Science.gov

Sample records for multi-type network approach

  1. mEducator: A Best Practice Network for Repurposing and Sharing Medical Educational Multi-type Content

    NASA Astrophysics Data System (ADS)

    Bamidis, Panagiotis D.; Kaldoudi, Eleni; Pattichis, Costas

    Although there is an abundance of medical educational content available in individual EU academic institutions, this is not widely available or easy to discover and retrieve, due to lack of standardized content sharing mechanisms. The mEducator EU project will face this lack by implementing and experimenting between two different sharing mechanisms, namely, one based one mashup technologies, and one based on semantic web services. In addition, the mEducator best practice network will critically evaluate existing standards and reference models in the field of e-learning in order to enable specialized state-of-the-art medical educational content to be discovered, retrieved, shared, repurposed and re-used across European higher academic institutions. Educational content included in mEducator covers and represents the whole range of medical educational content, from traditional instructional teaching to active learning and experiential teaching/studying approaches. It spans the whole range of types, from text to exam sheets, algorithms, teaching files, computer programs (simulators or games) and interactive objects (like virtual patients and electronically traced anatomies), while it covers a variety of topics. In this paper, apart from introducing the relevant project concepts and strategies, emphasis is also placed on the notion of (dynamic) user-generated content, its advantages and peculiarities, as well as, gaps in current research and technology practice upon its embedding into existing standards.

  2. Wireless Sensor Networks Approach

    NASA Technical Reports Server (NTRS)

    Perotti, Jose M.

    2003-01-01

    This viewgraph presentation provides information on hardware and software configurations for a network architecture for sensors. The hardware configuration uses a central station and remote stations. The software configuration uses the 'lost station' software algorithm. The presentation profiles a couple current examples of this network architecture in use.

  3. Network Routing Using the Network Tasking Order, a Chron Approach

    DTIC Science & Technology

    2015-03-26

    some advanced prediction techniques that are utilized for traffic routing and management as well as some synchronization techniques are presented...NETWORK ROUTING USING THE NETWORK TASKING ORDER, A CHRON APPROACH THESIS Nicholas J. Paltzer...MS-15-M-059 NETWORK ROUTING USING THE NETWORK TASKING ORDER, A CHRON APPROACH THESIS Presented to the Faculty Department of Electrical

  4. A network approach based on cliques

    NASA Astrophysics Data System (ADS)

    Fadigas, I. S.; Pereira, H. B. B.

    2013-05-01

    The characterization of complex networks is a procedure that is currently found in several research studies. Nevertheless, few studies present a discussion on networks in which the basic element is a clique. In this paper, we propose an approach based on a network of cliques. This approach consists not only of a set of new indices to capture the properties of a network of cliques but also of a method to characterize complex networks of cliques (i.e., some of the parameters are proposed to characterize the small-world phenomenon in networks of cliques). The results obtained are consistent with results from classical methods used to characterize complex networks.

  5. A Network Approach to Curriculum Quality Assessment

    ERIC Educational Resources Information Center

    Jordens, J. Zoe; Zepke, Nick

    2009-01-01

    This paper argues for an alternative approach to quality assurance in New Zealand universities that locates evaluation not with external auditors but with members of the teaching team. In the process, aspects of network theories are introduced as the basis for an approach to quality assurance. From this, the concept of networks is extended to…

  6. A Network Approach to Curriculum Quality Assessment

    ERIC Educational Resources Information Center

    Jordens, J. Zoe; Zepke, Nick

    2009-01-01

    This paper argues for an alternative approach to quality assurance in New Zealand universities that locates evaluation not with external auditors but with members of the teaching team. In the process, aspects of network theories are introduced as the basis for an approach to quality assurance. From this, the concept of networks is extended to…

  7. Process-in-Network: a comprehensive network processing approach.

    PubMed

    Urzaiz, Gabriel; Villa, David; Villanueva, Felix; Lopez, Juan Carlos

    2012-01-01

    A solid and versatile communications platform is very important in modern Ambient Intelligence (AmI) applications, which usually require the transmission of large amounts of multimedia information over a highly heterogeneous network. This article focuses on the concept of Process-in-Network (PIN), which is defined as the possibility that the network processes information as it is being transmitted, and introduces a more comprehensive approach than current network processing technologies. PIN can take advantage of waiting times in queues of routers, idle processing capacity in intermediate nodes, and the information that passes through the network.

  8. Process-in-Network: A Comprehensive Network Processing Approach

    PubMed Central

    Urzaiz, Gabriel; Villa, David; Villanueva, Felix; Lopez, Juan Carlos

    2012-01-01

    A solid and versatile communications platform is very important in modern Ambient Intelligence (AmI) applications, which usually require the transmission of large amounts of multimedia information over a highly heterogeneous network. This article focuses on the concept of Process-in-Network (PIN), which is defined as the possibility that the network processes information as it is being transmitted, and introduces a more comprehensive approach than current network processing technologies. PIN can take advantage of waiting times in queues of routers, idle processing capacity in intermediate nodes, and the information that passes through the network. PMID:22969390

  9. Network Medicine: A Network-based Approach to Human Diseases

    NASA Astrophysics Data System (ADS)

    Ghiassian, Susan Dina

    With the availability of large-scale data, it is now possible to systematically study the underlying interaction maps of many complex systems in multiple disciplines. Statistical physics has a long and successful history in modeling and characterizing systems with a large number of interacting individuals. Indeed, numerous approaches that were first developed in the context of statistical physics, such as the notion of random walks and diffusion processes, have been applied successfully to study and characterize complex systems in the context of network science. Based on these tools, network science has made important contributions to our understanding of many real-world, self-organizing systems, for example in computer science, sociology and economics. Biological systems are no exception. Indeed, recent studies reflect the necessity of applying statistical and network-based approaches in order to understand complex biological systems, such as cells. In these approaches, a cell is viewed as a complex network consisting of interactions among cellular components, such as genes and proteins. Given the cellular network as a platform, machinery, functionality and failure of a cell can be studied with network-based approaches, a field known as systems biology. Here, we apply network-based approaches to explore human diseases and their associated genes within the cellular network. This dissertation is divided in three parts: (i) A systematic analysis of the connectivity patterns among disease proteins within the cellular network. The quantification of these patterns inspires the design of an algorithm which predicts a disease-specific subnetwork containing yet unknown disease associated proteins. (ii) We apply the introduced algorithm to explore the common underlying mechanism of many complex diseases. We detect a subnetwork from which inflammatory processes initiate and result in many autoimmune diseases. (iii) The last chapter of this dissertation describes the

  10. An Evolutionary Approach to Designing Neural Networks

    DTIC Science & Technology

    1991-10-01

    Feature-Map Networks .. .. .. .. ... ... .... ... ... ... .... 42 4.5 Evolution of Learning: A Population Genetics Approach. .. .. .. .. ... .... .. 44...principles of biological evolution and population genetics provide the basis for such behavior. The processes of variation and selection, operating at...better understanding of the relationship among neural network theory, evolutionary and population genetics , and some aspects of dynamical systems

  11. The neural network approach to parton fitting

    SciTech Connect

    Rojo, Joan; Latorre, Jose I.; Del Debbio, Luigi; Forte, Stefano; Piccione, Andrea

    2005-10-06

    We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits.

  12. Qualitative networks: a symbolic approach to analyze biological signaling networks

    PubMed Central

    Schaub, Marc A; Henzinger, Thomas A; Fisher, Jasmin

    2007-01-01

    Background A central goal of Systems Biology is to model and analyze biological signaling pathways that interact with one another to form complex networks. Here we introduce Qualitative networks, an extension of Boolean networks. With this framework, we use formal verification methods to check whether a model is consistent with the laboratory experimental observations on which it is based. If the model does not conform to the data, we suggest a revised model and the new hypotheses are tested in-silico. Results We consider networks in which elements range over a small finite domain allowing more flexibility than Boolean values, and add target functions that allow to model a rich set of behaviors. We propose a symbolic algorithm for analyzing the steady state of these networks, allowing us to scale up to a system consisting of 144 elements and state spaces of approximately 1086 states. We illustrate the usefulness of this approach through a model of the interaction between the Notch and the Wnt signaling pathways in mammalian skin, and its extensive analysis. Conclusion We introduce an approach for constructing computational models of biological systems that extends the framework of Boolean networks and uses formal verification methods for the analysis of the model. This approach can scale to multicellular models of complex pathways, and is therefore a useful tool for the analysis of complex biological systems. The hypotheses formulated during in-silico testing suggest new avenues to explore experimentally. Hence, this approach has the potential to efficiently complement experimental studies in biology. PMID:17408511

  13. Network-based Approaches in Pharmacology.

    PubMed

    Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier

    2017-10-01

    In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  14. Approaching human language with complex networks.

    PubMed

    Cong, Jin; Liu, Haitao

    2014-12-01

    The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

  15. Approaching human language with complex networks

    NASA Astrophysics Data System (ADS)

    Cong, Jin; Liu, Haitao

    2014-12-01

    The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

  16. A Transfer Learning Approach for Network Modeling

    PubMed Central

    Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li

    2012-01-01

    Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804

  17. A neural network approach to cloud classification

    NASA Technical Reports Server (NTRS)

    Lee, Jonathan; Weger, Ronald C.; Sengupta, Sailes K.; Welch, Ronald M.

    1990-01-01

    It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery achieves an overall cloud identification accuracy of 93 percent. Cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96 percent, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92 percent, cumulus at 90 percent. The use of the neural network does not improve cirrus classification accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. While most cloud classification algorithms rely on linear parametric schemes, the present study is based on a nonlinear, nonparametric four-layer neural network approach. A three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure are compared. A significant finding is that significantly higher accuracies are attained with the nonparametric approaches using only 20 percent of the database as training data, compared to 67 percent of the database in the linear approach.

  18. An approach to metering and network modeling

    SciTech Connect

    Adibi, M.M. ); Clements, K.A. ); Kafka, R.J. ); Stovall, J.P. )

    1992-01-01

    Estimation of the static state of an electric power network has become a standard function in real-time monitoring and control. Its purpose is to use the network model and process the metering data in order to determine an accurate and reliable estimate of the system state in the real-time environment. In the models usually used it is assumed that the network parameters and topology are free of errors and the measurement system provides unbiased data having a known distribution. The network and metering models however, contain errors which frequently result in either non-convergent behavior of the state estimator or exceedingly large residuals, reducing the level of confidence in the results. This paper describes an approach minimizing the above uncertainties by analyzing the data which are routinely collected at the power system control center. The approach will improve the reliability of the real-time data-base while reducing the state estimator installation and maintenance effort.

  19. Queuing network approach for building evacuation planning

    NASA Astrophysics Data System (ADS)

    Ishak, Nurhanis; Khalid, Ruzelan; Baten, Md. Azizul; Nawawi, Mohd. Kamal Mohd.

    2014-12-01

    The complex behavior of pedestrians in a limited space layout can explicitly be modeled using an M/G/C/C state dependent queuing network. This paper implements the approach to study pedestrian flows through various corridors in a topological network. The best arrival rates and their impacts to the corridors' performances in terms of the throughput, blocking probability, expected number of occupants in the system and expected travel time were first measured using the M/G/C/C analytical model. These best arrival rates were then fed to its Network Flow Programming model to find the best arrival rates to source corridors and routes optimizing the network's total throughput. The analytical results were then validated using a simulation model. Various results of this study can be used to support the current Standard Operating Procedures (SOP) to efficiently and safely evacuate people in emergency cases.

  20. A Network Design Approach to Countering Terrorism

    DTIC Science & Technology

    2015-09-01

    APPROACH TO COUNTERING TERRORISM by Linus P. Torner September 2015 Thesis Advisor: Sean F. Everton Second Reader: Steven J. Iatrou THIS......comparative case study where performance of historical terrorist networks is examined to find recommendations for future counter- terrorism efforts. The

  1. Alternative approach to community detection in networks

    NASA Astrophysics Data System (ADS)

    Medus, A. D.; Dorso, C. O.

    2009-06-01

    The problem of community detection is relevant in many disciplines of science and modularity optimization is the widely accepted method for this purpose. It has recently been shown that this approach presents a resolution limit by which it is not possible to detect communities with sizes smaller than a threshold, which depends on the network size. Moreover, it might happen that the communities resulting from such an approach do not satisfy the usual qualitative definition of commune; i.e., nodes in a commune are more connected among themselves than to nodes outside the commune. In this paper we present a different method for community detection in complex networks. We define merit factors based on the weak and strong community definitions formulated by Radicchi [Proc. Natl. Acad. Sci. U.S.A. 101, 2658 (2004)] and we show that these local definitions avoid the resolution limit problem found in the modularity optimization approach.

  2. Neural network approaches for noisy language modeling.

    PubMed

    Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid

    2013-11-01

    Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.

  3. An approach to metering and network modeling

    SciTech Connect

    Adibi, M.M.; Clements, K.A.; Kafka, R.J.; Stovall, J.P.

    1992-06-01

    Estimation of the static state of an electric power network has become a standard function in real-time monitoring and control. Its purpose is to use the network model and process the metering data in order to determine an accurate and reliable estimate of the system state in the real-time environment. In the models usually used it is assumed that the network parameters and topology are free of errors and the measurement system provides unbiased data having a known distribution. The network and metering models however, contain errors which frequently result in either non-convergent behavior of the state estimator or exceedingly large residual, reducing the level of confidence in the results. This paper describes an approach minimizing the above uncertainties by analyzing the data which are routinely collected at the power system control center. The approach while improve the reliability of the real-time data-base while reducing the state estimator installation and maintenance effort. 5 refs.

  4. An approach to metering and network modeling

    SciTech Connect

    Adibi, M.M. ); Clements, K.A. ); Kafka, R.J. ); Stovall, J.P. )

    1992-01-01

    Estimation of the static state of an electric power network has become a standard function in real-time monitoring and control. Its purpose is to use the network model and process the metering data in order to determine an accurate and reliable estimate of the system state in the real-time environment. In the models usually used it is assumed that the network parameters and topology are free of errors and the measurement system provides unbiased data having a known distribution. The network and metering models however, contain errors which frequently result in either non-convergent behavior of the state estimator or exceedingly large residual, reducing the level of confidence in the results. This paper describes an approach minimizing the above uncertainties by analyzing the data which are routinely collected at the power system control center. The approach while improve the reliability of the real-time data-base while reducing the state estimator installation and maintenance effort. 5 refs.

  5. Systems approaches to the networks of aging.

    PubMed

    Kriete, Andres; Sokhansanj, Bahrad A; Coppock, Donald L; West, Geoffrey B

    2006-11-01

    The aging of an organism is the result of complex changes in structure and function of molecules, cells, tissues, and whole body systems. To increase our understanding of how aging works, we have to analyze and integrate quantitative evidence from multiple levels of biological organization. Here, we define a broader conceptual framework for a quantitative, computational systems biology approach to aging. Initially, we consider fractal supply networks that give rise to scaling laws relating body mass, metabolism and lifespan. This approach provides a top-down view of constrained cellular processes. Concomitantly, multi-omics data generation build such a framework from the bottom-up, using modeling strategies to identify key pathways and their physiological capacity. Multiscale spatio-temporal representations finally connect molecular processes with structural organization. As aging manifests on a systems level, it emerges as a highly networked process regulated through feedback loops between levels of biological organization.

  6. Insomnia and Personality—A Network Approach

    PubMed Central

    Dekker, Kim; Blanken, Tessa F.; Van Someren, Eus J. W.

    2017-01-01

    Studies on personality traits and insomnia have remained inconclusive about which traits show the most direct associations with insomnia severity. It has moreover hardly been explored how traits relate to specific characteristics of insomnia. We here used network analysis in a large sample (N = 2089) to obtain an integrated view on the associations of personality traits with both overall insomnia severity and different insomnia characteristics, while distinguishing direct from indirect associations. We first estimated a network describing the associations among the five factor model personality traits and overall insomnia severity. Overall insomnia severity was associated with neuroticism, agreeableness, and openness. Subsequently, we estimated a separate network describing the associations among the personality traits and each of the seven individual items of the Insomnia Severity Index. This revealed relatively separate clusters of daytime and nocturnal insomnia complaints, that both contributed to dissatisfaction with sleep, and were both most directly associated with neuroticism and conscientiousness. The approach revealed the strongest direct associations between personality traits and the severity of different insomnia characteristics and overall insomnia severity. Differentiating them from indirect associations identified the targets for improving Cognitive Behavioral Therapy for insomnia with the highest probability of effectively changing the network of associated complaints. PMID:28257084

  7. A Network Approach to Rare Disease Modeling

    NASA Astrophysics Data System (ADS)

    Ghiassian, Susan; Rabello, Sabrina; Sharma, Amitabh; Wiest, Olaf; Barabasi, Albert-Laszlo

    2011-03-01

    Network approaches have been widely used to better understand different areas of natural and social sciences. Network Science had a particularly great impact on the study of biological systems. In this project, using biological networks, candidate drugs as a potential treatment of rare diseases were identified. Developing new drugs for more than 2000 rare diseases (as defined by ORPHANET) is too expensive and beyond expectation. Disease proteins do not function in isolation but in cooperation with other interacting proteins. Research on FDA approved drugs have shown that most of the drugs do not target the disease protein but a protein which is 2 or 3 steps away from the disease protein in the Protein-Protein Interaction (PPI) network. We identified the already known drug targets in the disease gene's PPI subnetwork (up to the 3rd neighborhood) and among them those in the same sub cellular compartment and higher coexpression coefficient with the disease gene are expected to be stronger candidates. Out of 2177 rare diseases, 1092 were found not to have any drug target. Using the above method, we have found the strongest candidates among the rest in order to further experimental validations.

  8. Leveraging Modeling Approaches: Reaction Networks and Rules

    PubMed Central

    Blinov, Michael L.; Moraru, Ion I.

    2012-01-01

    We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high resolution and/or high throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatio-temporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks – the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks. PMID:22161349

  9. Leveraging modeling approaches: reaction networks and rules.

    PubMed

    Blinov, Michael L; Moraru, Ion I

    2012-01-01

    We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high-resolution and/or high-throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatiotemporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks - the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.

  10. An approach for modeling vulnerability of the network of networks

    NASA Astrophysics Data System (ADS)

    Zhang, Jianhua; Song, Bo; Zhang, Zhaojun; Liu, Haikuan

    2014-10-01

    In this paper, a framework is given to model the network of networks and to investigate the vulnerability of the network of networks subjected to failures. Because there are several redundant systems in infrastructure systems, the dependent intensity between two networks is introduced and adopted to discuss the vulnerability of the interdependent infrastructure networks subjected to failures. Shanghai electrified rail transit network is used to illustrate the feasibility and effectiveness of the proposed framework. Because the rail network is dependent on the power grid and communication network, the corresponding power grid and communication network are also included in this system. Meanwhile the failures to the power grid and communication network are utilized to investigate the vulnerability of the rail network. The results show that the rail network strongly depends on the power grid and weakly depends on the communication network, and the transport functionality loss of the rail network increases with the increase of dependent intensity. Meanwhile the highest betweenness node-based attack to the power grid and the largest degree node-based attack to the communication network can result in the most functionality losses to the rail network. Moreover, the functionality loss of the rail network has the smallest value when the tolerance parameter of the power grid equals 0.75 and the critical nodes of the power grid and communication network can be obtained by simulations.

  11. Differential Correlates of Multi-Type Maltreatment among Urban Youth

    ERIC Educational Resources Information Center

    Arata, Catalina M.; Langhinrichsen-Rohling, Jennifer; Bowers, David; O'Brien, Natalie

    2007-01-01

    Objective: The aim of this study was to examine the differential effects of multi-types of maltreatment in an adolescent sample. Different combinations of maltreatment (emotional, sexual, physical, neglect) were examined in relation to both negative affect and externalizing symptoms in male and female youth. Method: One thousand four hundred…

  12. Differential Correlates of Multi-Type Maltreatment among Urban Youth

    ERIC Educational Resources Information Center

    Arata, Catalina M.; Langhinrichsen-Rohling, Jennifer; Bowers, David; O'Brien, Natalie

    2007-01-01

    Objective: The aim of this study was to examine the differential effects of multi-types of maltreatment in an adolescent sample. Different combinations of maltreatment (emotional, sexual, physical, neglect) were examined in relation to both negative affect and externalizing symptoms in male and female youth. Method: One thousand four hundred…

  13. A Complex Network Approach to Stylometry.

    PubMed

    Amancio, Diego Raphael

    2015-01-01

    Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents.

  14. A Complex Network Approach to Stylometry

    PubMed Central

    Amancio, Diego Raphael

    2015-01-01

    Statistical methods have been widely employed to study the fundamental properties of language. In recent years, methods from complex and dynamical systems proved useful to create several language models. Despite the large amount of studies devoted to represent texts with physical models, only a limited number of studies have shown how the properties of the underlying physical systems can be employed to improve the performance of natural language processing tasks. In this paper, I address this problem by devising complex networks methods that are able to improve the performance of current statistical methods. Using a fuzzy classification strategy, I show that the topological properties extracted from texts complement the traditional textual description. In several cases, the performance obtained with hybrid approaches outperformed the results obtained when only traditional or networked methods were used. Because the proposed model is generic, the framework devised here could be straightforwardly used to study similar textual applications where the topology plays a pivotal role in the description of the interacting agents. PMID:26313921

  15. The NASA Science Internet: An integrated approach to networking

    NASA Technical Reports Server (NTRS)

    Rounds, Fred

    1991-01-01

    An integrated approach to building a networking infrastructure is an absolute necessity for meeting the multidisciplinary science networking requirements of the Office of Space Science and Applications (OSSA) science community. These networking requirements include communication connectivity between computational resources, databases, and library systems, as well as to other scientists and researchers around the world. A consolidated networking approach allows strategic use of the existing science networking within the Federal government, and it provides networking capability that takes into consideration national and international trends towards multivendor and multiprotocol service. It also offers a practical vehicle for optimizing costs and maximizing performance. Finally, and perhaps most important to the development of high speed computing is that an integrated network constitutes a focus for phasing to the National Research and Education Network (NREN). The NASA Science Internet (NSI) program, established in mid 1988, is structured to provide just such an integrated network. A description of the NSI is presented.

  16. A Sociospatial Approach to Understanding Terrorist Networks

    SciTech Connect

    Medina, Richard M; Hepner, George F.

    2011-01-01

    Terrorist networks operate in hybrid space where activities in social and geographic spaces are necessary for logistics and security. The Islamist terrorist network is analyzed as a sociospatial system using social network analysis, Geographic Information Science (GISc), and novel techniques designed for hybrid space analyses. This research focuses on identifying distance and sociospatial dependencies within the terrorist network. A methodology for analyzing sociospatial systems is developed and results lead to a greater understanding of terrorist network structures and activities. Distance and sociospatial dependencies are shown to exist for the Islamist terrorist network structure. These findings are discordant with recent literature that focuses on terrorist network tendencies toward decentralization in the information age. In this research, the Islamist terrorist network is theorized to use multiple structures of hierarchical and decentralized organization for effectiveness, efficiency, and resilience. Implications for counterterrorism policy and strategies are given.

  17. An artificial immune system algorithm approach for reconfiguring distribution network

    NASA Astrophysics Data System (ADS)

    Syahputra, Ramadoni; Soesanti, Indah

    2017-08-01

    This paper proposes an artificial immune system (AIS) algorithm approach for reconfiguring distribution network with the presence distributed generators (DG). The distribution network with high-performance is a network that has a low power loss, better voltage profile, and loading balance among feeders. The task for improving the performance of the distribution network is optimization of network configuration. The optimization has become a necessary study with the presence of DG in entire networks. In this work, optimization of network configuration is based on an AIS algorithm. The methodology has been tested in a model of 33 bus IEEE radial distribution networks with and without DG integration. The results have been showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the distribution network significantly.

  18. Making Connections: A Network Approach to University Disaster Preparedness

    ERIC Educational Resources Information Center

    Stein, Catherine H.; Vickio, Craig J.; Fogo, Wendy R.; Abraham, Kristen M.

    2007-01-01

    A network approach to disaster preparedness in university settings is described. Basic network concepts relevant for disaster preparedness and methods for analyzing network data without complex mathematics are presented. A case study of campus mental health and academic units at a midwestern university is presented to illustrate the practical…

  19. Input-state approach to Boolean networks.

    PubMed

    Cheng, Daizhan

    2009-03-01

    This paper investigates the structure of Boolean networks via input-state structure. Using the algebraic form proposed by the author, the logic-based input-state dynamics of Boolean networks, called the Boolean control networks, is converted into an algebraic discrete-time dynamic system. Then the structure of cycles of Boolean control systems is obtained as compounded cycles. Using the obtained input-state description, the structure of Boolean networks is investigated, and their attractors are revealed as nested compounded cycles, called rolling gears. This structure explains why small cycles mainly decide the behaviors of cellular networks. Some illustrative examples are presented.

  20. 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.

  1. Implementing the Fussy Baby Network[R] Approach

    ERIC Educational Resources Information Center

    Gilkerson, Linda; Hofherr, Jennifer; Heffron, Mary Claire; Sims, Jennifer Murphy; Jalowiec, Barbara; Bromberg, Stacey R.; Paul, Jennifer J.

    2012-01-01

    Erikson Institute Fussy Baby Network[R] (FBN) developed an approach to engaging parents around their urgent concerns about their baby's crying, sleeping, or feeding in a way which builds their longer-term capacities as parents. This approach, called the FAN, is now in place in new Fussy Baby Network programs around the country and is being infused…

  2. Implementing the Fussy Baby Network[R] Approach

    ERIC Educational Resources Information Center

    Gilkerson, Linda; Hofherr, Jennifer; Heffron, Mary Claire; Sims, Jennifer Murphy; Jalowiec, Barbara; Bromberg, Stacey R.; Paul, Jennifer J.

    2012-01-01

    Erikson Institute Fussy Baby Network[R] (FBN) developed an approach to engaging parents around their urgent concerns about their baby's crying, sleeping, or feeding in a way which builds their longer-term capacities as parents. This approach, called the FAN, is now in place in new Fussy Baby Network programs around the country and is being infused…

  3. Algebraic Approach for Recovering Topology in Distributed Camera Networks

    DTIC Science & Technology

    2009-01-14

    Algebraic Approach for Recovering Topology in Distributed Camera Networks Edgar J. Lobaton Parvez Ahammad S. Shankar Sastry Electrical Engineering...Topology in Distributed Camera Networks Edgar J. Lobaton , Parvez Ahammad, S. Shankar Sastry ∗† January 14, 2009 Abstract Camera networks are widely used...well as a real-world experimental set-up. Our proposed approach ∗E.J. Lobaton and S.S. Sastry are with the Electrical Engineering and Computer

  4. Unification of theoretical approaches for epidemic spreading on complex networks.

    PubMed

    Wang, Wei; Tang, Ming; Eugene Stanley, H; Braunstein, Lidia A

    2017-03-01

    Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks.

  5. Unification of theoretical approaches for epidemic spreading on complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Wei; Tang, Ming; Stanley, H. Eugene; Braunstein, Lidia A.

    2017-03-01

    Models of epidemic spreading on complex networks have attracted great attention among researchers in physics, mathematics, and epidemiology due to their success in predicting and controlling scenarios of epidemic spreading in real-world scenarios. To understand the interplay between epidemic spreading and the topology of a contact network, several outstanding theoretical approaches have been developed. An accurate theoretical approach describing the spreading dynamics must take both the network topology and dynamical correlations into consideration at the expense of increasing the complexity of the equations. In this short survey we unify the most widely used theoretical approaches for epidemic spreading on complex networks in terms of increasing complexity, including the mean-field, the heterogeneous mean-field, the quench mean-field, dynamical message-passing, link percolation, and pairwise approximation. We build connections among these approaches to provide new insights into developing an accurate theoretical approach to spreading dynamics on complex networks.

  6. Differential correlates of multi-type maltreatment among urban youth.

    PubMed

    Arata, Catalina M; Langhinrichsen-Rohling, Jennifer; Bowers, David; O'Brien, Natalie

    2007-04-01

    The aim of this study was to examine the differential effects of multi-types of maltreatment in an adolescent sample. Different combinations of maltreatment (emotional, sexual, physical, neglect) were examined in relation to both negative affect and externalizing symptoms in male and female youth. One thousand four hundred fifty-two middle and high school youth were recruited from urban schools and a mandated early warning truancy program. Youth completed an anonymous survey that included measures of child maltreatment, depression, suicide proneness, hopelessness, delinquency, hostility, substance use, and promiscuity. Respondents were categorized into groups of different combinations of maltreatment by their reports of sexual abuse, physical abuse, neglect (emotional and physical), and emotional abuse. Nearly two-thirds of boys and girls reported some form of maltreatment, and multi-type maltreatment was common (e.g., 13% reported experiencing both physical and sexual abuse and neglect). Individuals with maltreatment histories were more depressed (F=52.78, p<.0001), suicide prone (F=24.29, p<.001), and hopeless (F=32.07, p<.0001) than non-abused individuals. Maltreated adolescents were also more hostile (F=35.03, p<.0001), and they engaged in more delinquent behavior (F=26.76, p<.0001), promiscuity (F=8.54, p<.0001), and drug and alcohol use (F=9.61, p<.0001). Individuals experiencing multi-type maltreatment were the most symptomatic, particularly youth with histories of physical abuse, sexual abuse, and neglect. In general, gender differences in effects were not observed. The results highlight the importance of studying combined types of maltreatment, as well as understanding the particularly deleterious effects of neglect and emotional abuse. The results are generally consistent with an additive model of maltreatment effects.

  7. Overlapping community detection in weighted networks via a Bayesian approach

    NASA Astrophysics Data System (ADS)

    Chen, Yi; Wang, Xiaolong; Xiang, Xin; Tang, Buzhou; Chen, Qingcai; Fan, Shixi; Bu, Junzhao

    2017-02-01

    Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

  8. Considerations for Software Defined Networking (SDN): Approaches and use cases

    NASA Astrophysics Data System (ADS)

    Bakshi, K.

    Software Defined Networking (SDN) is an evolutionary approach to network design and functionality based on the ability to programmatically modify the behavior of network devices. SDN uses user-customizable and configurable software that's independent of hardware to enable networked systems to expand data flow control. SDN is in large part about understanding and managing a network as a unified abstraction. It will make networks more flexible, dynamic, and cost-efficient, while greatly simplifying operational complexity. And this advanced solution provides several benefits including network and service customizability, configurability, improved operations, and increased performance. There are several approaches to SDN and its practical implementation. Among them, two have risen to prominence with differences in pedigree and implementation. This paper's main focus will be to define, review, and evaluate salient approaches and use cases of the OpenFlow and Virtual Network Overlay approaches to SDN. OpenFlow is a communication protocol that gives access to the forwarding plane of a network's switches and routers. The Virtual Network Overlay relies on a completely virtualized network infrastructure and services to abstract the underlying physical network, which allows the overlay to be mobile to other physical networks. This is an important requirement for cloud computing, where applications and associated network services are migrated to cloud service providers and remote data centers on the fly as resource demands dictate. The paper will discuss how and where SDN can be applied and implemented, including research and academia, virtual multitenant data center, and cloud computing applications. Specific attention will be given to the cloud computing use case, where automated provisioning and programmable overlay for scalable multi-tenancy is leveraged via the SDN approach.

  9. Social Networks and Mourning: A Comparative Approach.

    ERIC Educational Resources Information Center

    Rubin, Nissan

    1990-01-01

    Suggests using social network theory to explain varieties of mourning behavior in different societies. Compares participation in funeral ceremonies of members of different social circles in American society and Israeli kibbutz. Concludes that results demonstrated validity of concepts deriving from social network analysis in study of bereavement,…

  10. Fault detection and diagnosis using neural network approaches

    NASA Technical Reports Server (NTRS)

    Kramer, Mark A.

    1992-01-01

    Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

  11. Control of tree water networks: A geometric programming approach

    NASA Astrophysics Data System (ADS)

    Sela Perelman, L.; Amin, S.

    2015-10-01

    This paper presents a modeling and operation approach for tree water supply systems. The network control problem is approximated as a geometric programming (GP) problem. The original nonlinear nonconvex network control problem is transformed into a convex optimization problem. The optimization model can be efficiently solved to optimality using state-of-the-art solvers. Two control schemes are presented: (1) operation of network actuators (pumps and valves) and (2) controlled demand shedding allocation between network consumers with limited resources. The dual of the network control problem is formulated and is used to perform sensitivity analysis with respect to hydraulic constraints. The approach is demonstrated on a small branched-topology network and later extended to a medium-size irrigation network. The results demonstrate an intrinsic trade-off between energy costs and demand shedding policy, providing an efficient decision support tool for active management of water systems.

  12. Insider Threat Mitigation Project: A Dynamic Network Approach (Poster)

    DTIC Science & Technology

    2014-10-23

    OCT 2014 2. REPORT TYPE N/A 3. DATES COVERED 4. TITLE AND SUBTITLE Insider Threat Mitigation Project: A Dynamic Network Approach 5a...Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Insider Threat Mitigation Project A Dynamic Network Approach Approach: • Semi-automated coding...to- external communication • Remove suspected distribution lists • Identify “normal behavior” using Enron • Develop pattern for “ insiders ” in

  13. Scalable Approaches to Control Network Dynamics: Prospects for City Networks

    NASA Astrophysics Data System (ADS)

    Motter, Adilson E.; Gray, Kimberly A.

    2014-07-01

    A city is a complex, emergent system and as such can be conveniently represented as a network of interacting components. A fundamental aspect of networks is that the systemic properties can depend as much on the interactions as they depend on the properties of the individual components themselves. Another fundamental aspect is that changes to one component can affect other components, in a process that may cause the entire or a substantial part of the system to change behavior. Over the past 2 decades, much research has been done on the modeling of large and complex networks involved in communication and transportation, disease propagation, and supply chains, as well as emergent phenomena, robustness and optimization in such systems...

  14. Efficient network meta-analysis: a confidence distribution approach*

    PubMed Central

    Yang, Guang; Liu, Dungang; Liu, Regina Y.; Xie, Minge; Hoaglin, David C.

    2014-01-01

    Summary Network meta-analysis synthesizes several studies of multiple treatment comparisons to simultaneously provide inference for all treatments in the network. It can often strengthen inference on pairwise comparisons by borrowing evidence from other comparisons in the network. Current network meta-analysis approaches are derived from either conventional pairwise meta-analysis or hierarchical Bayesian methods. This paper introduces a new approach for network meta-analysis by combining confidence distributions (CDs). Instead of combining point estimators from individual studies in the conventional approach, the new approach combines CDs which contain richer information than point estimators and thus achieves greater efficiency in its inference. The proposed CD approach can e ciently integrate all studies in the network and provide inference for all treatments even when individual studies contain only comparisons of subsets of the treatments. Through numerical studies with real and simulated data sets, the proposed approach is shown to outperform or at least equal the traditional pairwise meta-analysis and a commonly used Bayesian hierarchical model. Although the Bayesian approach may yield comparable results with a suitably chosen prior, it is highly sensitive to the choice of priors (especially the prior of the between-trial covariance structure), which is often subjective. The CD approach is a general frequentist approach and is prior-free. Moreover, it can always provide a proper inference for all the treatment effects regardless of the between-trial covariance structure. PMID:25067933

  15. Power grid vulnerability: A complex network approach

    NASA Astrophysics Data System (ADS)

    Arianos, S.; Bompard, E.; Carbone, A.; Xue, F.

    2009-03-01

    Power grids exhibit patterns of reaction to outages similar to complex networks. Blackout sequences follow power laws, as complex systems operating near a critical point. Here, the tolerance of electric power grids to both accidental and malicious outages is analyzed in the framework of complex network theory. In particular, the quantity known as efficiency is modified by introducing a new concept of distance between nodes. As a result, a new parameter called net-ability is proposed to evaluate the performance of power grids. A comparison between efficiency and net-ability is provided by estimating the vulnerability of sample networks, in terms of both the metrics.

  16. Power grid vulnerability: a complex network approach.

    PubMed

    Arianos, S; Bompard, E; Carbone, A; Xue, F

    2009-03-01

    Power grids exhibit patterns of reaction to outages similar to complex networks. Blackout sequences follow power laws, as complex systems operating near a critical point. Here, the tolerance of electric power grids to both accidental and malicious outages is analyzed in the framework of complex network theory. In particular, the quantity known as efficiency is modified by introducing a new concept of distance between nodes. As a result, a new parameter called net-ability is proposed to evaluate the performance of power grids. A comparison between efficiency and net-ability is provided by estimating the vulnerability of sample networks, in terms of both the metrics.

  17. Damage detection in bridge structures under moving loads with phase trajectory change of multi-type vibration measurements

    NASA Astrophysics Data System (ADS)

    Zhang, Weiwei; Li, Jun; Hao, Hong; Ma, Hongwei

    2017-03-01

    This paper presents a non-model based damage detection approach for bridge structures under moving loads based on the phase trajectory change of multi-type vibration measurements. A brief theoretical background on the vibration of a simply-supported bridge with a crack under moving load is described. The phase trajectories of multi-type dynamic responses are obtained and a damage index is defined as the separated distance between the trajectories of undamaged and damaged structures to indicate the damage location. Numerical studies on a simply-supported beam structure are conducted to investigate the sensitivity and robustness of the proposed approach to accurately identify the damage location. Experimental studies demonstrate that the proposed approach can be used to successfully identify the shear connection failure in a composite bridge model subjected to moving loads.

  18. A Sensible Approach to Wireless Networking.

    ERIC Educational Resources Information Center

    Ahmed, S. Faruq

    2002-01-01

    Discusses radio frequency (R.F.) wireless technology, including industry standards, range (coverage) and throughput (data rate), wireless compared to wired networks, and considerations before embarking on a large-scale wireless project. (EV)

  19. A Sensible Approach to Wireless Networking.

    ERIC Educational Resources Information Center

    Ahmed, S. Faruq

    2002-01-01

    Discusses radio frequency (R.F.) wireless technology, including industry standards, range (coverage) and throughput (data rate), wireless compared to wired networks, and considerations before embarking on a large-scale wireless project. (EV)

  20. Community Structures in Bipartite Networks: A Dual-Projection Approach

    PubMed Central

    Melamed, David

    2014-01-01

    Identifying communities or clusters in networked systems has received much attention across the physical and social sciences. Most of this work focuses on single layer or one-mode networks, including social networks between people or hyperlinks between websites. Multilayer or multi-mode networks, such as affiliation networks linking people to organizations, receive much less attention in this literature. Common strategies for discovering the community structure of multi-mode networks identify the communities of each mode simultaneously. Here I show that this combined approach is ineffective at discovering community structures when there are an unequal number of communities between the modes of a multi-mode network. I propose a dual-projection alternative for detecting communities in multi-mode networks that overcomes this shortcoming. The evaluation of synthetic networks with known community structures reveals that the dual-projection approach outperforms the combined approach when there are a different number of communities in the various modes. At the same time, results show that the dual-projection approach is as effective as the combined strategy when the number of communities is the same between the modes. PMID:24836376

  1. An Algebraic Approach to Inference in Complex Networked Structures

    DTIC Science & Technology

    2015-07-09

    AFRL-AFOSR-VA-TR-2015-0265 An Algebraic Approach to Inference in Complex Networked Structures Jose Moura CARNEGIE MELLON UNIVERSITY Final Report 07...31-03-2015 4.  TITLE AND SUBTITLE An Algebraic Approach to Inference in Complex Networked Structures 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER FA9550-12-1...Final Report: An Algebraic Approach to Inference in Complex Networked Structures FA9550-12-1-0087 4/1/2012-3/31/2015 José M F Moura Carnegie Mellon

  2. Network Medicine: A Network-based Approach to Human Disease

    PubMed Central

    Barabási, Albert-László; Gulbahce, Natali; Loscalzo, Joseph

    2011-01-01

    Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction are essential to identify new diseases genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases. PMID:21164525

  3. Sampling of Complex Networks: A Datamining Approach

    NASA Astrophysics Data System (ADS)

    Loecher, Markus; Dohrmann, Jakob; Bauer, Gernot

    2007-03-01

    Efficient and accurate sampling of big complex networks is still an unsolved problem. As the degree distribution is one of the most commonly used attributes to characterize a network, there have been many attempts in recent papers to derive the original degree distribution from the data obtained during a traceroute- like sampling process. This talk describes a strategy for predicting the original degree of a node using the data obtained from a network by traceroute-like sampling making use of datamining techniques. Only local quantities (the sampled degree k, the redundancy of node detection r, the time of the first discovery of a node t and the distance to the sampling source d) are used as input for the datamining models. Global properties like the betweenness centrality are ignored. These local quantities are examined theoretically and in simulations to increase their value for the predictions. The accuracy of the models is discussed as a function of the number of sources used in the sampling process and the underlying topology of the network. The purpose of this work is to introduce the techniques of the relatively young field of datamining to the discussion on network sampling.

  4. Network approach to patterns in stratocumulus clouds.

    PubMed

    Glassmeier, Franziska; Feingold, Graham

    2017-09-13

    Stratocumulus clouds (Sc) have a significant impact on the amount of sunlight reflected back to space, with important implications for Earth's climate. Representing Sc and their radiative impact is one of the largest challenges for global climate models. Sc fields self-organize into cellular patterns and thus lend themselves to analysis and quantification in terms of natural cellular networks. Based on large-eddy simulations of Sc fields, we present a first analysis of the geometric structure and self-organization of Sc patterns from this network perspective. Our network analysis shows that the Sc pattern is scale-invariant as a consequence of entropy maximization that is known as Lewis's Law (scaling parameter: 0.16) and is largely independent of the Sc regime (cloud-free vs. cloudy cell centers). Cells are, on average, hexagonal with a neighbor number variance of about 2, and larger cells tend to be surrounded by smaller cells, as described by an Aboav-Weaire parameter of 0.9. The network structure is neither completely random nor characteristic of natural convection. Instead, it emerges from Sc-specific versions of cell division and cell merging that are shaped by cell expansion. This is shown with a heuristic model of network dynamics that incorporates our physical understanding of cloud processes.

  5. A psychological approach to learning causal networks.

    PubMed

    Zargoush, Manaf; Alemi, Farrokh; Esposito Vinzi, Vinzenzo; Vang, Jee; Kheirbek, Raya

    2014-06-01

    We examine the role of a common cognitive heuristic in unsupervised learning of Bayesian probability networks from data. Human beings perceive a larger association between causal than diagnostic relationships. This psychological principal can be used to orient the arcs within Bayesian networks by prohibiting the direction that is less predictive. The heuristic increased predictive accuracy by an average of 0.51 % percent, a small amount. It also increased total agreement between different network learning algorithms (Max Spanning Tree, Taboo, EQ, SopLeq, and Taboo Order) by 25 %. Prior to use of the heuristic, the multiple raters Kappa between the algorithms was 0.60 (95 % confidence interval, CI, from 0.53 to 0.67) indicating moderate agreement among the networks learned through different algorithms. After the use of the heuristic, the multiple raters Kappa was 0.85 (95 % CI from 0.78 to 0.92). There was a statistically significant increase in agreement between the five algorithms (alpha < 0.05). These data suggest that the heuristic increased agreement between networks learned through use of different algorithms, without loss of predictive accuracy. Additional research is needed to see if findings persist in other data sets and to explain why a heuristic used by humans could improve construct validity of mathematical algorithms.

  6. A Networks Approach to Modeling Enzymatic Reactions.

    PubMed

    Imhof, P

    2016-01-01

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

  7. A New Approach to Create Image Control Networks in ISIS

    NASA Astrophysics Data System (ADS)

    Becker, K. J.; Berry, K. L.; Mapel, J. A.; Walldren, J. C.

    2017-06-01

    A new approach was used to create a feature-based control point network that required the development of new tools in the Integrated Software for Imagers and Spectrometers (ISIS3) system to process very large datasets.

  8. A Gaussian graphical model approach to climate networks

    SciTech Connect

    Zerenner, Tanja; Friederichs, Petra; Hense, Andreas; Lehnertz, Klaus

    2014-06-15

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.

  9. A Gaussian graphical model approach to climate networks.

    PubMed

    Zerenner, Tanja; Friederichs, Petra; Lehnertz, Klaus; Hense, Andreas

    2014-06-01

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.

  10. A Gateway Approach to Library System Networking.

    ERIC Educational Resources Information Center

    Anderson, David A.; Duggan, Michael T.

    1987-01-01

    Describes a technique for accessing a library system with limited interconnectivity by connecting the system to a gateway machine that is a host in the local area network and evaluates the performance of an existing prototype that has been implemented at the Los Alamos National Laboratory. (Author/CLB)

  11. A neural-network approach to robotic control

    NASA Technical Reports Server (NTRS)

    Graham, D. P. W.; Deleuterio, G. M. T.

    1993-01-01

    An artificial neural-network paradigm for the control of robotic systems is presented. The approach is based on the Cerebellar Model Articulation Controller created by James Albus and incorporates several extensions. First, recognizing the essential structure of multibody equations of motion, two parallel modules are used that directly reflect the dynamical characteristics of multibody systems. Second, the architecture of the proposed network is imbued with a self-organizational capability which improves efficiency and accuracy. Also, the networks can be arranged in hierarchical fashion with each subsequent network providing finer and finer resolution.

  12. Approach of Complex Networks for the Determination of Brain Death

    NASA Astrophysics Data System (ADS)

    Sun, Wei-Gang; Cao, Jian-Ting; Wang, Ru-Bin

    2011-06-01

    In clinical practice, brain death is the irreversible end of all brain activity. Compared to current statistical methods for the determination of brain death, we focus on the approach of complex networks for real-world electroencephalography in its determination. Brain functional networks constructed by correlation analysis are derived, and statistical network quantities used for distinguishing the patients in coma or brain death state, such as average strength, clustering coefficient and average path length, are calculated. Numerical results show that the values of network quantities of patients in coma state are larger than those of patients in brain death state. Our findings might provide valuable insights on the determination of brain death.

  13. Social network approaches to leadership: an integrative conceptual review.

    PubMed

    Carter, Dorothy R; DeChurch, Leslie A; Braun, Michael T; Contractor, Noshir S

    2015-05-01

    Contemporary definitions of leadership advance a view of the phenomenon as relational, situated in specific social contexts, involving patterned emergent processes, and encompassing both formal and informal influence. Paralleling these views is a growing interest in leveraging social network approaches to study leadership. Social network approaches provide a set of theories and methods with which to articulate and investigate, with greater precision and rigor, the wide variety of relational perspectives implied by contemporary leadership theories. Our goal is to advance this domain through an integrative conceptual review. We begin by answering the question of why-Why adopt a network approach to study leadership? Then, we offer a framework for organizing prior research. Our review reveals 3 areas of research, which we term: (a) leadership in networks, (b) leadership as networks, and (c) leadership in and as networks. By clarifying the conceptual underpinnings, key findings, and themes within each area, this review serves as a foundation for future inquiry that capitalizes on, and programmatically builds upon, the insights of prior work. Our final contribution is to advance an agenda for future research that harnesses the confluent ideas at the intersection of leadership in and as networks. Leadership in and as networks represents a paradigm shift in leadership research-from an emphasis on the static traits and behaviors of formal leaders whose actions are contingent upon situational constraints, toward an emphasis on the complex and patterned relational processes that interact with the embedding social context to jointly constitute leadership emergence and effectiveness.

  14. Complex network approach to fractional time series

    SciTech Connect

    Manshour, Pouya

    2015-10-15

    In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

  15. Chemical Approaches to Probe Metabolic Networks

    PubMed Central

    Medina-Cleghorn, Daniel; Nomura, Daniel K.

    2013-01-01

    One of the more provocative realizations that have come out of the genome sequencing projects is that organisms possess a large number of uncharacterized or poorly characterized enzymes. This finding belies the commonly held notion that our knowledge of cell metabolism is nearly complete, underscoring the vast landscape of unannotated metabolic and signaling networks that operate under normal physiological conditions, let alone in disease states where metabolic networks may be rewired, dysregulated, or altered to drive disease progression. Consequently, the functional annotation of enzymatic pathways represents a grand challenge for researchers in the post-genomic era. This review will highlight the chemical technologies that have been successfully used to characterize metabolism, and put forth some of the challenges we face as we expand our map of metabolic pathways. PMID:23296751

  16. Neural Flows in Hopfield Network Approach

    NASA Astrophysics Data System (ADS)

    Ionescu, Carmen; Panaitescu, Emilian; Stoicescu, Mihai

    2013-12-01

    In most of the applications involving neural networks, the main problem consists in finding an optimal procedure to reduce the real neuron to simpler models which still express the biological complexity but allow highlighting the main characteristics of the system. We effectively investigate a simple reduction procedure which leads from complex models of Hodgkin-Huxley type to very convenient binary models of Hopfield type. The reduction will allow to describe the neuron interconnections in a quite large network and to obtain information concerning its symmetry and stability. Both cases, on homogeneous voltage across the membrane and inhomogeneous voltage along the axon will be tackled out. Few numerical simulations of the neural flow based on the cable-equation will be also presented.

  17. Chemical approaches to study metabolic networks.

    PubMed

    Medina-Cleghorn, Daniel; Nomura, Daniel K

    2013-03-01

    One of the more provocative realizations that have come out of the genome sequencing projects is that organisms possess a large number of uncharacterized or poorly characterized enzymes. This finding belies the commonly held notion that our knowledge of cell metabolism is nearly complete, underscoring the vast landscape of unannotated metabolic and signaling networks that operate under normal physiological conditions, let alone in disease states where metabolic networks may be rewired, dysregulated, or altered to drive disease progression. Consequently, the functional annotation of enzymatic pathways represents a grand challenge for researchers in the post-genomic era. This review will highlight the chemical technologies that have been successfully used to characterize metabolism, and put forth some of the challenges we face as we expand our map of metabolic pathways.

  18. A Full Bayesian Approach for Boolean Genetic Network Inference

    PubMed Central

    Han, Shengtong; Wong, Raymond K. W.; Lee, Thomas C. M.; Shen, Linghao; Li, Shuo-Yen R.; Fan, Xiaodan

    2014-01-01

    Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data. PMID:25551820

  19. Electricity distribution networks: Changing regulatory approaches

    NASA Astrophysics Data System (ADS)

    Cambini, Carlo

    2016-09-01

    Increasing the penetration of distributed generation and smart grid technologies requires substantial investments. A study proposes an innovative approach that combines four regulatory tools to provide economic incentives for distribution system operators to facilitate these innovative practices.

  20. 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…

  1. Network-Oriented Approach to Distributed Generation Planning

    NASA Astrophysics Data System (ADS)

    Kochukov, O.; Mutule, A.

    2017-06-01

    The main objective of the paper is to present an innovative complex approach to distributed generation planning and show the advantages over existing methods. The approach will be most suitable for DNOs and authorities and has specific calculation targets to support the decision-making process. The method can be used for complex distribution networks with different arrangement and legal base.

  2. 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…

  3. Gender, Friendship Networks, and Delinquency: A Dynamic Network Approach**

    PubMed Central

    Haynie, Dana L.; Doogan, Nathan J.; Soller, Brian

    2014-01-01

    Researchers have examined selection and influence processes in shaping delinquency similarity among friends, but little is known about the role of gender in moderating these relationships. Our objective is to examine differences between adolescent boys and girls regarding delinquency-based selection and influence processes. Using longitudinal network data from adolescents attending two large schools in AddHealth (N = 1,857) and stochastic actor-oriented models, we evaluate whether girls are influenced to a greater degree by friends' violence or delinquency than boys (influence hypothesis) and whether girls are more likely to select friends based on violent or delinquent behavior than boys (selection hypothesis). The results indicate that girls are more likely than boys to be influenced by their friends' involvement in violence. Although a similar pattern emerges for nonviolent delinquency, the gender differences are not significant. Some evidence shows that boys are influenced toward increasing their violence or delinquency when exposed to more delinquent or violent friends but are immune to reducing their violence or delinquency when associating with less violent or delinquent friends. In terms of selection dynamics, although both boys and girls have a tendency to select friends based on friends' behavior, girls have a stronger tendency to do so, suggesting that among girls, friends' involvement in violence or delinquency is an especially decisive factor for determining friendship ties. PMID:26097241

  4. Automatic Distribution Network Reconfiguration: An Event-Driven Approach

    SciTech Connect

    Ding, Fei; Jiang, Huaiguang; Tan, Jin

    2016-11-14

    This paper proposes an event-driven approach for reconfiguring distribution systems automatically. Specifically, an optimal synchrophasor sensor placement (OSSP) is used to reduce the number of synchrophasor sensors while keeping the whole system observable. Then, a wavelet-based event detection and location approach is used to detect and locate the event, which performs as a trigger for network reconfiguration. With the detected information, the system is then reconfigured using the hierarchical decentralized approach to seek for the new optimal topology. In this manner, whenever an event happens the distribution network can be reconfigured automatically based on the real-time information that is observable and detectable.

  5. Reduction of streamflow monitoring networks by a reference point approach

    NASA Astrophysics Data System (ADS)

    Cetinkaya, Cem P.; Harmancioglu, Nilgun B.

    2014-05-01

    Adoption of an integrated approach to water management strongly forces policy and decision-makers to focus on hydrometric monitoring systems as well. Existing hydrometric networks need to be assessed and revised against the requirements on water quantity data to support integrated management. One of the questions that a network assessment study should resolve is whether a current monitoring system can be consolidated in view of the increased expenditures in time, money and effort imposed on the monitoring activity. Within the last decade, governmental monitoring agencies in Turkey have foreseen an audit on all their basin networks in view of prevailing economic pressures. In particular, they question how they can decide whether monitoring should be continued or terminated at a particular site in a network. The presented study is initiated to address this question by examining the applicability of a method called “reference point approach” (RPA) for network assessment and reduction purposes. The main objective of the study is to develop an easily applicable and flexible network reduction methodology, focusing mainly on the assessment of the “performance” of existing streamflow monitoring networks in view of variable operational purposes. The methodology is applied to 13 hydrometric stations in the Gediz Basin, along the Aegean coast of Turkey. The results have shown that the simplicity of the method, in contrast to more complicated computational techniques, is an asset that facilitates the involvement of decision makers in application of the methodology for a more interactive assessment procedure between the monitoring agency and the network designer. The method permits ranking of hydrometric stations with regard to multiple objectives of monitoring and the desired attributes of the basin network. Another distinctive feature of the approach is that it also assists decision making in cases with limited data and metadata. These features of the RPA approach

  6. An Overview of Data Routing Approaches for Wireless Sensor Networks

    PubMed Central

    Anisi, Mohammad Hossein; Abdullah, Abdul Hanan; Razak, Shukor Abd; Ngadi, Md. Asri

    2012-01-01

    Recent years have witnessed a growing interest in deploying large populations of microsensors that collaborate in a distributed manner to gather and process sensory data and deliver them to a sink node through wireless communications systems. Currently, there is a lot of interest in data routing for Wireless Sensor Networks (WSNs) due to their unique challenges compared to conventional routing in wired networks. In WSNs, each data routing approach follows a specific goal (goals) according to the application. Although the general goal of every data routing approach in WSNs is to extend the network lifetime and every approach should be aware of the energy level of the nodes, data routing approaches may focus on one (or some) specific goal(s) depending on the application. Thus, existing approaches can be categorized according to their routing goals. In this paper, the main goals of data routing approaches in sensor networks are described. Then, the best known and most recent data routing approaches in WSNs are classified and studied according to their specific goals. PMID:22666013

  7. An effective network reduction approach to find the dynamical repertoire of discrete dynamic networks

    NASA Astrophysics Data System (ADS)

    Zañudo, Jorge G. T.; Albert, Réka

    2013-06-01

    Discrete dynamic models are a powerful tool for the understanding and modeling of large biological networks. Although a lot of progress has been made in developing analysis tools for these models, there is still a need to find approaches that can directly relate the network structure to its dynamics. Of special interest is identifying the stable patterns of activity, i.e., the attractors of the system. This is a problem for large networks, because the state space of the system increases exponentially with network size. In this work, we present a novel network reduction approach that is based on finding network motifs that stabilize in a fixed state. Notably, we use a topological criterion to identify these motifs. Specifically, we find certain types of strongly connected components in a suitably expanded representation of the network. To test our method, we apply it to a dynamic network model for a type of cytotoxic T cell cancer and to an ensemble of random Boolean networks of size up to 200. Our results show that our method goes beyond reducing the network and in most cases can actually predict the dynamical repertoire of the nodes (fixed states or oscillations) in the attractors of the system.

  8. Network Reverse Engineering Approach in Synthetic Biology

    NASA Astrophysics Data System (ADS)

    Zhang, Haoqian; Liu, Ao; Lu, Yuheng; Sheng, Ying; Wu, Qianzhu; Yin, Zhenzhen; Chen, Yiwei; Liu, Zairan; Pan, Heng; Ouyang, Qi

    2013-12-01

    Synthetic biology is a new branch of interdisciplinary science that has been developed in recent years. The main purpose of synthetic biology is to apply successful principles that have been developed in electronic and chemical engineering to develop basic biological functional modules, and through rational design, develop man-made biological systems that have predicted useful functions. Here, we discuss an important principle in rational design of functional biological circuits: the reverse engineering design. We will use a research project that was conducted at Peking University for the International Genetic Engineering Machine Competition (iGEM) to illustrate the principle: synthesis a cell which has a semi-log dose-response to the environment. Through this work we try to demonstrate the potential application of network engineering in synthetic biology.

  9. Computational inference of gene regulatory networks: Approaches, limitations and opportunities.

    PubMed

    Banf, Michael; Rhee, Seung Y

    2017-01-01

    Gene regulatory networks lie at the core of cell function control. In E. coli and S. cerevisiae, the study of gene regulatory networks has led to the discovery of regulatory mechanisms responsible for the control of cell growth, differentiation and responses to environmental stimuli. In plants, computational rendering of gene regulatory networks is gaining momentum, thanks to the recent availability of high-quality genomes and transcriptomes and development of computational network inference approaches. Here, we review current techniques, challenges and trends in gene regulatory network inference and highlight challenges and opportunities for plant science. We provide plant-specific application examples to guide researchers in selecting methodologies that suit their particular research questions. Given the interdisciplinary nature of gene regulatory network inference, we tried to cater to both biologists and computer scientists to help them engage in a dialogue about concepts and caveats in network inference. Specifically, we discuss problems and opportunities in heterogeneous data integration for eukaryotic organisms and common caveats to be considered during network model evaluation. This article is part of a Special Issue entitled: Plant Gene Regulatory Mechanisms and Networks, edited by Dr. Erich Grotewold and Dr. Nathan Springer.

  10. Systems Approaches to Identifying Gene Regulatory Networks in Plants

    PubMed Central

    Long, Terri A.; Brady, Siobhan M.; Benfey, Philip N.

    2009-01-01

    Complex gene regulatory networks are composed of genes, noncoding RNAs, proteins, metabolites, and signaling components. The availability of genome-wide mutagenesis libraries; large-scale transcriptome, proteome, and metabalome data sets; and new high-throughput methods that uncover protein interactions underscores the need for mathematical modeling techniques that better enable scientists to synthesize these large amounts of information and to understand the properties of these biological systems. Systems biology approaches can allow researchers to move beyond a reductionist approach and to both integrate and comprehend the interactions of multiple components within these systems. Descriptive and mathematical models for gene regulatory networks can reveal emergent properties of these plant systems. This review highlights methods that researchers are using to obtain large-scale data sets, and examples of gene regulatory networks modeled with these data. Emergent properties revealed by the use of these network models and perspectives on the future of systems biology are discussed. PMID:18616425

  11. Space Network Control Conference on Resource Allocation Concepts and Approaches

    NASA Technical Reports Server (NTRS)

    Moe, Karen L. (Editor)

    1991-01-01

    The results are presented of the Space Network Control (SNC) Conference. In the late 1990s, when the Advanced Tracking and Data Relay Satellite System is operational, Space Network communication services will be supported and controlled by the SNC. The goals of the conference were to survey existing resource allocation concepts and approaches, to identify solutions applicable to the Space Network, and to identify avenues of study in support of the SNC development. The conference was divided into three sessions: (1) Concepts for Space Network Allocation; (2) SNC and User Payload Operations Control Center (POCC) Human-Computer Interface Concepts; and (3) Resource Allocation Tools, Technology, and Algorithms. Key recommendations addressed approaches to achieving higher levels of automation in the scheduling process.

  12. An approach to online network monitoring using clustered patterns

    DOE PAGES

    Kim, Jinoh; Sim, Alex; Suh, Sang C.; ...

    2017-03-13

    Network traffic monitoring is a core element in network operations and management for various purposes such as anomaly detection, change detection, and fault/failure detection. In this study, we introduce a new approach to online monitoring using a pattern-based representation of the network traffic. Unlike the past online techniques limited to a single variable to summarize (e.g., sketch), the focus of this study is on capturing the network state from the multivariate attributes under consideration. To this end, we employ clustering with its benefit of the aggregation of multidimensional variables. The clustered result represents the state of the network with regardmore » to the monitored variables, which can also be compared with the previously observed patterns visually and quantitatively. Finally, we demonstrate the proposed method with two popular use cases, one for estimating state changes and the other for identifying anomalous states, to confirm its feasibility.« less

  13. A thermostatistical approach to scale-free networks

    NASA Astrophysics Data System (ADS)

    da Cruz, João P.; Araújo, Nuno A. M.; Raischel, Frank; Lind, Pedro G.

    2015-11-01

    We describe an ensemble of growing scale-free networks in an equilibrium framework, providing insight into why the exponent of empirical scale-free networks in nature is typically robust. In an analogy to thermostatistics, to describe the canonical and microcanonical ensembles, we introduce a functional, whose maximum corresponds to a scale-free configuration. We then identify the equivalents to energy, Zeroth-law, entropy and heat capacity for scale-free networks. Discussing the merging of scale-free networks, we also establish an exact relation to predict their final "equilibrium" degree exponent. All analytic results are complemented with Monte Carlo simulations. Our approach illustrates the possibility to apply the tools of equilibrium statistical physics to study the properties of growing networks, and it also supports the recent arguments on the complementarity between equilibrium and nonequilibrium systems.

  14. Custodial Multicast in Delay Tolerant Networks: Challenges and Approaches

    DTIC Science & Technology

    2006-01-01

    Custodial Multicast in Delay Tolerant Networks Challenges and Approaches Susan Symington, Robert C. Durst , and Keith Scott The MITRE Corporation...McLean, Virginia susan@mitre.org, durst @mitre.org, kscott@mitre.org Abstract— Although custodial transmission of multicast bundles would be...Specification", draft-irtf- dtnrg-bundle-spec-05.txt , July 2005. [3] Symington, S., Durst , R., and Scott, K., “Delay-Tolerant Networking Custodial

  15. Next generation of network medicine: interdisciplinary signaling approaches.

    PubMed

    Korcsmaros, Tamas; Schneider, Maria Victoria; Superti-Furga, Giulio

    2017-02-20

    In the last decade, network approaches have transformed our understanding of biological systems. Network analyses and visualizations have allowed us to identify essential molecules and modules in biological systems, and improved our understanding of how changes in cellular processes can lead to complex diseases, such as cancer, infectious and neurodegenerative diseases. "Network medicine" involves unbiased large-scale network-based analyses of diverse data describing interactions between genes, diseases, phenotypes, drug targets, drug transport, drug side-effects, disease trajectories and more. In terms of drug discovery, network medicine exploits our understanding of the network connectivity and signaling system dynamics to help identify optimal, often novel, drug targets. Contrary to initial expectations, however, network approaches have not yet delivered a revolution in molecular medicine. In this review, we propose that a key reason for the limited impact, so far, of network medicine is a lack of quantitative multi-disciplinary studies involving scientists from different backgrounds. To support this argument, we present existing approaches from structural biology, 'omics' technologies (e.g., genomics, proteomics, lipidomics) and computational modeling that point towards how multi-disciplinary efforts allow for important new insights. We also highlight some breakthrough studies as examples of the potential of these approaches, and suggest ways to make greater use of the power of interdisciplinarity. This review reflects discussions held at an interdisciplinary signaling workshop which facilitated knowledge exchange from experts from several different fields, including in silico modelers, computational biologists, biochemists, geneticists, molecular and cell biologists as well as cancer biologists and pharmacologists.

  16. Energy Efficient Approach in RFID Network

    NASA Astrophysics Data System (ADS)

    Mahdin, Hairulnizam; Abawajy, Jemal; Salwani Yaacob, Siti

    2016-11-01

    Radio Frequency Identification (RFID) technology is among the key technology of Internet of Things (IOT). It is a sensor device that can monitor, identify, locate and tracking physical objects via its tag. The energy in RFID is commonly being used unwisely because they do repeated readings on the same tag as long it resides in the reader vicinity. Repeated readings are unnecessary because it only generate duplicate data that does not contain new information. The reading process need to be schedule accordingly to minimize the chances of repeated readings to save the energy. This will reduce operational cost and can prolong the tag's battery lifetime that cannot be replaced. In this paper, we propose an approach named SELECT to minimize energy spent during reading processes. Experiments conducted shows that proposed algorithm contribute towards significant energy savings in RFID compared to other approaches.

  17. Probabilistic Network Approach to Decision-Making

    NASA Astrophysics Data System (ADS)

    Nicolis, Grégoire; Nicolis, Stamatios C.

    2015-06-01

    A probabilistic approach to decision-making is developed in which the states of the underlying stochastic process, assumed to be of the Markov type, represent the competing options. The principal parameters determining the dominance of a particular option versus the others are identified and the transduction of information associated to the transitions between states is quantified using a set of entropy-like quantities.

  18. NetworkAnalyst - integrative approaches for protein–protein interaction network analysis and visual exploration

    PubMed Central

    Xia, Jianguo; Benner, Maia J.; Hancock, Robert E. W.

    2014-01-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required - identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. PMID:24861621

  19. Building a glaucoma interaction network using a text mining approach.

    PubMed

    Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

    2016-01-01

    The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of

  20. Contingent approach to Internet-based supply network integration

    NASA Astrophysics Data System (ADS)

    Ho, Jessica; Boughton, Nick; Kehoe, Dennis; Michaelides, Zenon

    2001-10-01

    The Internet is playing an increasingly important role in enhancing the operations of supply networks as many organizations begin to recognize the benefits of Internet- enabled supply arrangements. However, the developments and applications to-date do not extend significantly beyond the dyadic model, whereas the real advantages are to be made with the external and network models to support a coordinated and collaborative based approach. The DOMAIN research group at the University of Liverpool is currently defining new Internet- enabled approaches to enable greater collaboration across supply chains. Different e-business models and tools are focusing on different applications. Using inappropriate e- business models, tools or techniques will bring negative results instead of benefits to all the tiers in the supply network. Thus there are a number of issues to be considered before addressing Internet based supply network integration, in particular an understanding of supply chain management, the emergent business models and evaluating the effects of deploying e-business to the supply network or a particular tier. It is important to utilize a contingent approach to selecting the right e-business model to meet the specific supply chain requirements. This paper addresses the issues and provides a case study on the indirect materials supply networks.

  1. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

    SciTech Connect

    Shi, Cindy

    2015-07-17

    The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

  2. A mathematical programming approach for sequential clustering of dynamic networks

    NASA Astrophysics Data System (ADS)

    Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia

    2016-02-01

    A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.

  3. Information processing by biochemical networks: a dynamic approach

    PubMed Central

    Bowsher, Clive G.

    2011-01-01

    Understanding how information is encoded and transferred by biochemical networks is of fundamental importance in cellular and systems biology. This requires analysis of the relationships between the stochastic trajectories of the constituent molecular (or submolecular) species that comprise the network. We describe how to identify conditional independences between the trajectories or time courses of groups of species. These are robust network properties that provide important insight into how information is processed. An entire network can then be decomposed exactly into modules on informational grounds. In the context of signalling networks with multiple inputs, the approach identifies the routes and species involved in sequential information processing between input and output modules. An algorithm is developed which allows automated identification of decompositions for large networks and visualization using a tree that encodes the conditional independences. Only stoichiometric information is used and neither simulations nor knowledge of rate parameters are required. A bespoke version of the algorithm for signalling networks identifies the routes of sequential encoding between inputs and outputs, visualized as paths in the tree. Application to the toll-like receptor signalling network reveals that inputs can be informative in ways unanticipated by steady-state analyses, that the information processing structure is not well described as a bow tie, and that encoding for the interferon response is unusually sparse compared with other outputs of this innate immune system. PMID:20685691

  4. Network medicine approaches to the genetics of complex diseases.

    PubMed

    Silverman, Edwin K; Loscalzo, Joseph

    2012-08-01

    Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other -omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to -omics data have focused on a single -omics platform; the next generation of complex disease genetics studies will require integration of multiple types of -omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies.

  5. Network Medicine Approaches to the Genetics of Complex Diseases

    PubMed Central

    Silverman, Edwin K.; Loscalzo, Joseph

    2013-01-01

    Complex diseases are caused by perturbations of biological networks. Genetic analysis approaches focused on individual genetic determinants are unlikely to characterize the network architecture of complex diseases comprehensively. Network medicine, which applies systems biology and network science to complex molecular networks underlying human disease, focuses on identifying the interacting genes and proteins which lead to disease pathogenesis. The long biological path between a genetic risk variant and development of a complex disease involves a range of biochemical intermediates, including coding and non-coding RNA, proteins, and metabolites. Transcriptomics, proteomics, metabolomics, and other –omics technologies have the potential to provide insights into complex disease pathogenesis, especially if they are applied within a network biology framework. Most previous efforts to relate genetics to –omics data have focused on a single –omics platform; the next generation of complex disease genetics studies will require integration of multiple types of –omics data sets in a network context. Network medicine may also provide insight into complex disease heterogeneity, serve as the basis for new disease classifications that reflect underlying disease pathogenesis, and guide rational therapeutic and preventive strategies. PMID:22935211

  6. 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.

  7. Systems psychopharmacology: A network approach to developing novel therapies

    PubMed Central

    Gebicke-Haerter, Peter J

    2016-01-01

    The multifactorial origin of most chronic disorders of the brain, including schizophrenia, has been well accepted. Consequently, pharmacotherapy would require multi-targeted strategies. This contrasts to the majority of drug therapies used until now, addressing more or less specifically only one target molecule. Nevertheless, quite some searches for multiple molecular targets specific for mental disorders have been undertaken. For example, genome-wide association studies have been conducted to discover new target genes of disease. Unfortunately, these attempts have not fulfilled the great hopes they have started with. Polypharmacology and network pharmacology approaches of drug treatment endeavor to abandon the one-drug one-target thinking. To this end, most approaches set out to investigate network topologies searching for modules, endowed with “important” nodes, such as “hubs” or “bottlenecks”, encompassing features of disease networks, and being useful as tentative targets of drug therapies. This kind of research appears to be very promising. However, blocking or inhibiting “important” targets may easily result in destruction of network integrity. Therefore, it is suggested here to study functions of nodes with lower centrality for more subtle impact on network behavior. Targeting multiple nodes with low impact on network integrity by drugs with multiple activities (“dirty drugs”) or by several drugs, simultaneously, avoids to disrupt network integrity and may reset deviant dynamics of disease. Natural products typically display multi target functions and therefore could help to identify useful biological targets. Hence, future efforts should consider to combine drug-target networks with target-disease networks using mathematical (graph theoretical) tools, which could help to develop new therapeutic strategies in long-term psychiatric disorders. PMID:27014599

  8. Sensitivity of chemical reaction networks: a structural approach. 1. Examples and the carbon metabolic network.

    PubMed

    Mochizuki, Atsushi; Fiedler, Bernold

    2015-02-21

    In biological cells, chemical reaction pathways lead to complex network systems like metabolic networks. One experimental approach to the dynamics of such systems examines their "sensitivity": each enzyme mediating a reaction in the system is increased/decreased or knocked out separately, and the responses in the concentrations of chemicals or their fluxes are observed. In this study, we present a mathematical method, named structural sensitivity analysis, to determine the sensitivity of reaction systems from information on the network alone. We investigate how the sensitivity responses of chemicals in a reaction network depend on the structure of the network, and on the position of the perturbed reaction in the network. We establish and prove some general rules which relate the sensitivity response to the structure of the underlying network. We describe a hierarchical pattern in the flux response which is governed by branchings in the network. We apply our method to several hypothetical and real life chemical reaction networks, including the metabolic network of the Escherichia coli TCA cycle. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Neural Network Approach To Sensory Fusion

    NASA Astrophysics Data System (ADS)

    Pearson, John C.; Gelfand, Jack J.; Sullivan, W. E.; Peterson, Richard M.; Spence, Clay D.

    1988-08-01

    We present a neural network model for sensory fusion based on the design of the visual/acoustic target localiza-tion system of the barn owl. This system adaptively fuses its separate visual and acoustic representations of object position into a single joint representation used for head orientation. The building block in this system, as in much of the brain, is the neuronal map. Neuronal maps are large arrays of locally interconnected neurons that represent information in a map-like form, that is, parameter values are systematically encoded by the position of neural activation in the array. The computational load is distributed to a hierarchy of maps, and the computation is performed in stages by transforming the representation from map to map via the geometry of the projections between the maps and the local interactions within the maps. For example, azimuthal position is computed from the frequency and binaural phase information encoded in the signals of the acoustic sensors, while elevation is computed in a separate stream using binaural intensity information. These separate streams are merged in their joint projection onto the external nucleus of the inferior colliculus, a two dimensional array of cells which contains a map of acoustic space. This acoustic map, and the visual map of the retina, jointly project onto the optic tectum, creating a fused visual/acoustic representation of position in space that is used for object localization. In this paper we describe our mathematical model of the stage of visual/acoustic fusion in the optic tectum. The model assumes that the acoustic projection from the external nucleus onto the tectum is roughly topographic and one-to-many, while the visual projection from the retina onto the tectum is topographic and one-to-one. A simple process of self-organization alters the strengths of the acoustic connections, effectively forming a focused beam of strong acoustic connections whose inputs are coincident with the visual inputs

  10. Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

    PubMed Central

    Senior, Alistair M.; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J.

    2016-01-01

    Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. PMID:26858671

  11. Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach.

    PubMed

    Senior, Alistair M; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J

    2016-01-01

    Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.

  12. Bayesian network approach to spatial data mining: a case study

    NASA Astrophysics Data System (ADS)

    Huang, Jiejun; Wan, Youchuan

    2006-10-01

    Spatial data mining is a process of discovering interesting, novel, and potentially useful information or knowledge hidden in spatial data sets. It involves different techniques and different methods from various areas of research. A Bayesian network is a graphical model that encodes causal probabilistic relationships among variables of interest, which has a powerful ability for representing and reasoning and provides an effective way to spatial data mining. In this paper we give an introduction to Bayesian networks, and discuss using Bayesian networks for spatial data mining. We propose a framework of spatial data mining based on Bayesian networks. Then we show a case study and use the experimental results to validate the practical viability of the proposed approach to spatial data mining. Finally, the paper gives a summary and some remarks.

  13. A novel modulation classification approach using Gabor filter network.

    PubMed

    Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

    2014-01-01

    A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel.

  14. Speech transmission index from running speech: A neural network approach

    NASA Astrophysics Data System (ADS)

    Li, F. F.; Cox, T. J.

    2003-04-01

    Speech transmission index (STI) is an important objective parameter concerning speech intelligibility for sound transmission channels. It is normally measured with specific test signals to ensure high accuracy and good repeatability. Measurement with running speech was previously proposed, but accuracy is compromised and hence applications limited. A new approach that uses artificial neural networks to accurately extract the STI from received running speech is developed in this paper. Neural networks are trained on a large set of transmitted speech examples with prior knowledge of the transmission channels' STIs. The networks perform complicated nonlinear function mappings and spectral feature memorization to enable accurate objective parameter extraction from transmitted speech. Validations via simulations demonstrate the feasibility of this new method on a one-net-one-speech extract basis. In this case, accuracy is comparable with normal measurement methods. This provides an alternative to standard measurement techniques, and it is intended that the neural network method can facilitate occupied room acoustic measurements.

  15. Network biology: a direct approach to study biological function.

    PubMed

    Emmert-Streib, Frank; Glazko, Galina V

    2011-01-01

    In this paper we discuss the dualism of gene networks and their role in systems biology. We argue that gene networks (1) can serve as a conceptual framework, forming a fundamental level of a phenomenological description, and (2) are a means to represent and analyze data. The latter point does not only allow a systems analysis but is even amenable for a direct approach to study biological function. Here we focus on the clarity of our main arguments and conceptual meaning of gene networks, rather than the causal inference of gene networks from data. WIREs Syst Biol Med 2011 3 379-391 DOI: 10.1002/wsbm.134 For further resources related to this article, please visit the WIREs website. Copyright © 2010 John Wiley & Sons, Inc.

  16. A Novel Modulation Classification Approach Using Gabor Filter Network

    PubMed Central

    Ghauri, Sajjad Ahmed; Qureshi, Ijaz Mansoor; Cheema, Tanveer Ahmed; Malik, Aqdas Naveed

    2014-01-01

    A Gabor filter network based approach is used for feature extraction and classification of digital modulated signals by adaptively tuning the parameters of Gabor filter network. Modulation classification of digitally modulated signals is done under the influence of additive white Gaussian noise (AWGN). The modulations considered for the classification purpose are PSK 2 to 64, FSK 2 to 64, and QAM 4 to 64. The Gabor filter network uses the network structure of two layers; the first layer which is input layer constitutes the adaptive feature extraction part and the second layer constitutes the signal classification part. The Gabor atom parameters are tuned using Delta rule and updating of weights of Gabor filter using least mean square (LMS) algorithm. The simulation results show that proposed novel modulation classification algorithm has high classification accuracy at low signal to noise ratio (SNR) on AWGN channel. PMID:25126603

  17. Approaches to identify kinase dependencies in cancer signalling networks.

    PubMed

    Dermit, Maria; Dokal, Arran; Cutillas, Pedro R

    2017-09-01

    Cells integrate extracellular signals into appropriate responses through a complex network of biochemical reactions driven by the activity of protein and lipid kinases, among other proteins. In order to understand this complexity, new approaches, both experimental and computational, have recently been developed with the aim to identify regulatory kinases and infer their activation status in the context of their signalling network. Here, we review such approaches with particular focus on those based on phosphoproteomics. Integration of kinase activity measurements inferred from phosphoproteomics data with other 'omics' datasets is starting to be used to identify regulatory nodes in biochemical networks. These methodologies may in the future be used to identify patient-specific targets and thus advance personalised cancer medicine. © 2017 Federation of European Biochemical Societies.

  18. The Eigenfactor Metrics™: A Network Approach to Assessing Scholarly Journals

    ERIC Educational Resources Information Center

    West, Jevin D.; Bergstrom, Theodore C.; Bergstrom, Carl T.

    2010-01-01

    Limited time and budgets have created a legitimate need for quantitative measures of scholarly work. The well-known journal impact factor is the leading measure of this sort; here we describe an alternative approach based on the full structure of the scholarly citation network. The Eigenfactor Metrics--Eigenfactor Score and Article Influence…

  19. Evaluating Action Learning: A Critical Realist Complex Network Theory Approach

    ERIC Educational Resources Information Center

    Burgoyne, John G.

    2010-01-01

    This largely theoretical paper will argue the case for the usefulness of applying network and complex adaptive systems theory to an understanding of action learning and the challenge it is evaluating. This approach, it will be argued, is particularly helpful in the context of improving capability in dealing with wicked problems spread around…

  20. Connecting our resources: Louisiana's approach to community health network development.

    PubMed

    Broussard, Marsha; Blackwell, Robyn; Caillouet, L Philip; Nichols, Kristy Holloway; Shipman, Margaret

    2003-01-01

    Louisiana's rural community health systems are in crisis because of pressures fueled by the rising costs of health care, sustained poor health status, state budget shortfalls and changes in priorities, and a sliding rural economy. The development of community health networks is providing new infrastructure and capacity for communities to reprioritize, formulate innovative partnerships, and leverage new resources. Successful elements of Louisiana's network development experience include community commitment to engage in study and action; the availability of capable and motivated technical assistance; an approach that involves open-engagement, community-driven decision-making; and data-driven problem definition, prioritization, and solutions. Louisiana's experiences illustrate the benefits of developing networks along with, or as a result of, a community health plan. When a community owns its health improvement plan, it is more likely to support the new network as a structure for implementation. Broad-scale participation is also a principle of success. When social service agencies are included along with health agencies, more comprehensive strategies result, and they bring additional resources, resulting in more holistic solutions. The cases of 2 networks are presented as illustrations. One involves the facilitation of a community planning process for an existing network. The plan helped to expand the network's community connections and support and provided the content for a successful application for a Health Resources and Services Administration Community Access Program grant. In the second case, a new network was developed, and it leveraged federal funds from the federal Office of Rural Health Policy's Network Development Grant Program.

  1. Neural network for graphs: a contextual constructive approach.

    PubMed

    Micheli, Alessio

    2009-03-01

    This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected labeled graphs. In particular, the model can realize adaptive contextual transductions, learning the mapping from graphs for both classification and regression tasks. In contrast to previous neural networks for structures that had a recursive dynamics, NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data. Moreover, the incremental approach eliminates the need to introduce cyclic dependencies in the definition of the system state variables. In the traversal process, the NN4G units exploit (local) contextual information of the graphs vertices. In spite of the simplicity of the approach, we show that, through the compositionality of the contextual information developed by the learning, the model can deal with contextual information that is incrementally extended according to the graphs topology. The effectiveness and the generality of the new approach are investigated by analyzing its theoretical properties and providing experimental results.

  2. Alcohol expectancy multiaxial assessment: a memory network-based approach.

    PubMed

    Goldman, Mark S; Darkes, Jack

    2004-03-01

    Despite several decades of activity, alcohol expectancy research has yet to merge measurement approaches with developing memory theory. This article offers an expectancy assessment approach built on a conceptualization of expectancy as an information processing network. The authors began with multidimensional scaling models of expectancy space, which served as heuristics to suggest confirmatory factor analytic dimensional models for entry into covariance structure predictive models. It is argued that this approach permits a relatively thorough assessment of the broad range of potential expectancy dimensions in a format that is very flexible in terms of instrument length and specificity versus breadth of focus. ((c) 2004 APA, all rights reserved)

  3. Real Time Mars Approach Navigation Aided by the Mars Network

    NASA Technical Reports Server (NTRS)

    Ely, Todd A.; Duncan, Courtney; Lightsey, E. Glenn; Mogensen, Andreas

    2006-01-01

    A NASA Mars technology project is described that is building a prototype embedded real time Mars approach navigation capability which can be hosted on the Mars Network's Electra transceiver. The paper motivates the reason for doing real time Mars approach navigation via a set of analyses demonstrating its utility for enabling Mars pin-point landing (less than 1-km landing error). The development approach, software design, and test results are discussed. Finally, the way forward towards a flight demonstration on the Mars Science Laboratory (MSL) is presented.

  4. Real Time Mars Approach Navigation Aided by the Mars Network

    NASA Technical Reports Server (NTRS)

    Ely, Todd A.; Duncan, Courtney; Lightsey, E. Glenn; Mogensen, Andreas

    2006-01-01

    A NASA Mars technology project is described that is building a prototype embedded real time Mars approach navigation capability which can be hosted on the Mars Network's Electra transceiver. The paper motivates the reason for doing real time Mars approach navigation via a set of analyses demonstrating its utility for enabling Mars pin-point landing (< 1-km landing error). The development approach, software design, and test results are discussed. Finally, the way forward towards a flight demonstration on the Mars Science Laboratory is presented.

  5. Neural network approaches to dynamic collision-free trajectory generation.

    PubMed

    Yang, S X; Meng, M

    2001-01-01

    In this paper, dynamic collision-free trajectory generation in a nonstationary environment is studied using biologically inspired neural network approaches. The proposed neural network is topologically organized, where the dynamics of each neuron is characterized by a shunting equation or an additive equation. The state space of the neural network can be either the Cartesian workspace or the joint space of multi-joint robot manipulators. There are only local lateral connections among neurons. The real-time optimal trajectory is generated through the dynamic activity landscape of the neural network without explicitly searching over the free space nor the collision paths, without explicitly optimizing any global cost functions, without any prior knowledge of the dynamic environment, and without any learning procedures. Therefore the model algorithm is computationally efficient. The stability of the neural network system is guaranteed by the existence of a Lyapunov function candidate. In addition, this model is not very sensitive to the model parameters. Several model variations are presented and the differences are discussed. As examples, the proposed models are applied to generate collision-free trajectories for a mobile robot to solve a maze-type of problem, to avoid concave U-shaped obstacles, to track a moving target and at the same to avoid varying obstacles, and to generate a trajectory for a two-link planar robot with two targets. The effectiveness and efficiency of the proposed approaches are demonstrated through simulation and comparison studies.

  6. Multiple neural network approaches to clinical expert systems

    NASA Astrophysics Data System (ADS)

    Stubbs, Derek F.

    1990-08-01

    We briefly review the concept of computer aided medical diagnosis and more extensively review the the existing literature on neural network applications in the field. Neural networks can function as simple expert systems for diagnosis or prognosis. Using a public database we develop a neural network for the diagnosis of a major presenting symptom while discussing the development process and possible approaches. MEDICAL EXPERTS SYSTEMS COMPUTER AIDED DIAGNOSIS Biomedicine is an incredibly diverse and multidisciplinary field and it is not surprising that neural networks with their many applications are finding more and more applications in the highly non-linear field of biomedicine. I want to concentrate on neural networks as medical expert systems for clinical diagnosis or prognosis. Expert Systems started out as a set of computerized " ifthen" rules. Everything was reduced to boolean logic and the promised land of computer experts was said to be in sight. It never came. Why? First the computer code explodes as the number of " ifs" increases. All the " ifs" have to interact. Second experts are not very good at reducing expertise to language. It turns out that experts recognize patterns and have non-verbal left-brain intuition decision processes. Third learning by example rather than learning by rule is the way natural brains works and making computers work by rule-learning is hideously labor intensive. Neural networks can learn from example. They learn the results

  7. An Observing System Simulation Experiment Approach to Meteorological Network Assessment

    NASA Astrophysics Data System (ADS)

    Abbasnezhadi, K.; Rasmussen, P. F.; Stadnyk, T.; Boluwade, A.

    2016-12-01

    A proper knowledge of the spatiotemporal distribution of rainfall is important in order to conduct a mindful investigation of water movement and storage throughout a catchment. Currently, the most accurate precipitation information available for the remote Boreal ecozones of northern Manitoba is coming from the Canadian Precipitation Analysis (CaPA) data assimilation system. Throughout the Churchill River Basin (CRB), CaPA still does not have the proper skill due to the limited number of weather stations. A new approach to experimental network design was investigated based on the concept of Observing System Simulation Experiment (OSSE). The OSSE-based network assessment procedure which simulates the CaPA system provides a scientific and hydrologically significant tool to assess the sensitivity of CaPA precipitation analysis to observation network density throughout the CRB. To simulate CaPA system, synthetic background and station data were simulated, respectively, by adding spatially uncorrelated and correlated Gaussian noises to an assumingly true daily weather field synthesized by a gridded precipitation generator which simulates CaPA data. Given the true reference field on one hand, and a set of pseudo-CaPA analyses associated with different network realizations on the other hand, a WATFLOOD hydrological model was employed to compare the modeled runoff. The simulations showed that as network density increases, the accuracy of CaPA precipitation products improves up to a certain limit beyond which adding more stations to the network does not result in further accuracy.

  8. Kaolin Quality Prediction from Samples: A Bayesian Network Approach

    SciTech Connect

    Rivas, T.; Taboada, J.; Ordonez, C.; Matias, J. M.

    2009-08-13

    We describe the results of an expert system applied to the evaluation of samples of kaolin for industrial use in paper or ceramic manufacture. Different machine learning techniques - classification trees, support vector machines and Bayesian networks - were applied with the aim of evaluating and comparing their interpretability and prediction capacities. The predictive capacity of these models for the samples analyzed was highly satisfactory, both for ceramic quality and paper quality. However, Bayesian networks generally proved to be the most useful technique for our study, as this approach combines good predictive capacity with excellent interpretability of the kaolin quality structure, as it graphically represents relationships between variables and facilitates what-if analyses.

  9. Natural and anthropogenic multi-type hazards for loess territories

    NASA Astrophysics Data System (ADS)

    Mavlyanova, Nadira; Zakirova, Zulfiya

    2013-04-01

    developing of mining manufactures and their waste located in the foothill areas with high seismic risk and where manifested of dangerous geological processes as landslide, collapse, mud stream, rock falls and toxic contamination; 3) development of urbanization with manifestation of difference engineering geological processes in loess soil on the based of constructions in cities (collapse, liquefaction). That example of cascade effects when natural and anthropogenic multi type hazards in loess was the Gissar earthquake (1989) in Tajikistan when the earthquake of rather moderate intensity (M=5.2; H=5-7 km; I=7 - MSK scale) was triggered several landslides and mudslides connected with liquefaction of wetted loess and can cause a large number of human victims. In the pre 20 years steady irrigation of the slope area occurred for cotton field. This moistening has increase and the water content of the soil to wet 24-28%, up to a depth of 20-30 m that increased the vulnerability of this territory. The interactions between different natural hazards, include triggered, especially earthquakes, landslides, collapses, liquefaction in loess soil with taking account of anthropogenic hazard influence was investigate.

  10. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.

    PubMed

    Mo, Qianxing; Shen, Ronglai; Guo, Cui; Vannucci, Marina; Chan, Keith S; Hilsenbeck, Susan G

    2017-05-24

    Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  11. Implementing a Trauma-Informed Approach in Pediatric Healthcare Networks

    PubMed Central

    Marsac, Meghan L.; Kassam-Adams, Nancy; Hildenbrand, Aimee K.; Nicholls, Elizabeth; Winston, Flaura K.; Leff, Stephen S.; Fein, Joel

    2016-01-01

    Pediatric healthcare networks serve millions of children each year. Pediatric illness and injury are among the most common potential emotionally traumatic experiences for children and their families. Additionally, millions of children who present for medical care (including well visits) have been exposed to prior traumatic events such as violence or natural disasters. Given the daily challenges of working in pediatric healthcare networks, medical providers and support staff can experience trauma symptoms related to their work. The application of a trauma-informed approach to medical care has the potential to mitigate these negative consequences. Trauma-informed care minimizes the potential for medical care to become traumatic or trigger trauma reactions, addresses distress and provides emotional support for the entire family, encourages positive coping, and provides anticipatory guidance regarding the recovery process. When used in conjunction with family-centered practices, trauma-informed approaches enhance quality of care for patients and their families and the wellbeing of medical care providers and support staff. Barriers to routine integration of trauma-informed approaches into pediatric medicine include a lack of available training and unclear best practice guidelines. This paper highlights the importance of implementing a trauma-informed approach and offers a framework for training pediatric healthcare networks in trauma-informed care practices. PMID:26571032

  12. A neural network approach to complete coverage path planning.

    PubMed

    Yang, Simon X; Luo, Chaomin

    2004-02-01

    Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.

  13. Complex networks approach to geophysical time series analysis: Detecting paleoclimate transitions via recurrence networks

    NASA Astrophysics Data System (ADS)

    Donner, R. V.; Zou, Y.; Donges, J. F.; Marwan, N.; Kurths, J.

    2009-12-01

    We present a new approach for analysing structural properties of time series from complex systems. Starting from the concept of recurrences in phase space, the recurrence matrix of a time series is interpreted as the adjacency matrix of an associated complex network which links different points in time if the evolution of the considered states is very similar. A critical comparison of these recurrence networks with similar existing techniques is presented, revealing strong conceptual benefits of the new approach which can be considered as a unifying framework for transforming time series into complex networks that also includes other methods as special cases. Based on different model systems, we demonstrate that there are fundamental interrelationships between the topological properties of recurrence networks and the statistical properties of the phase space density of the underlying dynamical system. Hence, the network description yields new quantitative characteristics of the dynamical complexity of a time series, which substantially complement existing measures of recurrence quantification analysis. Finally, we illustrate the potential of our approach for detecting hidden dynamical transitions from geoscientific time series by applying it to different paleoclimate records. In particular, we are able to resolve previously unknown climatic regime shifts in East Africa during the last about 4 million years, which might have had a considerable influence on the evolution of hominids in the area.

  14. Fire detection from hyperspectral data using neural network approach

    NASA Astrophysics Data System (ADS)

    Piscini, Alessandro; Amici, Stefania

    2015-10-01

    This study describes an application of artificial neural networks for the recognition of flaming areas using hyper- spectral remote sensed data. Satellite remote sensing is considered an effective and safe way to monitor active fires for environmental and people safeguarding. Neural networks are an effective and consolidated technique for the classification of satellite images. Moreover, once well trained, they prove to be very fast in the application stage for a rapid response. At flaming temperature, thanks to its low excitation energy (about 4.34 eV), potassium (K) ionize with a unique doublet emission features. This emission features can be detected remotely providing a detection map of active fire which allows in principle to separate flaming from smouldering areas of vegetation even in presence of smoke. For this study a normalised Advanced K Band Difference (AKBD) has been applied to airborne hyper spectral sensor covering a range of 400-970 nm with resolution 2.9 nm. A back propagation neural network was used for the recognition of active fires affecting the hyperspectral image. The network was trained using all channels of sensor as inputs, and the corresponding AKBD indexes as target output. In order to evaluate its generalization capabilities, the neural network was validated on two independent data sets of hyperspectral images, not used during neural network training phase. The validation results for the independent data-sets had an overall accuracy round 100% for both image and a few commission errors (0.1%), therefore demonstrating the feasibility of estimating the presence of active fires using a neural network approach. Although the validation of the neural network classifier had a few commission errors, the producer accuracies were lower due to the presence of omission errors. Image analysis revealed that those false negatives lie in "smoky" portion fire fronts, and due to the low intensity of the signal. The proposed method can be considered

  15. A Spatial Clustering Approach for Stochastic Fracture Network Modelling

    NASA Astrophysics Data System (ADS)

    Seifollahi, S.; Dowd, P. A.; Xu, C.; Fadakar, A. Y.

    2014-07-01

    Fracture network modelling plays an important role in many application areas in which the behaviour of a rock mass is of interest. These areas include mining, civil, petroleum, water and environmental engineering and geothermal systems modelling. The aim is to model the fractured rock to assess fluid flow or the stability of rock blocks. One important step in fracture network modelling is to estimate the number of fractures and the properties of individual fractures such as their size and orientation. Due to the lack of data and the complexity of the problem, there are significant uncertainties associated with fracture network modelling in practice. Our primary interest is the modelling of fracture networks in geothermal systems and, in this paper, we propose a general stochastic approach to fracture network modelling for this application. We focus on using the seismic point cloud detected during the fracture stimulation of a hot dry rock reservoir to create an enhanced geothermal system; these seismic points are the conditioning data in the modelling process. The seismic points can be used to estimate the geographical extent of the reservoir, the amount of fracturing and the detailed geometries of fractures within the reservoir. The objective is to determine a fracture model from the conditioning data by minimizing the sum of the distances of the points from the fitted fracture model. Fractures are represented as line segments connecting two points in two-dimensional applications or as ellipses in three-dimensional (3D) cases. The novelty of our model is twofold: (1) it comprises a comprehensive fracture modification scheme based on simulated annealing and (2) it introduces new spatial approaches, a goodness-of-fit measure for the fitted fracture model, a measure for fracture similarity and a clustering technique for proposing a locally optimal solution for fracture parameters. We use a simulated dataset to demonstrate the application of the proposed approach

  16. A quantitative neural network approach to understanding aging phenotypes

    PubMed Central

    Ash, Jessica A.; Rapp, Peter R.

    2015-01-01

    Basic research on neurocognitive aging has traditionally adopted a reductionist approach in the search for the basis of cognitive preservation versus decline. However, increasing evidence suggests that a network level understanding of the brain can provide additional novel insight into the structural and functional organization from which complex behavior and dysfunction emerge. Using graph theory as a mathematical framework to characterize neural networks, recent data suggest that alterations in structural and functional networks may contribute to individual differences in cognitive phenotypes in advanced aging. This paper reviews literature that defines network changes in healthy and pathological aging phenotypes, while highlighting the substantial overlap in key features and patterns observed across aging phenotypes. Consistent with current efforts in this area, here we outline one analytic strategy that attempts to quantify graph theory metrics more precisely, with the goal of improving diagnostic sensitivity and predictive accuracy for differential trajectories in neurocognitive aging. Ultimately, such an approach may yield useful measures for gauging the efficacy of potential preventative interventions and disease modifying treatments early in the course of aging. PMID:24548925

  17. A DC programming approach for finding communities in networks.

    PubMed

    Le Thi, Hoai An; Nguyen, Manh Cuong; Dinh, Tao Pham

    2014-12-01

    Automatic discovery of community structures in complex networks is a fundamental task in many disciplines, including physics, biology, and the social sciences. The most used criterion for characterizing the existence of a community structure in a network is modularity, a quantitative measure proposed by Newman and Girvan (2004). The discovery community can be formulated as the so-called modularity maximization problem that consists of finding a partition of nodes of a network with the highest modularity. In this letter, we propose a fast and scalable algorithm called DCAM, based on DC (difference of convex function) programming and DCA (DC algorithms), an innovative approach in nonconvex programming framework for solving the modularity maximization problem. The special structure of the problem considered here has been well exploited to get an inexpensive DCA scheme that requires only a matrix-vector product at each iteration. Starting with a very large number of communities, DCAM furnishes, as output results, an optimal partition together with the optimal number of communities [Formula: see text]; that is, the number of communities is discovered automatically during DCAM's iterations. Numerical experiments are performed on a variety of real-world network data sets with up to 4,194,304 nodes and 30,359,198 edges. The comparative results with height reference algorithms show that the proposed approach outperforms them not only on quality and rapidity but also on scalability. Moreover, it realizes a very good trade-off between the quality of solutions and the run time.

  18. A brain network instantiating approach and avoidance motivation.

    PubMed

    Spielberg, Jeffrey M; Miller, Gregory A; Warren, Stacie L; Engels, Anna S; Crocker, Laura D; Banich, Marie T; Sutton, Bradley P; Heller, Wendy

    2012-09-01

    Research indicates that dorsolateral prefrontal cortex (DLPFC) is important for pursuing goals, and areas of DLPFC are differentially involved in approach and avoidance motivation. Given the complexity of the processes involved in goal pursuit, DLPFC is likely part of a network that includes orbitofrontal cortex (OFC), cingulate, amygdala, and basal ganglia. This hypothesis was tested with regard to one component of goal pursuit, the maintenance of goals in the face of distraction. Examination of connectivity with motivation-related areas of DLPFC supported the network hypothesis. Differential patterns of connectivity suggest a distinct role for DLPFC areas, with one involved in selecting approach goals, one in selecting avoidance goals, and one in selecting goal pursuit strategies. Finally, differences in trait motivation moderated connectivity between DLPFC and OFC, suggesting that this connectivity is important for instantiating motivation.

  19. An optimal control approach to probabilistic Boolean networks

    NASA Astrophysics Data System (ADS)

    Liu, Qiuli

    2012-12-01

    External control of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases. For this purpose, a number of stochastic optimal control approaches have been proposed. Probabilistic Boolean networks (PBNs) as powerful tools for modeling gene regulatory systems have attracted considerable attention in systems biology. In this paper, we deal with a problem of optimal intervention in a PBN with the help of the theory of discrete time Markov decision process. Specifically, we first formulate a control model for a PBN as a first passage model for discrete time Markov decision processes and then find, using a value iteration algorithm, optimal effective treatments with the minimal expected first passage time over the space of all possible treatments. In order to demonstrate the feasibility of our approach, an example is also displayed.

  20. A novel approach to characterize information radiation in complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Xiaoyang; Wang, Ying; Zhu, Lin; Li, Chao

    2016-06-01

    The traditional research of information dissemination is mostly based on the virus spreading model that the information is being spread by probability, which does not match very well to the reality, because the information that we receive is always more or less than what was sent. In order to quantitatively describe variations in the amount of information during the spreading process, this article proposes a safety information radiation model on the basis of communication theory, combining with relevant theories of complex networks. This model comprehensively considers the various influence factors when safety information radiates in the network, and introduces some concepts from the communication theory perspective, such as the radiation gain function, receiving gain function, information retaining capacity and information second reception capacity, to describe the safety information radiation process between nodes and dynamically investigate the states of network nodes. On a micro level, this article analyzes the influence of various initial conditions and parameters on safety information radiation through the new model simulation. The simulation reveals that this novel approach can reflect the variation of safety information quantity of each node in the complex network, and the scale-free network has better ;radiation explosive power;, while the small-world network has better ;radiation staying power;. The results also show that it is efficient to improve the overall performance of network security by selecting nodes with high degrees as the information source, refining and simplifying the information, increasing the information second reception capacity and decreasing the noises. In a word, this article lays the foundation for further research on the interactions of information and energy between internal components within complex systems.

  1. A Hybrid Satellite-Terrestrial Approach to Aeronautical Communication Networks

    NASA Technical Reports Server (NTRS)

    Kerczewski, Robert J.; Chomos, Gerald J.; Griner, James H.; Mainger, Steven W.; Martzaklis, Konstantinos S.; Kachmar, Brian A.

    2000-01-01

    Rapid growth in air travel has been projected to continue for the foreseeable future. To maintain a safe and efficient national and global aviation system, significant advances in communications systems supporting aviation are required. Satellites will increasingly play a critical role in the aeronautical communications network. At the same time, current ground-based communications links, primarily very high frequency (VHF), will continue to be employed due to cost advantages and legacy issues. Hence a hybrid satellite-terrestrial network, or group of networks, will emerge. The increased complexity of future aeronautical communications networks dictates that system-level modeling be employed to obtain an optimal system fulfilling a majority of user needs. The NASA Glenn Research Center is investigating the current and potential future state of aeronautical communications, and is developing a simulation and modeling program to research future communications architectures for national and global aeronautical needs. This paper describes the primary requirements, the current infrastructure, and emerging trends of aeronautical communications, including a growing role for satellite communications. The need for a hybrid communications system architecture approach including both satellite and ground-based communications links is explained. Future aeronautical communication network topologies and key issues in simulation and modeling of future aeronautical communications systems are described.

  2. Autonomous control of production networks using a pheromone approach

    NASA Astrophysics Data System (ADS)

    Armbruster, D.; de Beer, C.; Freitag, M.; Jagalski, T.; Ringhofer, C.

    2006-04-01

    The flow of parts through a production network is usually pre-planned by a central control system. Such central control fails in presence of highly fluctuating demand and/or unforeseen disturbances. To manage such dynamic networks according to low work-in-progress and short throughput times, an autonomous control approach is proposed. Autonomous control means a decentralized routing of the autonomous parts themselves. The parts’ decisions base on backward propagated information about the throughput times of finished parts for different routes. So, routes with shorter throughput times attract parts to use this route again. This process can be compared to ants leaving pheromones on their way to communicate with following ants. The paper focuses on a mathematical description of such autonomously controlled production networks. A fluid model with limited service rates in a general network topology is derived and compared to a discrete-event simulation model. Whereas the discrete-event simulation of production networks is straightforward, the formulation of the addressed scenario in terms of a fluid model is challenging. Here it is shown, how several problems in a fluid model formulation (e.g. discontinuities) can be handled mathematically. Finally, some simulation results for the pheromone-based control with both the discrete-event simulation model and the fluid model are presented for a time-dependent influx.

  3. An analysis of multi-type relational interactions in FMA using graph motifs with disjointness constraints.

    PubMed

    Zhang, Guo-Qiang; Luo, Lingyun; Ogbuji, Chime; Joslyn, Cliff; Mejino, Jose; Sahoo, Satya S

    2012-01-01

    The interaction of multiple types of relationships among anatomical classes in the Foundational Model of Anatomy (FMA) can provide inferred information valuable for quality assurance. This paper introduces a method called Motif Checking (MOCH) to study the effects of such multi-relation type interactions for detecting logical inconsistencies as well as other anomalies represented by the motifs. MOCH represents patterns of multi-type interaction as small labeled (with multiple types of edges) sub-graph motifs, whose nodes represent class variables, and labeled edges represent relational types. By representing FMA as an RDF graph and motifs as SPARQL queries, fragments of FMA are automatically obtained as auditing candidates. Leveraging the scalability and reconfigurability of Semantic Web Technology, we performed exhaustive analyses of a variety of labeled sub-graph motifs. The quality assurance feature of MOCH comes from the distinct use of a subset of the edges of the graph motifs as constraints for disjointness, whereby bringing in rule-based flavor to the approach as well. With possible disjointness implied by antonyms, we performed manual inspection of the resulting FMA fragments and tracked down sources of abnormal inferred conclusions (logical inconsistencies), which are amendable for programmatic revision of the FMA. Our results demonstrate that MOCH provides a unique source of valuable information for quality assurance. Since our approach is general, it is applicable to any ontological system with an OWL representation.

  4. An Analysis of Multi-type Relational Interactions in FMA Using Graph Motifs with Disjointness Constraints

    PubMed Central

    Zhang, Guo-Qiang; Luo, Lingyun; Ogbuji, Chime; Joslyn, Cliff; Mejino, Jose; Sahoo, Satya S

    2012-01-01

    The interaction of multiple types of relationships among anatomical classes in the Foundational Model of Anatomy (FMA) can provide inferred information valuable for quality assurance. This paper introduces a method called Motif Checking (MOCH) to study the effects of such multi-relation type interactions for detecting logical inconsistencies as well as other anomalies represented by the motifs. MOCH represents patterns of multi-type interaction as small labeled (with multiple types of edges) sub-graph motifs, whose nodes represent class variables, and labeled edges represent relational types. By representing FMA as an RDF graph and motifs as SPARQL queries, fragments of FMA are automatically obtained as auditing candidates. Leveraging the scalability and reconfigurability of Semantic Web Technology, we performed exhaustive analyses of a variety of labeled sub-graph motifs. The quality assurance feature of MOCH comes from the distinct use of a subset of the edges of the graph motifs as constraints for disjointness, whereby bringing in rule-based flavor to the approach as well. With possible disjointness implied by antonyms, we performed manual inspection of the resulting FMA fragments and tracked down sources of abnormal inferred conclusions (logical inconsistencies), which are amendable for programmatic revision of the FMA. Our results demonstrate that MOCH provides a unique source of valuable information for quality assurance. Since our approach is general, it is applicable to any ontological system with an OWL representation. PMID:23304382

  5. Assessing Collaboration Networks in Educational Research: A Co-Authorship-Based Social Network Analysis Approach

    ERIC Educational Resources Information Center

    Munoz, David Andres; Queupil, Juan Pablo; Fraser, Pablo

    2016-01-01

    Purpose: The purpose of this paper is to analyze collaboration networks and their patterns among higher education institutions (HEIs) in Chile and the Latin American region. This will provide evidence to educational managements in order to properly allocate their efforts to improve collaboration. Design/methodology/approach: This quantitative…

  6. Assessing Collaboration Networks in Educational Research: A Co-Authorship-Based Social Network Analysis Approach

    ERIC Educational Resources Information Center

    Munoz, David Andres; Queupil, Juan Pablo; Fraser, Pablo

    2016-01-01

    Purpose: The purpose of this paper is to analyze collaboration networks and their patterns among higher education institutions (HEIs) in Chile and the Latin American region. This will provide evidence to educational managements in order to properly allocate their efforts to improve collaboration. Design/methodology/approach: This quantitative…

  7. AutoCorrel: a neural network event correlation approach

    NASA Astrophysics Data System (ADS)

    Dondo, Maxwell G.; Japkowicz, Nathalie; Smith, Reuben

    2006-04-01

    Intrusion detection analysts are often swamped by multitudes of alerts originating from installed intrusion detection systems (IDS) as well as logs from routers and firewalls on the networks. Properly managing these alerts and correlating them to previously seen threats is critical in the ability to effectively protect a network from attacks. Manually correlating events can be a slow tedious task prone to human error. We present a two-stage alert correlation approach involving an artificial neural network (ANN) autoassociator and a single parameter decision threshold-setting unit. By clustering closely matched alerts together, this approach would be beneficial to the analyst. In this approach, alert attributes are extracted from each alert content and used to train an autoassociator. Based on the reconstruction error determined by the autoassociator, closely matched alerts are grouped together. Whenever a new alert is received, it is automatically categorised into one of the alert clusters which identify the type of attack and its severity level as previously known by the analyst. If the attack is entirely new and there is no match to the existing clusters, this would be appropriately reflected to the analyst. There are several advantages to using an ANN based approach. First, ANNs acquire knowledge straight from the data without the need for a human expert to build sets of domain rules and facts. Second, once trained, ANNs can be very fast, accurate and have high precision for near real-time applications. Finally, while learning, ANNs perform a type of dimensionality reduction allowing a user to input large amounts of information without fearing an effciency bottleneck. Thus, rather than storing the data in TCP Quad format (which stores only seven event attributes) and performing a multi-stage query on reduced information, the user can input all the relevant information available and instead allow the neural network to organise and reduce this knowledge in an

  8. A network approach to mixing delegates at meetings.

    PubMed

    Vaggi, Federico; Schiavinotto, Tommaso; Lawson, Jonathan Ld; Chessel, Anatole; Dodgson, James; Geymonat, Marco; Sato, Masamitsu; Carazo Salas, Rafael Edgardo; Csikász-Nagy, Attila

    2014-01-01

    Delegates at scientific meetings can come from diverse backgrounds and use very different methods in their research. Promoting interactions between these 'distant' delegates is challenging but such interactions could lead to novel interdisciplinary collaborations and unexpected breakthroughs. We have developed a network-based 'speed dating' approach that allows us to initiate such distant interactions by pairing every delegate with another delegate who might be of interest to them, but whom they might never have encountered otherwise. Here we describe our approach and its algorithmic implementation.

  9. Using cloud association rule data mining approach in optical networks

    NASA Astrophysics Data System (ADS)

    Ma, Bin

    2007-11-01

    In the current DWDM network, one of the critical design issues in the utilization of networks is careful planning to minimize burst dropping resulting from resource contention. The provision of suitable planning before metadata are sent is critical to improve the rate of successful transmission. In this paper, we attempt to adopt a novel data mining approaches to determining a suitable routing path in the OBS network. Instead of using label switching techniques in DWDM, we proposed the hybrid OBS routing planning on the basics of Cloud Association Rules Algorithm, thus reduced the transmission collision rate in OBS routing. This paper searches for the optimal routing path from all the possible routing paths using cloud association rule approach with Apriori-gen algorithm based on the PACNet topology. The heuristic rules discovered by Apriori-gen algorithm are stored in the Knowledge Base (KB) as references for determining the most suitable routing path. The Knowledge Base of the routing path are set up by means of optimal path routing with the highest successful rate which is mined from the database of historical routing paths using cloud association rules. The experiment results show that the successful rates of routing paths obtained by the proposed routing planning approach can effectively improve the successful rates of transmission.

  10. Hierarchical Brain Networks Active in Approach and Avoidance Goal Pursuit

    PubMed Central

    Spielberg, Jeffrey M.; Heller, Wendy; Miller, Gregory A.

    2013-01-01

    Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal-pursuit processes (e.g., motivation) has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity) vital to goal-pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging) with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures. PMID:23785328

  11. A Predictive Approach to Network Reverse-Engineering

    NASA Astrophysics Data System (ADS)

    Wiggins, Chris

    2005-03-01

    A central challenge of systems biology is the ``reverse engineering" of transcriptional networks: inferring which genes exert regulatory control over which other genes. Attempting such inference at the genomic scale has only recently become feasible, via data-intensive biological innovations such as DNA microrrays (``DNA chips") and the sequencing of whole genomes. In this talk we present a predictive approach to network reverse-engineering, in which we integrate DNA chip data and sequence data to build a model of the transcriptional network of the yeast S. cerevisiae capable of predicting the response of genes in unseen experiments. The technique can also be used to extract ``motifs,'' sequence elements which act as binding sites for regulatory proteins. We validate by a number of approaches and present comparison of theoretical prediction vs. experimental data, along with biological interpretations of the resulting model. En route, we will illustrate some basic notions in statistical learning theory (fitting vs. over-fitting; cross- validation; assessing statistical significance), highlighting ways in which physicists can make a unique contribution in data- driven approaches to reverse engineering.

  12. A neural network approach to dynamic task assignment of multirobots.

    PubMed

    Zhu, Anmin; Yang, Simon X

    2006-09-01

    In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies.

  13. A Continuum Approach For Neural Network Modelling Of Anisotropic Materials

    NASA Astrophysics Data System (ADS)

    Man, Hou; Furukawa, Tomonari

    2010-05-01

    This paper presents an approach for constitutive modelling of anisotropic materials using neural networks on a continuum basis. The proposed approach develops the models by using an error function formulated from the minimum total potential energy principle. The variation of the strain energy of a deformed geometry is approximated by using the full field strain measurement with the neural network constitutive model (NNCM) and the coordinate frame transformation. It is subsequently compared with the variation of the applied external work, such that the discrepancy is fed back to update the model properties. The proposed approach is, therefore, able to develop the NNCM without the presence of stress data. This not only facilitates the use of multi-axial load tests and non-standard specimens to produce more realistic experimental results, but also reduces the number of different specimen configurations used for the model development. A numerical example is presented in this paper to validate the performance and applicability of the proposed approach by modelling a carbon fibre reinforced plastic (CFRP) lamina. Artificial experimental results of tensile tests with two different specimens are used to facilitate the validation. The results emphasise the flexibility and applicability of the proposed approach for constitutive modelling of anisotropic materials.

  14. Risk prediction model: Statistical and artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  15. Stochastic Boolean networks: An efficient approach to modeling gene regulatory networks

    PubMed Central

    2012-01-01

    cell immune response dataset. An SBN can also implement the function of an asynchronous PBN and is potentially useful in a hybrid approach in combination with a continuous or single-molecule level stochastic model. Conclusions Stochastic Boolean networks (SBNs) are proposed as an efficient approach to modelling gene regulatory networks (GRNs). The SBN approach is able to recover biologically-proven regulatory behaviours, such as the oscillatory dynamics of the p53-Mdm2 network and the dynamic attractors in a T cell immune response network. The proposed approach can further predict the network dynamics when the genes are under perturbation, thus providing biologically meaningful insights for a better understanding of the dynamics of GRNs. The algorithms and methods described in this paper have been implemented in Matlab packages, which are attached as Additional files. PMID:22929591

  16. ADHD classification using bag of words approach on network features

    NASA Astrophysics Data System (ADS)

    Solmaz, Berkan; Dey, Soumyabrata; Rao, A. Ravishankar; Shah, Mubarak

    2012-02-01

    Attention Deficit Hyperactivity Disorder (ADHD) is receiving lots of attention nowadays mainly because it is one of the common brain disorders among children and not much information is known about the cause of this disorder. In this study, we propose to use a novel approach for automatic classification of ADHD conditioned subjects and control subjects using functional Magnetic Resonance Imaging (fMRI) data of resting state brains. For this purpose, we compute the correlation between every possible voxel pairs within a subject and over the time frame of the experimental protocol. A network of voxels is constructed by representing a high correlation value between any two voxels as an edge. A Bag-of-Words (BoW) approach is used to represent each subject as a histogram of network features; such as the number of degrees per voxel. The classification is done using a Support Vector Machine (SVM). We also investigate the use of raw intensity values in the time series for each voxel. Here, every subject is represented as a combined histogram of network and raw intensity features. Experimental results verified that the classification accuracy improves when the combined histogram is used. We tested our approach on a highly challenging dataset released by NITRC for ADHD-200 competition and obtained promising results. The dataset not only has a large size but also includes subjects from different demography and edge groups. To the best of our knowledge, this is the first paper to propose BoW approach in any functional brain disorder classification and we believe that this approach will be useful in analysis of many brain related conditions.

  17. A network approach for researching partnerships in health

    PubMed Central

    Lewis, Jenny M

    2005-01-01

    Background The last decade has witnessed a significant move towards new modes of governing that are based on coordination and collaboration. In particular, local level partnerships have been widely introduced around the world. There are few comprehensive approaches for researching the effects of these partnerships. The aim of this paper is to outline a network approach that combines structure and agency based explanations to research partnerships in health. Network research based on two Primary Care Partnerships (PCPs) in Victoria is used to demonstrate the utility of this approach. The paper examines multiple types of ties between people (structure), and the use and value of relationships to partners (agency), using interviews with the people involved in two PCPs – one in metropolitan Melbourne and one in a rural area. Results Network maps of ties based on work, strategic information and policy advice, show that there are many strong connections in both PCPs. Not surprisingly, PCP staff are central and highly connected. Of more interest are the ties that are dependent on these dedicated partnership staff, as they reveal which actors become weakly linked or disconnected without them. Network measures indicate that work ties are the most dispersed and strategic information ties are the most concentrated around fewer people. Divisions of general practice are weakly linked, while local government officials and Department of Human Services (DHS) regional staff appear to play important bridging roles. Finally, the relationships between partners have changed and improved, and most of those interviewed value their new or improved links with partners. Conclusion Improving service coordination and health promotion planning requires engaging people and building strong relationships. Mapping ties is a useful means for assessing the strengths and weaknesses of partnerships, and network analysis indicates concentration and dispersion, the importance of particular individuals

  18. A Passive Testing Approach for Protocols in Wireless Sensor Networks.

    PubMed

    Che, Xiaoping; Maag, Stephane; Tan, Hwee-Xian; Tan, Hwee-Pink; Zhou, Zhangbing

    2015-11-19

    Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN). However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results.

  19. A Passive Testing Approach for Protocols in Wireless Sensor Networks

    PubMed Central

    Che, Xiaoping; Maag, Stephane; Tan, Hwee-Xian; Tan, Hwee-Pink; Zhou, Zhangbing

    2015-01-01

    Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN). However, one stage that is often neglected before their deployment is the conformance testing process, a crucial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works propose to specify the protocol with state models or to analyze them with simulators and emulators, we here propose a logic-based approach for formally specifying some functional requirements of a novel WSN routing protocol. We provide an algorithm to evaluate these properties on collected protocol execution traces. Further, we demonstrate the efficiency and suitability of our approach by its application into common WSN functional properties, as well as specific ones designed from our own routing protocol. We provide relevant testing verdicts through a real indoor testbed and the implementation of our protocol. Furthermore, the flexibility, genericity and practicability of our approach have been proven by the experimental results. PMID:26610495

  20. A review on the computational approaches for gene regulatory network construction.

    PubMed

    Chai, Lian En; Loh, Swee Kuan; Low, Swee Thing; Mohamad, Mohd Saberi; Deris, Safaai; Zakaria, Zalmiyah

    2014-05-01

    Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described. Copyright © 2014 Elsevier Ltd. All rights reserved.

  1. Network Analysis: A Novel Approach to Understand Suicidal Behaviour

    PubMed Central

    de Beurs, Derek

    2017-01-01

    Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behaviour. Suicidal behaviour is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behaviour, but we do not yet understand how their interaction increases the risk for suicidal behaviour. A new approach called network analysis can help us better understand this process as it allows us to visualize and quantify the complex association between many different symptoms or risk factors. A network analysis of data containing information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

  2. A location-centric network approach to analyzing epidemic dynamics.

    PubMed

    Zhong, Shiran; Bian, Ling

    Recent health threats, such as the SARS, H1N1, and Ebola pandemics, have stimulated great interest in network models to study the transmission of communicable diseases through human interaction and mobility. Most current network models have focused on an individual-centric perspective where individuals are represented as nodes, and the interactions among them as edges. Few of these models are concerned with the discovery of the spatial patterns and dynamics of epidemics. We propose a location-centric, transmission network approach, in which nodes denote locations and edges denote possible disease transmissions between locations. We then identify the dynamics of transmission flows, the dynamics of critical locations, and the spatial-temporal extent of transmission pathways to assess the impact of these spatial dynamics on the evolution of an epidemic. Results show that transmission flows shift from elementary schools to middle schools and finally universities and professional schools at different phases of an epidemic. Critical locations, identified using network analysis, are responsible for the upsurge in transmission flows during the peaks of the epidemic. The length of transmission pathways shows a power law distribution and their spatial extent is rather small. Insights gained from this study will help devise spatially sensitive strategies to control communicable diseases.

  3. Chemical reaction network approaches to Biochemical Systems Theory.

    PubMed

    Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R

    2015-11-01

    This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed. Copyright © 2015 Elsevier Inc. All rights reserved.

  4. Mixed Neural Network Approach for Temporal Sleep Stage Classification.

    PubMed

    Dong, Hao; Supratak, Akara; Pan, Wei; Wu, Chao; Matthews, Paul M; Guo, Yike

    2017-07-28

    This paper proposes a practical approach to addressing limitations posed by using of single-channel electroencephalography (EEG) for sleep stage classification. EEG-based characterizations of sleep stage progression contribute the diagnosis and monitoring of the many pathologies of sleep. Several prior reports explored ways of automating the analysis of sleep EEG and of reducing the complexity of the data needed for reliable discrimination of sleep stages at lower cost in the home. However, these reports have involved recordings from electrodes placed on the cranial vertex or occiput, which are both uncomfortable and difficult to position. Previous studies of sleep stage scoring that used only frontal electrodes with a hierarchical decision tree motivated this paper, in which we have taken advantage of rectifier neural network for detecting hierarchical features and long short-term memory (LSTM) network for sequential data learning to optimize classification performance with single-channel recordings. After exploring alternative electrode placements, we found a comfortable configuration of a single-channel EEG on the forehead and have shown that it can be integrated with additional electrodes for simultaneous recording of the electrooculogram (EOG). Evaluation of data from 62 people (with 494 hours sleep) demonstrated better performance of our analytical algorithm than is available from existing approaches with vertex or occipital electrode placements. Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

  5. HEMODOSE: A Biodosimetry Tool Based on Multi-type Blood Cell Counts

    PubMed Central

    Hu, Shaowen; Blakely, William F.; Cucinotta, Francis A.

    2015-01-01

    Abstract Peripheral blood cell counts are important biomarkers of radiation exposure. In this work, a simplified compartmental modeling approach is applied to simulate the perturbation of the hematopoiesis system in humans after radiation exposure, and HemoDose software is reported to estimate individuals’ absorbed doses based on multi-type blood cell counts. Testing with patient data in some historical accidents indicates that either single or serial granulocyte, lymphocyte, leukocyte, and platelet counts after exposure can be robust indicators of the absorbed doses. In addition, such correlation exists not only in the early time window (1 or 2 d) but also in the late phase (up to 4 wk) after exposure, when the four types of cell counts are combined for analysis. These demonstrate the capability of HemoDose as a rapid point-of-care diagnostic or centralized high-throughput assay system for personnel exposed to unintended high doses of radiation, especially in large-scale nuclear/radiological disaster scenarios involving mass casualties. PMID:26011498

  6. Sport, how people choose it: A network analysis approach.

    PubMed

    Ferreri, Luca; Ivaldi, Marco; Daolio, Fabio; Giacobini, Mario; Rainoldi, Alberto; Tomassini, Marco

    2015-01-01

    In order to investigate the behaviour of athletes in choosing sports, we analyse data from part of the We-Sport database, a vertical social network that links athletes through sports. In particular, we explore connections between people sharing common sports and the role of age and gender by applying "network science" approaches and methods. The results show a disassortative tendency of athletes in choosing sports, a negative correlation between age and number of chosen sports and a positive correlation between age of connected athletes. Some interesting patterns of connection between age classes are depicted. In addition, we propose a method to classify sports, based on the analyses of the behaviour of people practising them. Thanks to this brand new classifications, we highlight the links of class of sports and their unexpected features. We emphasise some gender dependency affinity in choosing sport classes.

  7. Approaches for recognizing disease genes based on network.

    PubMed

    Zou, Quan; Li, Jinjin; Wang, Chunyu; Zeng, Xiangxiang

    2014-01-01

    Diseases are closely related to genes, thus indicating that genetic abnormalities may lead to certain diseases. The recognition of disease genes has long been a goal in biology, which may contribute to the improvement of health care and understanding gene functions, pathways, and interactions. However, few large-scale gene-gene association datasets, disease-disease association datasets, and gene-disease association datasets are available. A number of machine learning methods have been used to recognize disease genes based on networks. This paper states the relationship between disease and gene, summarizes the approaches used to recognize disease genes based on network, analyzes the core problems and challenges of the methods, and outlooks future research direction.

  8. Multidimensional stock network analysis: An Escoufier's RV coefficient approach

    NASA Astrophysics Data System (ADS)

    Lee, Gan Siew; Djauhari, Maman A.

    2013-09-01

    The current practice of stocks network analysis is based on the assumption that the time series of closed stock price could represent the behaviour of the each stock. This assumption leads to consider minimal spanning tree (MST) and sub-dominant ultrametric (SDU) as an indispensible tool to filter the economic information contained in the network. Recently, there is an attempt where researchers represent stock not only as a univariate time series of closed price but as a bivariate time series of closed price and volume. In this case, they developed the so-called multidimensional MST to filter the important economic information. However, in this paper, we show that their approach is only applicable for that bivariate time series only. This leads us to introduce a new methodology to construct MST where each stock is represented by a multivariate time series. An example of Malaysian stock exchange will be presented and discussed to illustrate the advantages of the method.

  9. Developing a space network interface simulator: The NTS approach

    NASA Technical Reports Server (NTRS)

    Hendrzak, Gary E.

    1993-01-01

    This paper describes the approach used to redevelop the Network Control Center (NCC) Test System (NTS), a hardware and software facility designed to make testing of the NCC Data System (NCCDS) software efficient, effective, and as rigorous as possible prior to operational use. The NTS transmits and receives network message traffic in real-time. Data transfer rates and message content are strictly controlled and are identical to that of the operational systems. NTS minimizes the need for costly and time-consuming testing with the actual external entities (e.g., the Hubble Space Telescope (HST) Payload Operations Control Center (POCC) and the White Sands Ground Terminal). Discussed are activities associated with the development of the NTS, lessons learned throughout the project's lifecycle, and resulting productivity and quality increases.

  10. A Neural Network Approach to Smarter Sensor Networks for Water Quality Monitoring

    PubMed Central

    O'Connor, Edel; Smeaton, Alan F.; O'Connor, Noel E.; Regan, Fiona

    2012-01-01

    Environmental monitoring is evolving towards large-scale and low-cost sensor networks operating reliability and autonomously over extended periods of time. Sophisticated analytical instrumentation such as chemo-bio sensors present inherent limitations because of the number of samples that they can take. In order to maximize their deployment lifetime, we propose the coordination of multiple heterogeneous information sources. We use rainfall radar images and information from a water depth sensor as input to a neural network (NN) to dictate the sampling frequency of a phosphate analyzer at the River Lee in Cork, Ireland. This approach shows varied performance for different times of the year but overall produces output that is very satisfactory for the application context in question. Our study demonstrates that even with limited training data, a system for controlling the sampling rate of the nutrient sensor can be set up and can improve the efficiency of the more sophisticated nodes of the sensor network. PMID:22666048

  11. Collaborative Distributed Scheduling Approaches for Wireless Sensor Network

    PubMed Central

    Niu, Jianjun; Deng, Zhidong

    2009-01-01

    Energy constraints restrict the lifetime of wireless sensor networks (WSNs) with battery-powered nodes, which poses great challenges for their large scale application. In this paper, we propose a family of collaborative distributed scheduling approaches (CDSAs) based on the Markov process to reduce the energy consumption of a WSN. The family of CDSAs comprises of two approaches: a one-step collaborative distributed approach and a two-step collaborative distributed approach. The approaches enable nodes to learn the behavior information of its environment collaboratively and integrate sleep scheduling with transmission scheduling to reduce the energy consumption. We analyze the adaptability and practicality features of the CDSAs. The simulation results show that the two proposed approaches can effectively reduce nodes' energy consumption. Some other characteristics of the CDSAs like buffer occupation and packet delay are also analyzed in this paper. We evaluate CDSAs extensively on a 15-node WSN testbed. The test results show that the CDSAs conserve the energy effectively and are feasible for real WSNs. PMID:22408491

  12. A Rawlsian Approach to Distribute Responsibilities in Networks

    PubMed Central

    2009-01-01

    Due to their non-hierarchical structure, socio-technical networks are prone to the occurrence of the problem of many hands. In the present paper an approach is introduced in which people’s opinions on responsibility are empirically traced. The approach is based on the Rawlsian concept of Wide Reflective Equilibrium (WRE) in which people’s considered judgments on a case are reflectively weighed against moral principles and background theories, ideally leading to a state of equilibrium. Application of the method to a hypothetical case with an artificially constructed network showed that it is possible to uncover the relevant data to assess a consensus amongst people in terms of their individual WRE. It appeared that the moral background theories people endorse are not predictive for their actual distribution of responsibilities but that they indicate ways of reasoning and justifying outcomes. Two ways of ascribing responsibilities were discerned, corresponding to two requirements of a desirable responsibility distribution: fairness and completeness. Applying the method triggered learning effects, both with regard to conceptual clarification and moral considerations, and in the sense that it led to some convergence of opinions. It is recommended to apply the method to a real engineering case in order to see whether this approach leads to an overlapping consensus on a responsibility distribution which is justifiable to all and in which no responsibilities are left unfulfilled, therewith trying to contribute to the solution of the problem of many hands. PMID:19626463

  13. A Rawlsian approach to distribute responsibilities in networks.

    PubMed

    Doorn, Neelke

    2010-06-01

    Due to their non-hierarchical structure, socio-technical networks are prone to the occurrence of the problem of many hands. In the present paper an approach is introduced in which people's opinions on responsibility are empirically traced. The approach is based on the Rawlsian concept of Wide Reflective Equilibrium (WRE) in which people's considered judgments on a case are reflectively weighed against moral principles and background theories, ideally leading to a state of equilibrium. Application of the method to a hypothetical case with an artificially constructed network showed that it is possible to uncover the relevant data to assess a consensus amongst people in terms of their individual WRE. It appeared that the moral background theories people endorse are not predictive for their actual distribution of responsibilities but that they indicate ways of reasoning and justifying outcomes. Two ways of ascribing responsibilities were discerned, corresponding to two requirements of a desirable responsibility distribution: fairness and completeness. Applying the method triggered learning effects, both with regard to conceptual clarification and moral considerations, and in the sense that it led to some convergence of opinions. It is recommended to apply the method to a real engineering case in order to see whether this approach leads to an overlapping consensus on a responsibility distribution which is justifiable to all and in which no responsibilities are left unfulfilled, therewith trying to contribute to the solution of the problem of many hands.

  14. Substrate independent approach for synthesis of graphene platelet networks

    NASA Astrophysics Data System (ADS)

    Shashurin, A.; Fang, X.; Zemlyanov, D.; Keidar, M.

    2017-06-01

    Graphene platelet networks (GPNs) comprised of randomly oriented graphene flakes two to three atomic layers thick are synthesized using a novel plasma-based approach. The approach uses a substrate capable of withstanding synthesis temperatures around 800 °C, but is fully independent of the substrate material. The synthesis occurs directly on the substrate surface without the necessity of any additional steps. GPNs were synthesized on various substrate materials including silicon (Si), thermally oxidized Si (SiO2), molybdenum (Mo), nickel (Ni) and copper (Cu), nickel-chromium (NiCr) alloy and alumina ceramics (Al2O3). The mismatch between the atomic structures of sp2 honeycomb carbon networks and the substrate material is fully eliminated shortly after the synthesis initiation, namely when about 100 nm thick deposits are formed on the substrate. GPN structures synthesized on a substrate at a temperature of about 800 °C are significantly more porous in comparison to the much denser packed amorphous carbon deposits synthesized at lower temperatures. The method proposed here can potentially revolutionize the area of electrochemical energy storage by offering a single-step direct approach for the manufacture of graphene-based electrodes for non-Faradaic supercapacitors. Mass production can be achieved using this method if a roll-to-roll system is utilized.

  15. An integrated network approach identifies the isobutanol response network of Escherichia coli

    PubMed Central

    Brynildsen, Mark P; Liao, James C

    2009-01-01

    Isobutanol has emerged as a potential biofuel due to recent metabolic engineering efforts. Here we used gene expression and transcription network connectivity data, genetic knockouts, and network component analysis (NCA) to map the initial isobutanol response network of Escherichia coli under aerobic conditions. NCA revealed profound perturbations to respiration. Further investigation showed ArcA as an important mediator of this response. Quinone/quinol malfunction was postulated to activate ArcA, Fur, and PhoB in this study. In support of this hypothesis, quinone-linked ArcA and Fur target expressions were significantly less perturbed by isobutanol under fermentative growth whereas quinol-linked PhoB target expressions remained activated, and isobutanol impeded growth on glycerol, which requires quinones, more than on glucose. In addition, ethanol, n-butanol, and isobutanol response networks were compared. n-Butanol and isobutanol responses were qualitatively similar, whereas ethanol had notable induction differences of pspABCDE and ndh, whose gene products manage proton motive force. The network described here could aid design and comprehension of alcohol tolerance, whereas the approach provides a general framework to characterize complex phenomena at the systems level. PMID:19536200

  16. Joint Network Selection and Discrete Power Control in Heterogeneous MIMO Networks: A Game Theoretical Approach

    NASA Astrophysics Data System (ADS)

    Chen, Gang; Tian, Hua; Xie, Wei; Zhong, Wei

    2013-09-01

    Next-generation wireless networks will integrate multiple wireless access technologies and the users will access the network using one of several available radio access technologies. In this paper, we study the spectrum access problem in heterogeneous multipleinput multiple-output (MIMO) networks through a game theoretic approach. The spectrum access problem in the considered system model is defined as joint network selection and discrete power control. We formulate the problem as a noncooperative game where the players are the multi-mode terminals and. The proposed common utility function takes both transmission rate and the power consumption into account. This game is shown to be a potential game which possess at least one pure strategy Nash equilibrium (NE) and the optimal strategy profile which maximizes the total energy efficiency of the heterogeneous MIMO network constitutes a pure strategy NE of our proposed game. Furthermore, we prove that the price of anarchy of the proposed game is equal to 1. In order to achieve the pure strategy NE, we design an iterative spectrum access algorithm. The convergence and the complexity of our designed algorithm is discussed. It is shown that the designed algorithm can achieve optimal performance with low complexity.

  17. Using a Multiobjective Approach to Balance Mission and Network Goals within a Delay Tolerant Network Topology

    DTIC Science & Technology

    2009-03-01

    9 Compton presented a slightly different approach, stating that the NTO should be structured similar format to the ATO and include each mission...Comparison [4] 40 The simplicity of the design of the DTN simulation program is inherent in the logical structured flow. All aspects required to...a metric section to keep track of mission and network performance. Decision trees are structures used to create a hierarchal logical flow diagram

  18. A Model-Driven Approach for Telecommunications Network Services Definition

    NASA Astrophysics Data System (ADS)

    Chiprianov, Vanea; Kermarrec, Yvon; Alff, Patrick D.

    Present day Telecommunications market imposes a short concept-to-market time for service providers. To reduce it, we propose a computer-aided, model-driven, service-specific tool, with support for collaborative work and for checking properties on models. We started by defining a prototype of the Meta-model (MM) of the service domain. Using this prototype, we defined a simple graphical modeling language specific for service designers. We are currently enlarging the MM of the domain using model transformations from Network Abstractions Layers (NALs). In the future, we will investigate approaches to ensure the support for collaborative work and for checking properties on models.

  19. Neural network approach to B →Xuℓν

    NASA Astrophysics Data System (ADS)

    Gambino, Paolo; Healey, Kristopher J.; Mondino, Cristina

    2016-07-01

    We use artificial neural networks to parametrize the shape functions in inclusive semileptonic B decays without charm. Our approach avoids the adoption of functional form models and allows for a straightforward implementation of all experimental and theoretical constraints on the shape functions. The results are used to extract |Vu b| in the GGOU framework and compared with the original GGOU paper and the latest HFAG results, finding good agreement in both cases. The possible impact of future Belle-II data on the MX distribution is also discussed.

  20. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks.

    PubMed

    Vitkin, Edward; Shlomi, Tomer

    2012-11-29

    Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files.

  1. Harnessing systems biology approaches to engineer functional microvascular networks.

    PubMed

    Sefcik, Lauren S; Wilson, Jennifer L; Papin, Jason A; Botchwey, Edward A

    2010-06-01

    Microvascular remodeling is a complex process that includes many cell types and molecular signals. Despite a continued growth in the understanding of signaling pathways involved in the formation and maturation of new blood vessels, approximately half of all compounds entering clinical trials will fail, resulting in the loss of much time, money, and resources. Most pro-angiogenic clinical trials to date have focused on increasing neovascularization via the delivery of a single growth factor or gene. Alternatively, a focus on the concerted regulation of whole networks of genes may lead to greater insight into the underlying physiology since the coordinated response is greater than the sum of its parts. Systems biology offers a comprehensive network view of the processes of angiogenesis and arteriogenesis that might enable the prediction of drug targets and whether or not activation of the targets elicits the desired outcome. Systems biology integrates complex biological data from a variety of experimental sources (-omics) and analyzes how the interactions of the system components can give rise to the function and behavior of that system. This review focuses on how systems biology approaches have been applied to microvascular growth and remodeling, and how network analysis tools can be utilized to aid novel pro-angiogenic drug discovery.

  2. A Systems Approach to Scalable Transportation Network Modeling

    SciTech Connect

    Perumalla, Kalyan S

    2006-01-01

    Emerging needs in transportation network modeling and simulation are raising new challenges with respect to scal-ability of network size and vehicular traffic intensity, speed of simulation for simulation-based optimization, and fidel-ity of vehicular behavior for accurate capture of event phe-nomena. Parallel execution is warranted to sustain the re-quired detail, size and speed. However, few parallel simulators exist for such applications, partly due to the challenges underlying their development. Moreover, many simulators are based on time-stepped models, which can be computationally inefficient for the purposes of modeling evacuation traffic. Here an approach is presented to de-signing a simulator with memory and speed efficiency as the goals from the outset, and, specifically, scalability via parallel execution. The design makes use of discrete event modeling techniques as well as parallel simulation meth-ods. Our simulator, called SCATTER, is being developed, incorporating such design considerations. Preliminary per-formance results are presented on benchmark road net-works, showing scalability to one million vehicles simu-lated on one processor.

  3. Patterns of work attitudes: A neural network approach

    NASA Astrophysics Data System (ADS)

    Mengov, George D.; Zinovieva, Irina L.; Sotirov, George R.

    2000-05-01

    In this paper we introduce a neural networks based approach to analyzing empirical data and models from work and organizational psychology (WOP), and suggest possible implications for the practice of managers and business consultants. With this method it becomes possible to have quantitative answers to a bunch of questions like: What are the characteristics of an organization in terms of its employees' motivation? What distinct attitudes towards the work exist? Which pattern is most desirable from the standpoint of productivity and professional achievement? What will be the dynamics of behavior as quantified by our method, during an ongoing organizational change or consultancy intervention? Etc. Our investigation is founded on the theoretical achievements of Maslow (1954, 1970) in human motivation, and of Hackman & Oldham (1975, 1980) in job diagnostics, and applies the mathematical algorithm of the dARTMAP variation (Carpenter et al., 1998) of the Adaptive Resonance Theory (ART) neural networks introduced by Grossberg (1976). We exploit the ART capabilities to visualize the knowledge accumulated in the network's long-term memory in order to interpret the findings in organizational research.

  4. Scalar and Multivariate Approaches for Optimal Network Design in Antarctica

    NASA Astrophysics Data System (ADS)

    Hryniw, Natalia

    Observations are crucial for weather and climate, not only for daily forecasts and logistical purposes, for but maintaining representative records and for tuning atmospheric models. Here scalar theory for optimal network design is expanded in a multivariate framework, to allow for optimal station siting for full field optimization. Ensemble sensitivity theory is expanded to produce the covariance trace approach, which optimizes for the trace of the covariance matrix. Relative entropy is also used for multivariate optimization as an information theory approach for finding optimal locations. Antarctic surface temperature data is used as a testbed for these methods. Both methods produce different results which are tied to the fundamental physical parameters of the Antarctic temperature field.

  5. Bridging the Gap between Genotype and Phenotype via Network Approaches

    PubMed Central

    Kim, Yoo-Ah; Przytycka, Teresa M.

    2013-01-01

    In the last few years we have witnessed tremendous progress in detecting associations between genetic variations and complex traits. While genome-wide association studies have been able to discover genomic regions that may influence many common human diseases, these discoveries created an urgent need for methods that extend the knowledge of genotype-phenotype relationships to the level of the molecular mechanisms behind them. To address this emerging need, computational approaches increasingly utilize a pathway-centric perspective. These new methods often utilize known or predicted interactions between genes and/or gene products. In this review, we survey recently developed network based methods that attempt to bridge the genotype-phenotype gap. We note that although these methods help narrow the gap between genotype and phenotype relationships, these approaches alone cannot provide the precise details of underlying mechanisms and current research is still far from closing the gap. PMID:23755063

  6. Random walk approach for dispersive transport in pipe networks

    NASA Astrophysics Data System (ADS)

    Sämann, Robert; Graf, Thomas; Neuweiler, Insa

    2016-04-01

    Keywords: particle transport, random walk, pipe, network, HYSTEM-EXTAN, OpenGeoSys After heavy pluvial events in urban areas the available drainage system may be undersized at peak flows (Fuchs, 2013). Consequently, rainwater in the pipe network is likely to spill out through manholes. The presence of hazardous contaminants in the pipe drainage system represents a potential risk to humans especially when the contaminated drainage water reaches the land surface. Real-time forecasting of contaminants in the drainage system needs a quick calculation. Numerical models to predict the fate of contaminants are usually based on finite volume methods. Those are not applicable here because of their volume averaging elements. Thus, a more efficient method is preferable, which is independent from spatial discretization. In the present study, a particle-based method is chosen to calculate transport paths and spatial distribution of contaminants within a pipe network. A random walk method for particles in turbulent flow in partially filled pipes has been developed. Different approaches for in-pipe-mixing and node-mixing with respect to the geometry in a drainage network are shown. A comparison of dispersive behavior and calculation time is given to find the fastest model. The HYSTEM-EXTRAN (itwh, 2002) model is used to provide hydrodynamic conditions in the pipe network according to surface runoff scenarios in order to real-time predict contaminant transport in an urban pipe network system. The newly developed particle-based model will later be coupled to the subsurface flow model OpenGeoSys (Kolditz et al., 2012). References: Fuchs, L. (2013). Gefährdungsanalyse zur Überflutungsvorsorge kommunaler Entwässerungssysteme. Sanierung und Anpassung von Entwässerungssystemen-Alternde Infrastruktur und Klimawandel, Österreichischer Wasser-und Abfallwirtschaftsverband, Wien, ISBN, 978-3. itwh (2002). Modellbeschreibung, Institut für technisch-wissenschaftliche Hydrologie Gmb

  7. A perturbation-theoretic approach to Lagrangian flow networks

    NASA Astrophysics Data System (ADS)

    Fujiwara, Naoya; Kirchen, Kathrin; Donges, Jonathan F.; Donner, Reik V.

    2017-03-01

    Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway, or airline infrastructures over industrial manufacturing to fluid dynamics. Here, we utilize a generic framework for describing the dynamics of geophysical flows such as ocean currents or atmospheric wind fields in terms of Lagrangian flow networks. In this approach, information on the passive advection of particles is transformed into a Markov chain based on transition probabilities of particles between the volume elements of a given partition of space for a fixed time step. We employ perturbation-theoretic methods to investigate the effects of modifications of transport processes in the underlying flow for three different problem classes: efficient absorption (corresponding to particle trapping or leaking), constant input of particles (with additional source terms modeling, e.g., localized contamination), and shifts of the steady state under probability mass conservation (as arising if the background flow is perturbed itself). Our results demonstrate that in all three cases, changes to the steady state solution can be analytically expressed in terms of the eigensystem of the unperturbed flow and the perturbation itself. These results are potentially relevant for developing more efficient strategies for coping with contaminations of fluid or gaseous media such as ocean and atmosphere by oil spills, radioactive substances, non-reactive chemicals, or volcanic aerosols.

  8. Pattern recognition tool based on complex network-based approach

    NASA Astrophysics Data System (ADS)

    Casanova, Dalcimar; Backes, André Ricardo; Martinez Bruno, Odemir

    2013-02-01

    This work proposed a generalization of the method proposed by the authors: 'A complex network-based approach for boundary shape analysis'. Instead of modelling a contour into a graph and use complex networks rules to characterize it, here, we generalize the technique. This way, the work proposes a mathematical tool for characterization signals, curves and set of points. To evaluate the pattern description power of the proposal, an experiment of plat identification based on leaf veins image are conducted. Leaf vein is a taxon characteristic used to plant identification proposes, and one of its characteristics is that these structures are complex, and difficult to be represented as a signal or curves and this way to be analyzed in a classical pattern recognition approach. Here, we model the veins as a set of points and model as graphs. As features, we use the degree and joint degree measurements in a dynamic evolution. The results demonstrates that the technique has a good power of discrimination and can be used for plant identification, as well as other complex pattern recognition tasks.

  9. Social network approaches to recruitment, HIV prevention, medical care, and medication adherence.

    PubMed

    Latkin, Carl A; Davey-Rothwell, Melissa A; Knowlton, Amy R; Alexander, Kamila A; Williams, Chyvette T; Boodram, Basmattee

    2013-06-01

    This article reviews the current issues and advancements in social network approaches to HIV prevention and care. Social network analysis can provide a method to understand health disparities in HIV rates, treatment access, and outcomes. Social network analysis is a valuable tool to link social structural factors to individual behaviors. Social networks provide an avenue for low-cost and sustainable HIV prevention interventions that can be adapted and translated into diverse populations. Social networks can be utilized as a viable approach to recruitment for HIV testing and counseling, HIV prevention interventions, optimizing HIV medical care, and medication adherence. Social network interventions may be face-to-face or through social media. Key issues in designing social network interventions are contamination due to social diffusion, network stability, density, and the choice and training of network members. There are also ethical issues involved in the development and implementation of social network interventions. Social network analyses can also be used to understand HIV transmission dynamics.

  10. A neural network based reputation bootstrapping approach for service selection

    NASA Astrophysics Data System (ADS)

    Wu, Quanwang; Zhu, Qingsheng; Li, Peng

    2015-10-01

    With the concept of service-oriented computing becoming widely accepted in enterprise application integration, more and more computing resources are encapsulated as services and published online. Reputation mechanism has been studied to establish trust on prior unknown services. One of the limitations of current reputation mechanisms is that they cannot assess the reputation of newly deployed services as no record of their previous behaviours exists. Most of the current bootstrapping approaches merely assign default reputation values to newcomers. However, by this kind of methods, either newcomers or existing services will be favoured. In this paper, we present a novel reputation bootstrapping approach, where correlations between features and performance of existing services are learned through an artificial neural network (ANN) and they are then generalised to establish a tentative reputation when evaluating new and unknown services. Reputations of services published previously by the same provider are also incorporated for reputation bootstrapping if available. The proposed reputation bootstrapping approach is seamlessly embedded into an existing reputation model and implemented in the extended service-oriented architecture. Empirical studies of the proposed approach are shown at last.

  11. Checking the reliability of a linear-programming based approach towards detecting community structures in networks.

    PubMed

    Chen, W Y C; Dress, A W M; Yu, W Q

    2007-09-01

    Here, the reliability of a recent approach to use parameterised linear programming for detecting community structures in network has been investigated. Using a one-parameter family of objective functions, a number of "perturbation experiments' document that our approach works rather well. A real-life network and a family of benchmark network are also analysed.

  12. Effective connectivity analysis of default mode network based on the Bayesian network learning approach

    NASA Astrophysics Data System (ADS)

    Li, Rui; Chen, Kewei; Zhang, Nan; Fleisher, Adam S.; Li, Yao; Wu, Xia

    2009-02-01

    This work proposed to use the linear Gaussian Bayesian network (BN) to construct the effective connectivity model of the brain's default mode network (DMN), a set of regions characterized by more increased neural activity during rest-state than most goal-oriented tasks. In a complete unsupervised data-driven manner, Bayesian information criterion (BIC) based learning approach was utilized to identify a highest scored network whose nodes (brain regions) were selected based on the result from the group independent component analysis (Group ICA) examining the DMN. We put forward to adopt the statistical significance testing method for regression coefficients used in stepwise regression analysis to further refine the network identified by BIC. The final established BN, learned from the functional magnetic resonance imaging (fMRI) data acquired from 12 healthy young subjects during rest-state, revealed that the hippocampus (HC) was the most influential brain region that affected activities in all other regions included in the BN. In contrast, the posterior cingulate cortex (PCC) was influenced by other regions, but had no reciprocal effects on any other region. Overall, the configuration of our BN illustrated that a prominent connection from HC to PCC existed in the DMN.

  13. A Network Approach to Non-Print Media Cataloging for Schools: A Report of an Indiana Department of Public Instruction and Indiana Cooperative Library Services Authority (INCOLSA) Project Using the OCLC System. Final Report.

    ERIC Educational Resources Information Center

    Alexander, Janice E.; Markuson, Barbara Evans

    This report describes a demonstration of cooperative cataloging of nonprint media in a network environment. The project was jointly managed by the Indiana Department of Public Instruction and the Indiana Cooperative Library Services Authority (INCOLSA), a state-wide multi-type library network. Staff at large school library media centers in Indiana…

  14. Neural network approach to classification of infrasound signals

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Chang

    As part of the International Monitoring Systems of the Preparatory Commissions for the Comprehensive Nuclear Test-Ban Treaty Organization, the Infrasound Group at the University of Alaska Fairbanks maintains and operates two infrasound stations to monitor global nuclear activity. In addition, the group specializes in detecting and classifying the man-made and naturally produced signals recorded at both stations by computing various characterization parameters (e.g. mean of the cross correlation maxima, trace velocity, direction of arrival, and planarity values) using the in-house developed weighted least-squares algorithm. Classifying commonly observed low-frequency (0.015--0.1 Hz) signals at out stations, namely mountain associated waves and high trace-velocity signals, using traditional approach (e.g. analysis of power spectral density) presents a problem. Such signals can be separated statistically by setting a window to the trace-velocity estimate for each signal types, and the feasibility of such technique is demonstrated by displaying and comparing various summary plots (e.g. universal, seasonal and azimuthal variations) produced by analyzing infrasound data (2004--2007) from the Fairbanks and Antarctic arrays. Such plots with the availability of magnetic activity information (from the College International Geophysical Observatory located at Fairbanks, Alaska) leads to possible physical sources of the two signal types. Throughout this thesis a newly developed robust algorithm (sum of squares of variance ratios) with improved detection quality (under low signal to noise ratios) over two well-known detection algorithms (mean of the cross correlation maxima and Fisher Statistics) are investigated for its efficacy as a new detector. A neural network is examined for its ability to automatically classify the two signals described above against clutter (spurious signals with common characteristics). Four identical perceptron networks are trained and validated (with

  15. Structure and organization of Stratocumulus fields: A network approach

    NASA Astrophysics Data System (ADS)

    Glassmeier, Franziska; Feingold, Graham

    2017-04-01

    The representation of Stratocumulus (Sc) clouds and their radiative impact is one of the large challenges for global climate models. Aerosol-cloud-precipitation interactions greatly contribute to this challenge by influencing the morphology of Sc fields: In the absence of rain, Sc are arranged in a relatively regular pattern of cloudy cells separated by cloud-free rings of down welling air ('closed cells'). Raining cloud fields, in contrast, exhibit an oscillating pattern of cloudy rings surrounding cloud free cells of negatively buoyant air caused by sedimentation and evaporation of rain ('open cells'). Surprisingly, these regular structures of open and closed cellular Sc fields and their potential for the development of new parameterizations have hardly been explored. In this contribution, we approach the organization of Sc from the perspective of a 2-dimensional random network. We find that cellular networks derived from LES simulations of open- and closed-cell Sc cases are almost indistinguishable and share the following features: (i) The distributions of nearest neighbors, or cell degree, are centered at six. This corresponds to approximately hexagonal cloud cells and is a direct mathematical consequence (Euler formula) of the triple junctions featured by Sc organization. (ii) The degree of individual cells is found to be proportional to the normalized size of the cells. This means that cell arrangement is independent of the typical cell size. (iii) Reflecting the continuously renewing dynamics of Sc fields, large (high-degree) cells tend to be neighbored by small (low-degree) cells and vice versa. These macroscopic network properties emerge independent of the state of the Sc field because the different processes governing the evolution of closed as compared to open cells correspond to topologically equivalent network dynamics. By developing a heuristic model, we show that open and closed cell dynamics can both be mimicked by versions of cell division and cell

  16. Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management

    EPA Science Inventory

    Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing i...

  17. Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management.

    EPA Science Inventory

    Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing in...

  18. Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management

    EPA Science Inventory

    Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing i...

  19. Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management.

    EPA Science Inventory

    Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing in...

  20. Automatic voice recognition using traditional and artificial neural network approaches

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1989-01-01

    The main objective of this research is to develop an algorithm for isolated-word recognition. This research is focused on digital signal analysis rather than linguistic analysis of speech. Features extraction is carried out by applying a Linear Predictive Coding (LPC) algorithm with order of 10. Continuous-word and speaker independent recognition will be considered in future study after accomplishing this isolated word research. To examine the similarity between the reference and the training sets, two approaches are explored. The first is implementing traditional pattern recognition techniques where a dynamic time warping algorithm is applied to align the two sets and calculate the probability of matching by measuring the Euclidean distance between the two sets. The second is implementing a backpropagation artificial neural net model with three layers as the pattern classifier. The adaptation rule implemented in this network is the generalized least mean square (LMS) rule. The first approach has been accomplished. A vocabulary of 50 words was selected and tested. The accuracy of the algorithm was found to be around 85 percent. The second approach is in progress at the present time.

  1. A neural-network approach for visual cryptography and authorization.

    PubMed

    Yue, Tai-Wen; Chiang, Suchen

    2004-06-01

    In this paper, we propose a neural-network approach for visual authorization, which is an application of visual cryptography (VC). The scheme contains a key-share and a set of user-shares. The administrator owns the key-share, and each user owns a user-share issued by the administrator from the user-share set. The shares in the user-share set are visually indistinguishable, i.e. they have the same pictorial meaning. However, the stacking of the key-share with different user-shares will reveal significantly different images. Therefore, the administrator (in fact, only the administrator) can visually recognize the authority assigned to a particular user by viewing the information appearing in the superposed image of key-share and user-share. This approach is completely different from traditional VC approaches. The salient features include: (i) the access schemes are described using a set of graytone images, and (ii) the codebooks to fulfil them are not required; and (iii) the size of share images is the same as the size of target image.

  2. A biplex approach to PageRank centrality: From classic to multiplex networks.

    PubMed

    Pedroche, Francisco; Romance, Miguel; Criado, Regino

    2016-06-01

    In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the usual PageRank algorithm to multiplex networks. We give some analytical relations between these new approaches and the classic PageRank centrality measure, and we illustrate the new parameters presented by computing them on real underground networks.

  3. Game theoretic approach for cooperative feature extraction in camera networks

    NASA Astrophysics Data System (ADS)

    Redondi, Alessandro E. C.; Baroffio, Luca; Cesana, Matteo; Tagliasacchi, Marco

    2016-07-01

    Visual sensor networks (VSNs) consist of several camera nodes with wireless communication capabilities that can perform visual analysis tasks such as object identification, recognition, and tracking. Often, VSN deployments result in many camera nodes with overlapping fields of view. In the past, such redundancy has been exploited in two different ways: (1) to improve the accuracy/quality of the visual analysis task by exploiting multiview information or (2) to reduce the energy consumed for performing the visual task, by applying temporal scheduling techniques among the cameras. We propose a game theoretic framework based on the Nash bargaining solution to bridge the gap between the two aforementioned approaches. The key tenet of the proposed framework is for cameras to reduce the consumed energy in the analysis process by exploiting the redundancy in the reciprocal fields of view. Experimental results in both simulated and real-life scenarios confirm that the proposed scheme is able to increase the network lifetime, with a negligible loss in terms of visual analysis accuracy.

  4. Multi-casting approach for vascular networks in cellularized hydrogels.

    PubMed

    Justin, Alexander W; Brooks, Roger A; Markaki, Athina E

    2016-12-01

    Vascularization is essential for living tissue and remains a major challenge in the field of tissue engineering. A lack of a perfusable channel network within a large and densely populated tissue engineered construct leads to necrotic core formation, preventing fabrication of functional tissues and organs. We report a new method for producing a hierarchical, three-dimensional (3D) and perfusable vasculature in a large, cellularized fibrin hydrogel. Bifurcating channels, varying in size from 1 mm to 200-250 µm, are formed using a novel process in which we convert a 3D printed thermoplastic material into a gelatin network template, by way of an intermediate alginate hydrogel. This enables a CAD-based model design, which is highly customizable, reproducible, and which can yield highly complex architectures, to be made into a removable material, which can be used in cellular environments. Our approach yields constructs with a uniform and high density of cells in the bulk, made from bioactive collagen and fibrin hydrogels. Using standard cell staining and immuno-histochemistry techniques, we showed good cell seeding and the presence of tight junctions between channel endothelial cells, and high cell viability and cell spreading in the bulk hydrogel.

  5. Multi-casting approach for vascular networks in cellularized hydrogels

    PubMed Central

    Justin, Alexander W.; Brooks, Roger A.

    2016-01-01

    Vascularization is essential for living tissue and remains a major challenge in the field of tissue engineering. A lack of a perfusable channel network within a large and densely populated tissue engineered construct leads to necrotic core formation, preventing fabrication of functional tissues and organs. We report a new method for producing a hierarchical, three-dimensional (3D) and perfusable vasculature in a large, cellularized fibrin hydrogel. Bifurcating channels, varying in size from 1 mm to 200–250 µm, are formed using a novel process in which we convert a 3D printed thermoplastic material into a gelatin network template, by way of an intermediate alginate hydrogel. This enables a CAD-based model design, which is highly customizable, reproducible, and which can yield highly complex architectures, to be made into a removable material, which can be used in cellular environments. Our approach yields constructs with a uniform and high density of cells in the bulk, made from bioactive collagen and fibrin hydrogels. Using standard cell staining and immuno-histochemistry techniques, we showed good cell seeding and the presence of tight junctions between channel endothelial cells, and high cell viability and cell spreading in the bulk hydrogel. PMID:27928031

  6. Multimodal approaches to define network oscillations in depression.

    PubMed

    Smart, Otis Lkuwamy; Tiruvadi, Vineet Ravi; Mayberg, Helen S

    2015-06-15

    The renaissance in the use of encephalography-based research methods to probe the pathophysiology of neuropsychiatric disorders is well afoot and continues to advance. Building on the platform of neuroimaging evidence on brain circuit models, magnetoencephalography, scalp electroencephalography, and even invasive electroencephalography are now being used to characterize brain network dysfunctions that underlie major depressive disorder using brain oscillation measurements and associated treatment responses. Such multiple encephalography modalities provide avenues to study pathologic network dynamics with high temporal resolution and over long time courses, opportunities to complement neuroimaging methods and findings, and new approaches to identify quantitative biomarkers that indicate critical targets for brain therapy. Such goals have been facilitated by the ongoing testing of novel invasive neuromodulation therapies, notably, deep brain stimulation, where clinically relevant treatment effects can be monitored at multiple brain sites in a time-locked causal manner. We review key brain rhythms identified in major depressive disorder as foundation for development of putative biomarkers for objectively evaluating neuromodulation success and for guiding deep brain stimulation or other target-based neuromodulation strategies for treatment-resistant depression patients.

  7. Omics and Exercise: Global Approaches for Mapping Exercise Biological Networks.

    PubMed

    Hoffman, Nolan J

    2017-03-27

    The application of global "-omics" technologies to exercise has introduced new opportunities to map the complexity and interconnectedness of biological networks underlying the tissue-specific responses and systemic health benefits of exercise. This review will introduce major research tracks and recent advancements in this emerging field, as well as critical gaps in understanding the orchestration of molecular exercise dynamics that will benefit from unbiased omics investigations. Furthermore, significant research hurdles that need to be overcome to effectively fill these gaps related to data collection, computation, interpretation, and integration across omics applications will be discussed. Collectively, a cross-disciplinary physiological and omics-based systems approach will lead to discovery of a wealth of novel exercise-regulated targets for future mechanistic validation. This frontier in exercise biology will aid the development of personalized therapeutic strategies to improve athletic performance and human health through precision exercise medicine.

  8. A network approach to diagnostic biomarkers in progressive supranuclear palsy.

    PubMed

    Santiago, Jose A; Potashkin, Judith A

    2014-04-01

    Diagnosis of progressive supranuclear palsy (PSP) remains challenging because of the clinical overlap with Parkinson's disease (PD). To date, disease-specific biomarkers have yet to be identified. In the absence of reliable biomarkers, we used an integrated network approach to identify genes and related biological pathways associated with PSP. We tested a highly ranked gene in cellular whole-blood samples from 122 patients enrolled in the Prognostic Biomarker Study. Biological and functional analysis identified 13 modules related to activation of leukocytes and lymphocytes, protein dephosphorylation, and phosphatase activity. Integration of these results with those from microarrays identified ptpn1 as a potential biomarker for PSP. Assessment of biomarker performance revealed that ptpn1 could be used to distinguish PSP patients from PD patients with 86% diagnostic accuracy. Ptpn1 may be a diagnostic marker useful for distinguishing PSP and PD. Further evaluation in a larger well-characterized prospective study is warranted.

  9. A tessellated continuum approach to thermal analysis: discontinuity networks

    NASA Astrophysics Data System (ADS)

    Jiang, C.; Davey, K.; Prosser, R.

    2017-01-01

    Tessellated continuum mechanics is an approach for the representation of thermo-mechanical behaviour of porous media on tessellated continua. It involves the application of iteration function schemes using affine contraction and expansion maps, respectively, for the creation of porous fractal materials and associated tessellated continua. Highly complex geometries can be produced using a modest number of contraction mappings. The associated tessellations form the mesh in a numerical procedure. This paper tests the hypothesis that thermal analysis of porous structures can be achieved using a discontinuous Galerkin finite element method on a tessellation. Discontinuous behaviour is identified at a discontinuity network in a tessellation; its use is shown to provide a good representation of the physics relating to cellular heat exchanger designs. Results for different cellular designs (with corresponding tessellations) are contrasted against those obtained from direct analysis and very high accuracy is observed.

  10. Kaolin Quality Prediction from Samples: A Bayesian Network Approach

    NASA Astrophysics Data System (ADS)

    Rivas, T.; Matías, J. M.; Taboada, J.; Ordóñez, C.

    2009-08-01

    We describe the results of an expert system applied to the evaluation of samples of kaolin for industrial use in paper or ceramic manufacture. Different machine learning techniques—classification trees, support vector machines and Bayesian networks—were applied with the aim of evaluating and comparing their interpretability and prediction capacities. The predictive capacity of these models for the samples analyzed was highly satisfactory, both for ceramic quality and paper quality. However, Bayesian networks generally proved to be the most useful technique for our study, as this approach combines good predictive capacity with excellent interpretability of the kaolin quality structure, as it graphically represents relationships between variables and facilitates what-if analyses.

  11. Linguistic complex networks: Rationale, application, interpretation, and directions. Reply to comments on "Approaching human language with complex networks"

    NASA Astrophysics Data System (ADS)

    Cong, Jin; Liu, Haitao

    2014-12-01

    Amid the enthusiasm for real-world networks of the new millennium, the enquiry into linguistic networks is flourishing not only as a productive branch of the new networks science but also as a promising approach to linguistic research. Although the complex network approach constitutes a potential opportunity to make linguistics a science, the world of linguistics seems unprepared to embrace it. For one thing, linguistics has been largely unaffected by quantitative methods. Those who are accustomed to qualitative linguistic methods may find it hard to appreciate the application of quantitative properties of language such as frequency and length, not to mention quantitative properties of language modeled as networks. With this in mind, in our review [1] we restrict ourselves to the basics of complex networks and the new insights into human language with the application of complex networks. For another, while breaking new grounds and posing new challenges for linguistics, the complex network approach to human language as a new tradition of linguistic research is faced with challenges and unsolved issues of its own. It is no surprise that the comments on our review, especially their skepticism and suggestions, focus on various different aspects of the complex network approach to human language. We are grateful to all the insightful and penetrating comments, which, together with our review, mark a significant impetus to linguistic research from the complex network approach. In this reply, we would like to address four major issues of the complex network approach to human language, namely, a) its theoretical rationale, b) its application in linguistic research, c) interpretation of the results, and d) directions of future research.

  12. A Comparison of Three Instructional Approaches in Teaching Network Analysis to Electronics Technology Students.

    ERIC Educational Resources Information Center

    Sappington, Hal M.; Miller, F. Milton

    1980-01-01

    Compares the effects of using three approaches to teach network analysis in a beginning college level electronics course upon cognitive achievement, knowledge retention, and attitude. Results show the electronics technology instructor may not need to teach both the mesh current approach and the superposition approach to network analysis. (CT)

  13. Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach.

    DTIC Science & Technology

    1998-05-01

    Coverage Probability with a Random Optimization Procedure: An Artificial Neural Network Approach by Biing T. Guan, George Z. Gertner, and Alan B...Modeling Training Site Vegetation Coverage Probability with a Random Optimizing Procedure: An Artificial Neural Network Approach 6. AUTHOR(S) Biing...coverage based on past coverage. Approach A literature survey was conducted to identify artificial neural network analysis techniques applicable for

  14. A peer-to-peer resource scheduling approach for photonic grid network based on OBGP

    NASA Astrophysics Data System (ADS)

    Wu, Runze; Ji, Yuefeng

    2005-11-01

    In this paper we present a resource scheduling mechanism for providing dynamic lightpaths to photonic grid network and point out that grid enabled by optical network has huge potential effect on pushing the next optical network applications. Furthermore we investigate photonic grid architecture and control plane based on peer-to-peer is also provided to control optical network communication resources dynamically. We also certificate the idea of extending BGP towards optical network, which is called Optical Border Gateway Protocol used to provide IP-based protocols to control optical network, and gives a dynamic lightpath scheduling approach over multi-wavelength optical network as a new grid service based on OBGP.

  15. An efficient approach of attractor calculation for large-scale Boolean gene regulatory networks.

    PubMed

    He, Qinbin; Xia, Zhile; Lin, Bin

    2016-11-07

    Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which improved the predecessor-based approach. Furthermore, the proposed approach combined with the identification of constant nodes and simplified Boolean networks to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks. If the average degree of the network is not too large, the algorithm can get all attractors of a Boolean network with dozens or even hundreds of nodes.

  16. Stochastic approach to efficient design of belt conveyor networks

    SciTech Connect

    Sevim, H.

    1985-07-01

    Currently, the design of belt conveyor networks is based either on deterministic production assumptions or on simulation models. In this research project, the stochastic process at the coal face is expressed and formulated by a Semi-Markovian technique, and the subject is used as input in a computerized heuristic design model. The author previously has used a Semi-Markovian process to analyze longwall and room-and-pillar production operations. Results indicated that a coal flow in the section belt of a room-and-pillar operation would be expected only 20% of the time in a steady-state operation mode. Similarly, longwall face operations indicated a 35 to 40% coal flow under steady-state conditions. In the present study, similar data from several production sections are used to compute the probabilities of different quantities of coal flowing at any given time during a shift on the belt in the submain entries. Depending upon the probabilities of coal flows on belts in sections and submain and main entries, the appropriate haulage units such as belt width, motor horsepower, idlers, etc., and belt speed are selected by a computerized model. After the development of this algorithm, now in progress, results of a case study undertaken at an existing coal mine in the Illinois Coal Basin using this stochastic approach will be compared with those obtained using an existing belt haulage system design approach.

  17. Personalized translational epilepsy research - Novel approaches and future perspectives: Part I: Clinical and network analysis approaches.

    PubMed

    Rosenow, Felix; van Alphen, Natascha; Becker, Albert; Chiocchetti, Andreas; Deichmann, Ralf; Deller, Thomas; Freiman, Thomas; Freitag, Christine M; Gehrig, Johannes; Hermsen, Anke M; Jedlicka, Peter; Kell, Christian; Klein, Karl Martin; Knake, Susanne; Kullmann, Dimitri M; Liebner, Stefan; Norwood, Braxton A; Omigie, Diana; Plate, Karlheinz; Reif, Andreas; Reif, Philipp S; Reiss, Yvonne; Roeper, Jochen; Ronellenfitsch, Michael W; Schorge, Stephanie; Schratt, Gerhard; Schwarzacher, Stephan W; Steinbach, Joachim P; Strzelczyk, Adam; Triesch, Jochen; Wagner, Marlies; Walker, Matthew C; von Wegner, Frederic; Bauer, Sebastian

    2017-09-13

    Despite the availability of more than 15 new "antiepileptic drugs", the proportion of patients with pharmacoresistant epilepsy has remained constant at about 20-30%. Furthermore, no disease-modifying treatments shown to prevent the development of epilepsy following an initial precipitating brain injury or to reverse established epilepsy have been identified to date. This is likely in part due to the polyetiologic nature of epilepsy, which in turn requires personalized medicine approaches. Recent advances in imaging, pathology, genetics and epigenetics have led to new pathophysiological concepts and the identification of monogenic causes of epilepsy. In the context of these advances, the First International Symposium on Personalized Translational Epilepsy Research (1st ISymPTER) was held in Frankfurt on September 8, 2016, to discuss novel approaches and future perspectives for personalized translational research. These included new developments and ideas in a range of experimental and clinical areas such as deep phenotyping, quantitative brain imaging, EEG/MEG-based analysis of network dysfunction, tissue-based translational studies, innate immunity mechanisms, microRNA as treatment targets, functional characterization of genetic variants in human cell models and rodent organotypic slice cultures, personalized treatment approaches for monogenic epilepsies, blood-brain barrier dysfunction, therapeutic focal tissue modification, computational modeling for target and biomarker identification, and cost analysis in (monogenic) disease and its treatment. This report on the meeting proceedings is aimed at stimulating much needed investments of time and resources in personalized translational epilepsy research. Part I includes the clinical phenotyping and diagnostic methods, EEG network-analysis, biomarkers, and personalized treatment approaches. In Part II, experimental and translational approaches will be discussed (Bauer et al., 2017) [1]. Copyright © 2017 Elsevier Inc

  18. Describing spatial pattern in stream networks: A practical approach

    USGS Publications Warehouse

    Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

    2005-01-01

    The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

  19. A geostatistical approach for describing spatial pattern in stream networks

    USGS Publications Warehouse

    Ganio, L.M.; Torgersen, C.E.; Gresswell, R.E.

    2005-01-01

    The shape and configuration of branched networks influence ecological patterns and processes. Recent investigations of network influences in riverine ecology stress the need to quantify spatial structure not only in a two-dimensional plane, but also in networks. An initial step in understanding data from stream networks is discerning non-random patterns along the network. On the other hand, data collected in the network may be spatially autocorrelated and thus not suitable for traditional statistical analyses. Here we provide a method that uses commercially available software to construct an empirical variogram to describe spatial pattern in the relative abundance of coastal cutthroat trout in headwater stream networks. We describe the mathematical and practical considerations involved in calculating a variogram using a non-Euclidean distance metric to incorporate the network pathway structure in the analysis of spatial variability, and use a non-parametric technique to ascertain if the pattern in the empirical variogram is non-random.

  20. A Network Approach to Predict Pathogenic Genes for Fusarium graminearum

    PubMed Central

    Liu, Xiaoping; Tang, Wei-Hua; Zhao, Xing-Ming; Chen, Luonan

    2010-01-01

    Fusarium graminearum is the pathogenic agent of Fusarium head blight (FHB), which is a destructive disease on wheat and barley, thereby causing huge economic loss and health problems to human by contaminating foods. Identifying pathogenic genes can shed light on pathogenesis underlying the interaction between F. graminearum and its plant host. However, it is difficult to detect pathogenic genes for this destructive pathogen by time-consuming and expensive molecular biological experiments in lab. On the other hand, computational methods provide an alternative way to solve this problem. Since pathogenesis is a complicated procedure that involves complex regulations and interactions, the molecular interaction network of F. graminearum can give clues to potential pathogenic genes. Furthermore, the gene expression data of F. graminearum before and after its invasion into plant host can also provide useful information. In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data. With a small number of known pathogenic genes as seed genes, a subnetwork that consists of potential pathogenic genes is identified from the protein-protein interaction network (PPIN) of F. graminearum, where the genes in the subnetwork are further required to be differentially expressed before and after the invasion of the pathogenic fungus. Therefore, the candidate genes in the subnetwork are expected to be involved in the same biological processes as seed genes, which imply that they are potential pathogenic genes. The prediction results show that most of the pathogenic genes of F. graminearum are enriched in two important signal transduction pathways, including G protein coupled receptor pathway and MAPK signaling pathway, which are known related to pathogenesis in other fungi. In addition, several pathogenic genes predicted by our method are verified in other pathogenic fungi, which

  1. A Complex Network Approach to Distributional Semantic Models

    PubMed Central

    Utsumi, Akira

    2015-01-01

    A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

  2. A Complex Network Approach to Distributional Semantic Models.

    PubMed

    Utsumi, Akira

    2015-01-01

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

  3. Identification of Gene Networks: An Approach Based on Mathematical Modeling

    DTIC Science & Technology

    2014-08-21

    from global interaction networks of all experimentally validated regulatory interactions in either Escherichia coli7 or Saccharomyces cerevisiae1...ranged from 100 to 1,500 for E. coli and 500 to 4,000 for S. cerevisiae . For each species, the largest network generated is smaller than the number of...appears to increase linearly with increasing network size (Figure 1). For S. cerevisiae networks, assignments and measurements seem to increase faster

  4. Networked Learning a Relational Approach: Weak and Strong Ties

    ERIC Educational Resources Information Center

    Jones, C. R.; Ferreday, D.; Hodgson, V.

    2008-01-01

    In this paper, we explore the idea of weak ties in networked learning. We go back to the original conception of the strength of weak ties and relate this to Bakhtin and a dialogic understanding of networked learning. These theoretical ideas are applied to the examination of two networked settings in which educational leaders exchange ideas and…

  5. Heuristic urban transportation network design method, a multilayer coevolution approach

    NASA Astrophysics Data System (ADS)

    Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun

    2017-08-01

    The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.

  6. The Embedded Self: A Social Networks Approach to Identity Theory

    ERIC Educational Resources Information Center

    Walker, Mark H.; Lynn, Freda B.

    2013-01-01

    Despite the fact that key sociological theories of self and identity view the self as fundamentally rooted in networks of interpersonal relationships, empirical research investigating how personal network structure influences the self is conspicuously lacking. To address this gap, we examine links between network structure and role identity…

  7. Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties

    SciTech Connect

    Chinthavali, Supriya

    2016-04-01

    Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.

  8. From Microactions to Macrostructure and Back: A Structurational Approach to the Evolution of Organizational Networks

    ERIC Educational Resources Information Center

    Whitbred, Robert; Fonti, Fabio; Steglich, Christian; Contractor, Noshir

    2011-01-01

    Structuration theory (ST) and network analysis are promising approaches for studying the emergence of communication networks. We offer a model that integrates the conceptual richness of structuration with the precision of relevant concepts and mechanisms offered from communication network research. We leverage methodological advancements (i.e.,…

  9. A network-based feature selection approach to identify metabolic signatures in disease.

    PubMed

    Netzer, Michael; Kugler, Karl G; Müller, Laurin A J; Weinberger, Klaus M; Graber, Armin; Baumgartner, Christian; Dehmer, Matthias

    2012-10-07

    The identification and interpretation of metabolic biomarkers is a challenging task. In this context, network-based approaches have become increasingly a key technology in systems biology allowing to capture complex interactions in biological systems. In this work, we introduce a novel network-based method to identify highly predictive biomarker candidates for disease. First, we infer two different types of networks: (i) correlation networks, and (ii) a new type of network called ratio networks. Based on these networks, we introduce scores to prioritize features using topological descriptors of the vertices. To evaluate our method we use an example dataset where quantitative targeted MS/MS analysis was applied to a total of 52 blood samples from 22 persons with obesity (BMI >30) and 30 healthy controls. Using our network-based feature selection approach we identified highly discriminating metabolites for obesity (F-score >0.85, accuracy >85%), some of which could be verified by the literature.

  10. Extending network approach to language dynamics and human cognition. Comment on "Approaching human language with complex networks" by Cong and Liu

    NASA Astrophysics Data System (ADS)

    Gong, Tao; Shuai, Lan; Wu, Yicheng

    2014-12-01

    By analyzing complex networks constructed from authentic language data, Cong and Liu [1] advance linguistics research into the big data era. The network approach has revealed many intrinsic generalities and crucial differences at both the macro and micro scales between human languages. The axiom behind this research is that language is a complex adaptive system [2]. Although many lexical, semantic, or syntactic features have been discovered by means of analyzing the static and dynamic linguistic networks of world languages, available network-based language studies have not explicitly addressed the evolutionary dynamics of language systems and the correlations between language and human cognition. This commentary aims to provide some insights on how to use the network approach to study these issues.

  11. Comorbidities of Psoriasis - Exploring the Links by Network Approach.

    PubMed

    Sundarrajan, Sudharsana; Arumugam, Mohanapriya

    2016-01-01

    Increasing epidemiological studies in patients with psoriasis report the frequent occurrence of one or more associated disorders. Psoriasis is associated with multiple comorbidities including autoimmune disease, neurological disorders, cardiometabolic diseases and inflammatory-bowel disease. An integrated system biology approach is utilized to decipher the molecular alliance of psoriasis with its comorbidities. An unbiased integrative network medicine methodology is adopted for the investigation of diseasome, biological process and pathways of five most common psoriasis associated comorbidities. A significant overlap was observed between genes acting in similar direction in psoriasis and its comorbidities proving the mandatory occurrence of either one of its comorbidities. The biological processes involved in inflammatory response and cell signaling formed a common basis between psoriasis and its associated comorbidities. The pathway analysis revealed the presence of few common pathways such as angiogenesis and few uncommon pathways which includes CCKR signaling map and gonadotrophin-realising hormone receptor pathway overlapping in all the comorbidities. The work shed light on few common genes and pathways that were previously overlooked. These fruitful targets may serve as a starting point for diagnosis and/or treatment of psoriasis comorbidities. The current research provides an evidence for the existence of shared component hypothesis between psoriasis and its comorbidities.

  12. Probabilistic approaches to fault detection in networked discrete event systems.

    PubMed

    Athanasopoulou, Eleftheria; Hadjicostis, Christoforos N

    2005-09-01

    In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example.

  13. Comorbidities of Psoriasis - Exploring the Links by Network Approach

    PubMed Central

    Sundarrajan, Sudharsana; Arumugam, Mohanapriya

    2016-01-01

    Increasing epidemiological studies in patients with psoriasis report the frequent occurrence of one or more associated disorders. Psoriasis is associated with multiple comorbidities including autoimmune disease, neurological disorders, cardiometabolic diseases and inflammatory-bowel disease. An integrated system biology approach is utilized to decipher the molecular alliance of psoriasis with its comorbidities. An unbiased integrative network medicine methodology is adopted for the investigation of diseasome, biological process and pathways of five most common psoriasis associated comorbidities. A significant overlap was observed between genes acting in similar direction in psoriasis and its comorbidities proving the mandatory occurrence of either one of its comorbidities. The biological processes involved in inflammatory response and cell signaling formed a common basis between psoriasis and its associated comorbidities. The pathway analysis revealed the presence of few common pathways such as angiogenesis and few uncommon pathways which includes CCKR signaling map and gonadotrophin-realising hormone receptor pathway overlapping in all the comorbidities. The work shed light on few common genes and pathways that were previously overlooked. These fruitful targets may serve as a starting point for diagnosis and/or treatment of psoriasis comorbidities. The current research provides an evidence for the existence of shared component hypothesis between psoriasis and its comorbidities. PMID:26966903

  14. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks

    PubMed Central

    2012-01-01

    Genome-scale metabolic network reconstructions are considered a key step in quantifying the genotype-phenotype relationship. We present a novel gap-filling approach, MetabolIc Reconstruction via functionAl GEnomics (MIRAGE), which identifies missing network reactions by integrating metabolic flux analysis and functional genomics data. MIRAGE's performance is demonstrated on the reconstruction of metabolic network models of E. coli and Synechocystis sp. and validated via existing networks for these species. Then, it is applied to reconstruct genome-scale metabolic network models for 36 sequenced cyanobacteria amenable for constraint-based modeling analysis and specifically for metabolic engineering. The reconstructed network models are supplied via standard SBML files. PMID:23194418

  15. Nationwide Network of TalentPoints: The Hungarian Approach to Talent Support

    ERIC Educational Resources Information Center

    Csermely, Peter; Rajnai, Gabor; Sulyok, Katalin

    2013-01-01

    In 2006 a novel approach to talent support was promoted by several talent support programmes in Hungary. The new idea was a network approach. The nationwide network of so-called TalentPoints and its framework, the Hungarian Genius Program, gained substantial European Union funding in 2009, and today it is growing rapidly. A novel concept of talent…

  16. Nationwide Network of TalentPoints: The Hungarian Approach to Talent Support

    ERIC Educational Resources Information Center

    Csermely, Peter; Rajnai, Gabor; Sulyok, Katalin

    2013-01-01

    In 2006 a novel approach to talent support was promoted by several talent support programmes in Hungary. The new idea was a network approach. The nationwide network of so-called TalentPoints and its framework, the Hungarian Genius Program, gained substantial European Union funding in 2009, and today it is growing rapidly. A novel concept of talent…

  17. Bayesian analysis of multi-type recurrent events and dependent termination with nonparametric covariate functions

    PubMed Central

    Lin, Li-An; Luo, Sheng; Chen, Bingshu E.; Davis, Barry R.

    2016-01-01

    Multi-type recurrent event data occur frequently in longitudinal studies. Dependent termination may occur when the terminal time is correlated to recurrent event times. In this article, we simultaneously model the multi-type recurrent events and a dependent terminal event, both with nonparametric covariate functions modeled by B-splines. We develop a Bayesian multivariate frailty model to account for the correlation among the dependent termination and various types of recurrent events. Extensive simulation results suggest that misspecifying nonparametric covariate functions may introduce bias in parameter estimation. This method development has been motivated by and applied to the lipid-lowering trial (LLT) component of the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). PMID:26546256

  18. Baboon (Papio anubis) social complexity--a network approach.

    PubMed

    Lehmann, Julia; Ross, Caroline

    2011-08-01

    Although many studies have analyzed the causes and consequences of social relationships, few studies have explicitly assessed how measures of social relationships are affected by the choice of behaviors used to quantify them. The use of many behaviors to measure social relationships in primates has long been advocated, but it was analytically difficult to implement this framework into primatological work. However, recent advances in social network analysis (SNA) now allow the comparison of multiple networks created from different behaviors. Here we use our database of baboon social behavior (Papio anubis, Gashaka Gumti National Park, Nigeria) to investigate (i) to what extent social networks created from different behaviors overlap, (ii) to what extent individuals occupy similar social positions in these networks and (iii) how sex affects social network position in this population of baboons. We used data on grooming, aggression, displacement, mounting and presenting, which were collected over a 15-month period. We calculated network parameters separately for each behavior. Networks based on displacement, mounting and presenting were very similar to each other, whereas grooming and aggression networks differed both from each other and from mounting, displacement and presenting networks. Overall, individual network positions were strongly affected by sex. Individuals central in one network tended to be central in most other networks as well, whereas other measures such as clustering coefficient were found to vary depending on the behavior analyzed. Thus, our results suggest that a baboon's social environment is best described by a multiplex network based on affiliative, aggressive and sexual behavior. Modern SNA provides a number of useful tools that will help us to better understand animals' social environment. We also discuss potential caveats related to their use. © 2011 Wiley-Liss, Inc.

  19. Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification.

    PubMed

    Tian, Xue W; Lim, Joon S

    2015-01-01

    Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.

  20. Mining Functional Modules in Heterogeneous Biological Networks Using Multiplex PageRank Approach

    PubMed Central

    Li, Jun; Zhao, Patrick X.

    2016-01-01

    Identification of functional modules/sub-networks in large-scale biological networks is one of the important research challenges in current bioinformatics and systems biology. Approaches have been developed to identify functional modules in single-class biological networks; however, methods for systematically and interactively mining multiple classes of heterogeneous biological networks are lacking. In this paper, we present a novel algorithm (called mPageRank) that utilizes the Multiplex PageRank approach to mine functional modules from two classes of biological networks. We demonstrate the capabilities of our approach by successfully mining functional biological modules through integrating expression-based gene-gene association networks and protein-protein interaction networks. We first compared the performance of our method with that of other methods using simulated data. We then applied our method to identify the cell division cycle related functional module and plant signaling defense-related functional module in the model plant Arabidopsis thaliana. Our results demonstrated that the mPageRank method is effective for mining sub-networks in both expression-based gene-gene association networks and protein-protein interaction networks, and has the potential to be adapted for the discovery of functional modules/sub-networks in other heterogeneous biological networks. The mPageRank executable program, source code, the datasets and results of the presented two case studies are publicly and freely available at http://plantgrn.noble.org/MPageRank/. PMID:27446133

  1. Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach

    PubMed Central

    Wang, Pei; Lü, Jinhu; Yu, Xinghuo

    2014-01-01

    Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine. PMID:25170616

  2. Identification of important nodes in directed biological networks: a network motif approach.

    PubMed

    Wang, Pei; Lü, Jinhu; Yu, Xinghuo

    2014-01-01

    Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

  3. Application of Game Theory Approaches in Routing Protocols for Wireless Networks

    NASA Astrophysics Data System (ADS)

    Javidi, Mohammad M.; Aliahmadipour, Laya

    2011-09-01

    An important and essential issue for wireless networks is routing protocol design that is a major technical challenge due to the function of the network. Game theory is a powerful mathematical tool that analyzes the strategic interactions among multiple decision makers and the results of researches show that applied game theory in routing protocol lead to improvement the network performance through reduce overhead and motivates selfish nodes to collaborate in the network. This paper presents a review and comparison for typical representatives of routing protocols designed that applied game theory approaches for various wireless networks such as ad hoc networks, mobile ad hoc networks and sensor networks that all of them lead to improve the network performance.

  4. Game Theoretic, Multi-agent Approach to Network Traffic Monitoring

    DTIC Science & Technology

    2012-01-16

    models are based on observations of effects of real attackers on our university network and their influence on the network characteristics. Using these...performed on CAMNEP, an intrusion detection system based on analysis of NetFlow data, and were performed on the university network over one month. Index...dynamics,” Center for Rationality and Interactive Decision Theory, Hebrew University , Jerusalem, Discussion Paper Series dp490, Sep. 2008. [16] J. Shamma

  5. Neural Network Approach Towards Logic Testing and Design for Testability.

    DTIC Science & Technology

    2007-11-02

    conventional Hopfield network of N neurons describes only binary relations between neurons. With this model gates having more than two inputs need...neuron doubles the search space. Thus, finding a valid test set using Hopfield model is either increasingly hard or the network converges to an invalid...This report considers the problem of applying neural network for logic testing and proposes an efficient method based on the hyperneural model. The

  6. The Cable and Wireless approach to network synchronization

    NASA Technical Reports Server (NTRS)

    Calvert, Robert D.

    1990-01-01

    The philosophy adopted by Cable and Wireless for the synchronization of its world-wide network is presented. The architectures of some clock systems already deployed and how network synchronization had been implemented at selected locations are discussed. This includes some innovative designs as the network spans both first and third world countries with a combination of North Amercan and European hierarchy equipment. Different parts of the global network are linked together by a combination of terrestrial microwave, submarine cable and satellite technology. The impact of synchronization on Intelsat Intermediate Data Rate (IDR) operation and the restoration of submarine cable systems are addressed.

  7. Locus minimization in breed prediction using artificial neural network approach.

    PubMed

    Iquebal, M A; Ansari, M S; Sarika; Dixit, S P; Verma, N K; Aggarwal, R A K; Jayakumar, S; Rai, A; Kumar, D

    2014-12-01

    Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible (http://nabg.iasri.res.in/bisgoat) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51,850 samples of allelic data of microsatellite-marker-based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio-piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs.

  8. Cluster Approach to Network Interaction in Pedagogical University

    ERIC Educational Resources Information Center

    Chekaleva, Nadezhda V.; Makarova, Natalia S.; Drobotenko, Yulia B.

    2016-01-01

    The study presented in the article is devoted to the analysis of theory and practice of network interaction within the framework of education clusters. Education clusters are considered to be a novel form of network interaction in pedagogical education in Russia. The aim of the article is to show the advantages and disadvantages of the cluster…

  9. A Graph Oriented Approach for Network Forensic Analysis

    ERIC Educational Resources Information Center

    Wang, Wei

    2010-01-01

    Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex…

  10. An approach to efficient mobility management in intelligent networks

    NASA Technical Reports Server (NTRS)

    Murthy, K. M. S.

    1995-01-01

    Providing personal communication systems supporting full mobility require intelligent networks for tracking mobile users and facilitating outgoing and incoming calls over different physical and network environments. In realizing the intelligent network functionalities, databases play a major role. Currently proposed network architectures envision using the SS7-based signaling network for linking these DB's and also for interconnecting DB's with switches. If the network has to support ubiquitous, seamless mobile services, then it has to support additionally mobile application parts, viz., mobile origination calls, mobile destination calls, mobile location updates and inter-switch handovers. These functions will generate significant amount of data and require them to be transferred between databases (HLR, VLR) and switches (MSC's) very efficiently. In the future, the users (fixed or mobile) may use and communicate with sophisticated CPE's (e.g. multimedia, multipoint and multisession calls) which may require complex signaling functions. This will generate volumness service handling data and require efficient transfer of these message between databases and switches. Consequently, the network providers would be able to add new services and capabilities to their networks incrementally, quickly and cost-effectively.

  11. How Fast Can Networks Synchronize? A Random Matrix Theory Approach

    NASA Astrophysics Data System (ADS)

    Timme, Marc; Wolf, Fred; Geisel, Theo

    2004-03-01

    Pulse-coupled oscillators constitute a paradigmatic class of dynamical systems interacting on networks because they model a variety of biological systems including flashing fireflies and chirping crickets as well as pacemaker cells of the heart and neural networks. Synchronization is one of the most simple and most prevailing kinds of collective dynamics on such networks. Here we study collective synchronization [1] of pulse-coupled oscillators interacting on asymmetric random networks. Using random matrix theory we analytically determine the speed of synchronization in such networks in dependence on the dynamical and network parameters [2]. The speed of synchronization increases with increasing coupling strengths. Surprisingly, however, it stays finite even for infinitely strong interactions. The results indicate that the speed of synchronization is limited by the connectivity of the network. We discuss the relevance of our findings to general equilibration processes on complex networks. [5mm] [1] M. Timme, F. Wolf, T. Geisel, Phys. Rev. Lett. 89:258701 (2002). [2] M. Timme, F. Wolf, T. Geisel, cond-mat/0306512 (2003).

  12. A Graph Oriented Approach for Network Forensic Analysis

    ERIC Educational Resources Information Center

    Wang, Wei

    2010-01-01

    Network forensic analysis is a process that analyzes intrusion evidence captured from networked environment to identify suspicious entities and stepwise actions in an attack scenario. Unfortunately, the overwhelming amount and low quality of output from security sensors make it difficult for analysts to obtain a succinct high-level view of complex…

  13. Reasoning about Complex Networks: A Logic Programming Approach

    DTIC Science & Technology

    2013-01-01

    susceptibility” ( Aral and Walker 2012) can be integrated into our framework via this component. We represent these criteria with a formula of non-fluent network...J008346/1 (“PrOQAW”). References Aral , S. and Walker, D. 2012. Identifying Influential and Susceptible Members of Social Networks. Science 337, 6092

  14. Development of a decentralized multi-axis synchronous control approach for real-time networks.

    PubMed

    Xu, Xiong; Gu, Guo-Ying; Xiong, Zhenhua; Sheng, Xinjun; Zhu, Xiangyang

    2017-03-23

    The message scheduling and the network-induced delays of real-time networks, together with the different inertias and disturbances in different axes, make the synchronous control of the real-time network-based systems quite challenging. To address this challenge, a decentralized multi-axis synchronous control approach is developed in this paper. Due to the limitations of message scheduling and network bandwidth, error of the position synchronization is firstly defined in the proposed control approach as a subset of preceding-axis pairs. Then, a motion message estimator is designed to reduce the effect of network delays. It is proven that position and synchronization errors asymptotically converge to zero in the proposed controller with the delay compensation. Finally, simulation and experimental results show that the developed control approach can achieve the good position synchronization performance for the multi-axis motion over the real-time network.

  15. Efficiency comparison of graphical approaches for designing contaminant detection networks in groundwater

    NASA Astrophysics Data System (ADS)

    Hudak, Paul F.

    2002-12-01

    Graphical approaches for locating monitoring wells near landfills in aquifers dominated by intergranular porosity were evaluated. Both perpendicular groundwater monitoring networks (wells constrained to a monitoring locus perpendicular to flow) and equidistant networks (wells located the same distance along flow paths) were considered, along with several setbacks between wells and a landfill and different flow fields. For an orthogonal landfill oblique to groundwater flow, equidistant networks generally outperformed their perpendicular counterparts. Equidistant monitoring networks with well locations compressed 10-20% closer to the downgradient corner of a landfill outperformed other networks over a wide range of setbacks. However, compression reduced the detection efficiency of an equidistant network at a field setting with divergent flow, a nonlinear buffer zone boundary, and discharge zones near the sides of a landfill. Graphical approaches described in this paper identify effective monitoring networks that can be refined to site-specific conditions.

  16. Complex network approach to classifying classical piano compositions

    NASA Astrophysics Data System (ADS)

    Xin, Chen; Zhang, Huishu; Huang, Jiping

    2016-10-01

    Complex network has been regarded as a useful tool handling systems with vague interactions. Hence, numerous applications have arised. In this paper we construct complex networks for 770 classical piano compositions of Mozart, Beethoven and Chopin based on musical note pitches and lengths. We find prominent distinctions among network edges of different composers. Some stylized facts can be explained by such parameters of network structures and topologies. Further, we propose two classification methods for music styles and genres according to the discovered distinctions. These methods are easy to implement and the results are sound. This work suggests that complex network could be a decent way to analyze the characteristics of musical notes, since it could provide a deep view into understanding of the relationships among notes in musical compositions and evidence for classification of different composers, styles and genres of music.

  17. Distributed Reinforcement Learning Approach for Vehicular Ad Hoc Networks

    NASA Astrophysics Data System (ADS)

    Wu, Celimuge; Kumekawa, Kazuya; Kato, Toshihiko

    In Vehicular Ad hoc Networks (VANETs), general purpose ad hoc routing protocols such as AODV cannot work efficiently due to the frequent changes in network topology caused by vehicle movement. This paper proposes a VANET routing protocol QLAODV (Q-Learning AODV) which suits unicast applications in high mobility scenarios. QLAODV is a distributed reinforcement learning routing protocol, which uses a Q-Learning algorithm to infer network state information and uses unicast control packets to check the path availability in a real time manner in order to allow Q-Learning to work efficiently in a highly dynamic network environment. QLAODV is favored by its dynamic route change mechanism, which makes it capable of reacting quickly to network topology changes. We present an analysis of the performance of QLAODV by simulation using different mobility models. The simulation results show that QLAODV can efficiently handle unicast applications in VANETs.

  18. Modeling pedestrian's conformity violation behavior: a complex network based approach.

    PubMed

    Zhou, Zhuping; Hu, Qizhou; Wang, Wei

    2014-01-01

    Pedestrian injuries and fatalities present a problem all over the world. Pedestrian conformity violation behaviors, which lead to many pedestrian crashes, are common phenomena at the signalized intersections in China. The concepts and metrics of complex networks are applied to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings. First, a network of pedestrians crossing the street is established, and the network's degree distributions are analyzed. Then, by using the basic idea of SI model, a spreading model of pedestrian illegal crossing behavior is proposed. Finally, through simulation analysis, pedestrian's illegal crossing behavior trends are obtained in different network structures and different spreading rates. Some conclusions are drawn: as the waiting time increases, more pedestrians will join in the violation crossing once a pedestrian crosses on red firstly. And pedestrian's conformity violation behavior will increase as the spreading rate increases.

  19. Modeling Pedestrian's Conformity Violation Behavior: A Complex Network Based Approach

    PubMed Central

    Zhou, Zhuping; Hu, Qizhou; Wang, Wei

    2014-01-01

    Pedestrian injuries and fatalities present a problem all over the world. Pedestrian conformity violation behaviors, which lead to many pedestrian crashes, are common phenomena at the signalized intersections in China. The concepts and metrics of complex networks are applied to analyze the structural characteristics and evolution rules of pedestrian network about the conformity violation crossings. First, a network of pedestrians crossing the street is established, and the network's degree distributions are analyzed. Then, by using the basic idea of SI model, a spreading model of pedestrian illegal crossing behavior is proposed. Finally, through simulation analysis, pedestrian's illegal crossing behavior trends are obtained in different network structures and different spreading rates. Some conclusions are drawn: as the waiting time increases, more pedestrians will join in the violation crossing once a pedestrian crosses on red firstly. And pedestrian's conformity violation behavior will increase as the spreading rate increases. PMID:25530755

  20. Introduction to Focus Issue: Quantitative Approaches to Genetic Networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; Collins, James J.; Glass, Leon

    2013-06-01

    All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

  1. Introduction to focus issue: quantitative approaches to genetic networks.

    PubMed

    Albert, Réka; Collins, James J; Glass, Leon

    2013-06-01

    All cells of living organisms contain similar genetic instructions encoded in the organism's DNA. In any particular cell, the control of the expression of each different gene is regulated, in part, by binding of molecular complexes to specific regions of the DNA. The molecular complexes are composed of protein molecules, called transcription factors, combined with various other molecules such as hormones and drugs. Since transcription factors are coded by genes, cellular function is partially determined by genetic networks. Recent research is making large strides to understand both the structure and the function of these networks. Further, the emerging discipline of synthetic biology is engineering novel gene circuits with specific dynamic properties to advance both basic science and potential practical applications. Although there is not yet a universally accepted mathematical framework for studying the properties of genetic networks, the strong analogies between the activation and inhibition of gene expression and electric circuits suggest frameworks based on logical switching circuits. This focus issue provides a selection of papers reflecting current research directions in the quantitative analysis of genetic networks. The work extends from molecular models for the binding of proteins, to realistic detailed models of cellular metabolism. Between these extremes are simplified models in which genetic dynamics are modeled using classical methods of systems engineering, Boolean switching networks, differential equations that are continuous analogues of Boolean switching networks, and differential equations in which control is based on power law functions. The mathematical techniques are applied to study: (i) naturally occurring gene networks in living organisms including: cyanobacteria, Mycoplasma genitalium, fruit flies, immune cells in mammals; (ii) synthetic gene circuits in Escherichia coli and yeast; and (iii) electronic circuits modeling genetic networks

  2. Statistical approaches used to assess and redesign surface water-quality-monitoring networks.

    PubMed

    Khalil, B; Ouarda, T B M J

    2009-11-01

    An up-to-date review of the statistical approaches utilized for the assessment and redesign of surface water quality monitoring (WQM) networks is presented. The main technical aspects of network design are covered in four sections, addressing monitoring objectives, water quality variables, sampling frequency and spatial distribution of sampling locations. This paper discusses various monitoring objectives and related procedures used for the assessment and redesign of long-term surface WQM networks. The appropriateness of each approach for the design, contraction or expansion of monitoring networks is also discussed. For each statistical approach, its advantages and disadvantages are examined from a network design perspective. Possible methods to overcome disadvantages and deficiencies in the statistical approaches that are currently in use are recommended.

  3. A multiple network learning approach to capture system-wide condition-specific responses

    PubMed Central

    Roy, Sushmita; Werner-Washburne, Margaret; Lane, Terran

    2011-01-01

    Motivation: Condition-specific networks capture system-wide behavior under varying conditions such as environmental stresses, cell types or tissues. These networks frequently comprise parts that are unique to each condition, and parts that are shared among related conditions. Existing approaches for learning condition-specific networks typically identify either only differences or only similarities across conditions. Most of these approaches first learn networks per condition independently, and then identify similarities and differences in a post-learning step. Such approaches do not exploit the shared information across conditions during network learning. Results: We describe an approach for learning condition-specific networks that identifies the shared and unique subgraphs during network learning simultaneously, rather than as a post-processing step. Our approach learns networks across condition sets, shares data from different conditions and produces high-quality networks that capture biologically meaningful information. On simulated data, our approach outperformed an existing approach that learns networks independently for each condition, especially for small training datasets. On microarray data of hundreds of deletion mutants in two, yeast stationary-phase cell populations, the inferred network structure identified several common and population-specific effects of these deletion mutants and several high-confidence cases of double-deletion pairs, which can be experimentally tested. Our results are consistent with and extend the existing knowledge base of differentiated cell populations in yeast stationary phase. Availability and Implementation: C++ code can be accessed from http://www.broadinstitute.org/~sroy/condspec/ Contact: sroy@broadinstitute.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21551143

  4. SNMP-SI: A Network Management Tool Based on Slow Intelligence System Approach

    NASA Astrophysics Data System (ADS)

    Colace, Francesco; de Santo, Massimo; Ferrandino, Salvatore

    The last decade has witnessed an intense spread of computer networks that has been further accelerated with the introduction of wireless networks. Simultaneously with, this growth has increased significantly the problems of network management. Especially in small companies, where there is no provision of personnel assigned to these tasks, the management of such networks is often complex and malfunctions can have significant impacts on their businesses. A possible solution is the adoption of Simple Network Management Protocol. Simple Network Management Protocol (SNMP) is a standard protocol used to exchange network management information. It is part of the Transmission Control Protocol/Internet Protocol (TCP/IP) protocol suite. SNMP provides a tool for network administrators to manage network performance, find and solve network problems, and plan for network growth. SNMP has a big disadvantage: its simple design means that the information it deals with is neither detailed nor well organized enough to deal with the expanding modern networking requirements. Over the past years much efforts has been given to improve the lack of Simple Network Management Protocol and new frameworks has been developed: A promising approach involves the use of Ontology. This is the starting point of this paper where a novel approach to the network management based on the use of the Slow Intelligence System methodologies and Ontology based techniques is proposed. Slow Intelligence Systems is a general-purpose systems characterized by being able to improve performance over time through a process involving enumeration, propagation, adaptation, elimination and concentration. Therefore, the proposed approach aims to develop a system able to acquire, according to an SNMP standard, information from the various hosts that are in the managed networks and apply solutions in order to solve problems. To check the feasibility of this model first experimental results in a real scenario are showed.

  5. A multi-layer network approach to MEG connectivity analysis.

    PubMed

    Brookes, Matthew J; Tewarie, Prejaas K; Hunt, Benjamin A E; Robson, Sian E; Gascoyne, Lauren E; Liddle, Elizabeth B; Liddle, Peter F; Morris, Peter G

    2016-05-15

    Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia.

  6. A multi-layer network approach to MEG connectivity analysis

    PubMed Central

    Brookes, Matthew J.; Tewarie, Prejaas K.; Hunt, Benjamin A.E.; Robson, Sian E.; Gascoyne, Lauren E.; Liddle, Elizabeth B.; Liddle, Peter F.; Morris, Peter G.

    2016-01-01

    Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia. PMID:26908313

  7. A complex network approach for the growth of aerogels

    NASA Astrophysics Data System (ADS)

    Morales, R. V.; da Cunha, C. R.; Rambo, C. R.

    2014-07-01

    The formation of the gel structure of a finite set of inorganic particles interacting via long range potentials is studied via Monte Carlo simulations for different conditions of temperature and concentration. We found that there are certain specific conditions wherein gelation can occur. Moreover, the integrated network of connected particles is investigated. A divergence of an order parameter was observed and indicates the transition from random Erdös-Rényi to scale-free networks. The effects of a reaction limited process are also investigated and indicate that the dissociation of particles favors the formation of random networks.

  8. Trauma-Exposed Latina Immigrants' Networks: A Social Network Analysis Approach.

    PubMed

    Hurtado-de-Mendoza, Alejandra; Serrano, Adriana; Gonzales, Felisa A; Fernandez, Nicole C; Cabling, Mark; Kaltman, Stacey

    2016-11-01

    Trauma exposure among Latina immigrants is common. Social support networks can buffer the impact of trauma on mental health. This study characterizes the social networks of trauma-exposed Latina immigrants using a social network analysis perspective. In 2011-2012 a convenience sample (n=28) of Latina immigrants with trauma exposure and presumptive depression or posttraumatic stress disorder was recruited from a community clinic in Washington DC. Participants completed a social network assessment and listed up to ten persons in their network (alters). E-Net was used to describe the aggregate structural, interactional, and functional characteristics of networks and Node-XL was used in a case study to diagram one network. Most participants listed children (93%), siblings (82%), and friends (71%) as alters, and most alters lived in the US (69%). Perceived emotional support and positive social interaction were higher compared to tangible, language, information, and financial support. A case study illustrates the use of network visualizations to assess the strengths and weaknesses of social networks. Targeted social network interventions to enhance supportive networks among trauma-exposed Latina immigrants are warranted.

  9. A Collaborative Learning Network Approach to Improvement: The CUSP Learning Network.

    PubMed

    Weaver, Sallie J; Lofthus, Jennifer; Sawyer, Melinda; Greer, Lee; Opett, Kristin; Reynolds, Catherine; Wyskiel, Rhonda; Peditto, Stephanie; Pronovost, Peter J

    2015-04-01

    Collaborative improvement networks draw on the science of collaborative organizational learning and communities of practice to facilitate peer-to-peer learning, coaching, and local adaption. Although significant improvements in patient safety and quality have been achieved through collaborative methods, insight regarding how collaborative networks are used by members is needed. Improvement Strategy: The Comprehensive Unit-based Safety Program (CUSP) Learning Network is a multi-institutional collaborative network that is designed to facilitate peer-to-peer learning and coaching specifically related to CUSP. Member organizations implement all or part of the CUSP methodology to improve organizational safety culture, patient safety, and care quality. Qualitative case studies developed by participating members examine the impact of network participation across three levels of analysis (unit, hospital, health system). In addition, results of a satisfaction survey designed to evaluate member experiences were collected to inform network development. Common themes across case studies suggest that members found value in collaborative learning and sharing strategies across organizational boundaries related to a specific improvement strategy. The CUSP Learning Network is an example of network-based collaborative learning in action. Although this learning network focuses on a particular improvement methodology-CUSP-there is clear potential for member-driven learning networks to grow around other methods or topic areas. Such collaborative learning networks may offer a way to develop an infrastructure for longer-term support of improvement efforts and to more quickly diffuse creative sustainment strategies.

  10. Trauma-Exposed Latina Immigrants’ Networks: A Social Network Analysis Approach

    PubMed Central

    Hurtado-de-Mendoza, Alejandra; Serrano, Adriana; Gonzales, Felisa A.; Fernandez, Nicole C.; Cabling, Mark; Kaltman, Stacey

    2015-01-01

    Objective Trauma exposure among Latina immigrants is common. Social support networks can buffer the impact of trauma on mental health. This study characterizes the social networks of trauma-exposed Latina immigrants using a social network analysis perspective. Methods In 2011–2012 a convenience sample (n=28) of Latina immigrants with trauma exposure and presumptive depression or posttraumatic stress disorder was recruited from a community clinic in Washington DC. Participants completed a social network assessment and listed up to ten persons in their network (alters). E-Net was used to describe the aggregate structural, interactional, and functional characteristics of networks and Node-XL was used in a case study to diagram one network. Results Most participants listed children (93%), siblings (82%), and friends (71%) as alters, and most alters lived in the US (69%). Perceived emotional support and positive social interaction were higher compared to tangible, language, information, and financial support. A case study illustrates the use of network visualizations to assess the strengths and weaknesses of social networks. Conclusions Targeted social network interventions to enhance supportive networks among trauma-exposed Latina immigrants are warranted. PMID:28078194

  11. The complex networks approach for authorship attribution of books

    NASA Astrophysics Data System (ADS)

    Mehri, Ali; Darooneh, Amir H.; Shariati, Ashrafalsadat

    2012-04-01

    Authorship analysis by means of textual features is an important task in linguistic studies. We employ complex networks theory to tackle this disputed problem. In this work, we focus on some measurable quantities of word co-occurrence network of each book for authorship characterization. Based on the network features, attribution probability is defined for authorship identification. Furthermore, two scaling exponents, q-parameter and α-exponent, are combined to classify personal writing style with acceptable high resolution power. The q-parameter, generally known as the nonextensivity measure, is calculated for degree distribution and the α-exponent comes from a power law relationship between number of links and number of nodes in the co-occurrence network constructed for different books written by each author. The applicability of the presented method is evaluated in an experiment with thirty six books of five Persian litterateurs. Our results show high accuracy rate in authorship attribution.

  12. Network topology analysis approach on China's QFII stock investment behavior

    NASA Astrophysics Data System (ADS)

    Zhang, Yongjie; Cao, Xing; He, Feng; Zhang, Wei

    2017-05-01

    In this paper, the investment behavior of QFII in China stock market from 2004 to 2015 is studied with the network topology method. Based on the nodes topological characteristics, stock holding fluctuations correlation is studied from the micro network level. We conclude that the QFII mutual stock holding network have both scale free and small world properties, which presented mainly small world characteristics from 2005 to 2011, and scale free characteristics from 2012 to 2015. Moreover, fluctuations correlation is different with different nodes topological characteristics. In different economic periods, QFII represented different connection patterns and they reacted to the market crash spontaneously. Thus, this paper provides the first evidence of complex network research on QFII' investment behavior in China as an emerging market.

  13. Inference Approaches to Constructing Covert Social Network Topologies

    NASA Astrophysics Data System (ADS)

    Rhodes, Christopher J.

    Social network analysis techniques are being increasingly employed in counter-terrorism and counter-insurgency operations to develop an understanding of the organisation, capabilities and vulnerabilities of adversary groups. However, the covert nature of these groups makes the construction of social network topologies very challenging. An additional constraint is that such constructions often have to be made on a fast time-scale using data that has a limited shelf-life. Consequently, developing effective processes for constructing network representations from incomplete and limited data of variable quality is a topic of much current interest. Here we show how Bayesian inference techniques can be used to construct candidate network topologies and predict missing links in two different analysis scenarios. The techniques are illustrated by application to data from open-source publications.

  14. Approaches to Forecasting Demands for Library Network Services.

    ERIC Educational Resources Information Center

    Kang, Jong H.; Rouse, William B.

    1980-01-01

    Considers the problem of predicting monthly demands for library network services, especially the use of forecasts as inputs to policy analysis models and as aids to budgeting and staffing decisions. Several forecasting methods are discussed. (FM)

  15. An adaptive neural swarm approach for intrusion defense in ad hoc networks

    NASA Astrophysics Data System (ADS)

    Cannady, James

    2011-06-01

    Wireless sensor networks (WSN) and mobile ad hoc networks (MANET) are being increasingly deployed in critical applications due to the flexibility and extensibility of the technology. While these networks possess numerous advantages over traditional wireless systems in dynamic environments they are still vulnerable to many of the same types of host-based and distributed attacks common to those systems. Unfortunately, the limited power and bandwidth available in WSNs and MANETs, combined with the dynamic connectivity that is a defining characteristic of the technology, makes it extremely difficult to utilize traditional intrusion detection techniques. This paper describes an approach to accurately and efficiently detect potentially damaging activity in WSNs and MANETs. It enables the network as a whole to recognize attacks, anomalies, and potential vulnerabilities in a distributive manner that reflects the autonomic processes of biological systems. Each component of the network recognizes activity in its local environment and then contributes to the overall situational awareness of the entire system. The approach utilizes agent-based swarm intelligence to adaptively identify potential data sources on each node and on adjacent nodes throughout the network. The swarm agents then self-organize into modular neural networks that utilize a reinforcement learning algorithm to identify relevant behavior patterns in the data without supervision. Once the modular neural networks have established interconnectivity both locally and with neighboring nodes the analysis of events within the network can be conducted collectively in real-time. The approach has been shown to be extremely effective in identifying distributed network attacks.

  16. A Risk Based Approach to Node Insertion Within Social Networks

    DTIC Science & Technology

    2015-03-26

    operations. In an ideal world, SNA would provide a perfect course of action to eliminate dangerous situations that terrorist organizations bring...world, SNA would provide perfect insight as to the correct course of ac- tion (COA) to eliminate the dangerous situations that terrorism and its...assumption in order to provide seemingly valuable information for analysts. Early work in social networks assumed perfect information of the network

  17. A Holistic Approach to Networked Information Systems Design and Analysis

    DTIC Science & Technology

    2016-04-15

    delivery of information over wireless communication networks. Our methods enable the wireless networking and storage systems to provide provable...the min-max of the utility function, where the max is over all protocol suites published and followed by the good nodes, while the min is over all...systems, data from sensor nodes should be delivered to other consumer nodes such as actuators in a regular fashion. But, in practical systems over

  18. A Supervised Approach to Windowing Detection on Dynamic Networks

    DTIC Science & Technology

    2017-07-01

    defined, and is only a matter of degrees away from time scale detection, which asks for a segmentation of the sequence. Implicitly, however, change point...Work In numeric time series, this problem is often termed ‘segmen- tation,’ and segmentation of numeric times series has a long history which is outside...finding change points do implicitly segment the dynamic network - the goal is not necessarily to find a good representation of the network for the

  19. RMB Exchange Rate Forecast Approach Based on BP Neural Network

    NASA Astrophysics Data System (ADS)

    Ye, Sun

    RMB exchange rate system has reformed since July, 2005. This article chose RMB exchange rate data during a period from July, 2005 to September 2010 to establish BP neural network model to forecast RMB exchange rate in the future by using MATLAB software. The result showed that BP neural network is effective to forecast RMB exchange rate and also indicated that RMB exchange rate will continue to appreciate in the future.

  20. Community-level demographic consequences of urbanization: an ecological network approach.

    PubMed

    Rodewald, Amanda D; Rohr, Rudolf P; Fortuna, Miguel A; Bascompte, Jordi

    2014-11-01

    Ecological networks are known to influence ecosystem attributes, but we poorly understand how interspecific network structure affect population demography of multiple species, particularly for vertebrates. Establishing the link between network structure and demography is at the crux of being able to use networks to understand population dynamics and to inform conservation. We addressed the critical but unanswered question, does network structure explain demographic consequences of urbanization? We studied 141 ecological networks representing interactions between plants and nesting birds in forests across an urbanization gradient in Ohio, USA, from 2001 to 2011. Nest predators were identified by video-recording nests and surveyed from 2004 to 2011. As landscapes urbanized, bird-plant networks were more nested, less compartmentalized and dominated by strong interactions between a few species (i.e. low evenness). Evenness of interaction strengths promoted avian nest survival, and evenness explained demography better than urbanization, level of invasion, numbers of predators or other qualitative network metrics. Highly uneven networks had approximately half the nesting success as the most even networks. Thus, nest survival reflected how urbanization altered species interactions, particularly with respect to how nest placement affected search efficiency of predators. The demographic effects of urbanization were not direct, but were filtered through bird-plant networks. This study illustrates how network structure can influence demography at the community level and further, that knowledge of species interactions and a network approach may be requisite to understanding demographic responses to environmental change. © 2014 The Authors. Journal of Animal Ecology © 2014 British Ecological Society.

  1. Traffic network and distribution of cars: Maximum-entropy approach

    SciTech Connect

    Das, N.C.; Chakrabarti, C.G.; Mazumder, S.K.

    2000-02-01

    An urban transport system plays a vital role in the modeling of the modern cosmopolis. A great emphasis is needed for the proper development of a transport system, particularly the traffic network and flow, to meet possible future demand. There are various mathematical models of traffic network and flow. The role of Shannon entropy in the modeling of traffic network and flow was stressed by Tomlin and Tomlin (1968) and Tomlin (1969). In the present note the authors study the role of maximum-entropy principle in the solution of an important problem associated with the traffic network flow. The maximum-entropy principle initiated by Jaynes is a powerful optimization technique of determining the distribution of a random system in the case of partial or incomplete information or data available about the system. This principle has now been broadened and extended and has found wide applications in different fields of science and technology. In the present note the authors show how the Jaynes' maximum-entropy principle, slightly modified, can be successfully applied in determining the flow or distribution of cars in different paths of a traffic network when incomplete information is available about the network.

  2. 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.

  3. Directional MAC Approach for Wireless Body Area Networks

    PubMed Central

    Hussain, Md. Asdaque; Alam, Md. Nasre; Kwak, Kyung Sup

    2011-01-01

    Wireless Body Area Networks (WBANs) designed for medical, sports, and entertainment applications, have drawn the attention of academia and industry alike. A WBAN is a special purpose network, designed to operate autonomously to connect various medical sensors and appliances, located inside and/or outside of a human body. This network enables physicians to remotely monitor vital signs of patients and provide real time feedback for medical diagnosis and consultations. The WBAN system can offer two significant advantages: patient mobility due to their use of portable monitoring devices and a location independent monitoring facility. With its appealing dimensions, it brings about a new set of challenges, which we do not normally consider in such small sensor networks. It requires a scalable network in terms of heterogeneous data traffic, low power consumption of sensor nodes, integration in and around the body networking and coexistence. This work presents a medium access control protocol for WBAN which tries to overcome the aforementioned challenges. We consider the use of multiple beam adaptive arrays (MBAA) at BAN Coordinator (BAN_C) node. When used as a BAN_C, an MBAA can successfully receive two or more overlapping packets at the same time. Each beam captures a different packet by automatically pointing its pattern toward one packet while annulling other contending packets. This paper describes how an MBAA can be integrated into a single hope star topology as a BAN_C. Simulation results show the performance of our proposed protocol. PMID:22346602

  4. Social network approaches to recruitment, HIV prevention, medical care, and medication adherence

    PubMed Central

    Latkin, Carl A.; Davey-Rothwell, Melissa A.; Knowlton, Amy R.; Alexander, Kamila A.; Williams, Chyvette T.; Boodram, Basmattee

    2013-01-01

    This article reviews current issues and advancements in social network approaches to HIV prevention and care. Social network analysis can provide a method to understand health disparities in HIV rates and treatment access and outcomes. Social network analysis is a value tool to link social structural factors to individual behaviors. Social networks provide an avenue for low cost and sustainable HIV prevention interventions that can be adapted and translated into diverse populations. Social networks can be utilized as a viable approach to recruitment for HIV testing and counseling, HIV prevention interventions, and optimizing HIV medical care and medication adherence. Social network interventions may be face-to-face or through social media. Key issues in designing social network interventions are contamination due to social diffusion, network stability, density, and the choice and training of network members. There are also ethical issues involved in the development and implementation of social network interventions. Social network analyses can also be used to understand HIV transmission dynamics. PMID:23673888

  5. Unified Approach to Modeling and Simulation of Space Communication Networks and Systems

    NASA Technical Reports Server (NTRS)

    Barritt, Brian; Bhasin, Kul; Eddy, Wesley; Matthews, Seth

    2010-01-01

    Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks

  6. An automated approach to network features of protein structure ensembles

    PubMed Central

    Bhattacharyya, Moitrayee; Bhat, Chanda R; Vishveshwara, Saraswathi

    2013-01-01

    Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html. PMID:23934896

  7. GPM ground validation via commercial cellular networks: an exploratory approach

    NASA Astrophysics Data System (ADS)

    Rios Gaona, Manuel Felipe; Overeem, Aart; Leijnse, Hidde; Brasjen, Noud; Uijlenhoet, Remko

    2016-04-01

    The suitability of commercial microwave link networks for ground validation of GPM (Global Precipitation Measurement) data is evaluated here. Two state-of-the-art rainfall products are compared over the land surface of the Netherlands for a period of 7 months, i.e., rainfall maps from commercial cellular communication networks and Integrated Multi-satellite Retrievals for GPM (IMERG). Commercial microwave link networks are nowadays the core component in telecommunications worldwide. Rainfall rates can be retrieved from measurements of attenuation between transmitting and receiving antennas. If adequately set up, these networks enable rainfall monitoring tens of meters above the ground at high spatiotemporal resolutions (temporal sampling of seconds to tens of minutes, and spatial sampling of hundreds of meters to tens of kilometers). The GPM mission is the successor of TRMM (Tropical Rainfall Measurement Mission). For two years now, IMERG offers rainfall estimates across the globe (180°W - 180°E and 60°N - 60°S) at spatiotemporal resolutions of 0.1° x 0.1° every 30 min. These two data sets are compared against a Dutch gauge-adjusted radar data set, considered to be the ground truth given its accuracy, spatiotemporal resolution and availability. The suitability of microwave link networks in satellite rainfall evaluation is of special interest, given the independent character of this technique, its high spatiotemporal resolutions and availability. These are valuable assets for water management and modeling of floods, landslides, and weather extremes; especially in places where rain gauge networks are scarce or poorly maintained, or where weather radar networks are too expensive to acquire and/or maintain.

  8. Multi-type Childhood Abuse, Strategies of Coping, and Psychological Adaptations in Young Adults

    PubMed Central

    Sesar, Kristina; Šimić, Nataša; Barišić, Marijana

    2010-01-01

    Aim To retrospectively analyze the rate of multi-type abuse in childhood and the effects of childhood abuse and type of coping strategies on the psychological adaptation of young adults in a sample form the student population of the University of Mostar. Methods The study was conducted on a convenience sample of 233 students from the University of Mostar (196 female and 37 male), with a median age of 20 (interquartile range, 2). Exposure to abuse was determined using the Child Maltreatment Scales for Adults, which assesses emotional, physical, and sexual abuse, neglect, and witnessing family violence. Psychological adaptation was explored by the Trauma Symptom Checklist, which assesses anxiety/depression, sexual problems, trauma symptoms, and somatic symptoms. Strategies of coping with stress were explored by the Coping Inventory for Stressful Situations. Results Multi-type abuse in childhood was experienced by 172 participants (74%) and all types of abuse by 11 (5%) participants. Emotional and physical maltreatment were the most frequent types of abuse and mostly occurred together with other types of abuse. Significant association was found between all types of abuse (r = 0.436-0.778, P < 0.050). Exposure to sexual abuse in childhood and coping strategies were significant predictors of anxiety/depression (R2 = 0.3553), traumatic symptoms (R2 = 0.2299), somatic symptoms (R2 = 0.2173), and sexual problems (R2 = 0.1550, P < 0.001). Conclusion Exposure to multi-type abuse in childhood is a traumatic experience with long-term negative effects. Problem-oriented coping strategies ensure a better psychosocial adaptation than emotion-oriented strategies. PMID:20960590

  9. A network approach for distinguishing ethical issues in research and development.

    PubMed

    Zwart, Sjoerd D; van de Poel, Ibo; van Mil, Harald; Brumsen, Michiel

    2006-10-01

    In this paper we report on our experiences with using network analysis to discern and analyse ethical issues in research into, and the development of, a new wastewater treatment technology. Using network analysis, we preliminarily interpreted some of our observations in a Group Decision Room (GDR) session where we invited important stakeholders to think about the risks of this new technology. We show how a network approach is useful for understanding the observations, and suggests some relevant ethical issues. We argue that a network approach is also useful for ethical analysis of issues in other fields of research and development. The abandoning of the overarching rationality assumption, which is central to network approaches, does not have to lead to ethical relativism.

  10. An empirical Bayes approach to network recovery using external knowledge.

    PubMed

    Kpogbezan, Gino B; van der Vaart, Aad W; van Wieringen, Wessel N; Leday, Gwenaël G R; van de Wiel, Mark A

    2017-09-01

    Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure that automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for posterior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study, we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In particular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Network analysis: a new approach to study endocrine disorders.

    PubMed

    Stevens, A; De Leonibus, C; Hanson, D; Dowsey, A W; Whatmore, A; Meyer, S; Donn, R P; Chatelain, P; Banerjee, I; Cosgrove, K E; Clayton, P E; Dunne, M J

    2014-02-01

    Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

  12. EXAMINE: a computational approach to reconstructing gene regulatory networks.

    PubMed

    Deng, Xutao; Geng, Huimin; Ali, Hesham

    2005-08-01

    Reverse-engineering of gene networks using linear models often results in an underdetermined system because of excessive unknown parameters. In addition, the practical utility of linear models has remained unclear. We address these problems by developing an improved method, EXpression Array MINing Engine (EXAMINE), to infer gene regulatory networks from time-series gene expression data sets. EXAMINE takes advantage of sparse graph theory to overcome the excessive-parameter problem with an adaptive-connectivity model and fitting algorithm. EXAMINE also guarantees that the most parsimonious network structure will be found with its incremental adaptive fitting process. Compared to previous linear models, where a fully connected model is used, EXAMINE reduces the number of parameters by O(N), thereby increasing the chance of recovering the underlying regulatory network. The fitting algorithm increments the connectivity during the fitting process until a satisfactory fit is obtained. We performed a systematic study to explore the data mining ability of linear models. A guideline for using linear models is provided: If the system is small (3-20 elements), more than 90% of the regulation pathways can be determined correctly. For a large-scale system, either clustering is needed or it is necessary to integrate information in addition to expression profile. Coupled with the clustering method, we applied EXAMINE to rat central nervous system development (CNS) data with 112 genes. We were able to efficiently generate regulatory networks with statistically significant pathways that have been predicted previously.

  13. Efficient learning strategy of Chinese characters based on network approach.

    PubMed

    Yan, Xiaoyong; Fan, Ying; Di, Zengru; Havlin, Shlomo; Wu, Jinshan

    2013-01-01

    We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved.

  14. Financial fluctuations anchored to economic fundamentals: A mesoscopic network approach.

    PubMed

    Sharma, Kiran; Gopalakrishnan, Balagopal; Chakrabarti, Anindya S; Chakraborti, Anirban

    2017-08-14

    We demonstrate the existence of an empirical linkage between nominal financial networks and the underlying economic fundamentals, across countries. We construct the nominal return correlation networks from daily data to encapsulate sector-level dynamics and infer the relative importance of the sectors in the nominal network through measures of centrality and clustering algorithms. Eigenvector centrality robustly identifies the backbone of the minimum spanning tree defined on the return networks as well as the primary cluster in the multidimensional scaling map. We show that the sectors that are relatively large in size, defined with three metrics, viz., market capitalization, revenue and number of employees, constitute the core of the return networks, whereas the periphery is mostly populated by relatively smaller sectors. Therefore, sector-level nominal return dynamics are anchored to the real size effect, which ultimately shapes the optimal portfolios for risk management. Our results are reasonably robust across 27 countries of varying degrees of prosperity and across periods of market turbulence (2008-09) as well as periods of relative calmness (2012-13 and 2015-16).

  15. A NETWORK-THEORETICAL APPROACH TO UNDERSTANDING INTERSTELLAR CHEMISTRY

    SciTech Connect

    Jolley, Craig C.; Douglas, Trevor

    2010-10-20

    Recent years have seen dramatic advances in computational models of chemical processes in the interstellar medium (ISM). Typically, these models have been used to calculate changes in chemical abundances with time; the calculated abundances can then be compared with chemical abundances derived from observations. In this study, the output from an astrochemical simulation has been used to generate directed graphs with weighted edges; these have been analyzed with the tools of network theory to uncover whole-network properties of reaction systems in dark molecular clouds. The results allow the development of a model in which global network properties can be rationalized in terms of the basic physical properties of the reaction system. The ISM network exhibits an exponential degree distribution, which is likely to be a generic feature of chemical networks involving a broad range of reaction rate constants. While species abundances span several orders of magnitude, the formation and destruction rates for most species are approximately balanced-departures from this rule indicate species (such as CO) that play a critical role in shaping the dynamics of the system. Future theoretical or observational studies focusing on individual molecular species will be able to situate them in terms of their role in the complete system or quantify the degree to which they deviate from the typical system behavior.

  16. A SOCIAL NETWORK ANALYSIS APPROACH TO UNDERSTAND CHANGES IN A CANCER DISPARITIES COMMUNITY PARTNERSHIP NETWORK

    PubMed Central

    Luque, John S.; Tyson, Dinorah Martinez; Bynum, Shalanda A.; Noel-Thomas, Shalewa; Wells, Kristen J.; Vadaparampil, Susan T.; Gwede, Clement K.; Meade, Cathy D.

    2013-01-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of the Community Network Program sites funded (2005–10) by the National Cancer Institute’s Center to Reduce Cancer Health Disparities. TBCCN was tasked to form a sustainable, community-based partnership network focused on the goal of reducing cancer health disparities among racial–ethnic minority and medically underserved populations. This article reports evaluation outcome results from a social network analysis and discusses the varying TBCCN partner roles—in education, training, and research—over a span of three years (2007–09). The network analysis included 20 local community partner organizations covering a tricounty area in Southwest Florida. In addition, multiple externally funded, community-based participatory research pilot projects with community–academic partners have either been completed or are currently in progress, covering research topics including culturally targeted colorectal and prostate cancer screening education, patient navigation focused on preventing cervical cancer in rural Latinas, and community perceptions of biobanking. The social network analysis identified a trend toward increased network decentralization based on betweenness centrality and overall increase in number of linkages, suggesting network sustainability. Degree centrality, trust, and multiplexity exhibited stability over the three-year time period. These results suggest increased interaction and interdependence among partner organizations and less dependence on the cancer center. Social network analysis enabled us to quantitatively evaluate partnership network functioning of TBCCN in terms of network structure and information and resources flows, which are integral to understanding effective coalition practice based on Community Coalition Action Theory ( Butterfoss and Kegler 2009). Sharing the results of the social network analysis with the partnership network is an important component of our coalition

  17. A SOCIAL NETWORK ANALYSIS APPROACH TO UNDERSTAND CHANGES IN A CANCER DISPARITIES COMMUNITY PARTNERSHIP NETWORK.

    PubMed

    Luque, John S; Tyson, Dinorah Martinez; Bynum, Shalanda A; Noel-Thomas, Shalewa; Wells, Kristen J; Vadaparampil, Susan T; Gwede, Clement K; Meade, Cathy D

    2011-11-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of the Community Network Program sites funded (2005-10) by the National Cancer Institute's Center to Reduce Cancer Health Disparities. TBCCN was tasked to form a sustainable, community-based partnership network focused on the goal of reducing cancer health disparities among racial-ethnic minority and medically underserved populations. This article reports evaluation outcome results from a social network analysis and discusses the varying TBCCN partner roles-in education, training, and research-over a span of three years (2007-09). The network analysis included 20 local community partner organizations covering a tricounty area in Southwest Florida. In addition, multiple externally funded, community-based participatory research pilot projects with community-academic partners have either been completed or are currently in progress, covering research topics including culturally targeted colorectal and prostate cancer screening education, patient navigation focused on preventing cervical cancer in rural Latinas, and community perceptions of biobanking. The social network analysis identified a trend toward increased network decentralization based on betweenness centrality and overall increase in number of linkages, suggesting network sustainability. Degree centrality, trust, and multiplexity exhibited stability over the three-year time period. These results suggest increased interaction and interdependence among partner organizations and less dependence on the cancer center. Social network analysis enabled us to quantitatively evaluate partnership network functioning of TBCCN in terms of network structure and information and resources flows, which are integral to understanding effective coalition practice based on Community Coalition Action Theory ( Butterfoss and Kegler 2009). Sharing the results of the social network analysis with the partnership network is an important component of our coalition building efforts. A

  18. Combing the hairball with BioFabric: a new approach for visualization of large networks

    PubMed Central

    2012-01-01

    Background The analysis of large, complex networks is an important aspect of ongoing biological research. Yet there is a need for entirely new, scalable approaches for network visualization that can provide more insight into the structure and function of these complex networks. Results To address this need, we have developed a software tool named BioFabric, which uses a novel network visualization technique that depicts nodes as one-dimensional horizontal lines arranged in unique rows. This is in distinct contrast to the traditional approach that represents nodes as discrete symbols that behave essentially as zero-dimensional points. BioFabric then depicts each edge in the network using a vertical line assigned to its own unique column, which spans between the source and target rows, i.e. nodes. This method of displaying the network allows a full-scale view to be organized in a rational fashion; interesting network structures, such as sets of nodes with similar connectivity, can be quickly scanned and visually identified in the full network view, even in networks with well over 100,000 edges. This approach means that the network is being represented as a fundamentally linear, sequential entity, where the horizontal scroll bar provides the basic navigation tool for browsing the entire network. Conclusions BioFabric provides a novel and powerful way of looking at any size of network, including very large networks, using horizontal lines to represent nodes and vertical lines to represent edges. It is freely available as an open-source Java application. PMID:23102059

  19. An entropy-driven matrix completion (E-MC) approach to complex network mapping

    NASA Astrophysics Data System (ADS)

    Koochakzadeh, Ali; Pal, Piya

    2016-05-01

    Mapping the topology of a complex network in a resource-efficient manner is a challenging problem with applications in internet mapping, social network inference, and so forth. We propose a new entropy driven algorithm leveraging ideas from matrix completion, to map the network using monitors (or sensors) which, when placed on judiciously selected nodes, are capable of discovering their immediate neighbors. The main challenge is to maximize the portion of discovered network using only a limited number of available monitors. To this end, (i) a new measure of entropy or uncertainty is associated with each node, in terms of the currently discovered edges incident on that node, and (ii) a greedy algorithm is developed to select a candidate node for monitor placement based on its entropy. Utilizing the fact that many complex networks of interest (such as social networks), have a low-rank adjacency matrix, a matrix completion algorithm, namely 1-bit matrix completion, is combined with the greedy algorithm to further boost its performance. The low rank property of the network adjacency matrix can be used to extrapolate a portion of missing edges, and consequently update the node entropies, so as to efficiently guide the network discovery algorithm towards placing monitors on the nodes that can turn out to be more informative. Simulations performed on a variety of real world networks such as social networks and peer networks demonstrate the superior performance of the matrix-completion guided approach in discovering the network topology.

  20. Research into alternative network approaches for space operations

    NASA Technical Reports Server (NTRS)

    Kusmanoff, Antone L.; Barton, Timothy J.

    1990-01-01

    The main goal is to resolve the interoperability problem of applications employing DOD TCP/IP (Department of Defence Transmission Control Protocol/Internet Protocol) family of protocols on a CCITT/ISO based network. The objective is to allow them to communicate over the CCITT/ISO protocol GPLAN (General Purpose Local Area Network) network without modification to the user's application programs. There were two primary assumptions associated with the solution that was actually realized. The first is that the solution had to allow for future movement to the exclusive use of the CCITT/ISO standards. The second is that the solution had to be software transparent to the currently installed TCP/IP and CCITT/ISO user application programs.

  1. Lagrangian Flow Network approach to an open flow model

    NASA Astrophysics Data System (ADS)

    Ser-Giacomi, Enrico; Rodríguez-Méndez, Víctor; López, Cristóbal; Hernández-García, Emilio

    2017-06-01

    Concepts and tools from network theory, the so-called Lagrangian Flow Network framework, have been successfully used to obtain a coarse-grained description of transport by closed fluid flows. Here we explore the application of this methodology to open chaotic flows, and check it with numerical results for a model open flow, namely a jet with a localized wave perturbation. We find that network nodes with high values of out-degree and of finite-time entropy in the forward-in-time direction identify the location of the chaotic saddle and its stable manifold, whereas nodes with high in-degree and backwards finite-time entropy highlight the location of the saddle and its unstable manifold. The cyclic clustering coefficient, associated to the presence of periodic orbits, takes non-vanishing values at the location of the saddle itself.

  2. A simulated annealing approach for redesigning a warehouse network problem

    NASA Astrophysics Data System (ADS)

    Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia

    2017-09-01

    Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.

  3. Quantifying gene network connectivity in silico: Scalability and accuracy of a modular approach

    PubMed Central

    Yalamanchili, Nirupama; Zak, Daniel E.; Ogunnaike, Babatunde A.; Schwaber, James S.; Kriete, Andres; Kholodenko, Boris N.

    2008-01-01

    Large, complex datasets that are generated from microarray experiments create a need for systematic analysis techniques to unravel the underlying connectivity of gene regulatory networks. A modular approach, previously proposed by Kholodenko and co-workers, helps to scale down the network complexity into more computationally manageable entities called modules. A functional module includes a gene’s mRNA, promoter and resulting products, thus encompassing a large set of interacting states. The essential elements of this approach are described in detail for a three-gene model network and later extended to a ten-gene model network, demonstrating scalability. The network architecture is identified by analyzing in silico steady-state changes in the activities of only the module outputs -communicating intermediates- that result from specific perturbations applied to the network modules one at a time. These steady-state changes form the system response matrix, which is used to compute the network connectivity or network interaction map. By employing a known biochemical network, we are able to evaluate the accuracy of the modular approach and its sensitivity to key assumptions. PMID:16986625

  4. Quantitative Approaches to Analyzing the Structure of Continuing Professional Development Networks.

    ERIC Educational Resources Information Center

    West, Russell F.

    A review of the antecedents of the recent interest in social network analysis makes a case for using network information in mapping professional groups as part of the program planning process. Three major streams of theory and research have converged into a broad multidisciplinary approach to viewing social structure that is termed "network…

  5. Quantitative Approaches to Analyzing the Structure of Continuing Professional Development Networks.

    ERIC Educational Resources Information Center

    West, Russell F.

    A review of the antecedents of the recent interest in social network analysis makes a case for using network information in mapping professional groups as part of the program planning process. Three major streams of theory and research have converged into a broad multidisciplinary approach to viewing social structure that is termed "network…

  6. An information-based network approach for protein classification

    PubMed Central

    Wan, Xiaogeng; Zhao, Xin; Yau, Stephen S. T.

    2017-01-01

    Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method. PMID:28350835

  7. A Systematic Approach to Local Area Network Administration

    DTIC Science & Technology

    1989-03-01

    1988 10:49pm HELP BAT 1152 Apr 19, 1988 09:04pmro~ ort lJjame D> Cop =Typ e LEL0 ~L ]~~~o The 1 DIR Version 3.50 - Copyright (c) Bourbaki , Inc. 1984...1989 01:llpm The 1 DIR Version 3.50 - Copyright (c) Bourbaki , Inc. 1984, 1985 45 3. 3COM NETWORK D:\\APPS\\DBASE3P IDIR SCREEN \\APPS\\DBASE3P iveD Name... Bourbaki , Inc. 1984, 1985 4. TOKEN RING NETWORK BATCH FILE DIRECTORY Drive D Name Ext Size Statistics Toggles Select=> IDIR COM 49823 Disk Usage Optons

  8. Constrained off-line synthesis approach of model predictive control for networked control systems with network-induced delays.

    PubMed

    Tang, Xiaoming; Qu, Hongchun; Wang, Ping; Zhao, Meng

    2015-03-01

    This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  9. A feedback-based secure path approach for wireless sensor network data collection.

    PubMed

    Mao, Yuxin; Wei, Guiyi

    2010-01-01

    The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose.

  10. Networks and social capital: a relational approach to primary healthcare reform

    PubMed Central

    Scott, Catherine; Hofmeyer, Anne

    2007-01-01

    Collaboration among health care providers and across systems is proposed as a strategy to improve health care delivery the world over. Over the past two decades, health care providers have been encouraged to work in partnership and build interdisciplinary teams. More recently, the notion of networks has entered this discourse but the lack of consensus and understanding about what is meant by adopting a network approach in health services limits its use. Also crucial to this discussion is the work of distinguishing the nature and extent of the impact of social relationships – generally referred to as social capital. In this paper, we review the rationale for collaboration in health care systems; provide an overview and synthesis of key concepts; dispel some common misconceptions of networks; and apply the theory to an example of primary healthcare network reform in Alberta (Canada). Our central thesis is that a relational approach to systems change, one based on a synthesis of network theory and social capital can provide the fodation for a multi-focal approach to primary healthcare reform. Action strategies are recommended to move from an awareness of 'networks' to fully translating knowledge from existing theory to guide planning and practice innovations. Decision-makers are encouraged to consider a multi-focal approach that effectively incorporates a network and social capital approach in planning and evaluating primary healthcare reform. PMID:17894868

  11. Reorganization of river networks under changing spatiotemporal precipitation patterns: An optimal channel network approach

    NASA Astrophysics Data System (ADS)

    Abed-Elmdoust, Armaghan; Miri, Mohammad-Ali; Singh, Arvind

    2016-11-01

    We investigate the impact of changing nonuniform spatial and temporal precipitation patterns on the evolution of river networks. To achieve this, we develop a two-dimensional optimal channel network (OCN) model with a controllable rainfall distribution to simulate the evolution of river networks, governed by the principle of minimum energy expenditure, inside a prescribed boundary. We show that under nonuniform precipitation conditions, river networks reorganize significantly toward new patterns with different geomorphic and hydrologic signatures. This reorganization is mainly observed in the form of migration of channels of different orders, widening or elongation of basins as well as formation and extinction of channels and basins. In particular, when the precipitation gradient is locally increased, the higher-order channels, including the mainstream river, migrate toward regions with higher precipitation intensity. Through pertinent examples, the reorganization of the drainage network is quantified via stream parameters such as Horton-Strahler and Tokunaga measures, order-based channel total length and river long profiles obtained via simulation of three-dimensional basin topography, while the hydrologic response of the evolved network is investigated using metrics such as hydrograph and power spectral density of simulated streamflows at the outlet of the network. In addition, using OCNs, we investigate the effect of orographic precipitation patterns on multicatchment landscapes composed of several interacting basins. Our results show that network-inspired methods can be utilized as insightful and versatile models for directly exploring the effects of climate change on the evolution of river drainage systems.

  12. Variable sampling approach to mitigate instability in networked control systems with delays.

    PubMed

    López-Echeverría, Daniel; Magaña, Mario E

    2012-01-01

    This paper analyzes a new alternative approach to compensate for the effects of time delays on a dynamic networked control system (NCS). The approach is based on the use of time-delay-predicted values as the sampling times of the NCS. We use a one-step-ahead prediction algorithm based on an adaptive time delay neural network. The application of pole placement and linear quadratic regulator methods to compute the feedback gains taking into account the estimated time delays is investigated.

  13. Geometry of Dynamic Large Networks: A Scaling and Renormalization Group Approach

    DTIC Science & Technology

    2013-12-11

    Geometry of Dynamic Large Networks - A Scaling and Renormalization Group Approach IRAJ SANIEE LUCENT TECHNOLOGIES INC 12/11/2013 Final Report...Z39.18 Final Performance Report Grant Title: Geometry of Dynamic Large Networks: A Scaling and Renormalization Group Approach Grant Award Number...prototypical synthetic and real -life large graphs, congestion metrics and analytical estimation of these metrics for such graphs. In our earlier

  14. Investigating cellular network heterogeneity and modularity in cancer: a network entropy and unbalanced motif approach.

    PubMed

    Cheng, Feixiong; Liu, Chuang; Shen, Bairong; Zhao, Zhongming

    2016-08-26

    Cancer is increasingly recognized as a cellular system phenomenon that is attributed to the accumulation of genetic or epigenetic alterations leading to the perturbation of the molecular network architecture. Elucidation of network properties that can characterize tumor initiation and progression, or pinpoint the molecular targets related to the drug sensitivity or resistance, is therefore of critical importance for providing systems-level insights into tumorigenesis and clinical outcome in the molecularly targeted cancer therapy. In this study, we developed a network-based framework to quantitatively examine cellular network heterogeneity and modularity in cancer. Specifically, we constructed gene co-expressed protein interaction networks derived from large-scale RNA-Seq data across 8 cancer types generated in The Cancer Genome Atlas (TCGA) project. We performed gene network entropy and balanced versus unbalanced motif analysis to investigate cellular network heterogeneity and modularity in tumor versus normal tissues, different stages of progression, and drug resistant versus sensitive cancer cell lines. We found that tumorigenesis could be characterized by a significant increase of gene network entropy in all of the 8 cancer types. The ratio of the balanced motifs in normal tissues is higher than that of tumors, while the ratio of unbalanced motifs in tumors is higher than that of normal tissues in all of the 8 cancer types. Furthermore, we showed that network entropy could be used to characterize tumor progression and anticancer drug responses. For example, we found that kinase inhibitor resistant cancer cell lines had higher entropy compared to that of sensitive cell lines using the integrative analysis of microarray gene expression and drug pharmacological data collected from the Genomics of Drug Sensitivity in Cancer database. In addition, we provided potential network-level evidence that smoking might increase cancer cellular network heterogeneity and

  15. Nursing Home Care Quality: Insights from a Bayesian Network Approach

    ERIC Educational Resources Information Center

    Goodson, Justin; Jang, Wooseung; Rantz, Marilyn

    2008-01-01

    Purpose: The purpose of this research is twofold. The first purpose is to utilize a new methodology (Bayesian networks) for aggregating various quality indicators to measure the overall quality of care in nursing homes. The second is to provide new insight into the relationships that exist among various measures of quality and how such measures…

  16. Multi-modal Social Networks: A MRF Learning Approach

    DTIC Science & Technology

    2016-06-20

    number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. University of Texas at Austin 101 East 27th Street Suite 5.300 Austin, TX 78712 -1532...14, Toronto, ON, Canada. : , . Topic modeling from network spread, The 2014 ACM international conference. 16-JUN-14, Austin, Texas , USA

  17. Measuring Service Quality in the Networked Environment: Approaches and Considerations.

    ERIC Educational Resources Information Center

    Bertot, John Carlo

    2001-01-01

    This article offers a number of statistics and performance measures that libraries may find useful in determining the overall quality of their network-based services; identifies a number of service quality criteria; and provides a framework to assist librarians in selecting statistics and performance measures based on service quality criteria.…

  18. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  19. Neural Networks Based Approach to Enhance Space Hardware Reliability

    NASA Technical Reports Server (NTRS)

    Zebulum, Ricardo S.; Thakoor, Anilkumar; Lu, Thomas; Franco, Lauro; Lin, Tsung Han; McClure, S. S.

    2011-01-01

    This paper demonstrates the use of Neural Networks as a device modeling tool to increase the reliability analysis accuracy of circuits targeted for space applications. The paper tackles a number of case studies of relevance to the design of Flight hardware. The results show that the proposed technique generates more accurate models than the ones regularly used to model circuits.

  20. The Teenage Expertise Network (TEN): An Online Ethnographic Approach

    ERIC Educational Resources Information Center

    Johnson, Nicola F.; Humphry, Nicoli

    2012-01-01

    The take-up of digital technology by young people is a well-known phenomenon and has been subject to socio-cultural analysis in areas such as youth studies and cultural studies. The Teenage Expertise Network (TEN) research project investigates how teenagers develop technological expertise in techno-cultural contexts via the use of a purposefully…

  1. Neural Network Approach to Locating Cryptography in Object Code

    SciTech Connect

    Jason L. Wright; Milos Manic

    2009-09-01

    Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (Neural Net for Locating Cryptography) is presented and results of applying this system to various libraries are described.

  2. A Cultural Approach to Networked-Based Mobile Education

    ERIC Educational Resources Information Center

    Koskimaa, Raine; Lehtonen, Miika; Heinonen, Ulla; Ruokamo, Heli; Tissari, Varpu; Vahtivuori-Hanninen, Sanna; Tella, Seppo

    2007-01-01

    This paper discusses cultural conditions for networked-based mobile education. In our paper, we demonstrate how an Integrated Meta-Model that we have been developing in our MOMENTS project, i.e. Models and Methods for Future Knowledge Construction: Interdisciplinary Implementations with Mobile Technologies, can be used as a heuristic tool for…

  3. The Teenage Expertise Network (TEN): An Online Ethnographic Approach

    ERIC Educational Resources Information Center

    Johnson, Nicola F.; Humphry, Nicoli

    2012-01-01

    The take-up of digital technology by young people is a well-known phenomenon and has been subject to socio-cultural analysis in areas such as youth studies and cultural studies. The Teenage Expertise Network (TEN) research project investigates how teenagers develop technological expertise in techno-cultural contexts via the use of a purposefully…

  4. Analysing Interactions in a Teacher Network Forum: A Sociometric Approach

    ERIC Educational Resources Information Center

    Lisboa, Eliana Santana; Coutinho, Clara Pereira

    2013-01-01

    This article presents the sociometric analysis of the interactions in a forum of a social network created for the professional development of Portuguese-speaking teachers. The main goal of the forum, which was titled Stricto Sensu, was to discuss the educational value of programmes that joined the distance learning model in Brazil. The empirical…

  5. Neural Networks Based Approach to Enhance Space Hardware Reliability

    NASA Technical Reports Server (NTRS)

    Zebulum, Ricardo S.; Thakoor, Anilkumar; Lu, Thomas; Franco, Lauro; Lin, Tsung Han; McClure, S. S.

    2011-01-01

    This paper demonstrates the use of Neural Networks as a device modeling tool to increase the reliability analysis accuracy of circuits targeted for space applications. The paper tackles a number of case studies of relevance to the design of Flight hardware. The results show that the proposed technique generates more accurate models than the ones regularly used to model circuits.

  6. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  7. Small "p" Publishing: A Networked Blogging Approach to Academic Discourse

    ERIC Educational Resources Information Center

    Martin, Julia W.; Hughes, Brian

    2012-01-01

    This article highlights a middle ground for academic publishing between formal peer-reviewed journals and informal blogging that we call "Small "p" Publishing." Having implemented and tested a publishing network that illustrates this middle ground, we describe its unique contributions to scholars and learning communities. Three features that…

  8. Forecasting ENSO events: A neural network-extended EOF approach

    SciTech Connect

    Tangang, F.T.; Tang, B.; Monahan, A.H.; Hsieh, W.W.

    1998-01-01

    The authors constructed neural network models to forecast the sea surface temperature anomalies (SSTA) for three regions: Nino 4. Nino 3.5, and Nino 3, representing the western-central, the central, and the eastern-central parts of the equatorial Pacific Ocean, respectively. The inputs were the extended empirical orthogonal functions (EEOF) of the sea level pressure (SLP) field that covered the tropical Indian and Pacific Oceans and evolved for a duration of 1 yr. The EEOFs greatly reduced the size of the neural networks from those of the authors` earlier papers using EOFs. The Nino 4 region appeared to be the best forecasted region, with useful skills up to a year lead time for the 1982-93 forecast period. By network pruning analysis and spectral analysis, four important inputs were identified: modes 1, 2, and 6 of the SLP EEOFs and the SSTA persistence. Mode 1 characterized the low-frequency oscillation (LFO, with 4-5-yr period), and was seen as the typical ENSO signal, while mode 2, with a period of 2-5 yr, characterized the quasi-biennial oscillation (QBO) plus the LFO. Mode 6 was dominated by decadal and interdecadal variations. Thus, forecasting ENSO required information from the QBO, and the decadal-interdecadal oscillations. The nonlinearity of the networks tended to increase with lead time and to become stronger for the eastern regions of the equatorial Pacific Ocean. 35 refs., 14 figs., 4 tabs.

  9. Nursing Home Care Quality: Insights from a Bayesian Network Approach

    ERIC Educational Resources Information Center

    Goodson, Justin; Jang, Wooseung; Rantz, Marilyn

    2008-01-01

    Purpose: The purpose of this research is twofold. The first purpose is to utilize a new methodology (Bayesian networks) for aggregating various quality indicators to measure the overall quality of care in nursing homes. The second is to provide new insight into the relationships that exist among various measures of quality and how such measures…

  10. A Maximal Flow Approach to Dynamic Routing in Communication Networks,

    DTIC Science & Technology

    1980-05-01

    of nodes. In Appendix B we provide a computer program in Fortran for finding the maximal flow in these networks, based on the algorithm of Edmons and... Edmons and Karp is implemented by a Fortran Subroutine called MAXFL. The algorithm finds the shortest path between source and destination on which an

  11. Biology Inspired Approach for Communal Behavior in Sensor Networks

    NASA Technical Reports Server (NTRS)

    Jones, Kennie H.; Lodding, Kenneth N.; Olariu, Stephan; Wilson, Larry; Xin, Chunsheng

    2006-01-01

    Research in wireless sensor network technology has exploded in the last decade. Promises of complex and ubiquitous control of the physical environment by these networks open avenues for new kinds of science and business. Due to the small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors working in concert. Although the reduction in size has been phenomenal it results in severe limitations on the computing, communicating, and power capabilities of these devices. Under these constraints, research efforts have concentrated on developing techniques for performing relatively simple tasks with minimal energy expense assuming some form of centralized control. Unfortunately, centralized control does not scale to massive size networks and execution of simple tasks in sparsely populated networks will not lead to the sophisticated applications predicted. These must be enabled by new techniques dependent on local and autonomous cooperation between sensors to effect global functions. As a step in that direction, in this work we detail a technique whereby a large population of sensors can attain a global goal using only local information and by making only local decisions without any form of centralized control.

  12. Fraction Calculation--A Didactic Approach to Constructing Mathematical Networks.

    ERIC Educational Resources Information Center

    Steiner, Gerhard F.; Stoecklin, Markus

    1997-01-01

    Thirty-eight sixth graders were trained in fraction calculation through progressive transformation dialectics (PT) whereas a control group of 38 was taught through a traditional mathematics education framework. The PT group, encouraged to form network-type knowledge representations, performed better on problems that required more than mere…

  13. Small "p" Publishing: A Networked Blogging Approach to Academic Discourse

    ERIC Educational Resources Information Center

    Martin, Julia W.; Hughes, Brian

    2012-01-01

    This article highlights a middle ground for academic publishing between formal peer-reviewed journals and informal blogging that we call "Small "p" Publishing." Having implemented and tested a publishing network that illustrates this middle ground, we describe its unique contributions to scholars and learning communities. Three features that…

  14. Real-time, large scale optimization of water network systems using a subdomain approach.

    SciTech Connect

    van Bloemen Waanders, Bart Gustaaf; Biegler, Lorenz T.; Laird, Carl Damon

    2005-03-01

    Certain classes of dynamic network problems can be modeled by a set of hyperbolic partial differential equations describing behavior along network edges and a set of differential and algebraic equations describing behavior at network nodes. In this paper, we demonstrate real-time performance for optimization problems in drinking water networks. While optimization problems subject to partial differential, differential, and algebraic equations can be solved with a variety of techniques, efficient solutions are difficult for large network problems with many degrees of freedom and variable bounds. Sequential optimization strategies can be inefficient for this problem due to the high cost of computing derivatives with respect to many degrees of freedom. Simultaneous techniques can be more efficient, but are difficult because of the need to solve a large nonlinear program; a program that may be too large for current solver. This study describes a dynamic optimization formulation for estimating contaminant sources in drinking water networks, given concentration measurements at various network nodes. We achieve real-time performance by combining an efficient large-scale nonlinear programming algorithm with two problem reduction techniques. D Alembert's principle can be applied to the partial differential equations governing behavior along the network edges (distribution pipes). This allows us to approximate the time-delay relationships between network nodes, removing the need to discretize along the length of the pipes. The efficiency of this approach alone, however, is still dependent on the size of the network and does not scale indefinitely to larger network models. We further reduce the problem size with a subdomain approach and solve smaller inversion problems using a geographic window around the area of contamination. We illustrate the effectiveness of this overall approach and these reduction techniques on an actual metropolitan water network model.

  15. Excluded Volume Approach for Ultrathin Carbon Nanotube Network Stabilization: A Mesoscopic Distinct Element Method Study.

    PubMed

    Wang, Yuezhou; Drozdov, Grigorii; Hobbie, Erik K; Dumitrica, Traian

    2017-04-04

    Ultrathin carbon nanotube films have gathered attention for flexible electronics applications. Unfortunately, their network structure changes significantly even under small applied strains. We perform mesoscopic distinct element method simulations and develop an atomic-scale picture of the network stress relaxation. On this basis, we put forward the concept of mesoscale design by the addition of excluded-volume interactions. We integrate silicon nanoparticles into our model and show that the nanoparticle-filled networks present superior stability and mechanical response relative to those of pure films. The approach opens new possibilities for tuning the network microstructure in a manner that is compatible with flexible electronics applications.

  16. A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks.

    PubMed

    Yin, Junming; Ho, Qirong; Xing, Eric P

    2013-01-01

    We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction.

  17. Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management

    NASA Astrophysics Data System (ADS)

    Kreakie, B. J.; Hychka, K. C.; Belaire, J. A.; Minor, E.; Walker, H. A.

    2016-02-01

    Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing internet-based social networks, and use an existing traditional (survey-based) case study to illustrate in a familiar context the deviations in methods and results. Internet-based approaches to SNA offer a means to overcome institutional hurdles to conducting survey-based SNA, provide unique insight into an institution's web presences, allow for easy snowballing (iterative process that incorporates new nodes in the network), and afford monitoring of social networks through time. The internet-based approaches differ in link definition: hyperlink is based on links on a website that redirect to a different website and relatedness links are based on a Google's "relatedness" operator that identifies pages "similar" to a URL. All networks were initiated with the same start nodes [members of a conservation alliance for the Calumet region around Chicago ( n = 130)], but the resulting networks vary drastically from one another. Interpretation of the resulting networks is highly contingent upon how the links were defined.

  18. A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

    PubMed

    Röhl, Annika; Bockmayr, Alexander

    2017-01-03

    Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

  19. Internet-Based Approaches to Building Stakeholder Networks for Conservation and Natural Resource Management.

    PubMed

    Kreakie, B J; Hychka, K C; Belaire, J A; Minor, E; Walker, H A

    2016-02-01

    Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing internet-based social networks, and use an existing traditional (survey-based) case study to illustrate in a familiar context the deviations in methods and results. Internet-based approaches to SNA offer a means to overcome institutional hurdles to conducting survey-based SNA, provide unique insight into an institution's web presences, allow for easy snowballing (iterative process that incorporates new nodes in the network), and afford monitoring of social networks through time. The internet-based approaches differ in link definition: hyperlink is based on links on a website that redirect to a different website and relatedness links are based on a Google's "relatedness" operator that identifies pages "similar" to a URL. All networks were initiated with the same start nodes [members of a conservation alliance for the Calumet region around Chicago (n = 130)], but the resulting networks vary drastically from one another. Interpretation of the resulting networks is highly contingent upon how the links were defined.

  20. An improved spanning tree approach for the reliability analysis of supply chain collaborative network

    NASA Astrophysics Data System (ADS)

    Lam, C. Y.; Ip, W. H.

    2012-11-01

    A higher degree of reliability in the collaborative network can increase the competitiveness and performance of an entire supply chain. As supply chain networks grow more complex, the consequences of unreliable behaviour become increasingly severe in terms of cost, effort and time. Moreover, it is computationally difficult to calculate the network reliability of a Non-deterministic Polynomial-time hard (NP-hard) all-terminal network using state enumeration, as this may require a huge number of iterations for topology optimisation. Therefore, this paper proposes an alternative approach of an improved spanning tree for reliability analysis to help effectively evaluate and analyse the reliability of collaborative networks in supply chains and reduce the comparative computational complexity of algorithms. Set theory is employed to evaluate and model the all-terminal reliability of the improved spanning tree algorithm and present a case study of a supply chain used in lamp production to illustrate the application of the proposed approach.

  1. Analysis of bHLH coding genes using gene co-expression network approach.

    PubMed

    Srivastava, Swati; Sanchita; Singh, Garima; Singh, Noopur; Srivastava, Gaurava; Sharma, Ashok

    2016-07-01

    Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species.

  2. From partnerships to networks: new approaches for measuring U.S. National Heritage Area effectiveness.

    PubMed

    Laven, Daniel N; Krymkowski, Daniel H; Ventriss, Curtis L; Manning, Robert E; Mitchell, Nora J

    2010-08-01

    National Heritage Areas (NHAs) are an alternative and increasingly popular form of protected area management in the United States. NHAs seek to integrate environmental objectives with community and economic objectives at regional or landscape scales. NHA designations have increased rapidly in the last 20 years, generating a substantial need for evaluative information about (a) how NHAs work; (b) outcomes associated with the NHA process; and (c) the costs and benefits of investing public moneys into the NHA approach. Qualitative evaluation studies recently conducted at three NHAs have identified the importance of understanding network structure and function in the context of evaluating NHA management effectiveness. This article extends these case studies by examining quantitative network data from each of the sites. The authors analyze these data using both a descriptive approach and a statistically more robust approach known as exponential random graph modeling. Study findings indicate the presence of transitive structures and the absence of three-cycle structures in each of these networks. This suggests that these networks are relatively ''open,'' which may be desirable, given the uncertainty of the environments in which they operate. These findings also suggest, at least at the sites reported here, that the NHA approach may be an effective way to activate and develop networks of intersectoral organizational partners. Finally, this study demonstrates the utility of using quantitative network analysis to better understand the effectiveness of protected area management models that rely on partnership networks to achieve their intended outcomes.

  3. A network approach in analysis of the matching hypothesis

    NASA Astrophysics Data System (ADS)

    Jia, Tao; Spivey, Robert; Korniss, Gyorgy; Szymanski, Boleslaw

    2014-03-01

    The matching hypothesis in social psychology claimed that people are more likely to form a committed relationship with someone who is equally attractive. This phenomenon can be well interpreted by the principle of homophily that people are apt to get in touch with others similar to them. Yet, social experiments indicate that people in general tend to prefer more attractive individuals regardless of their own attractiveness. Here study the stochastic matching process for different underlying networks and different attractiveness distributions. We showed that the correlation of attractiveness within couples could purely due to the limited number of acquaintance each person has and such correlation decreases as the network becomes more sparse. We also analyzed the effect of the degree distribution and the attractiveness on the number of individuals that can not find their partners. This work is supported by ARL NS-CTA, ARO, and ONR.

  4. An enhanced stream mining approach for network anomaly detection

    NASA Astrophysics Data System (ADS)

    Bellaachia, Abdelghani; Bhatt, Rajat

    2005-03-01

    Network anomaly detection is one of the hot topics in the market today. Currently, researchers are trying to find a way in which machines could automatically learn both normal and anomalous behavior and thus detect anomalies if and when they occur. Most important applications which could spring out of these systems is intrusion detection and spam mail detection. In this paper, the primary focus on the problem and solution of "real time" network intrusion detection although the underlying theory discussed may be used for other applications of anomaly detection (like spam detection or spy-ware detection) too. Since a machine needs a learning process on its own, data mining has been chosen as a preferred technique. The object of this paper is to present a real time clustering system; we call Enhanced Stream Mining (ESM) which could analyze packet information (headers, and data) to determine intrusions.

  5. Architectural approach for quality and safety aware healthcare social networks.

    PubMed

    López, Diego M; Blobel, Bernd; González, Carolina

    2012-01-01

    Quality of information and privacy and safety issues are frequently identified as main limitations to make most benefit from social media in healthcare. The objective of the paper is to contribute to the analysis of healthcare social networks (SN), and online healthcare social network services (SNS) by proposing a formal architectural analysis of healthcare SN and SNS, considering the complexity of both systems, but stressing on quality, safety and usability aspects. Quality policies are necessary to control the quality of content published by experts and consumers. Privacy and safety policies protect against inappropriate use of information and users responsibility for sharing information. After the policies are established and documented, a proof of concept online SNS supporting primary healthcare promotion is presented in the paper.

  6. A coclustering approach for mining large protein-protein interaction networks.

    PubMed

    Pizzuti, Clara; Rombo, Simona E

    2012-01-01

    Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks. Indeed, methods returning high accurate results are often able to cover only small parts of the input PPI network, especially when low-characterized networks are considered. We present a coclustering-based technique able to generate both overlapping and nonoverlapping clusters. The density of the clusters to search for can also be set by the user. We tested our method on the two networks of yeast and human, and compared it to other five well-known techniques on the same interaction data sets. The results showed that, for all the examples considered, our approach always reaches a good compromise between accuracy and network coverage. Furthermore, the behavior of our algorithm is not influenced by the structure of the input network, different from all the techniques considered in the comparison, which returned very good results on the yeast network, while on the human network their outcomes are rather poor.

  7. Applying a social network analysis (SNA) approach to understanding radiologists' performance in reading mammograms

    NASA Astrophysics Data System (ADS)

    Tavakoli Taba, Seyedamir; Hossain, Liaquat; Heard, Robert; Brennan, Patrick; Lee, Warwick; Lewis, Sarah

    2017-03-01

    Rationale and objectives: Observer performance has been widely studied through examining the characteristics of individuals. Applying a systems perspective, while understanding of the system's output, requires a study of the interactions between observers. This research explains a mixed methods approach to applying a social network analysis (SNA), together with a more traditional approach of examining personal/ individual characteristics in understanding observer performance in mammography. Materials and Methods: Using social networks theories and measures in order to understand observer performance, we designed a social networks survey instrument for collecting personal and network data about observers involved in mammography performance studies. We present the results of a study by our group where 31 Australian breast radiologists originally reviewed 60 mammographic cases (comprising of 20 abnormal and 40 normal cases) and then completed an online questionnaire about their social networks and personal characteristics. A jackknife free response operating characteristic (JAFROC) method was used to measure performance of radiologists. JAFROC was tested against various personal and network measures to verify the theoretical model. Results: The results from this study suggest a strong association between social networks and observer performance for Australian radiologists. Network factors accounted for 48% of variance in observer performance, in comparison to 15.5% for the personal characteristics for this study group. Conclusion: This study suggest a strong new direction for research into improving observer performance. Future studies in observer performance should consider social networks' influence as part of their research paradigm, with equal or greater vigour than traditional constructs of personal characteristics.

  8. Impact of environmental inputs on reverse-engineering approach to network structures.

    PubMed

    Wu, Jianhua; Sinfield, James L; Buchanan-Wollaston, Vicky; Feng, Jianfeng

    2009-12-04

    Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.

  9. Design Approaches for Stealthy Probing Mechanisms in Battlefield Networks

    DTIC Science & Technology

    2008-09-01

    pings are an effective tool for detecting network nodes that have been compromised by an attacker who tries to delay or drop traffic passing through...the captured node. However, an intelligent attacker may evade detection by giving preferential treatment to probe traffic. This is usually possible...stealthy manner so as to avoid identification of probes by an attacker and to ensure the collection of accurate system health statistics. In this

  10. A statistical mechanics approach to autopoietic immune networks

    NASA Astrophysics Data System (ADS)

    Barra, Adriano; Agliari, Elena

    2010-07-01

    In this work we aim to bridge theoretical immunology and disordered statistical mechanics. We introduce a model for the behavior of B-cells which naturally merges the clonal selection theory and the autopoietic network theory as a whole. From the analysis of its features we recover several basic phenomena such as low-dose tolerance, dynamical memory of antigens and self/non-self discrimination.

  11. Characterizing Teamwork in Cardiovascular Care Outcomes: A Network Analytics Approach.

    PubMed

    Carson, Matthew B; Scholtens, Denise M; Frailey, Conor N; Gravenor, Stephanie J; Powell, Emilie S; Wang, Amy Y; Kricke, Gayle Shier; Ahmad, Faraz S; Mutharasan, R Kannan; Soulakis, Nicholas D

    2016-11-01

    The nature of teamwork in healthcare is complex and interdisciplinary, and provider collaboration based on shared patient encounters is crucial to its success. Characterizing the intensity of working relationships with risk-adjusted patient outcomes supplies insight into provider interactions in a hospital environment. We extracted 4 years of patient, provider, and activity data for encounters in an inpatient cardiology unit from Northwestern Medicine's Enterprise Data Warehouse. We then created a provider-patient network to identify healthcare providers who jointly participated in patient encounters and calculated satisfaction rates for provider-provider pairs. We demonstrated the application of a novel parameter, the shared positive outcome ratio, a measure that assesses the strength of a patient-sharing relationship between 2 providers based on risk-adjusted encounter outcomes. We compared an observed collaboration network of 334 providers and 3453 relationships to 1000 networks with shared positive outcome ratio scores based on randomized outcomes and found 188 collaborative relationships between pairs of providers that showed significantly higher than expected patient satisfaction ratings. A group of 22 providers performed exceptionally in terms of patient satisfaction. Our results indicate high variability in collaboration scores across the network and highlight our ability to identify relationships with both higher and lower than expected scores across a set of shared patient encounters. Satisfaction rates seem to vary across different teams of providers. Team collaboration can be quantified using a composite measure of collaboration across provider pairs. Tracking provider pair outcomes over a sufficient set of shared encounters may inform quality improvement strategies such as optimizing team staffing, identifying characteristics and practices of high-performing teams, developing evidence-based team guidelines, and redesigning inpatient care processes.

  12. Coevolutionary network approach to cultural dynamics controlled by intolerance

    NASA Astrophysics Data System (ADS)

    Gracia-Lázaro, Carlos; Quijandría, Fernando; Hernández, Laura; Floría, Luis Mario; Moreno, Yamir

    2011-12-01

    Starting from Axelrod's model of cultural dissemination, we introduce a rewiring probability, enabling agents to cut the links with their unfriendly neighbors if their cultural similarity is below a tolerance parameter. For low values of tolerance, rewiring promotes the convergence to a frozen monocultural state. However, intermediate tolerance values prevent rewiring once the network is fragmented, resulting in a multicultural society even for values of initial cultural diversity in which the original Axelrod model reaches globalization.

  13. Tracing Road Network Bottleneck by Data Driven Approach

    PubMed Central

    Qi, Hongsheng; Liu, Meiqi; Zhang, Lihui; Wang, Dianhai

    2016-01-01

    Urban road congestions change both temporally and spatially. They are essentially caused by network bottlenecks. Therefore, understanding bottleneck dynamics is critical in the goal of reasonably allocating transportation resources. In general, a typical bottleneck experiences the stages of formation, propagation and dispersion. In order to understand the three stages of a bottle neck and how the bottleneck moves on a road network, traffic flow data can be used to reconstruct these dynamics. However, raw traffic flow data is usually flawed in many ways. For instance some portion of data may be missing due to the failure of data collection devices, or some random factors in the data make it hard to identify real bottlenecks. In this paper a “user voting method” is proposed to deal with such raw-data-related issues. In this method, road links are ranked according to the weighed sum of certain performance measures and the links that are ranked relatively high are regarded as recurrent bottlenecks in a network, and several bottlenecks form a bottleneck area. A series of bottleneck parameters can be defined based on the identified bottleneck areas, such as bottleneck coverage, bottleneck link length, etc. Identifying bottleneck areas and calculating the bottleneck parameters for each time interval can reflect the evolution of the bottlenecks and also help trace how the bottlenecks move. PMID:27228150

  14. Predicting forest insect flight activity: A Bayesian network approach.

    PubMed

    Pawson, Stephen M; Marcot, Bruce G; Woodberry, Owen G

    2017-01-01

    Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.

  15. A review of network-based approaches to drug repositioning.

    PubMed

    Lotfi Shahreza, Maryam; Ghadiri, Nasser; Mousavi, Sayed Rasoul; Varshosaz, Jaleh; Green, James R

    2017-02-27

    Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.

  16. Approach and analysis of contention resolution in optical switching network

    NASA Astrophysics Data System (ADS)

    Yang, Xiaolong; Dang, Mingrui; Mao, Youju; Zhang, Min; Li, Lemin

    2002-09-01

    As the Internet traffic exponentially growing, the next generation IP network is forced to scale far beyond its present performances. The more and more mature optical switching technology, such as optical burst switching, is expected to provide an ideal infrastructure for meeting the demands. However in optical switching, there is one critical issue, namely contention, which roots from multiple optical data requesting the same output port How to resolve contention in optical domain will have a significant effect on the performance (in terms of the burst-loss rate, average delay time and network throughput) of optical switching network. The paper proposes a contention resolution scheme based on FDL, AWG and TWC. Here FDL is used as two functions, i.e. forwarding and feedback for smaller or longer buffering time requirements respectively. In the paper we incorporate the scheme into the design of optical switch. We descript the optical data buffering strategy when contention occurs. We also study the performance of the scheme in a Markov process model under the assumption of uniform Bernoulli traffic, and validate the analysis through numerical simulation. The computer simulation results show that the scheme can efficiently use FDL buffering and AWG switching capacities, hence can obviously reduce the contentions.

  17. Nursing home care quality: insights from a Bayesian network approach.

    PubMed

    Goodson, Justin; Jang, Wooseung; Rantz, Marilyn

    2008-06-01

    The purpose of this research is twofold. The first purpose is to utilize a new methodology (Bayesian networks) for aggregating various quality indicators to measure the overall quality of care in nursing homes. The second is to provide new insight into the relationships that exist among various measures of quality and how such measures affect the overall quality of nursing home care as measured by the Observable Indicators of Nursing Home Care Quality Instrument. In contrast to many methods used for the same purpose, our method yields both qualitative and quantitative insight into nursing home care quality. We construct several Bayesian networks to study the influences among factors associated with the quality of nursing home care; we compare and measure their accuracy against other predictive models. We find the best Bayesian network to perform better than other commonly used methods. We also identify key factors, including number of certified nurse assistant hours, prevalence of bedfast residents, and prevalence of daily physical restraints, that significantly affect the quality of nursing home care. Furthermore, the results of our analysis identify their probabilistic relationships. The findings of this research indicate that nursing home care quality is most accurately represented through a mix of structural, process, and outcome measures of quality. We also observe that the factors affecting the quality of nursing home care collectively determine the overall quality. Hence, focusing on only key factors without addressing other related factors may not substantially improve the quality of nursing home care.

  18. Integrated healthcare networks' performance: a growth curve modeling approach.

    PubMed

    Wan, Thomas T H; Wang, Bill B L

    2003-05-01

    This study examines the effects of integration on the performance ratings of the top 100 integrated healthcare networks (IHNs) in the United States. A strategic-contingency theory is used to identify the relationship of IHNs' performance to their structural and operational characteristics and integration strategies. To create a database for the panel study, the top 100 IHNs selected by the SMG Marketing Group in 1998 were followed up in 1999 and 2000. The data were merged with the Dorenfest data on information system integration. A growth curve model was developed and validated by the Mplus statistical program. Factors influencing the top 100 IHNs' performance in 1998 and their subsequent rankings in the consecutive years were analyzed. IHNs' initial performance scores were positively influenced by network size, number of affiliated physicians and profit margin, and were negatively associated with average length of stay and technical efficiency. The continuing high performance, judged by maintaining higher performance scores, tended to be enhanced by the use of more managerial or executive decision-support systems. Future studies should include time-varying operational indicators to serve as predictors of network performance.

  19. A network biology approach to denitrification in Pseudomonas aeruginosa

    DOE PAGES

    Arat, Seda; Bullerjahn, George S.; Laubenbacher, Reinhard

    2015-02-23

    Pseudomonas aeruginosa is a metabolically flexible member of the Gammaproteobacteria. Under anaerobic conditions and the presence of nitrate, P. aeruginosa can perform (complete) denitrification, a respiratory process of dissimilatory nitrate reduction to nitrogen gas via nitrite (NO₂), nitric oxide (NO) and nitrous oxide (N₂O). This study focuses on understanding the influence of environmental conditions on bacterial denitrification performance, using a mathematical model of a metabolic network in P. aeruginosa. To our knowledge, this is the first mathematical model of denitrification for this bacterium. Analysis of the long-term behavior of the network under changing concentration levels of oxygen (O₂), nitrate (NO₃),more » and phosphate (PO₄) suggests that PO₄ concentration strongly affects denitrification performance. The model provides three predictions on denitrification activity of P. aeruginosa under various environmental conditions, and these predictions are either experimentally validated or supported by pertinent biological literature. One motivation for this study is to capture the effect of PO₄ on a denitrification metabolic network of P. aeruginosa in order to shed light on mechanisms for greenhouse gas N₂O accumulation during seasonal oxygen depletion in aquatic environments such as Lake Erie (Laurentian Great Lakes, USA). Simulating the microbial production of greenhouse gases in anaerobic aquatic systems such as Lake Erie allows a deeper understanding of the contributing environmental effects that will inform studies on, and remediation strategies for, other hypoxic sites worldwide.« less

  20. Active traffic management on road networks: a macroscopic approach.

    PubMed

    Kurzhanskiy, Alex A; Varaiya, Pravin

    2010-10-13

    Active traffic management (ATM) is the ability to dynamically manage recurrent and non-recurrent congestion based on prevailing traffic conditions in order to maximize the effectiveness and efficiency of road networks. It is a continuous process of (i) obtaining and analysing traffic measurement data, (ii) operations planning, i.e. simulating various scenarios and control strategies, (iii) implementing the most promising control strategies in the field, and (iv) maintaining a real-time decision support system that filters current traffic measurements to predict the traffic state in the near future, and to suggest the best available control strategy for the predicted situation. ATM relies on a fast and trusted traffic simulator for the rapid quantitative assessment of a large number of control strategies for the road network under various scenarios, in a matter of minutes. The open-source macrosimulation tool Aurora ROAD NETWORK MODELER is a good candidate for this purpose. The paper describes the underlying dynamical traffic model and what it takes to prepare the model for simulation; covers the traffic performance measures and evaluation of scenarios as part of operations planning; introduces the framework within which the control strategies are modelled and evaluated; and presents the algorithm for real-time traffic state estimation and short-term prediction.

  1. Embedded Efficiency: A Social Networks Approach to Popular Support and Dark Network Structure

    DTIC Science & Technology

    2016-03-01

    SUPPORT AND DARK NETWORK STRUCTURE 5. FUNDING NUMBERS 6. AUTHOR(S) Leopele S. Raabe and Gary S. Blount 7. PERFORMING ORGANIZATION NAME(S) AND...ADDRESS(ES) Naval Postgraduate School Monterey, CA 93943-5000 8. PERFORMING ORGANIZATION REPORT NUMBER 9. SPONSORING /MONITORING AGENCY NAME(S... organizational behavior within dark networks? The objectives of this thesis are twofold. The primary objective is to illuminate the interaction between

  2. Network-based stochastic competitive learning approach to disambiguation in collaborative networks

    NASA Astrophysics Data System (ADS)

    Christiano Silva, Thiago; Raphael Amancio, Diego

    2013-03-01

    Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

  3. Network-based stochastic competitive learning approach to disambiguation in collaborative networks.

    PubMed

    Christiano Silva, Thiago; Raphael Amancio, Diego

    2013-03-01

    Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

  4. Multiscale complex network analysis: An approach to study spatiotemporal rainfall pattern in south Germany

    NASA Astrophysics Data System (ADS)

    Agarwal, Ankit; Marwan, Norbert; Rathinasamy, Maheswaran; Oeztuerk, Ugur; Merz, Bruno; Kurths, Jürgen

    2017-04-01

    Understanding of the climate sytems has been of tremendous importance to different branches such as agriculture, flood, drought and water resources management etc. In this regard, complex networks analysis and time series analysis attracted considerable attention, owing to their potential role in understanding the climate system through characteristic properties. One of the basic requirements in studying climate network dynamics is to identify connections in space or time or space-time, depending upon the purpose. Although a wide variety of approaches have been developed and applied to identify and analyse spatio-temporal relationships by climate networks, there is still further need for improvements in particular when considering precipitation time series or interactions on different scales. In this regard, recent developments in the area of network theory, especially complex networks, offer new avenues, both for their generality about systems and for their holistic perspective about spatio-temporal relationships. The present study has made an attempt to apply the ideas developed in the field of complex networks to examine connections in regional climate networks with particular focus on multiscale spatiotemporal connections. This paper proposes a novel multiscale understanding of regional climate networks using wavelets. The proposed approach is applied to daily precipitation records observed at 543 selected stations from south Germany for a period of 110 years (1901-2010). Further, multiscale community mining is performed on the same study region to shed more light on the underlying processes at different time scales. Various network measure and tools so far employed provide micro-level (individual station) and macro-level (community structure) information of the network. It is interesting to investigate how the result of this study can be useful for future climate predictions and for evaluating climate models on their implementation regarding heavy

  5. Heuristic approaches for energy-efficient shared restoration in WDM networks

    NASA Astrophysics Data System (ADS)

    Alilou, Shahab

    In recent years, there has been ongoing research on the design of energy-efficient Wavelength Division Multiplexing (WDM) networks. The explosive growth of Internet traffic has led to increased power consumption of network components. Network survivability has also been a relevant research topic, as it plays a crucial role in assuring continuity of service with no disruption, regardless of network component failure. Network survivability mechanisms tend to utilize considerable resources such as spare capacity in order to protect and restore information. This thesis investigates techniques for reducing energy demand and enhancing energy efficiency in the context of network survivability. We propose two novel heuristic energy-efficient shared protection approaches for WDM networks. These approaches intend to save energy by setting on sleep mode devices that are not being used while providing shared backup paths to satisfy network survivability. The first approach exploits properties of a math series in order to assign weight to the network links. It aims at reducing power consumption at the network indirectly by aggregating traffic on a set of nodes and links with high traffic load level. Routing traffic on links and nodes that are already under utilization makes it possible for the links and nodes with no load to be set on sleep mode. The second approach is intended to dynamically route traffic through nodes and links with high traffic load level. Similar to the first approach, this approach computes a pair of paths for every newly arrived demand. It computes these paths for every new demand by comparing the power consumption of nodes and links in the network before the demand arrives with their potential power consumption if they are chosen along the paths of this demand. Simulations of two different networks were used to compare the total network power consumption obtained using the proposed techniques against a standard shared-path restoration scheme. Shared

  6. Touch-Based Interaction Approach for Network Science Research and Visualization

    DTIC Science & Technology

    2016-01-01

    Laboratory (ARL) Visualization Framework presents a language -agnostic, platform-independent approach to connecting data published by probes to... Visualization by John P Hancock, Mathew Aguirre, and Andrew Toth Approved for public release; distribution is unlimited...Laboratory Touch-Based Interaction Approach for Network Science Research and Visualization by John P Hancock and Mathew Aguirre ArtisTech, Inc

  7. Disrupting Terrorist Networks — A Dynamic Fitness Landscape Approach

    NASA Astrophysics Data System (ADS)

    Fellman, Philip V.; Clemens, Jonathan P.; Wright, Roxana; Post, Jonathan Vos; Dadmun, Matthew

    The study of terrorist networks as well as the study of how to impede their successful functioning has been the topic of considerable attention since the odious event of the 2001 World Trade Center disaster. While serious students of terrorism were indeed engaged in the subject prior to this time, a far more general concern has arisen subsequently. Nonetheless, much of the subject remains shrouded in obscurity, not the least because of difficulties with language and the representation or translation of names, and the inherent complexity and ambiguity of the subject matter.

  8. A subset polynomial neural networks approach for breast cancer diagnosis.

    PubMed

    O'Neill, T J; Penm, Jack; Penm, Jonathan

    2007-01-01

    Breast cancer is a very common and serious cancer for women that is diagnosed in one of every eight Australian women before the age of 85. The conventional method of breast cancer diagnosis is mammography. However, mammography has been reported to have poor diagnostic capability. In this paper we have used subset polynomial neural network techniques in conjunction with fine needle aspiration cytology to undertake this difficult task of predicting breast cancer. The successful findings indicate that adoption of NNs is likely to lead to increased survival of women with breast cancer, improved electronic healthcare, and enhanced quality of life.

  9. Deep space network resource scheduling approach and application

    NASA Technical Reports Server (NTRS)

    Eggemeyer, William C.; Bowling, Alan

    1987-01-01

    Deep Space Network (DSN) resource scheduling is the process of distributing ground-based facilities to track multiple spacecraft. The Jet Propulsion Laboratory has carried out extensive research to find ways of automating this process in an effort to reduce time and manpower costs. This paper presents a resource-scheduling system entitled PLAN-IT with a description of its design philosophy. The PLAN-IT's current on-line usage and limitations in scheduling the resources of the DSN are discussed, along with potential enhancements for DSN application.

  10. Link removal for the control of stochastically evolving epidemics over networks: A comparison of approaches

    PubMed Central

    Brandeau, Margaret L.

    2015-01-01

    For many communicable diseases, knowledge of the underlying contact network through which the disease spreads is essential to determining appropriate control measures. When behavior change is the primary intervention for disease prevention, it is important to understand how to best modify network connectivity using the limited resources available to control disease spread. We describe and compare four algorithms for selecting a limited number of links to remove from a network: two “preventive” approaches (edge centrality, R0 minimization), where the decision of which links to remove is made prior to any disease outbreak and depends only on the network structure; and two “reactive” approaches (S-I edge centrality, optimal quarantining), where information about the initial disease states of the nodes is incorporated into the decision of which links to remove. We evaluate the performance of these algorithms in minimizing the total number of infections that occur over the course of an acute outbreak of disease. We consider different network structures, including both static and dynamic Erdős-Rényi random networks with varying levels of connectivity, a real-world network of residential hotels connected through injection drug use, and a network exhibiting community structure. We show that reactive approaches outperform preventive approaches in averting infections. Among reactive approaches, removing links in order of S-I edge centrality is favored when the link removal budget is small, while optimal quarantining performs best when the link removal budget is sufficiently large. The budget threshold above which optimal quarantining outperforms the S-I edge centrality algorithm is a function of both network structure (higher for unstructured Erdős-Rényi random networks compared to networks with community structure or the real-world network) and disease infectiousness (lower for highly infectious diseases). We conduct a value-of-information analysis of knowing

  11. Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach

    NASA Astrophysics Data System (ADS)

    Chiadamrong, N.; Piyathanavong, V.

    2017-04-01

    Models that aim to optimize the design of supply chain networks have gained more interest in the supply chain literature. Mixed-integer linear programming and discrete-event simulation are widely used for such an optimization problem. We present a hybrid approach to support decisions for supply chain network design using a combination of analytical and discrete-event simulation models. The proposed approach is based on iterative procedures until the difference between subsequent solutions satisfies the pre-determined termination criteria. The effectiveness of proposed approach is illustrated by an example, which shows closer to optimal results with much faster solving time than the results obtained from the conventional simulation-based optimization model. The efficacy of this proposed hybrid approach is promising and can be applied as a powerful tool in designing a real supply chain network. It also provides the possibility to model and solve more realistic problems, which incorporate dynamism and uncertainty.

  12. Bilayer linearized tensor renormalization group approach for thermal tensor networks

    NASA Astrophysics Data System (ADS)

    Dong, Yong-Liang; Chen, Lei; Liu, Yun-Jing; Li, Wei

    2017-04-01

    Thermal tensor networks constitute an efficient and versatile representation for quantum lattice models at finite temperatures. By Trotter-Suzuki decomposition, one obtains a D +1 dimensional TTN for the D -dimensional quantum system and then employs efficient renormalizaton group (RG) contractions to obtain the thermodynamic properties with high precision. The linearized tensor renormalization group (LTRG) method, which can be used to contract TTN efficiently and calculate the thermodynamics, is briefly reviewed and then generalized to a bilayer form. We dub this bilayer algorithm as LTRG++ and explore its performance in both finite- and infinite-size systems, finding the numerical accuracy significantly improved compared to single-layer algorithm. Moreover, we show that the LTRG++ algorithm in an infinite-size system is in essence equivalent to transfer-matrix renormalization group method, while reformulated in a tensor network language. As an application of LTRG++, we simulate an extended fermionic Hubbard model numerically, where the phase separation phenomenon, ground-state phase diagram, as well as quantum criticality-enhanced magnetocaloric effects, are investigated.

  13. Decentralized Approach of Cable Network Structures for Their Vibration Control

    NASA Astrophysics Data System (ADS)

    Nishio, Mayuko; Tanaka, Hiroaki; Natori, M. C.; Kishimoto, Naoko; Fujino, Yozo

    This paper proposes a decentralized vibration control method for cable-network structures in space antennas, which are large and so flexible structures and show non-linear behaviors. The main advantage of this method is that it is possible to gain adaptability to changes of models, such as size or depth changes of parabolas that come from diversity of space mission and manufacturing errors. The control method is decentralized direct velocity and displacement feedback (DVDFB). Force Density Method (FDM) is used for structural analysis of cable-network structures. The index “Force Density (FD),” which shows tension condition of each cable, can be used for decision of the placement of sensors and actuators. The results show that, even though non-linear behaviors such as large displacement and slack of cables are occurred, vibration control of this structure system is possible. And then, it is also shown that the shape-changed model can obtain the control effect by this method.

  14. A neural network approach to lung nodule segmentation

    NASA Astrophysics Data System (ADS)

    Hu, Yaoxiu; Menon, Prahlad G.

    2016-03-01

    Computed tomography (CT) imaging is a sensitive and specific lung cancer screening tool for the high-risk population and shown to be promising for detection of lung cancer. This study proposes an automatic methodology for detecting and segmenting lung nodules from CT images. The proposed methods begin with thorax segmentation, lung extraction and reconstruction of the original shape of the parenchyma using morphology operations. Next, a multi-scale hessian-based vesselness filter is applied to extract lung vasculature in lung. The lung vasculature mask is subtracted from the lung region segmentation mask to extract 3D regions representing candidate pulmonary nodules. Finally, the remaining structures are classified as nodules through shape and intensity features which are together used to train an artificial neural network. Up to 75% sensitivity and 98% specificity was achieved for detection of lung nodules in our testing dataset, with an overall accuracy of 97.62%+/-0.72% using 11 selected features as input to the neural network classifier, based on 4-fold cross-validation studies. Receiver operator characteristics for identifying nodules revealed an area under curve of 0.9476.

  15. Model-Driven Approach for Body Area Network Application Development

    PubMed Central

    Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata

    2016-01-01

    This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application. PMID:27187394

  16. Model-Driven Approach for Body Area Network Application Development.

    PubMed

    Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata

    2016-05-12

    This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.

  17. Direction-dependent learning approach for radial basis function networks.

    PubMed

    Singla, Puneet; Subbarao, Kamesh; Junkins, John L

    2007-01-01

    Direction-dependent scaling, shaping, and rotation of Gaussian basis functions are introduced for maximal trend sensing with minimal parameter representations for input output approximation. It is shown that shaping and rotation of the radial basis functions helps in reducing the total number of function units required to approximate any given input-output data, while improving accuracy. Several alternate formulations that enforce minimal parameterization of the most general radial basis functions are presented. A novel "directed graph" based algorithm is introduced to facilitate intelligent direction based learning and adaptation of the parameters appearing in the radial basis function network. Further, a parameter estimation algorithm is incorporated to establish starting estimates for the model parameters using multiple windows of the input-output data. The efficacy of direction-dependent shaping and rotation in function approximation is evaluated by modifying the minimal resource allocating network and considering different test examples. The examples are drawn from recent literature to benchmark the new algorithm versus existing methods.

  18. Associative nature of event participation dynamics: A network theory approach

    PubMed Central

    Smiljanić, Jelena; Mitrović Dankulov, Marija

    2017-01-01

    The affiliation with various social groups can be a critical factor when it comes to quality of life of each individual, making such groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group’s ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist’s engagement in conference activities of the specific scientific community depends on the balance between the numbers of previous attendances and non-attendances and is directly related to scientist’s association with that community. Here we show that the same holds for leisure groups of the Meetup website and further quantify individual members’ association with the group. We examine how structure of personal social networks is evolving with the event attendance. Our results show that member’s increasing engagement in the group activities is primarily associated with the strengthening of already existing ties and increase in the bonding social capital. We also show that Meetup social networks mostly grow trough big events, while small events contribute to the groups cohesiveness. PMID:28166305

  19. Denominator estimation: approaches in the Hamburg paediatric sentinel network.

    PubMed Central

    Kellerhof, M; Gritz, K; Brand, H

    1995-01-01

    STUDY OBJECTIVE--The aims were to develop an estimator for the size of paediatric practices to be used as a denominator for purposes of comparison; to analyse the age structure of the patients attending paediatric practices and to check the necessity for an age specific denominator; and to validate the denominator information by other available data. DESIGN--This was an observational study. SETTING/PARTICIPANTS--A sentinel network was set up comprising 26 self selected paediatric practices. Weekly patient contacts in relation to age and sex were counted three times during the study period of two years. In addition, accounting data, including the total number of children treated in a given three month period (quarter), were available. MAIN RESULTS--Weekly patient contact counts were stable over time, not in terms of the absolute number of contacts but in the rank positions of the practices (rs = 0.86) and in their age structure. The age distribution of weekly patient contacts differed significantly between the practices. Cross validation of the weekly contact count by means of the quarterly accounting data resulted in a rank correlation of rs = 0.90. CONCLUSIONS--Sentinel networks with paediatric practices should use age specific denominator information. Weekly contact group, estimated by counts in a sample of weeks, is a stable and easily available denominator for sentinel practices in the context of the German health care system. Images PMID:7561666

  20. Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

    NASA Astrophysics Data System (ADS)

    Wu, Kai; Liu, Jing; Wang, Shuai

    2016-11-01

    Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy.

  1. Reconstructing Networks from Profit Sequences in Evolutionary Games via a Multiobjective Optimization Approach with Lasso Initialization

    PubMed Central

    Wu, Kai; Liu, Jing; Wang, Shuai

    2016-01-01

    Evolutionary games (EG) model a common type of interactions in various complex, networked, natural and social systems. Given such a system with only profit sequences being available, reconstructing the interacting structure of EG networks is fundamental to understand and control its collective dynamics. Existing approaches used to handle this problem, such as the lasso, a convex optimization method, need a user-defined constant to control the tradeoff between the natural sparsity of networks and measurement error (the difference between observed data and simulated data). However, a shortcoming of these approaches is that it is not easy to determine these key parameters which can maximize the performance. In contrast to these approaches, we first model the EG network reconstruction problem as a multiobjective optimization problem (MOP), and then develop a framework which involves multiobjective evolutionary algorithm (MOEA), followed by solution selection based on knee regions, termed as MOEANet, to solve this MOP. We also design an effective initialization operator based on the lasso for MOEA. We apply the proposed method to reconstruct various types of synthetic and real-world networks, and the results show that our approach is effective to avoid the above parameter selecting problem and can reconstruct EG networks with high accuracy. PMID:27886244

  2. Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

    PubMed Central

    2014-01-01

    Background Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely

  3. Emerging late adolescent friendship networks and Big Five personality traits: a social network approach.

    PubMed

    Selfhout, Maarten; Burk, William; Branje, Susan; Denissen, Jaap; van Aken, Marcel; Meeus, Wim

    2010-04-01

    The current study focuses on the emergence of friendship networks among just-acquainted individuals, investigating the effects of Big Five personality traits on friendship selection processes. Sociometric nominations and self-ratings on personality traits were gathered from 205 late adolescents (mean age=19 years) at 5 time points during the first year of university. SIENA, a novel multilevel statistical procedure for social network analysis, was used to examine effects of Big Five traits on friendship selection. Results indicated that friendship networks between just-acquainted individuals became increasingly more cohesive within the first 3 months and then stabilized. Whereas individuals high on Extraversion tended to select more friends than those low on this trait, individuals high on Agreeableness tended to be selected more as friends. In addition, individuals tended to select friends with similar levels of Agreeableness, Extraversion, and Openness.

  4. Copercolating Networks: An Approach for Realizing High-Performance Transparent Conductors using Multicomponent Nanostructured Networks

    NASA Astrophysics Data System (ADS)

    Das, Suprem R.; Sadeque, Sajia; Jeong, Changwook; Chen, Ruiyi; Alam, Muhammad A.; Janes, David B.

    2016-06-01

    Although transparent conductive oxides such as indium tin oxide (ITO) are widely employed as transparent conducting electrodes (TCEs) for applications such as touch screens and displays, new nanostructured TCEs are of interest for future applications, including emerging transparent and flexible electronics. A number of twodimensional networks of nanostructured elements have been reported, including metallic nanowire networks consisting of silver nanowires, metallic carbon nanotubes (m-CNTs), copper nanowires or gold nanowires, and metallic mesh structures. In these single-component systems, it has generally been difficult to achieve sheet resistances that are comparable to ITO at a given broadband optical transparency. A relatively new third category of TCEs consisting of networks of 1D-1D and 1D-2D nanocomposites (such as silver nanowires and CNTs, silver nanowires and polycrystalline graphene, silver nanowires and reduced graphene oxide) have demonstrated TCE performance comparable to, or better than, ITO. In such hybrid networks, copercolation between the two components can lead to relatively low sheet resistances at nanowire densities corresponding to high optical transmittance. This review provides an overview of reported hybrid networks, including a comparison of the performance regimes achievable with those of ITO and single-component nanostructured networks. The performance is compared to that expected from bulk thin films and analyzed in terms of the copercolation model. In addition, performance characteristics relevant for flexible and transparent applications are discussed. The new TCEs are promising, but significant work must be done to ensure earth abundance, stability, and reliability so that they can eventually replace traditional ITO-based transparent conductors.

  5. Neighborhoods and Adolescent Health-Risk Behavior: An Ecological Network Approach1

    PubMed Central

    Browning, Christopher R.; Soller, Brian; Jackson, Aubrey L.

    2014-01-01

    This study integrates insights from social network analysis, activity space perspectives, and theories of urban and spatial processes to present an innovative approach to neighborhood effects on health-risk behavior among youth. We suggest spatial patterns of neighborhood residents’ non-home routine activities may be conceptualized as ecological, or “eco”-networks, which are two-mode networks that indirectly link residents through socio-spatial overlap in routine activities. We further argue structural configurations of eco-networks are consequential for youth’s behavioral health. In this study we focus on a key structural feature of eco-networks—the neighborhood-level extent to which households share two or more activity locations, or eco-network reinforcement—and its association with two dimensions of health-risk behavior, substance use and delinquency/sexual activity. Using geographic data on non-home routine activity locations among respondents from the Los Angeles Family and Neighborhood Survey (L.A.FANS), we constructed neighborhood-specific eco-networks by connecting sampled households to “activity clusters,” which are sets of spatially-proximate activity locations. We then measured eco-network reinforcement and examined its association with adolescent dimensions of health risk behavior employing a sample of 830 youth ages 12-17 nested in 65 census tracts. We also examined whether neighborhood-level social processes (collective efficacy and intergenerational closure) mediate the association between eco-network reinforcement and the outcomes considered. Results indicated eco-network reinforcement exhibits robust negative associations with both substance use and delinquency/sexual activity scales. Eco-network reinforcement effects were not explained by potential mediating variables. In addition to introducing a novel theoretical and empirical approach to neighborhood effects on youth, our findings highlight the importance of eco-network

  6. Neighborhoods and adolescent health-risk behavior: an ecological network approach.

    PubMed

    Browning, Christopher R; Soller, Brian; Jackson, Aubrey L

    2015-01-01

    This study integrates insights from social network analysis, activity space perspectives, and theories of urban and spatial processes to present an novel approach to neighborhood effects on health-risk behavior among youth. We suggest spatial patterns of neighborhood residents' non-home routines may be conceptualized as ecological, or "eco"-networks, which are two-mode networks that indirectly link residents through socio-spatial overlap in routine activities. We further argue structural configurations of eco-networks are consequential for youth's behavioral health. In this study we focus on a key structural feature of eco-networks--the neighborhood-level extent to which household dyads share two or more activity locations, or eco-network reinforcement--and its association with two dimensions of health-risk behavior, substance use and delinquency/sexual activity. Using geographic data on non-home routine activity locations among respondents from the Los Angeles Family and Neighborhood Survey (L.A.FANS), we constructed neighborhood-specific eco-networks by connecting sampled households to "activity clusters," which are sets of spatially-proximate activity locations. We then measured eco-network reinforcement and examined its association with dimensions of adolescent health risk behavior employing a sample of 830 youth ages 12-17 nested in 65 census tracts. We also examined whether neighborhood-level social processes (collective efficacy and intergenerational closure) mediate the association between eco-network reinforcement and the outcomes considered. Results indicated eco-network reinforcement exhibits robust negative associations with both substance use and delinquency/sexual activity scales. Eco-network reinforcement effects were not explained by potential mediating variables. In addition to introducing a novel theoretical and empirical approach to neighborhood effects on youth, our findings highlight the importance of intersecting conventional routines for

  7. Stock market networks: The dynamic conditional correlation approach

    NASA Astrophysics Data System (ADS)

    Lyócsa, Štefan; Výrost, Tomáš; Baumöhl, Eduard

    2012-08-01

    We demonstrate the economic relevance of minimum spanning trees (MSTs) constructed from dynamic conditional correlations (DCC) for a sample of S&P 100 constituents. An empirical comparison of MST properties shows that using the standard approach of rolling (or sliding-window) correlations yields trees that are more robust, have higher densities and exhibit higher industry clustering than MSTs based on DCC. Our results suggest that these properties are achieved at the expense of the smoothing of market dynamics, which is better preserved by DCC. The DCC approach offers a new perspective for the analysis of complex systems such as stock markets.

  8. Study on uncertainty of geospatial semantic Web services composition based on broker approach and Bayesian networks

    NASA Astrophysics Data System (ADS)

    Yang, Xiaodong; Cui, Weihong; Liu, Zhen; Ouyang, Fucheng

    2008-10-01

    The Semantic Web has a major weakness which is lacking of a principled means to represent and reason about uncertainty. This is also located in the services composition approaches such as BPEL4WS and Semantic Description Model. We analyze the uncertainty of Geospatial Web Service composition through mining the knowledge in historical records of composition based on Broker approach and Bayesian Networks. We proved this approach is effective and efficient through a sample scenario in this paper.

  9. A game-theoretical approach to multimedia social networks security.

    PubMed

    Liu, Enqiang; Liu, Zengliang; Shao, Fei; Zhang, Zhiyong

    2014-01-01

    The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party's benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders.

  10. A Bayesian Network approach for flash flood risk assessment

    NASA Astrophysics Data System (ADS)

    Boutkhamouine, Brahim; Roux, Hélène; Pérès, François

    2017-04-01

    Climate change is contributing to the increase of natural disasters such as extreme weather events. Sometimes, these events lead to sudden flash floods causing devastating effects on life and property. Most recently, many regions of the French Mediterranean perimeter have endured such catastrophic flood events; Var (October 2015), Ardèche (November 2014), Nîmes (October 2014), Hérault, Gard and Languedoc (September 2014), and Pyrenees mountains (Jun 2013). Altogether, it resulted in dozens of victims and property damages amounting to millions of euros. With this heavy loss in mind, development of hydrological forecasting and warning systems is becoming an essential element in regional and national strategies. Flash flood forecasting but also monitoring is a difficult task because small ungauged catchments ( 10 km2) are often the most destructive ones as for the extreme flash flood event of September 2002 in the Cévennes region (France) (Ruin et al., 2008). The problem of measurement/prediction uncertainty is particularly crucial when attempting to develop operational flash-flood forecasting methods. Taking into account the uncertainty related to the model structure itself, to the model parametrization or to the model forcing (spatio-temporal rainfall, initial conditions) is crucial in hydrological modelling. Quantifying these uncertainties is of primary importance for risk assessment and decision making. Although significant improvements have been made in computational power and distributed hydrologic modelling, the issue dealing with integration of uncertainties into flood forecasting remains up-to-date and challenging. In order to develop a framework which could handle these uncertainties and explain their propagation through the model, we propose to explore the potential of graphical models (GMs) and, more precisely, Bayesian Networks (BNs). These networks are Directed Acyclic Graphs (DAGs) in which knowledge of a certain phenomenon is represented by

  11. Genetic variants in Alzheimer disease - molecular and brain network approaches.

    PubMed

    Gaiteri, Chris; Mostafavi, Sara; Honey, Christopher J; De Jager, Philip L; Bennett, David A

    2016-07-01

    Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models.

  12. A scenario planning approach for disasters on Swiss road network

    NASA Astrophysics Data System (ADS)

    Mendes, G. A.; Axhausen, K. W.; Andrade, J. S.; Herrmann, H. J.

    2014-05-01

    We study a vehicular traffic scenario on Swiss roads in an emergency situation, calculating how sequentially roads block due to excessive traffic load until global collapse (gridlock) occurs and in this way displays the fragilities of the system. We used a database from Bundesamt für Raumentwicklung which contains length and maximum allowed speed of all roads in Switzerland. The present work could be interesting for government agencies in planning and managing for emergency logistics for a country or a big city. The model used to generate the flux on the Swiss road network was proposed by Mendes et al. [Physica A 391, 362 (2012)]. It is based on the conservation of the number of vehicles and allows for an easy and fast way to follow the formation of traffic jams in large systems. We also analyze the difference between a nonlinear and a linear model and the distribution of fluxes on the Swiss road.

  13. Decentralised ? - filtering of networked control systems: a jump system approach

    NASA Astrophysics Data System (ADS)

    Al-Radhawi, Muhammad Ali; Bettayeb, Maamar

    2014-10-01

    We consider the problem of decentralised estimation of discrete-time interconnected systems with local estimators communicating with their subsystems over lossy communication channels. Assuming that the packet losses follow the Gilbert-Elliot model, the networked estimation problem can be formulated into a Markovian jump linear system framework. Modelling subsystem interactions as sum quadratic constrained uncertainties, we design mode-dependent decentralised ?-estimators that robustly stabilise the estimator system and guarantee a given disturbance attenuation level. The estimation gains are derived with necessary and sufficient rank-constrained linear matrix inequality conditions. Results are also provided for local mode-dependent estimators. Estimator synthesis is done using a cone-complementarity linearisation algorithm for the rank-constraints. The results are illustrated via an example.

  14. The generalized quadratic knapsack problem. A neuronal network approach.

    PubMed

    Talaván, Pedro M; Yáñez, Javier

    2006-05-01

    The solution of an optimization problem through the continuous Hopfield network (CHN) is based on some energy or Lyapunov function, which decreases as the system evolves until a local minimum value is attained. A new energy function is proposed in this paper so that any 0-1 linear constrains programming with quadratic objective function can be solved. This problem, denoted as the generalized quadratic knapsack problem (GQKP), includes as particular cases well-known problems such as the traveling salesman problem (TSP) and the quadratic assignment problem (QAP). This new energy function generalizes those proposed by other authors. Through this energy function, any GQKP can be solved with an appropriate parameter setting procedure, which is detailed in this paper. As a particular case, and in order to test this generalized energy function, some computational experiments solving the traveling salesman problem are also included.

  15. Predictive vector quantization using a neural network approach

    NASA Astrophysics Data System (ADS)

    Mohsenian, Nader; Rizvi, Syed A.; Nasrabadi, Nasser M.

    1993-07-01

    A new predictive vector quantization (PVQ) technique capable of exploring the nonlinear dependencies in addition to the linear dependencies that exist between adjacent blocks (vectors) of pixels is introduced. The two components of the PVQ scheme, the vector predictor and the vector quantizer, are implemented by two different classes of neural networks. A multilayer perceptron is used for the predictive component and Kohonen self- organizing feature maps are used to design the codebook for the vector quantizer. The multilayer perceptron uses the nonlinearity condition associated with its processing units to perform a nonlinear vector prediction. The second component of the PVQ scheme vector quantizers the residual vector that is formed by subtracting the output of the perceptron from the original input vector. The joint-optimization task of designing the two components of the PVQ scheme is also achieved. Simulation results are presented for still images with high visual quality.

  16. Structural Approaches on the Toughness in Double Network Hydrogels

    NASA Astrophysics Data System (ADS)

    Tominaga, Taiki; Osada, Yoshihito; Gong, Jian Ping

    Most hydrogels are mechanically too weak to be used as any load bearing devices. We have overcome this problem by synthesizing hydrogels with a double network (DN) structure. Despite the presence of 90% water in their composition, these tough gels exhibit a fracture stress of 170 kg/cm2, similar to that of cartilage. The relation between their mechanical strength and structure for a wide range of conditions should be analyzed to apprehend the origin of the toughness of the DN-gels. We recently reported some experi- mental results obtained by dynamic light scattering and small angle neutron scattering. Some new experimental results obtained by neutron scattering in both deformed and undeformed conditions provided for a new under- standing of the origin of toughness. We review the studies on the structure of DN-gels towards understanding of the toughness origin. Studies on DN-gels for biomedical applications are also described.

  17. A Game-Theoretical Approach to Multimedia Social Networks Security

    PubMed Central

    Liu, Enqiang; Liu, Zengliang; Shao, Fei; Zhang, Zhiyong

    2014-01-01

    The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party's benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders. PMID:24977226

  18. A competitive neural network approach for meteorological situation clustering

    NASA Astrophysics Data System (ADS)

    Turias, Ignacio J.; González, Francisco J.; Martín, M. a. Luz; Galindo, Pedro L.

    A complete competitive scheme is proposed in this work in order to perform a classification analysis of meteorological data in the 'Campo de Gibraltar' region (in the South of Spain) from 1999 to 2002. The main objectives of the study presented here have been the characterization of the meteorological conditions in the area, using a competitive neural network based on Kohonen learning rule. Standard Principal Component Analysis (PCA) and VARIMAX rotation have allowed interpreting the physical meaning of the classes obtained from the competitive scheme. Quantitative (using three quality indices) and qualitative (from the analysis of the data projection) criteria based on Fisher Discriminant Analysis were introduced to verify the results of the clustering. A randomized procedure is developed to assure the best performance of the models and to select the best model in the experiments. The different experiments developed extracted five classes, which were related to typical meteorological conditions in the area.

  19. Transfer Error and Correction Approach in Mobile Network

    NASA Astrophysics Data System (ADS)

    Xiao-kai, Wu; Yong-jin, Shi; Da-jin, Chen; Bing-he, Ma; Qi-li, Zhou

    With the development of information technology and social progress, human demand for information has become increasingly diverse, wherever and whenever people want to be able to easily, quickly and flexibly via voice, data, images and video and other means to communicate. Visual information to the people direct and vivid image, image / video transmission also been widespread attention. Although the third generation mobile communication systems and the emergence and rapid development of IP networks, making video communications is becoming the main business of the wireless communications, however, the actual wireless and IP channel will lead to error generation, such as: wireless channel multi- fading channels generated error and blocking IP packet loss and so on. Due to channel bandwidth limitations, the video communication compression coding of data is often beyond the data, and compress data after the error is very sensitive to error conditions caused a serious decline in image quality.

  20. System Review about Function Role of ESCC Driver Gene KDM6A by Network Biology Approach.

    PubMed

    Ran, Jihua; Li, Hui; Li, Huiwu

    2016-01-01

    Background. KDM6A (Lysine (K)-Specific Demethylase 6A) is the driver gene related to esophageal squamous cell carcinoma (ESCC). In order to provide more biological insights into KDM6A, in this paper, we treat PPI (protein-protein interaction) network derived from KDM6A as a conceptual framework and follow it to review its biological function. Method. We constructed a PPI network with Cytoscape software and performed clustering of network with Clust&See. Then, we evaluate the pathways, which are statistically involved in the network derived from KDM6A. Lastly, gene ontology analysis of clusters of genes in the network was conducted. Result. The network includes three clusters that consist of 74 nodes connected via 453 edges. Fifty-five pathways are statistically involved in the network and most of them are functionally related to the processes of cell cycle, gene expression, and carcinogenesis. The biology themes of clusters 1, 2, and 3 are chromatin modification, regulation of gene expression by transcription factor complex, and control of cell cycle, respectively. Conclusion. The PPI network presents a panoramic view which can facilitate for us to understand the function role of KDM6A. It is a helpful way by network approach to perform system review on a certain gene.

  1. Mean-field approach to evolving spatial networks, with an application to osteocyte network formation

    NASA Astrophysics Data System (ADS)

    Taylor-King, Jake P.; Basanta, David; Chapman, S. Jonathan; Porter, Mason A.

    2017-07-01

    We consider evolving networks in which each node can have various associated properties (a state) in addition to those that arise from network structure. For example, each node can have a spatial location and a velocity, or it can have some more abstract internal property that describes something like a social trait. Edges between nodes are created and destroyed, and new nodes enter the system. We introduce a "local state degree distribution" (LSDD) as the degree distribution at a particular point in state space. We then make a mean-field assumption and thereby derive an integro-partial differential equation that is satisfied by the LSDD. We perform numerical experiments and find good agreement between solutions of the integro-differential equation and the LSDD from stochastic simulations of the full model. To illustrate our theory, we apply it to a simple model for osteocyte network formation within bones, with a view to understanding changes that may take place during cancer. Our results suggest that increased rates of differentiation lead to higher densities of osteocytes, but with a smaller number of dendrites. To help provide biological context, we also include an introduction to osteocytes, the formation of osteocyte networks, and the role of osteocytes in bone metastasis.

  2. A review of active learning approaches to experimental design for uncovering biological networks

    PubMed Central

    2017-01-01

    Various types of biological knowledge describe networks of interactions among elementary entities. For example, transcriptional regulatory networks consist of interactions among proteins and genes. Current knowledge about the exact structure of such networks is highly incomplete, and laboratory experiments that manipulate the entities involved are conducted to test hypotheses about these networks. In recent years, various automated approaches to experiment selection have been proposed. Many of these approaches can be characterized as active machine learning algorithms. Active learning is an iterative process in which a model is learned from data, hypotheses are generated from the model to propose informative experiments, and the experiments yield new data that is used to update the model. This review describes the various models, experiment selection strategies, validation techniques, and successful applications described in the literature; highlights common themes and notable distinctions among methods; and identifies likely directions of future research and open problems in the area. PMID:28570593

  3. A Frame-Relay Approach for a State-Wide Health Information Network

    PubMed Central

    Reed-Fourquet, LL; Beaudin, S; Courtway, P; Pappanikou, J; Trask, J; Durand, D; Bonacci, J; Rhodes, K; Pendleton, R; Vogler, E; Lynch, JT

    1994-01-01

    An approach for a frame-relay implementation is described which is intended to establish connectivity between the health care providers in the state of Connecticut with a gateway to the internet. While other health care networking efforts have based the interconnectivity efforts on direct connections to the internet for each institution, our design takes a more cost effective approach by establishing a private health care network with a single entry point to the internet. This will not only provide the advantages of internet connections to all participating providers, but it will also isolate intrastate patient care traffic from the internet, reserving internet traffic for those information needs not available within the statewide network. In addition to the network solution, an extensive user support infrastructure is also presented. PMID:7949962

  4. An Iterative Approach for the Optimization of Pavement Maintenance Management at the Network Level

    PubMed Central

    Torres-Machí, Cristina; Chamorro, Alondra; Videla, Carlos; Yepes, Víctor

    2014-01-01

    Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach. PMID:24741352

  5. An iterative approach for the optimization of pavement maintenance management at the network level.

    PubMed

    Torres-Machí, Cristina; Chamorro, Alondra; Videla, Carlos; Pellicer, Eugenio; Yepes, Víctor

    2014-01-01

    Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach.

  6. Systems Biology Approach to Identify Gene Network Signatures for Colorectal Cancer

    PubMed Central

    Sonachalam, Madhankumar; Shen, Jeffrey; Huang, Hui; Wu, Xiaogang

    2012-01-01

    In this work, we integrated prior knowledge from gene signatures and protein interactions with gene set enrichment analysis (GSEA), and gene/protein network modeling together to identify gene network signatures from gene expression microarray data. We demonstrated how to apply this approach into discovering gene network signatures for colorectal cancer (CRC) from microarray datasets. First, we used GSEA to analyze the microarray data through enriching differential genes in different CRC-related gene sets from two publicly available up-to-date gene set databases – Molecular Signatures Database (MSigDB) and Gene Signatures Database (GeneSigDB). Second, we compared the enriched gene sets through enrichment score, false-discovery rate, and nominal p-value. Third, we constructed an integrated protein–protein interaction (PPI) network through connecting these enriched genes by high-quality interactions from a human annotated and predicted protein interaction database, with a confidence score labeled for each interaction. Finally, we mapped differential gene expressions onto the constructed network to build a comprehensive network model containing visualized transcriptome and proteome data. The results show that although MSigDB has more CRC-relevant gene sets than GeneSigDB, the integrated PPI network connecting the enriched genes from both MSigDB and GeneSigDB can provide a more complete view for discovering gene network signatures. We also found several important sub-network signatures for CRC, such as TP53 sub-network, PCNA sub-network, and IL8 sub-network, corresponding to apoptosis, DNA repair, and immune response, respectively. PMID:22629282

  7. Why network approach can promote a new way of thinking in biology

    PubMed Central

    Giuliani, Alessandro; Filippi, Simonetta; Bertolaso, Marta

    2014-01-01

    This work deals with the particular nature of network-based approach in biology. We will comment about the shift from the consideration of the molecular layer as the definitive place where causative process start to the elucidation of the among elements (at any level of biological organization they are located) interaction network as the main goal of scientific explanation. This shift comes from the intrinsic nature of networks where the properties of a specific node are determined by its position in the entire network (top-down explanation) while the global network characteristics emerge from the nodes wiring pattern (bottom-up explanation). This promotes a “middle-out” paradigm formally identical to the time honored chemical thought holding big promises in the study of biological regulation. PMID:24782892

  8. Why network approach can promote a new way of thinking in biology.

    PubMed

    Giuliani, Alessandro; Filippi, Simonetta; Bertolaso, Marta

    2014-01-01

    This work deals with the particular nature of network-based approach in biology. We will comment about the shift from the consideration of the molecular layer as the definitive place where causative process start to the elucidation of the among elements (at any level of biological organization they are located) interaction network as the main goal of scientific explanation. This shift comes from the intrinsic nature of networks where the properties of a specific node are determined by its position in the entire network (top-down explanation) while the global network characteristics emerge from the nodes wiring pattern (bottom-up explanation). This promotes a "middle-out" paradigm formally identical to the time honored chemical thought holding big promises in the study of biological regulation.

  9. Establishment of a hydrological monitoring network in a tropical African catchment: An integrated participatory approach

    NASA Astrophysics Data System (ADS)

    Gomani, M. C.; Dietrich, O.; Lischeid, G.; Mahoo, H.; Mahay, F.; Mbilinyi, B.; Sarmett, J.

    Sound decision making for water resources management has to be based on good knowledge of the dominant hydrological processes of a catchment. This information can only be obtained through establishing suitable hydrological monitoring networks. Research catchments are typically established without involving the key stakeholders, which results in instruments being installed at inappropriate places as well as at high risk of theft and vandalism. This paper presents an integrated participatory approach for establishing a hydrological monitoring network. We propose a framework with six steps beginning with (i) inception of idea; (ii) stakeholder identification; (iii) defining the scope of the network; (iv) installation; (v) monitoring; and (vi) feedback mechanism integrated within the participatory framework. The approach is illustrated using an example of the Ngerengere catchment in Tanzania. In applying the approach, the concept of establishing the Ngerengere catchment monitoring network was initiated in 2008 within the Resilient Agro-landscapes to Climate Change in Tanzania (ReACCT) research program. The main stakeholders included: local communities; Sokoine University of Agriculture; Wami Ruvu Basin Water Office and the ReACCT Research team. The scope of the network was based on expert experience in similar projects and lessons learnt from literature review of similar projects from elsewhere integrated with local expert knowledge. The installations involved reconnaissance surveys, detailed surveys, and expert consultations to identify best sites. First, a Digital Elevation Model, land use, and soil maps were used to identify potential monitoring sites. Local and expert knowledge was collected on flow regimes, indicators of shallow groundwater plant species, precipitation pattern, vegetation, and soil types. This information was integrated and used to select sites for installation of an automatic weather station, automatic rain gauges, river flow gauging stations

  10. A quantitative approach to measure road network information based on edge diversity

    NASA Astrophysics Data System (ADS)

    Wu, Xun; Zhang, Hong; Lan, Tian; Cao, Weiwei; He, Jing

    2015-12-01

    The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.

  11. Integrative network modeling approaches to personalized cancer medicine

    PubMed Central

    Kidd, Brian A; Readhead, Ben P; Eden, Caroline; Parekh, Samir; Dudley, Joel T

    2016-01-01

    The ability to collect millions of molecular measurements from patients is a now a reality for clinical medicine. This reality has created the challenge of how to integrate these vast amounts of data into models that accurately predict complex pathophysiology and can translate this complexity into clinically actionable outputs. Integrative informatics and data-driven approaches provide a framework for analyzing large-scale datasets and combining them into multiscale models that can be used to determine the key drivers of disease and identify optimal therapies for treating tumors. In this perspective we discuss how an integrative modeling approach is being used to inform individual treatment decisions, highlighting a recent case report that illustrates the challenges and opportunities for personalized oncology. PMID:27019658

  12. Analysis and models of bilateral investment treaties using a social networks approach

    NASA Astrophysics Data System (ADS)

    Saban, Daniela; Bonomo, Flavia; Stier-Moses, Nicolás E.

    2010-09-01

    Bilateral investment treaties (BITs) are agreements between two countries for the reciprocal encouragement, promotion and protection of investments in each other’s territories by companies based in either country. Germany and Pakistan signed the first BIT in 1959 and since then, BITs are one of the most popular and widespread form of international agreement. In this work we study the proliferation of BITs using a social networks approach. We propose a network growth model that dynamically replicates the empirical topological characteristics of the BIT network.

  13. Linear mixed model approach to network meta-analysis for continuous outcomes in periodontal research.

    PubMed

    Tu, Yu-Kang

    2015-02-01

    Analysing continuous outcomes for network meta-analysis by means of linear mixed models is a great challenge, as it requires statistical software packages to specify special patterns of model error variance and covariance structure. This article demonstrates a non-Bayesian approach to network meta-analysis for continuous outcomes in periodontal research with a special focus on the adjustment of data dependency. Seventeen studies on guided tissue regeneration were used to illustrate how the proposed linear mixed models for network meta-analysis of continuous outcomes. Arm-based network meta-analysis use treatment arms from each study as the unit of analysis; when patients are randomly assigned to each arm, data are deemed independent and therefore no adjustment is required for multi-arm trials. Trial-based network meta-analysis use treatment contrasts as the unit of analysis, and therefore treatment contrasts within a multi-arm trial are not independent. This data dependency occurs also in split-mouth studies, and adjustments for data dependency are therefore required. Arm-based analysis is the preferred approach to network meta-analysis, when all included studies use the parallel group design and some compare more than two treatment arms. When included studies used designs that yield dependent data, the trial-based analysis is the preferred approach. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  14. An evolutionary game approach for determination of the structural conflicts in signed networks

    PubMed Central

    Tan, Shaolin; Lü, Jinhu

    2016-01-01

    Social or biochemical networks can often divide into two opposite alliances in response to structural conflicts between positive (friendly, activating) and negative (hostile, inhibiting) interactions. Yet, the underlying dynamics on how the opposite alliances are spontaneously formed to minimize the structural conflicts is still unclear. Here, we demonstrate that evolutionary game dynamics provides a felicitous possible tool to characterize the evolution and formation of alliances in signed networks. Indeed, an evolutionary game dynamics on signed networks is proposed such that each node can adaptively adjust its choice of alliances to maximize its own fitness, which yet leads to a minimization of the structural conflicts in the entire network. Numerical experiments show that the evolutionary game approach is universally efficient in quality and speed to find optimal solutions for all undirected or directed, unweighted or weighted signed networks. Moreover, the evolutionary game approach is inherently distributed. These characteristics thus suggest the evolutionary game dynamic approach as a feasible and effective tool for determining the structural conflicts in large-scale on-line signed networks. PMID:26915581

  15. An evolutionary game approach for determination of the structural conflicts in signed networks

    NASA Astrophysics Data System (ADS)

    Tan, Shaolin; Lü, Jinhu

    2016-02-01

    Social or biochemical networks can often divide into two opposite alliances in response to structural conflicts between positive (friendly, activating) and negative (hostile, inhibiting) interactions. Yet, the underlying dynamics on how the opposite alliances are spontaneously formed to minimize the structural conflicts is still unclear. Here, we demonstrate that evolutionary game dynamics provides a felicitous possible tool to characterize the evolution and formation of alliances in signed networks. Indeed, an evolutionary game dynamics on signed networks is proposed such that each node can adaptively adjust its choice of alliances to maximize its own fitness, which yet leads to a minimization of the structural conflicts in the entire network. Numerical experiments show that the evolutionary game approach is universally efficient in quality and speed to find optimal solutions for all undirected or directed, unweighted or weighted signed networks. Moreover, the evolutionary game approach is inherently distributed. These characteristics thus suggest the evolutionary game dynamic approach as a feasible and effective tool for determining the structural conflicts in large-scale on-line signed networks.

  16. Parameter estimation in spiking neural networks: a reverse-engineering approach.

    PubMed

    Rostro-Gonzalez, H; Cessac, B; Vieville, T

    2012-04-01

    This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing the fact that the parameter estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to be considered. Here, this assumption has been reformulated as a linear programming (LP) problem in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes the fact that the reverse engineering of a neural network can be performed from the observation of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a generalization of this approach to the design of input-output (I/O) transformations as a practical method to 'program' a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.

  17. Designing optimal transportation networks: a knowledge-based computer-aided multicriteria approach

    SciTech Connect

    Tung, S.I.

    1986-01-01

    The dissertation investigates the applicability of using knowledge-based expert systems (KBES) approach to solve the single-mode (automobile), fixed-demand, discrete, multicriteria, equilibrium transportation-network-design problem. Previous works on this problem has found that mathematical programming method perform well on small networks with only one objective. Needed is a solution technique that can be used on large networks having multiple, conflicting criteria with different relative importance weights. The KBES approach developed in this dissertation represents a new way to solve network design problems. The development of an expert system involves three major tasks: knowledge acquisition, knowledge representation, and testing. For knowledge acquisition, a computer aided network design/evaluation model (UFOS) was developed to explore the design space. This study is limited to the problem of designing an optimal transportation network by adding and deleting capacity increments to/from any link in the network. Three weighted criteria were adopted for use in evaluating each design alternative: cost, average V/C ratio, and average travel time.

  18. A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

    PubMed Central

    Luan, Bo; Sörös, Peter; Sejdić, Ervin

    2013-01-01

    Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia. PMID:24009758

  19. A generative modeling approach to connectivity-Electrical conduction in vascular networks.

    PubMed

    Hald, Bjørn Olav

    2016-06-21

    The physiology of biological structures is inherently dynamic and emerges from the interaction and assembly of large collections of small entities. The extent of coupled entities complicates modeling and increases computational load. Here, microvascular networks are used to present a novel generative approach to connectivity based on the observation that biological organization is hierarchical and composed of a limited set of building blocks, i.e. a vascular network consists of blood vessels which in turn are composed by one or more cell types. Fast electrical communication is crucial to synchronize vessel tone across the vast distances within a network. We hypothesize that electrical conduction capacity is delimited by the size of vascular structures and connectivity of the network. Generation and simulation of series of dynamical models of electrical spread within vascular networks of different size and composition showed that (1) Conduction is enhanced in models harboring long and thin endothelial cells that couple preferentially along the longitudinal axis. (2) Conduction across a branch point depends on endothelial connectivity between branches. (3) Low connectivity sub-networks are more sensitive to electrical perturbations. In summary, the capacity for electrical signaling in microvascular networks is strongly shaped by the morphology and connectivity of vascular (particularly endothelial) cells. While the presented software can be used by itself or as a starting point for more sophisticated models of vascular dynamics, the generative approach can be applied to other biological systems, e.g. nervous tissue, the lymphatics, or the biliary system. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks.

    PubMed

    Salehi, Mehraveh; Karbasi, Amin; Shen, Xilin; Scheinost, Dustin; Constable, R Todd

    2017-09-04

    Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of "submodularity" to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i.e. the node-to-network assignment vectors). The sex prediction finding illustrates that individual parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls for example or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications. Copyright © 2017. Published by Elsevier Inc.

  1. An Efficient Hardware-Software Approach to Network Fault Tolerance with InfiniBand

    SciTech Connect

    Vishnu, Abhinav; Krishnan, Manoj Kumar; Panda, Dhabaleswar K.

    2009-09-01

    In the last decade or so, clusters have observed a tremendous rise in popularity due to excellent price to performance ratio. A variety of Interconnects have been proposed during this period, with InfiniBand leading the way due to its high performance and open standard. Increasing size of the InfiniBand clusters has reduced the mean time between failures of various components of these clusters tremendously. In this paper, we specifically focus on the network component failure and propose a hybrid hardware-software approach to handling network faults. The hybrid approach leverages the user-transparent network fault detection and recovery using Automatic Path Migration (APM), and the software approach is used in the wake of APM failure. Using Global Arrays as the programming model, we implement this approach with Aggregate Remote Memory Copy Interface (ARMCI), the runtime system of Global Arrays. We evaluate our approach using various benchmarks (siosi7, pentane, h2o7 and siosi3) with NWChem, a very popular {\\em ab initio} quantum chemistry application. Using the proposed approach, the applications run to completion without restart on emulated network faults and acceptable overhead for benchmarks executing for a longer period of time.

  2. An approach of community evolution based on gravitational relationship refactoring in dynamic networks

    NASA Astrophysics Data System (ADS)

    Yin, Guisheng; Chi, Kuo; Dong, Yuxin; Dong, Hongbin

    2017-04-01

    In this paper, an approach of community evolution based on gravitational relationship refactoring between the nodes in a dynamic network is proposed, and it can be used to simulate the process of community evolution. A static community detection algorithm and a dynamic community evolution algorithm are included in the approach. At first, communities are initialized by constructing the core nodes chains, the nodes can be iteratively searched and divided into corresponding communities via the static community detection algorithm. For a dynamic network, an evolutionary process is divided into three phases, and behaviors of community evolution can be judged according to the changing situation of the core nodes chain in each community. Experiments show that the proposed approach can achieve accuracy and availability in the synthetic and real world networks.

  3. Approximate-master-equation approach for the Kinouchi-Copelli neural model on networks.

    PubMed

    Wang, Chong-Yang; Wu, Zhi-Xi; Chen, Michael Z Q

    2017-01-01

    In this work, we use the approximate-master-equation approach to study the dynamics of the Kinouchi-Copelli neural model on various networks. By categorizing each neuron in terms of its state and also the states of its neighbors, we are able to uncover how the coupled system evolves with respective to time by directly solving a set of ordinary differential equations. In particular, we can easily calculate the statistical properties of the time evolution of the network instantaneous response, the network response curve, the dynamic range, and the critical point in the framework of the approximate-master-equation approach. The possible usage of the proposed theoretical approach to other spreading phenomena is briefly discussed.

  4. Approximate-master-equation approach for the Kinouchi-Copelli neural model on networks

    NASA Astrophysics Data System (ADS)

    Wang, Chong-Yang; Wu, Zhi-Xi; Chen, Michael Z. Q.

    2017-01-01

    In this work, we use the approximate-master-equation approach to study the dynamics of the Kinouchi-Copelli neural model on various networks. By categorizing each neuron in terms of its state and also the states of its neighbors, we are able to uncover how the coupled system evolves with respective to time by directly solving a set of ordinary differential equations. In particular, we can easily calculate the statistical properties of the time evolution of the network instantaneous response, the network response curve, the dynamic range, and the critical point in the framework of the approximate-master-equation approach. The possible usage of the proposed theoretical approach to other spreading phenomena is briefly discussed.

  5. Protein interaction network for Alzheimer's disease using computational approach.

    PubMed

    Srinivasa Rao, V; Srinivas, K; Kumar, G N Sunand; Sujin, G N

    2013-01-01

    Alzheimer's disease (AD) is the most common form of dementia. It is the sixth leading cause of death in old age people. Despite recent advances in the field of drug design, the medical treatment for the disease is purely symptomatic and hardly effective. Thus there is a need to understand the molecular mechanism behind the disease in order to improve the drug aspects of the disease. We provided two contributions in the field of proteomics in drug design. First, we have constructed a protein-protein interaction network for Alzheimer's disease reviewed proteins with 1412 interactions predicted among 969 proteins. Second, the disease proteins were given confidence scores to prioritize and then analyzed for their homology nature with respect to paralogs and homologs. The homology persisted with the mouse giving a basis for drug design phase. The method will create a new drug design technique in the field of bioinformatics by linking drug design process with protein-protein interactions via signal pathways. This method can be improvised for other diseases in future.

  6. Estimation of incident clearance times using Bayesian Networks approach.

    PubMed

    Ozbay, Kaan; Noyan, Nebahat

    2006-05-01

    Effective incident management requires a full understanding of various characteristics of incidents to accurately estimate incident durations and to help make more efficient decisions to reduce the impact of non-recurring congestion due to these accidents. Our goal is thus to have a comprehensive and clear description of incident clearance patterns and to represent these patterns with formalisms based on Bayesian Networks (BNs). BNs can be used to create dynamic incident duration estimation trees that can be extracted in the presence of a real incident for which data might only be partially available. This capability will enable traffic operators to create case-specific incident management strategies in the presence of incomplete information. In this paper, we employ a unique database created using incident data collected in Northern Virginia. This database is then used to demonstrate the advantages of employing BNs as a powerful modeling and analysis tool especially due to their ability to consider the stochastic variations of the data and to allow bi-directional induction in decision-making. In addition to the presentation of the basic theory behind BNs in the context of our problem and the validation of our estimation results, the dependency relations among all variables in the estimated BN that can be used for both quantitative and qualitative analysis are also discussed in detail.

  7. Integrated Approach to Reconstruction of Microbial Regulatory Networks

    SciTech Connect

    Rodionov, Dmitry A; Novichkov, Pavel S

    2013-11-04

    This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated in RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.

  8. MAGMA: A Liquid Software Approach to Fault Tolerance, Computer Network Security, and Survivable Networking

    DTIC Science & Technology

    2001-12-01

    and Lieutenant Namik Kaplan , Turkish Navy. Maj Tiefert’s thesis, “Modeling Control Channel Dynamics of SAAM using NS Network Simulation”, helped lay...DEC99] Deconinck , Dr. ir. Geert, Fault Tolerant Systems, ESAT / Division ACCA , Katholieke Universiteit Leuven, October 1999. [FRE00] Freed...Systems”, Addison-Wesley, 1989. [KAP99] Kaplan , Namik, “Prototyping of an Active and Lightweight Router,” March 1999 [KAT99] Kati, Effraim

  9. Optimal Control of Gene Regulatory Networks with Effectiveness of Multiple Drugs: A Boolean Network Approach

    PubMed Central

    Kobayashi, Koichi; Hiraishi, Kunihiko

    2013-01-01

    Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of gene regulatory networks, and gene expression is expressed by a binary value (0 or 1). In the control problem, we assume that the concentration level of a part of genes is arbitrarily determined as the control input. However, there are cases that no gene satisfying this assumption exists, and it is important to consider structural control via external stimuli. Furthermore, these controls are realized by multiple drugs, and it is also important to consider multiple effects such as duration of effect and side effects. In this paper, we propose a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. First, a BN model and duration of drug effectiveness are discussed. Next, the optimal control problem is formulated and is reduced to an integer linear programming problem. Finally, numerical simulations are shown. PMID:24058904

  10. Optimal control of gene regulatory networks with effectiveness of multiple drugs: a Boolean network approach.

    PubMed

    Kobayashi, Koichi; Hiraishi, Kunihiko

    2013-01-01

    Developing control theory of gene regulatory networks is one of the significant topics in the field of systems biology, and it is expected to apply the obtained results to gene therapy technologies in the future. In this paper, a control method using a Boolean network (BN) is studied. A BN is widely used as a model of gene regulatory networks, and gene expression is expressed by a binary value (0 or 1). In the control problem, we assume that the concentration level of a part of genes is arbitrarily determined as the control input. However, there are cases that no gene satisfying this assumption exists, and it is important to consider structural control via external stimuli. Furthermore, these controls are realized by multiple drugs, and it is also important to consider multiple effects such as duration of effect and side effects. In this paper, we propose a BN model with two types of the control inputs and an optimal control method with duration of drug effectiveness. First, a BN model and duration of drug effectiveness are discussed. Next, the optimal control problem is formulated and is reduced to an integer linear programming problem. Finally, numerical simulations are shown.

  11. Algebraic approach to small-world network models

    NASA Astrophysics Data System (ADS)

    Rudolph-Lilith, Michelle; Muller, Lyle E.

    2014-01-01

    We introduce an analytic model for directed Watts-Strogatz small-world graphs and deduce an algebraic expression of its defining adjacency matrix. The latter is then used to calculate the small-world digraph's asymmetry index and clustering coefficient in an analytically exact fashion, valid nonasymptotically for all graph sizes. The proposed approach is general and can be applied to all algebraically well-defined graph-theoretical measures, thus allowing for an analytical investigation of finite-size small-world graphs.

  12. Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach

    NASA Astrophysics Data System (ADS)

    Lin, Guo-Qiang; Ao, Bin; Chen, Jia-Wei; Wang, Wen-Xu; Di, Zeng-Ru

    2014-12-01

    Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering.

  13. The network approach and interventions to prevent HIV among injection drug users.

    PubMed Central

    Neaigus, A

    1998-01-01

    OBJECTIVE: To review human immunodeficiency virus (HIV) risk reduction interventions among injecting drug users (IDUs) that have adopted a network approach. METHODS: The design and outcomes of selected network-based interventions among IDUs are reviewed using the network concepts of the dyad (two-person relationship), the personal risk network (an index person and all of his or her relationship), and the "sociometric" network (the complete set of relations between people in a population) and community. RESULTS: In a dyad intervention among HIV-serodiscordant couples, many of which included IDUs, there were no HIV seroconversions. Participants in personal risk network interventions were more likely to reduce drug risks and in some of these interventions, sexual risks, than were participants in individual-based interventions. Sociometric network interventions reached more IDUs and may be more cost-effective than individual-based interventions. CONCLUSION: Network-based HIV risk reduction interventions among IDUs, and others at risk for HIV, hold promise and should be encouraged. PMID:9722819

  14. A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data

    PubMed Central

    Bringmann, Laura F.; Vissers, Nathalie; Wichers, Marieke; Geschwind, Nicole; Kuppens, Peter; Peeters, Frenk; Borsboom, Denny; Tuerlinckx, Francis

    2013-01-01

    In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and within-subject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed. PMID:23593171

  15. Battery Performance Modelling ad Simulation: a Neural Network Based Approach

    NASA Astrophysics Data System (ADS)

    Ottavianelli, Giuseppe; Donati, Alessandro

    2002-01-01

    This project has developed on the background of ongoing researches within the Control Technology Unit (TOS-OSC) of the Special Projects Division at the European Space Operations Centre (ESOC) of the European Space Agency. The purpose of this research is to develop and validate an Artificial Neural Network tool (ANN) able to model, simulate and predict the Cluster II battery system's performance degradation. (Cluster II mission is made of four spacecraft flying in tetrahedral formation and aimed to observe and study the interaction between sun and earth by passing in and out of our planet's magnetic field). This prototype tool, named BAPER and developed with a commercial neural network toolbox, could be used to support short and medium term mission planning in order to improve and maximise the batteries lifetime, determining which are the future best charge/discharge cycles for the batteries given their present states, in view of a Cluster II mission extension. This study focuses on the five Silver-Cadmium batteries onboard of Tango, the fourth Cluster II satellite, but time restrains have allowed so far to perform an assessment only on the first battery. In their most basic form, ANNs are hyper-dimensional curve fits for non-linear data. With their remarkable ability to derive meaning from complicated or imprecise history data, ANN can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. ANNs learn by example, and this is why they can be described as an inductive, or data-based models for the simulation of input/target mappings. A trained ANN can be thought of as an "expert" in the category of information it has been given to analyse, and this expert can then be used, as in this project, to provide projections given new situations of interest and answer "what if" questions. The most appropriate algorithm, in terms of training speed and memory storage requirements, is clearly the Levenberg

  16. Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

    NASA Technical Reports Server (NTRS)

    Grantham, Katie

    2003-01-01

    Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

  17. An Optimization Approach to Coexistence of Bluetooth and Wi-Fi Networks Operating in ISM Environment

    NASA Astrophysics Data System (ADS)

    Klajbor, Tomasz; Rak, Jacek; Wozniak, Jozef

    Unlicensed ISM band is used by various wireless technologies. Therefore, issues related to ensuring the required efficiency and quality of operation of coexisting networks become essential. The paper addresses the problem of mutual interferences between IEEE 802.11b transmitters (commercially named Wi-Fi) and Bluetooth (BT) devices.An optimization approach to modeling the topology of BT scatternets is introduced, resulting in more efficient utilization of ISM environment consisting of BT and Wi-Fi networks. To achieve it, the Integer Linear Programming approach has been proposed. Example results presented in the paper illustrate significant benefits of using the proposed modeling strategy.

  18. Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

    NASA Astrophysics Data System (ADS)

    Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

    2015-07-01

    Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.

  19. Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

    PubMed Central

    Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

    2015-01-01

    Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states. PMID:26154191

  20. Costal vulnerability systems-network using Fuzzy and Bayesian approaches

    NASA Astrophysics Data System (ADS)

    Taramelli, A.; Valentini, E.; Filipponi, F.; Nguyen Xuan, A.; Arosio, M.

    2016-12-01

    Marine drivers such as surge in the context of SLR, are threatening low-lying coastal plains. In order to deal with disturbances a deeper understanding of benefits deriving from ecosystem services assesment, management and planning (e.g. the role of dune ridges in surge mitigation and climate adaptation) can enhance the resilience of coastal systems. In this frame assessing the vulnerability is a key concern of many SOS (social, ecological, institutional) that deals with several challenges like the definition of Essential Variables (EVs) able to synthesize the required information, the assignment of different weight to be attributed to each considered variable, the selection of method for combining the relevant variables, etc.. To this end it is unclear how SLR, subsidence and erosion might affect coastal subsistence resources because of highly complex interactions and because of the subjective system of weighting many variables and their interaction within the systems. In this contribution, making the best use of many EO products, in situ data and modelling, we propose a multidimensional surge vulnerability assessment that aims at combining together geophysical and socioeconomic variable on the base of different approaches: 1) Fuzzy Logic; 2) Bayesian approach. The final goal is providing insight in understanding how to quantify regulating ecosystem services.

  1. Computer Mediated Social Network Approach to Software Support and Maintenance

    DTIC Science & Technology

    2010-06-01

    mathematics (Euler, 1741;  Sachs, Stiebitz, & Wilson, 1988), philosophy ( Durkheim , 2001), the social science domain (Granovetter  1973; 1983; Milgram, 1967...to philosophy  ( Durkheim , 2001), to the strength of the connections a (Granovetter 1973; Granovetter, 1983) and the  number of connections (Milgram...Qualitative, quantitative, and mixed method approaches  (Second ed.) Sage Publications Inc.   Durkheim , É. (2001). The elementary forms of religious life, New

  2. Bayesian Belief Networks Approach for Modeling Irrigation Behavior

    NASA Astrophysics Data System (ADS)

    Andriyas, S.; McKee, M.

    2012-12-01

    Canal operators need information to manage water deliveries to irrigators. Short-term irrigation demand forecasts can potentially valuable information for a canal operator who must manage an on-demand system. Such forecasts could be generated by using information about the decision-making processes of irrigators. Bayesian models of irrigation behavior can provide insight into the likely criteria which farmers use to make irrigation decisions. This paper develops a Bayesian belief network (BBN) to learn irrigation decision-making behavior of farmers and utilizes the resulting model to make forecasts of future irrigation decisions based on factor interaction and posterior probabilities. Models for studying irrigation behavior have been rarely explored in the past. The model discussed here was built from a combination of data about biotic, climatic, and edaphic conditions under which observed irrigation decisions were made. The paper includes a case study using data collected from the Canal B region of the Sevier River, near Delta, Utah. Alfalfa, barley and corn are the main crops of the location. The model has been tested with a portion of the data to affirm the model predictive capabilities. Irrigation rules were deduced in the process of learning and verified in the testing phase. It was found that most of the farmers used consistent rules throughout all years and across different types of crops. Soil moisture stress, which indicates the level of water available to the plant in the soil profile, was found to be one of the most significant likely driving forces for irrigation. Irrigations appeared to be triggered by a farmer's perception of soil stress, or by a perception of combined factors such as information about a neighbor irrigating or an apparent preference to irrigate on a weekend. Soil stress resulted in irrigation probabilities of 94.4% for alfalfa. With additional factors like weekend and irrigating when a neighbor irrigates, alfalfa irrigation

  3. Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks

    SciTech Connect

    Jin, R; McCallen, S; Almaas, E

    2007-05-28

    Complex networks have been used successfully in scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. Until recently, studies of complex networks have mainly focused on their network topology. However, in many real world applications, the edges and vertices have associated attributes that are frequently represented as vertex or edge weights. Furthermore, these weights are often not static, instead changing with time and forming a time series. Hence, to fully understand the dynamics of the complex network, we have to consider both network topology and related time series data. In this work, we propose a motif mining approach to identify trend motifs for such purposes. Simply stated, a trend motif describes a recurring subgraph where each of its vertices or edges displays similar dynamics over a userdefined period. Given this, each trend motif occurrence can help reveal significant events in a complex system; frequent trend motifs may aid in uncovering dynamic rules of change for the system, and the distribution of trend motifs may characterize the global dynamics of the system. Here, we have developed efficient mining algorithms to extract trend motifs. Our experimental validation using three disparate empirical datasets, ranging from the stock market, world trade, to a protein interaction network, has demonstrated the efficiency and effectiveness of our approach.

  4. Hybrid Evolutionary Approaches to Maximum Lifetime Routing and Energy Efficiency in Sensor Mesh Networks.

    PubMed

    Rahat, Alma A M; Everson, Richard M; Fieldsend, Jonathan E

    2015-01-01

    Mesh network topologies are becoming increasingly popular in battery-powered wireless sensor networks, primarily because of the extension of network range. However, multihop mesh networks suffer from higher energy costs, and the routing strategy employed directly affects the lifetime of nodes with limited energy resources. Hence when planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a multiobjective routing optimisation approach using hybrid evolutionary algorithms to approximate the optimal trade-off between the minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly, our approach prunes the search space using k-shortest path pruning and a graph reduction method that finds candidate routes promoting long minimum lifetimes. When arbitrarily many routes from a node to the base station are permitted, optimal routes may be found as the solution to a well-known linear program. We present an evolutionary algorithm that finds good routes when each node is allowed only a small number of paths to the base station. On a real network deployed in the Victoria & Albert Museum, London, these solutions, using only three paths per node, are able to achieve minimum lifetimes of over 99% of the optimum linear program solution's time to first sensor battery failure.

  5. Approach of virtual observations generation of a multi-reference GPS station network

    NASA Astrophysics Data System (ADS)

    Yu, Guorong

    2007-11-01

    The generation of virtual reference station observations to relay the corrections to the rover receiver for use with standard RTK software is one of important architectures of reference station networks RTK positioning. The approach of virtual observations generation based on a multi-reference GPS station network is presented in this paper. Ambiguities for the baselines in the reference network are determined firstly. The inter-reference-station differential spatially-correlated errors are estimated using highly accurate coordinates of the reference stations and resolved ambiguities. These spatially-correlated errors are interpolated among the network region as corrections. These network-generated corrections are used to correct the zero-differential observables of one reference station, which is usually the closest one to the rover (the so-called primary reference station). These corrected zero-differential observables, named virtual observations, are processed using conventional single reference station differential GPS algorithms. A test conducted using regional reference networks in Jiangsu(China) demonstrates the effectiveness of the approach to reduce the time to integer ambiguity resolution, and to increase the distance over which centimeter level accuracies can be achieved.

  6. A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches.

    PubMed

    Cantone, Irene; Marucci, Lucia; Iorio, Francesco; Ricci, Maria Aurelia; Belcastro, Vincenzo; Bansal, Mukesh; Santini, Stefania; di Bernardo, Mario; di Bernardo, Diego; Cosma, Maria Pia

    2009-04-03

    Systems biology approaches are extensively used to model and reverse engineer gene regulatory networks from experimental data. Conversely, synthetic biology allows "de novo" construction of a regulatory network to seed new functions in the cell. At present, the usefulness and predictive ability of modeling and reverse engineering cannot be assessed and compared rigorously. We built in the yeast Saccharomyces cerevisiae a synthetic network, IRMA, for in vivo "benchmarking" of reverse-engineering and modeling approaches. The network is composed of five genes regulating each other through a variety of regulatory interactions; it is negligibly affected by endogenous genes, and it is responsive to small molecules. We measured time series and steady-state expression data after multiple perturbations. These data were used to assess state-of-the-art modeling and reverse-engineering techniques. A semiquantitative model was able to capture and predict the behavior of the network. Reverse engineering based on differential equations and Bayesian networks correctly inferred regulatory interactions from the experimental data.

  7. Modeling the Attractor Landscape of Disease Progression: a Network-Based Approach

    PubMed Central

    Taherian Fard, Atefeh; Ragan, Mark A.

    2017-01-01

    Genome-wide regulatory networks enable cells to function, develop, and survive. Perturbation of these networks can lead to appearance of a disease phenotype. Inspired by Conrad Waddington's epigenetic landscape of cell development, we use a Hopfield network formalism to construct an attractor landscape model of disease progression based on protein- or gene-correlation networks of Parkinson's disease, glioma, and colorectal cancer. Attractors in this landscape correspond to normal and disease states of the cell. We introduce approaches to estimate the size and robustness of these attractors, and take a network-based approach to study their biological features such as the key genes and their functions associated with the attractors. Our results show that the attractor of cancer cells is wider than the attractor of normal cells, suggesting a heterogeneous nature of cancer. Perturbation analysis shows that robustness depends on characteristics of the input data (number of samples per time-point, and the fraction which converge to an attractor). We identify unique gene interactions at each stage, which reflect the temporal rewiring of the gene regulatory network (GRN) with disease progression. Our model of the attractor landscape, constructed from large-scale gene expression profiles of individual patients, captures snapshots of disease progression and identifies gene interactions specific to different stages, opening the way for development of stage-specific therapeutic strategies. PMID:28458684

  8. Brain networks, structural realism, and local approaches to the scientific realism debate.

    PubMed

    Yan, Karen; Hricko, Jonathon

    2017-08-01

    We examine recent work in cognitive neuroscience that investigates brain networks. Brain networks are characterized by the ways in which brain regions are functionally and anatomically connected to one another. Cognitive neuroscientists use various noninvasive techniques (e.g., fMRI) to investigate these networks. They represent them formally as graphs. And they use various graph theoretic techniques to analyze them further. We distinguish between knowledge of the graph theoretic structure of such networks (structural knowledge) and knowledge of what instantiates that structure (nonstructural knowledge). And we argue that this work provides structural knowledge of brain networks. We explore the significance of this conclusion for the scientific realism debate. We argue that our conclusion should not be understood as an instance of a global structural realist claim regarding the structure of the unobservable part of the world, but instead, as a local structural realist attitude towards brain networks in particular. And we argue that various local approaches to the realism debate, i.e., approaches that restrict realist commitments to particular theories and/or entities, are problematic insofar as they don't allow for the possibility of such a local structural realist attitude. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Novel delay-partitioning stabilization approach for networked control system via Wirtinger-based inequalities.

    PubMed

    Li, Zhichen; Bai, Yan; Huang, Congzhi; Cai, Yunfei

    2016-03-01

    This paper studies the problems of stability analysis and state feedback stabilization for networked control system. By developing a novel delay-partitioning approach, the information on both the range of network-induced delay and the maximum number of consecutive data packet dropouts can be taken into full consideration. Various augmented Lyapunov-Krasovskii functionals (LKFs) with triple-integral terms are constructed for the two delay subintervals. Moreover, the Wirtinger-based inequalities in combination with an improved reciprocal convexity are utilized to estimate the derivatives of LKFs more accurately. The proposed approaches have improved the stability conditions without increasing much computational complexity. Based on the obtained stability criterion, a stabilization controller design approach is also given. Finally, four numerical examples are presented to illustrate the effectiveness and outperformance of the proposed approaches.

  10. A new collaborative knowledge-based approach for wireless sensor networks.

    PubMed

    Canada-Bago, Joaquin; Fernandez-Prieto, Jose Angel; Gadeo-Martos, Manuel Angel; Velasco, Juan Ramón

    2010-01-01

    This work presents a new approach for collaboration among sensors in Wireless Sensor Networks. These networks are composed of a large number of sensor nodes with constrained resources: limited computational capability, memory, power sources, etc. Nowadays, there is a growing interest in the integration of Soft Computing technologies into Wireless Sensor Networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks. The objective of this work is to design a collaborative knowledge-based network, in which each sensor executes an adapted Fuzzy Rule-Based System, which presents significant advantages such as: experts can define interpretable knowledge with uncertainty and imprecision, collaborative knowledge can be separated from control or modeling knowledge and the collaborative approach may support neighbor sensor failures and communication errors. As a real-world application of this approach, we demonstrate a collaborative modeling system for pests, in which an alarm about the development of olive tree fly is inferred. The results show that knowledge-based sensors are suitable for a wide range of applications and that the behavior of a knowledge-based sensor may be modified by inferences and knowledge of neighbor sensors in order to obtain a more accurate and reliable output.

  11. Teaching the bioinformatics of signaling networks: an integrated approach to facilitate multi-disciplinary learning.

    PubMed

    Korcsmaros, Tamas; Dunai, Zsuzsanna A; Vellai, Tibor; Csermely, Peter

    2013-09-01

    The number of bioinformatics tools and resources that support molecular and cell biology approaches is continuously expanding. Moreover, systems and network biology analyses are accompanied more and more by integrated bioinformatics methods. Traditional information-centered university teaching methods often fail, as (1) it is impossible to cover all existing approaches in the frame of a single course, and (2) a large segment of the current bioinformation can become obsolete in a few years. Signaling network offers an excellent example for teaching bioinformatics resources and tools, as it is both focused and complex at the same time. Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network sciences. We show that several bioinformatics resources and tools, as well as important concepts and current trends, can also be integrated to signaling network studies. The research-type hands-on experiences we show enable the students to improve key competences such as teamworking, creative and critical thinking and problem solving. Our classroom course curriculum can be re-formulated as an e-learning material or applied as a part of a specific training course. The multi-disciplinary approach and the mosaic setup of the course have the additional benefit to support the advanced teaching of talented students.

  12. Neural network approach to damage detection in a building from ambient vibration measurements

    NASA Astrophysics Data System (ADS)

    Nakamura, Mitsuru; Masri, Sami F.; Chassiakos, A. G.; Caughey, T. K.

    1998-04-01

    A neural network-based approach is presented for the detection of changes in the characteristics of structure- unknown systems. The approach relies on the use of vibration measurements from a `healthy' system to train a neural network for identification purposes. Subsequently, the trained network is fed comparable vibration measurements from the same structure under different episodes of response in order to monitor the health of the structure. It is shown, through simulation studies with linear as well as nonlinear models typically encountered in the applied mechanics field, that the proposed damage detection methodology is capable of detecting relatively small changes in the structural parameters. The methodology is applied to actual data obtained from ambient vibration measurements on a steel building structure, which was damaged under strong seismic motion during the Hyogo-Ken Nanbu Earthquake of January 17, 1995. The measurements were done before and after repairs to the damaged frame were made. A neural network is trained with data after the repairs, which represents `healthy' condition of the building. The trained network, which is subsequently fed data before the repairs, successfully identified the difference between damaged story and undamaged story. Through this study, it is shown that the proposed approach has the potential of being a practical tool for damage detection methodology, which leads to smart civil structures.

  13. The effects of alcohol on the nonhuman primate brain: A network science approach to neuroimaging

    PubMed Central

    Telesford, Qawi K.; Laurienti, Paul J.; Friedman, David P.; Kraft, Robert A.; Daunais, James B.

    2013-01-01

    Background Animal studies have long been an important tool for basic research as they offer a degree of control often lacking in clinical studies. Of particular value is the use of nonhuman primates (NHPs) for neuroimaging studies. Currently, studies have been published using functional magnetic resonance imaging (fMRI) to understand the default mode network in the NHP brain. Network science provides an alternative approach to neuroimaging allowing for evaluation of whole-brain connectivity. In this study we used network science to build NHP brain networks from fMRI data to understand the basic functional organization of the NHP brain. We also explored how the brain network is affected following an acute ethanol pharmacological challenge. Methods Baseline resting state fMRI was acquired in an adult male rhesus macaque (n=1) and a cohort of vervet monkeys (n=10). A follow-up scan was conducted in the rhesus macaque to assess network variability and to assess the effects of an acute ethanol challenge on the brain network. Results The most connected regions in the resting state networks were similar across species and matched regions identified as the default mode network in previous NHP fMRI studies. Under an acute ethanol challenge, the functional organization of the brain was significantly impacted. Conclusion Network science offers a great opportunity to understand the brain as a complex system and how pharmacological conditions can affect the system globally. These models are sensitive to changes in the brain and may prove to be a valuable tool in long-term studies on alcohol exposure. PMID:23905720

  14. Modeling approaches for qualitative and semi-quantitative analysis of cellular signaling networks

    PubMed Central

    2013-01-01

    A central goal of systems biology is the construction of predictive models of bio-molecular networks. Cellular networks of moderate size have been modeled successfully in a quantitative way based on differential equations. However, in large-scale networks, knowledge of mechanistic details and kinetic parameters is often too limited to allow for the set-up of predictive quantitative models. Here, we review methodologies for qualitative and semi-quantitative modeling of cellular signal transduction networks. In particular, we focus on three different but related formalisms facilitating modeling of signaling processes with different levels of detail: interaction graphs, logical/Boolean networks, and logic-based ordinary differential equations (ODEs). Albeit the simplest models possible, interaction graphs allow the identification of important network properties such as signaling paths, feedback loops, or global interdependencies. Logical or Boolean models can be derived from interaction graphs by constraining the logical combination of edges. Logical models can be used to study the basic input–output behavior of the system under investigation and to analyze its qualitative dynamic properties by discrete simulations. They also provide a suitable framework to identify proper intervention strategies enforcing or repressing certain behaviors. Finally, as a third formalism, Boolean networks can be transformed into logic-based ODEs enabling studies on essential quantitative and dynamic features of a signaling network, where time and states are continuous. We describe and illustrate key methods and applications of the different modeling formalisms and discuss their relationships. In particular, as one important aspect for model reuse, we will show how these three modeling approaches can be combined to a modeling pipeline (or model hierarchy) allowing one to start with the simplest representation of a signaling network (interaction graph), which can later be refined to

  15. Communication, Collaboration and Cooperation: An Evaluation of Nova Scotia's Borrow Anywhere, Return Anywhere (BARA) Multi-Type Library Initiative

    ERIC Educational Resources Information Center

    van den Hoogen, Suzanne; Parrott, Denise

    2012-01-01

    Partnerships and collaborations among libraries are proven to enhance collective resources. The collaboration of multi-type libraries offers a unique opportunity to explore the potential of different libraries working together to provide the best possible service to their community members. This article provides a detailed report of a multi-type…

  16. Communication, Collaboration and Cooperation: An Evaluation of Nova Scotia's Borrow Anywhere, Return Anywhere (BARA) Multi-Type Library Initiative

    ERIC Educational Resources Information Center

    van den Hoogen, Suzanne; Parrott, Denise

    2012-01-01

    Partnerships and collaborations among libraries are proven to enhance collective resources. The collaboration of multi-type libraries offers a unique opportunity to explore the potential of different libraries working together to provide the best possible service to their community members. This article provides a detailed report of a multi-type…

  17. Child Multi-Type Maltreatment and Associated Depression and PTSD Symptoms: The Role of Social Support and Stress

    ERIC Educational Resources Information Center

    Vranceanu, Ana-Maria; Hobfoll, Stevan E.; Johnson, Robert J.

    2007-01-01

    Objective: This retrospective, cross-sectional study explored the hypothesis that multiple forms of child abuse and neglect (child multi-type maltreatment; CMM) would be associated with women's lower social support and higher stress in adulthood, and that this, in turn, would amplify their vulnerability to symptoms of depression and posttraumatic…

  18. The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach

    PubMed Central

    Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan

    2015-01-01

    This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key factor determining the viral prevalence. Specifically, the value range of the maximum eigenvalue is partitioned into three subintervals: viruses tend to extinction very quickly or approach extinction or persist depending on into which subinterval the maximum eigenvalue of the propagation network falls. Consequently, computer virus can be contained by adjusting the propagation network so that its maximum eigenvalue falls into the desired subinterval. PMID:26222539

  19. Differential neural network approach in information process for prediction of roadside air pollution by peat fire

    NASA Astrophysics Data System (ADS)

    Lozhkin, V.; Tarkhov, D.; Timofeev, V.; Lozhkina, O.; Vasilyev, A.

    2016-11-01

    The paper presents a novel differential neural network model estimating the dispersion of CO emissions from a peat fire near a highway. We have developed approaches for the optimization of the model on the base of simulated and experimental measurements of CO concentrations in the area of dispersion of the smoke cloud. The numerical solutions of the problem are presented in the form of neural network approximations by the Gaussian model and in the form of neural network approximate solutions of partial differential equations. The trained neural network model can be used for the prediction of emergency when wind speed and direction and other fire parameters are changing. The method is also recommended for the development of air quality monitoring and predicting information systems.

  20. t-LSE: a novel robust geometric approach for modeling protein-protein interaction networks.

    PubMed

    Zhu, Lin; You, Zhu-Hong; Huang, De-Shuang; Wang, Bing

    2013-01-01

    Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.

  1. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

    PubMed Central

    Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

    2014-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

  2. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    PubMed

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent.

  3. A network theory approach for a better understanding of overland flow connectivity

    NASA Astrophysics Data System (ADS)

    Masselink, Rens; Heckmann, Tobias; Temme, Arnaud; Anders, Niels; Keesstra, Saskia

    2016-04-01

    Hydrological connectivity describes the physical coupling, or linkages of different elements within a landscape regarding (sub)surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for e.g. habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has been recognised within the scientific community as well, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterise overland flow connectivity dynamics on hillslopes in a humid sub-Mediterranean environment by using a combination of high-resolution digital-terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow on agricultural areas and semi-natural shrubs areas. Positive correlations between connectivity and precipitation characteristics were found, while negative correlations between connectivity and soil moisture were found, probably due to soil water repellency. The combination of a structural network to determine potential connectivity with dynamic networks to determine the actual connectivity proved a powerful tool in analysing overland flow connectivity.

  4. Compression approach of street networks considering the structural and functional features of streets

    NASA Astrophysics Data System (ADS)

    Liu, Gang; He, Jing; Zhang, Xiping

    2015-10-01

    The compression of networks is an important aspect of complex networks and spatial generalization. Previous studies show that the dual graph for street-street relationships more accurately reflects the morphological features of street networks than the traditional methods. In this study, a dual graph for street-street relationship is constructed based on complex networks theory. We introduce the concept of m-order neighbors and take into account the factors of the node’s degree, closeness centrality, betweenness centrality, and distance within the dual graph. We also consider the importance contributions of the node itself and its 1- to m-order neighbors and define the evaluation model of node importance. We then propose a street compression process based on the evaluation of node importance for dual graph by considering the structural and functional features of streets. The degree distribution and topological similarity index are introduced to evaluate the level of maintaining the global structure and topological characteristics of the road network and to validate the efficiency of the proposed method. A real urban road network is used for the experiments. Results show that the proposed approach can be used in selecting important streets that can retain the global structural properties and topological connectivity of the street network.

  5. Networking between community health programs: a team-work approach to improving health service provision

    PubMed Central

    2014-01-01

    Background Networking between non-government organisations in the health sector is recognised as an effective method of improving service delivery. The Uttarakhand Cluster was established in 2008 as a collaboration of community health programs in rural north India with the aim of building capacity, increasing visibility and improving linkages with the government. This qualitative research, conducted between 2011-2012, examined the factors contributing to formation and sustainability of this clustering approach. Methods Annual focus group discussions, indicator surveys and participant observation were used to document and observe the factors involved in the formation and sustainability of an NGO network in North India. Results The analysis demonstrated that relationships were central to the formation and sustainability of the cluster. The elements of small group relationships: forming, storming, norming and performing emerged as a helpful way to describe the phases which have contributed to the functioning of this network with common values, strong leadership, resource sharing and visible progress encouraging the ongoing commitment of programs to the network goals. Conclusions In conclusion, this case study demonstrates an example of a successful and effective network of community health programs. The development of relationships was seen to be to be an important part of promoting effective resource sharing, training opportunities, government networking and resource mobilisation and will be important for other health networks to consider. PMID:25015212

  6. Brief Report: Databases in the Asia-Pacific Region: The Potential for a Distributed Network Approach.

    PubMed

    Lai, Edward Chia-Cheng; Man, Kenneth K C; Chaiyakunapruk, Nathorn; Cheng, Ching-Lan; Chien, Hsu-Chih; Chui, Celine S L; Dilokthornsakul, Piyameth; Hardy, N Chantelle; Hsieh, Cheng-Yang; Hsu, Chung Y; Kubota, Kiyoshi; Lin, Tzu-Chieh; Liu, Yanfang; Park, Byung Joo; Pratt, Nicole; Roughead, Elizabeth E; Shin, Ju-Young; Watcharathanakij, Sawaeng; Wen, Jin; Wong, Ian C K; Yang, Yea-Huei Kao; Zhang, Yinghong; Setoguchi, Soko

    2015-11-01

    This study describes the availability and characteristics of databases in Asian-Pacific countries and assesses the feasibility of a distributed network approach in the region. A web-based survey was conducted among investigators using healthcare databases in the Asia-Pacific countries. Potential survey participants were identified through the Asian Pharmacoepidemiology Network. Investigators from a total of 11 databases participated in the survey. Database sources included four nationwide claims databases from Japan, South Korea, and Taiwan; two nationwide electronic health records from Hong Kong and Singapore; a regional electronic health record from western China; two electronic health records from Thailand; and cancer and stroke registries from Taiwan. We identified 11 databases with capabilities for distributed network approaches. Many country-specific coding systems and terminologies have been already converted to international coding systems. The harmonization of health expenditure data is a major obstacle for future investigations attempting to evaluate issues related to medical costs.

  7. Performance of the Levenberg-Marquardt neural network approach in nuclear mass prediction

    NASA Astrophysics Data System (ADS)

    Zhang, Hai Fei; Hao Wang, Li; Yin, Jing Peng; Chen, Peng Hui; Zhang, Hong Fei

    2017-04-01

    Resorting to a neural network approach we refined several representative and sophisticated global nuclear mass models within the latest atomic mass evaluation (AME2012). In the training process, a quite robust algorithm named the Levenberg-Marquardt (LM) method is employed to determine the weights and biases of the neural network. As a result, this LM neural network approach demonstrates a very useful tool for further improving the accuracy of mass models. For a simple liquid drop formula the root mean square (rms) deviation between the predictions and the 2353 experimental known masses are sharply reduced from 2.455 MeV to 0.235 MeV, and for the other revisited mass models, the rms is remarkably improved by about 30%.

  8. Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: a comparison of three approaches.

    PubMed

    Havemann, Frank; Gläser, Jochen; Heinz, Michael; Struck, Alexander

    2012-01-01

    The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

  9. Multimodal signalling in the North American barn swallow: a phenotype network approach

    PubMed Central

    Wilkins, Matthew R.; Shizuka, Daizaburo; Joseph, Maxwell B.; Hubbard, Joanna K.; Safran, Rebecca J.

    2015-01-01

    Complex signals, involving multiple components within and across modalities, are common in animal communication. However, decomposing complex signals into traits and their interactions remains a fundamental challenge for studies of phenotype evolution. We apply a novel phenotype network approach for studying complex signal evolution in the North American barn swallow (Hirundo rustica erythrogaster). We integrate model testing with correlation-based phenotype networks to infer the contributions of female mate choice and male–male competition to the evolution of barn swallow communication. Overall, the best predictors of mate choice were distinct from those for competition, while moderate functional overlap suggests males and females use some of the same traits to assess potential mates and rivals. We interpret model results in the context of a network of traits, and suggest this approach allows researchers a more nuanced view of trait clustering patterns that informs new hypotheses about the evolution of communication systems. PMID:26423842

  10. An Approach to Spatiotemporally Resolve Protein Interaction Networks in Living Cells.

    PubMed

    Lobingier, Braden T; Hüttenhain, Ruth; Eichel, Kelsie; Miller, Kenneth B; Ting, Alice Y; von Zastrow, Mark; Krogan, Nevan J

    2017-04-06

    Cells operate through protein interaction networks organized in space and time. Here, we describe an approach to resolve both dimensions simultaneously by using proximity labeling mediated by engineered ascorbic acid peroxidase (APEX). APEX has been used to capture entire organelle proteomes with high temporal resolution, but its breadth of labeling is generally thought to preclude the higher spatial resolution necessary to interrogate specific protein networks. We provide a solution to this problem by combining quantitative proteomics with a system of spatial references. As proof of principle, we apply this approach to interrogate proteins engaged by G-protein-coupled receptors as they dynamically signal and traffic in response to ligand-induced activation. The method resolves known binding partners, as well as previously unidentified network components. Validating its utility as a discovery pipeline, we establish that two of these proteins promote ubiquitin-linked receptor downregulation after prolonged activation.

  11. A network-based gene-weighting approach for pathway analysis.

    PubMed

    Fang, Zhaoyuan; Tian, Weidong; Ji, Hongbin

    2012-03-01

    Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the "over-counting" bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called "Gene Associaqtion Network-based Pathway Analysis" (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation.

  12. Computational approaches to the topology, stability and dynamics of metabolic networks.

    PubMed

    Steuer, Ralf

    2007-01-01

    Cellular metabolism is characterized by an intricate network of interactions between biochemical fluxes, metabolic compounds and regulatory interactions. To investigate and eventually understand the emergent global behavior arising from such networks of interaction is not possible by intuitive reasoning alone. This contribution seeks to describe recent computational approaches that aim to asses the topological and functional properties of metabolic networks. In particular, based on a recently proposed method, it is shown that it is possible to acquire a quantitative picture of the possible dynamics of metabolic systems, without assuming detailed knowledge of the underlying enzyme-kinetic rate equations and parameters. Rather, the method builds upon a statistical exploration of the comprehensive parameter space to evaluate the dynamic capabilities of a metabolic system, thus providing a first step towards the transition from topology to function of metabolic pathways. Utilizing this approach, the role of feedback mechanisms in the maintenance of stability is discussed using minimal models of cellular pathways.

  13. Multimodal signalling in the North American barn swallow: a phenotype network approach.

    PubMed

    Wilkins, Matthew R; Shizuka, Daizaburo; Joseph, Maxwell B; Hubbard, Joanna K; Safran, Rebecca J

    2015-10-07

    Complex signals, involving multiple components within and across modalities, are common in animal communication. However, decomposing complex signals into traits and their interactions remains a fundamental challenge for studies of phenotype evolution. We apply a novel phenotype network approach for studying complex signal evolution in the North American barn swallow (Hirundo rustica erythrogaster). We integrate model testing with correlation-based phenotype networks to infer the contributions of female mate choice and male-male competition to the evolution of barn swallow communication. Overall, the best predictors of mate choice were distinct from those for competition, while moderate functional overlap suggests males and females use some of the same traits to assess potential mates and rivals. We interpret model results in the context of a network of traits, and suggest this approach allows researchers a more nuanced view of trait clustering patterns that informs new hypotheses about the evolution of communication systems.

  14. Infrared and multi-type images fusion algorithm based on contrast pyramid transform

    NASA Astrophysics Data System (ADS)

    Xu, Hua; Wang, Yan; Wu, Yujing; Qian, Yunsheng

    2016-09-01

    A fusion algorithm for infrared and multi-type images based on contrast pyramid transform (CPT) combined with Otsu method and morphology is proposed in this paper. Firstly, two sharpened images are combined to the first fused image based on information entropy weighted scheme. Afterwards, two enhanced images and the first fused one are decomposed into a series of images with different dimensions and spatial frequencies. To the low-frequency layer, the Otsu method is applied to calculate the optimal segmentation threshold of the first fused image, which is subsequently used to determine the pixel values in top layer fused image. With respect to the high-frequency layers, the top-bottom hats morphological transform is employed to each layer before maximum selection criterion. Finally, the series of decomposed images are reconstructed and then superposed with the enhanced image processed by morphological gradient operation as a second fusion to get the final fusion image. Infrared and visible images fusion, infrared and low-light-level (LLL) images fusion, infrared intensity and infrared polarization images fusion, and multi-focus images fusion are discussed in this paper. Both experimental results and objective metrics demonstrate the effectiveness and superiority of the proposed algorithm over the conventional ones used to compare.

  15. Livelihood diversification in tropical coastal communities: a network-based approach to analyzing 'livelihood landscapes'.

    PubMed

    Cinner, Joshua E; Bodin, Orjan

    2010-08-11

    Diverse livelihood portfolios are frequently viewed as a critical component of household economies in developing countries. Within the context of natural resources governance in particular, the capacity of individual households to engage in multiple occupations has been shown to influence important issues such as whether fishers would exit a declining fishery, how people react to policy, the types of resource management systems that may be applicable, and other decisions about natural resource use. This paper uses network analysis to provide a novel methodological framework for detailed systemic analysis of household livelihood portfolios. Paying particular attention to the role of natural resource-based occupations such as fisheries, we use network analyses to map occupations and their interrelationships- what we refer to as 'livelihood landscapes'. This network approach allows for the visualization of complex information about dependence on natural resources that can be aggregated at different scales. We then examine how the role of natural resource-based occupations changes along spectra of socioeconomic development and population density in 27 communities in 5 western Indian Ocean countries. Network statistics, including in- and out-degree centrality, the density of the network, and the level of network centralization are compared along a multivariate index of community-level socioeconomic development and a gradient of human population density. The combination of network analyses suggests an increase in household-level specialization with development for most occupational sectors, including fishing and farming, but that at the community-level, economies remained diversified. The novel modeling approach introduced here provides for various types of livelihood portfolio analyses at different scales of social aggregation. Our livelihood landscapes approach provides insights into communities' dependencies and usages of natural resources, and shows how patterns of

  16. Collaboration Levels in Asynchronous Discussion Forums: A Social Network Analysis Approach

    ERIC Educational Resources Information Center

    Luhrs, Cecilia; McAnally-Salas, Lewis

    2016-01-01

    Computer Supported Collaborative Learning literature relates high levels of collaboration to enhanced learning outcomes. However, an agreement on what is considered a high level of collaboration is unclear, especially if a qualitative approach is taken. This study describes how methods of Social Network Analysis were used to design a collaboration…

  17. A Networking Approach to Groundwater Education in West Oakland County, Michigan.

    ERIC Educational Resources Information Center

    Dean, Lillian F.

    1984-01-01

    A public education project in West Oakland County (Michigan) was started to encourage individual citizen responsibility and action related to groundwater protection. A description of the project is provided, showing the effectiveness of using a networking approach for broad public education. (JN)

  18. Competence-Based Vocational Education and Training (VET): An Approach of Shaping and Networking

    ERIC Educational Resources Information Center

    Bohne, Christoph; Eicker, Friedhelm; Haseloff, Gesine

    2017-01-01

    Purpose: The purpose of this paper is to develop a vocational scientific constructivist concept meant for shaping competence-based and networked teaching and learning in vocational education and training (VET). Design/methodology/approach: VET must enable learners to shape work within the context of conceptions based on the development of society.…

  19. Approaches to Forecasting Demands for Library Network Services. Report No. 10.

    ERIC Educational Resources Information Center

    Kang, Jong Hoa

    The problem of forecasting monthly demands for library network services is considered in terms of using forecasts as inputs to policy analysis models, and in terms of using forecasts to aid in the making of budgeting and staffing decisions. Box-Jenkins time-series methodology, adaptive filtering, and regression approaches are examined and compared…

  20. An Approach Based on Social Network Analysis Applied to a Collaborative Learning Experience

    ERIC Educational Resources Information Center

    Claros, Iván; Cobos, Ruth; Collazos, César A.

    2016-01-01

    The Social Network Analysis (SNA) techniques allow modelling and analysing the interaction among individuals based on their attributes and relationships. This approach has been used by several researchers in order to measure the social processes in collaborative learning experiences. But oftentimes such measures were calculated at the final state…

  1. Approaches to Forecasting Demands for Library Network Services. Report No. 10.

    ERIC Educational Resources Information Center

    Kang, Jong Hoa

    The problem of forecasting monthly demands for library network services is considered in terms of using forecasts as inputs to policy analysis models, and in terms of using forecasts to aid in the making of budgeting and staffing decisions. Box-Jenkins time-series methodology, adaptive filtering, and regression approaches are examined and compared…

  2. Competence-Based Vocational Education and Training (VET): An Approach of Shaping and Networking

    ERIC Educational Resources Information Center

    Bohne, Christoph; Eicker, Friedhelm; Haseloff, Gesine

    2017-01-01

    Purpose: The purpose of this paper is to develop a vocational scientific constructivist concept meant for shaping competence-based and networked teaching and learning in vocational education and training (VET). Design/methodology/approach: VET must enable learners to shape work within the context of conceptions based on the development of society.…

  3. Collaboration Levels in Asynchronous Discussion Forums: A Social Network Analysis Approach

    ERIC Educational Resources Information Center

    Luhrs, Cecilia; McAnally-Salas, Lewis

    2016-01-01

    Computer Supported Collaborative Learning literature relates high levels of collaboration to enhanced learning outcomes. However, an agreement on what is considered a high level of collaboration is unclear, especially if a qualitative approach is taken. This study describes how methods of Social Network Analysis were used to design a collaboration…

  4. A Networking Approach to Groundwater Education in West Oakland County, Michigan.

    ERIC Educational Resources Information Center

    Dean, Lillian F.

    1984-01-01

    A public education project in West Oakland County (Michigan) was started to encourage individual citizen responsibility and action related to groundwater protection. A description of the project is provided, showing the effectiveness of using a networking approach for broad public education. (JN)

  5. 3D imaging of soil pore network: two different approaches

    NASA Astrophysics Data System (ADS)

    Matrecano, M.; Di Matteo, B.; Mele, G.; Terribile, F.

    2009-04-01

    system but on less noisy images. SSAT system showed more flexibility in terms of sample size although both techniques allowed investigation on REVs (Representative Elementary Volumes) for most of macroscopic properties describing soil processes. Morover, undoubted advantages of not destructivity and ease sample preparation for the Skysan 1172 are balanced by lower overall costs for the SSAT and its potential of producing 3D representation of soil features different from the simple solid/porous phases. Both approaches allow to use exactly the same image analysis procedures on the reconstructed 3D images although require some specific pre-processing treatments.

  6. Multi-level security for computer networking - SAC digital network approach

    NASA Astrophysics Data System (ADS)

    Griess, W.; Poutre, D. L.

    The functional features and architecture of the SACDIN (SAC digital network) are detailed. SACDIN is the new data transmission segment for directing SAC's strategic forces. The system has 135 processor nodes at 32 locations and processes, distributes and stores data of any level of security classification. The sophistication of access nodes is dependent on the location. A reference monitor mediates the multilevel security by implementation of the multi-state machine concept, i.e., the Bell-LaPadula model (1973, 1974), which concludes that a secure state can never lead to an unsecure state. The monitor is controlled by the internal access control mechanism, which resides in PROM. Details of the access process are provided, including message flow on trusted paths appropriate to the security clearance of the user.

  7. Networking of networks: a 1990s approach to information for development.

    PubMed

    1992-06-01

    The ability to access and use information is increasingly becoming a crucial determinant of a country's ability to achieve sustainable socioeconomic development. Countries which are able to manage and utilize data and information have a competitive advantage over other nations. Countries which fail to tap into the growing global knowledge base, develop a complementary local knowledge base, promote the dissemination and use of knowledge, and invest in institutional and technical human capital will, however, simply remain or fall behind the competition. Many developing countries lack appropriate strategy, financial support for information centers and networks, timely adoption and use of new technology, adequate telecommunications infrastructure, and coordination at national and regional levels. Further, telecommunications services are costly, research on user group behavior is inadequate, few technically skilled people are available, and governments fail to recognize the importance of joining international information networks. Policy development, maternal-child health and family planning, and information, education, and communication are 3 of the most significant population issues worldwide. To best address these issues, international development agencies are urged to veer from providing capital and to directly support greater access to information and enhanced knowledge leading to sustainable national development. Thus far the UN has helped create global information systems in certain areas, and regional cooperative information systems are being developed. ESCAP has taken the lead in Asia and the Pacific. Gradually, population libraries and information centers are becoming computerized. Greater effort is recommended to recover costs for services and products. Further, donors and country organizations should stress that information is only useful as far as it is used.

  8. Infectious disease surveillance in animal movement networks: An approach based on the friendship paradox.

    PubMed

    Amaku, Marcos; Grisi-Filho, José Henrique de Hildebrand; Negreiros, Rísia Lopes; Dias, Ricardo Augusto; Ferreira, Fernando; Ferreira Neto, José Soares; Cipullo, Rafael Ishibashi; Marques, Fernando Silveira; Ossada, Raul

    2015-10-01

    The network of animal movements among livestock premises is an important topological structure for the spread of infectious diseases. The central focus of this study was to analyze strategies for selecting premises based on the friendship paradox ("your friends have more friends than you do") - in which premises that neighbor randomly selected premises are sampled for surveillance or control - to determine whether these strategies are viable alternatives for the surveillance and control of diseases in scenarios with insufficient data on animal movement. To test the effectiveness of these strategies, we performed three sets of simulations. In the first set, we examined the risk of spreading an infectious disease using the cattle movement network of the state of Mato Grosso, Brazil. All tested strategies based on the friendship paradox have comparable performance to the hub control strategy (controlling premises that sold more animals) and superior performance to random sampling in terms of both reducing the risk of purchasing infected animals and the number of premises that need to be controlled. In the second and third sets of simulations, we observed that the friendship paradox strategies were more sensitive than the random sampling strategy to detect cases and disease, respectively. The survey of the entire animal movement network to identify animal premises with a key role in trade is not always possible, either because the data are insufficient or because informal trade is significant. If surveying the network is not possible, all approaches based on knowledge of the network become useless. As an alternative, knowing that there is a hidden movement network that follows rules inherent to all networks, such as the friendship paradox, can be used to our advantage. Strategies based on the friendship paradox do not assume knowledge of the animal movement network and therefore may be viable alternatives for the surveillance or control of infectious diseases in the

  9. A two-stage approach for a multi-objective component assignment problem for a stochastic-flow network

    NASA Astrophysics Data System (ADS)

    Lin, Yi-Kuei; Yeh, Cheng-Ta

    2013-03-01

    Many real-life systems, such as computer systems, manufacturing systems and logistics systems, are modelled as stochastic-flow networks (SFNs) to evaluate network reliability. Here, network reliability, defined as the probability that the network successfully transmits d units of data/commodity from an origin to a destination, is a performance indicator of the systems. Network reliability maximization is a particular objective, but is costly for many system supervisors. This article solves the multi-objective problem of reliability maximization and cost minimization by finding the optimal component assignment for SFN, in which a set of multi-state components is ready to be assigned to the network. A two-stage approach integrating Non-dominated Sorting Genetic Algorithm II and simple additive weighting are proposed to solve this problem, where network reliability is evaluated in terms of minimal paths and recursive sum of disjoint products. Several practical examples related to computer networks are utilized to demonstrate the proposed approach.

  10. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

    NASA Astrophysics Data System (ADS)

    Schlechtingen, Meik; Ferreira Santos, Ilmar

    2011-07-01

    This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.

  11. Spectrum-space-divided spectrum allocation approaches in software-defined elastic optical networks

    NASA Astrophysics Data System (ADS)

    Chen, Bowen; Yu, Xiaosong; Zhao, Yongli

    2017-08-01

    Recently, the architecture of elastic optical network (EON) has been proposed as a candidate solution to accommodate both huge bandwidth requirements and flexible connections in next generation optical networks. In order to improve the spectrum efficiency, we propose different spectrum-space-divided approaches and develop two integer linear programming (ILP) models and several spectrum-space-divided spectrum allocation approaches with and without dedicated-path protection in software-defined elastic optical networks (SD-EONs). Simulation results show that the ILP models achieve better performance in terms of the number of frequency slots and hop counts than the proposed spectrum-space-divided spectrum allocation approaches with and without dedicated-path protection under the static scenario of connection requests. Furthermore, we apply the spectrum-space-divided spectrum allocation approaches with and without dedicated-path protection to reduce the blocking probability and to improve spectrum efficiency under the dynamic connection requests compared to the traditional first-fit spectrum allocation approach in SD-EONs.

  12. Statistical methods and neural network approaches for classification of data from multiple sources

    NASA Technical Reports Server (NTRS)

    Benediktsson, Jon Atli; Swain, Philip H.

    1990-01-01

    Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

  13. Functional characterization of bacterial sRNAs using a network biology approach.

    PubMed

    Modi, Sheetal R; Camacho, Diogo M; Kohanski, Michael A; Walker, Graham C; Collins, James J

    2011-09-13

    Small RNAs (sRNAs) are important components of posttranscriptional regulation. These molecules are prevalent in bacterial and eukaryotic organisms, and involved in a variety of responses to environmental stresses. The functional characterization of sRNAs is challenging and requires highly focused and extensive experimental procedures. Here, using a network biology approach and a compendium of gene expression profiles, we predict functional roles and regulatory interactions for sRNAs in Escherichia coli. We experimentally validate predictions for three sRNAs in our inferred network: IsrA, GlmZ, and GcvB. Specifically, we validate a predicted role for IsrA and GlmZ in the SOS response, and we expand on current knowledge of the GcvB sRNA, demonstrating its broad role in the regulation of amino acid metabolism and transport. We also show, using the inferred network coupled with experiments, that GcvB and Lrp, a transcription factor, repress each other in a mutually inhibitory network. This work shows that a network-based approach can be used to identify the cellular function of sRNAs and characterize the relationship between sRNAs and transcription factors.

  14. Propagation of New Innovations: An Approach to Classify Human Behavior and Movement from Available Social Network Data

    NASA Technical Reports Server (NTRS)

    Mahmud, Faisal; Samiul, Hasan

    2010-01-01

    It is interesting to observe new innovations, products, or ideas propagating into the society. One important factor of this propagation is the role of individual's social network; while another factor is individual's activities. In this paper, an approach will be made to analyze the propagation of different ideas in a popular social network. Individuals' responses to different activities in the network will be analyzed. The properties of network will also be investigated for successful propagation of innovations.

  15. Fostering Earth Observation Regional Networks - Integrative and iterative approaches to capacity building

    NASA Astrophysics Data System (ADS)

    Habtezion, S.

    2015-12-01

    Fostering Earth Observation Regional Networks - Integrative and iterative approaches to capacity building Fostering Earth Observation Regional Networks - Integrative and iterative approaches to capacity building Senay Habtezion (shabtezion@start.org) / Hassan Virji (hvirji@start.org)Global Change SySTem for Analysis, Training and Research (START) (www.start.org) 2000 Florida Avenue NW, Suite 200 Washington, DC 20009 USA As part of the Global Observation of Forest and Land Cover Dynamics (GOFC-GOLD) project partnership effort to promote use of earth observations in advancing scientific knowledge, START works to bridge capacity needs related to earth observations (EOs) and their applications in the developing world. GOFC-GOLD regional networks, fostered through the support of regional and thematic workshops, have been successful in (1) enabling participation of scientists for developing countries and from the US to collaborate on key GOFC-GOLD and Land Cover and Land Use Change (LCLUC) issues, including NASA Global Data Set validation and (2) training young developing country scientists to gain key skills in EOs data management and analysis. Members of the regional networks are also engaged and reengaged in other EOs programs (e.g. visiting scientists program; data initiative fellowship programs at the USGS EROS Center and Boston University), which has helped strengthen these networks. The presentation draws from these experiences in advocating for integrative and iterative approaches to capacity building through the lens of the GOFC-GOLD partnership effort. Specifically, this presentation describes the role of the GODC-GOLD partnership in nurturing organic networks of scientists and EOs practitioners in Asia, Africa, Eastern Europe and Latin America.

  16. Behavior-based network management: a unique model-based approach to implementing cyber superiority

    NASA Astrophysics Data System (ADS)

    Seng, Jocelyn M.

    2016-05-01

    Behavior-Based Network Management (BBNM) is a technological and strategic approach to mastering the identification and assessment of network behavior, whether human-driven or machine-generated. Recognizing that all five U.S. Air Force (USAF) mission areas rely on the cyber domain to support, enhance and execute their tasks, BBNM is designed to elevate awareness and improve the ability to better understand the degree of reliance placed upon a digital capability and the operational risk.2 Thus, the objective of BBNM is to provide a holistic view of the digital battle space to better assess the effects of security, monitoring, provisioning, utilization management, allocation to support mission sustainment and change control. Leveraging advances in conceptual modeling made possible by a novel advancement in software design and implementation known as Vector Relational Data Modeling (VRDM™), the BBNM approach entails creating a network simulation in which meaning can be inferred and used to manage network behavior according to policy, such as quickly detecting and countering malicious behavior. Initial research configurations have yielded executable BBNM models as combinations of conceptualized behavior within a network management simulation that includes only concepts of threats and definitions of "good" behavior. A proof of concept assessment called "Lab Rat," was designed to demonstrate the simplicity of network modeling and the ability to perform adaptation. The model was tested on real world threat data and demonstrated adaptive and inferential learning behavior. Preliminary results indicate this is a viable approach towards achieving cyber superiority in today's volatile, uncertain, complex and ambiguous (VUCA) environment.

  17. A New Approach in Advance Network Reservation and Provisioning for High-Performance Scientific Data Transfers

    SciTech Connect

    Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex

    2010-01-28

    Scientific applications already generate many terabytes and even petabytes of data from supercomputer runs and large-scale experiments. The need for transferring data chunks of ever-increasing sizes through the network shows no sign of abating. Hence, we need high-bandwidth high speed networks such as ESnet (Energy Sciences Network). Network reservation systems, i.e. ESnet's OSCARS (On-demand Secure Circuits and Advance Reservation System) establish guaranteed bandwidth of secure virtual circuits at a certain time, for a certain bandwidth and length of time. OSCARS checks network availability and capacity for the specified period of time, and allocates requested bandwidth for that user if it is available. If the requested reservation cannot be granted, no further suggestion is returned back to the user. Further, there is no possibility from the users view-point to make an optimal choice. We report a new algorithm, where the user specifies the total volume that needs to be transferred, a maximum bandwidth that he/she can use, and a desired time period within which the transfer should be done. The algorithm can find alternate allocation possibilities, including earliest time for completion, or shortest transfer duration - leaving the choice to the user. We present a novel approach for path finding in time-dependent networks, and a new polynomial algorithm to find possible reservation options according to given constraints. We have implemented our algorithm for testing and incorporation into a future version of ESnet?s OSCARS. Our approach provides a basis for provisioning end-to-end high performance data transfers over storage and network resources.

  18. A practical approach for outdoors distributed target localization in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Béjar, Benjamín; Zazo, Santiago

    2012-12-01

    Wireless sensor networks are posed as the new communication paradigm where the use of small, low-complexity, and low-power devices is preferred over costly centralized systems. The spectra of potential applications of sensor networks is very wide, ranging from monitoring, surveillance, and localization, among others. Localization is a key application in sensor networks and the use of simple, efficient, and distributed algorithms is of paramount practical importance. Combining convex optimization tools with consensus algorithms we propose a distributed localization algorithm for scenarios where received signal strength indicator readings are used. We approach the localization problem by formulating an alternative problem that uses distance estimates locally computed at each node. The formulated problem is solved by a relaxed version using semidefinite relaxation technique. Conditions under which the relaxed problem yields to the same solution as the original problem are given and a distributed consensus-based implementation of the algorithm is proposed based on an augmented Lagrangian approach and primal-dual decomposition methods. Although suboptimal, the proposed approach is very suitable for its implementation in real sensor networks, i.e., it is scalable, robust against node failures and requires only local communication among neighboring nodes. Simulation results show that running an additional local search around the found solution can yield performance close to the maximum likelihood estimate.

  19. Prognostic transcriptional association networks: a new supervised approach based on regression trees

    PubMed Central

    Nepomuceno-Chamorro, Isabel; Azuaje, Francisco; Devaux, Yvan; Nazarov, Petr V.; Muller, Arnaud; Aguilar-Ruiz, Jesús S.; Wagner, Daniel R.

    2011-01-01

    Motivation: The application of information encoded in molecular networks for prognostic purposes is a crucial objective of systems biomedicine. This approach has not been widely investigated in the cardiovascular research area. Within this area, the prediction of clinical outcomes after suffering a heart attack would represent a significant step forward. We developed a new quantitative prediction-based method for this prognostic problem based on the discovery of clinically relevant transcriptional association networks. This method integrates regression trees and clinical class-specific networks, and can be applied to other clinical domains. Results: Before analyzing our cardiovascular disease dataset, we tested the usefulness of our approach on a benchmark dataset with control and disease patients. We also compared it to several algorithms to infer transcriptional association networks and classification models. Comparative results provided evidence of the prediction power of our approach. Next, we discovered new models for predicting good and bad outcomes after myocardial infarction. Using blood-derived gene expression data, our models reported areas under the receiver operating characteristic curve above 0.70. Our model could also outperform different techniques based on co-expressed gene modules. We also predicted processes that may represent novel therapeutic targets for heart disease, such as the synthesis of leucine and isoleucine. Availability: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/. Contact: inepomuceno@us.es Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21098433

  20. A Geovisual Analytic Approach to Understanding Geo-Social Relationships in the International Trade Network

    PubMed Central

    Luo, Wei; Yin, Peifeng; Di, Qian; Hardisty, Frank; MacEachren, Alan M.

    2014-01-01

    The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly ‘balkanized’ (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above. PMID:24558409