Science.gov

Sample records for market network framework

  1. Network model of bilateral power markets based on complex networks

    NASA Astrophysics Data System (ADS)

    Wu, Yang; Liu, Junyong; Li, Furong; Yan, Zhanxin; Zhang, Li

    2014-06-01

    The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.

  2. MarketBayes: A distributed, market-based Bayesian network

    SciTech Connect

    Pennock, D.M.

    1996-12-31

    This paper presents initial work on a system called MarketBayes, a computational market economy where distributed agents trade in uncertain propositions. For any Bayesian network, we have defined a corresponding economy of goods, consumers and producers that essentially {open_quotes}computes{close_quotes} the same information. Although our research thus far has only verified the existence of a market structure capable of Bayesian calculations, our hope is that such a system may address a variety of interesting problems of distributed uncertain reasoning. For example, the economic framework should be well suited for belief aggregation, since the bids of numerous agents with varying beliefs, confidence levels and wealth are concisely {open_quotes}summarized{close_quotes} in the going prices of goods.

  3. Markets on Networks

    NASA Astrophysics Data System (ADS)

    Toroczkai, Zoltan; Anghel, Marian; Bassler, Kevin; Korniss, Gyorgy

    2003-03-01

    The dynamics of human, and most biological populations is characterized by competition for resources. By its own nature, this dynamics creates the group of "elites", formed by those agents who have strategies that are the most successful in the given situation, and therefore the rest of the agents will tend to follow, imitate, or interact with them, creating a social structure of leadership in the agent society. These inter-agent communications generate a complex social network with small-world character which itself forms the substrate for a second network, the action network. The latter is a highly dynamic, adaptive, directed network, defined by those inter-agent communication links on the substrate along which the passed information /prediction is acted upon by the other agents. By using the minority game for competition dynamics, here we show that when the substrate network is highly connected, the action network spontaneously develops hubs with a broad distribution of out-degrees, defining a robust leadership structure that is scale-free. Furthermore, in certain, realistic parameter ranges, facilitated by information passing on the action network, agents can spontaneously generate a high degree of cooperation making the collective almost maximally efficient.

  4. Marketing Network Centric Warfare

    DTIC Science & Technology

    2007-11-02

    must transition from a Navy concept to a joint product. NCW advocates can effect this transition by using basic business principles to market NCW...They can tailor a solid mix of product, price, place, and promotion to target and win over operational commanders. This is the first and vital step to successfully introduce NCW as the way of the future.

  5. Theoretical framework for quantum networks

    NASA Astrophysics Data System (ADS)

    Chiribella, Giulio; D'Ariano, Giacomo Mauro; Perinotti, Paolo

    2009-08-01

    We present a framework to treat quantum networks and all possible transformations thereof, including as special cases all possible manipulations of quantum states, measurements, and channels, such as, e.g., cloning, discrimination, estimation, and tomography. Our framework is based on the concepts of quantum comb—which describes all transformations achievable by a given quantum network—and link product—the operation of connecting two quantum networks. Quantum networks are treated both from a constructive point of view—based on connections of elementary circuits—and from an axiomatic one—based on a hierarchy of admissible quantum maps. In the axiomatic context a fundamental property is shown, which we call universality of quantum memory channels: any admissible transformation of quantum networks can be realized by a suitable sequence of memory channels. The open problem whether this property fails for some nonquantum theory, e.g., for no-signaling boxes, is posed.

  6. Developing a water market readiness assessment framework

    NASA Astrophysics Data System (ADS)

    Wheeler, Sarah Ann; Loch, Adam; Crase, Lin; Young, Mike; Grafton, R. Quentin

    2017-09-01

    Water markets are increasingly proposed as a demand-management strategy to deal with water scarcity. Water trading arrangements, on their own, are not about setting bio-physical limits to water-use. Nevertheless, water trading that mitigates scarcity constraints can assist regulators of water resources to keep water-use within limits at the lowest possible cost, and may reduce the cost of restoring water system health. While theoretically attractive, many practitioners have, at best, only a limited understanding of the practical usefulness of markets and how they might be most appropriately deployed. Using lessons learned from jurisdictions around the world where water markets have been implemented, this study attempts to fill the existing water market development gap and provide an initial framework (the water market readiness assessment (WMRA)) to describe the policy and administrative conditions/reforms necessary to enable governments/jurisdictions to develop water trading arrangements that are efficient, equitable and within sustainable limits. Our proposed framework consists of three key steps: 1) an assessment of hydrological and institutional needs; 2) a market evaluation, including assessment of development and implementation issues; and 3) the monitoring, continuous/review and assessment of future needs; with a variety of questions needing assessment at each stage. We apply the framework to three examples: regions in Australia, the United States and Spain. These applications indicate that WMRA can provide key information for water planners to consider on the usefulness of water trading processes to better manage water scarcity; but further practical applications and tests of the framework are required to fully evaluate its effectiveness.

  7. Structurally Dynamic Spin Market Networks

    NASA Astrophysics Data System (ADS)

    Horváth, Denis; Kuscsik, Zoltán

    The agent-based model of stock price dynamics on a directed evolving complex network is suggested and studied by direct simulation. The stationary regime is maintained as a result of the balance between the extremal dynamics, adaptivity of strategic variables and reconnection rules. The inherent structure of node agent "brain" is modeled by a recursive neural network with local and global inputs and feedback connections. For specific parametric combination the complex network displays small-world phenomenon combined with scale-free behavior. The identification of a local leader (network hub, agent whose strategies are frequently adapted by its neighbors) is carried out by repeated random walk process through network. The simulations show empirically relevant dynamics of price returns and volatility clustering. The additional emerging aspects of stylized market statistics are Zipfian distributions of fitness.

  8. A dynamic network model for interbank market

    NASA Astrophysics Data System (ADS)

    Xu, Tao; He, Jianmin; Li, Shouwei

    2016-12-01

    In this paper, a dynamic network model based on agent behavior is introduced to explain the formation mechanism of interbank market network. We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network and cumulative degree follows the power-law distribution. We find that the interbank network structure keeps dynamic stability in the network evolution process. With the increase of bank credit lending preference, network clustering coefficient increases and average shortest path length decreases monotonously, which improves the stability of the network structure. External shocks are main threats for the interbank market and the reduction of bank external investment yield rate and deposits fluctuations contribute to improve the resilience of the banking system.

  9. Developing Functional Networks of Frontier Capital Markets

    DTIC Science & Technology

    2011-10-01

    ABSTRACT Examining the structure , dynamics, and unique characteristics of a capital market network in which it operates is vital to understanding...opportunities. Examining the structure , dynamics, and unique characteristics of the capital market network in which they operate is vital to...behavior and economics. The individual motivations, information availability, transaction systems , and cultural realities in these markets provide a rich

  10. Framework for State-Level Renewable Energy Market Potential Studies

    EPA Pesticide Factsheets

    This document provides a framework and next steps for state officials who require estimates of renewable energy market potential. The report gives insight into how to conduct a market potential study.

  11. Building clinical networks: a developmental evaluation framework.

    PubMed

    Carswell, Peter; Manning, Benjamin; Long, Janet; Braithwaite, Jeffrey

    2014-05-01

    Clinical networks have been designed as a cross-organisational mechanism to plan and deliver health services. With recent concerns about the effectiveness of these structures, it is timely to consider an evidence-informed approach for how they can be developed and evaluated. To document an evaluation framework for clinical networks by drawing on the network evaluation literature and a 5-year study of clinical networks. We searched literature in three domains: network evaluation, factors that aid or inhibit network development, and on robust methods to measure network characteristics. This material was used to build a framework required for effective developmental evaluation. The framework's architecture identifies three stages of clinical network development; partner selection, network design and network management. Within each stage is evidence about factors that act as facilitators and barriers to network growth. These factors can be used to measure progress via appropriate methods and tools. The framework can provide for network growth and support informed decisions about progress. For the first time in one place a framework incorporating rigorous methods and tools can identify factors known to affect the development of clinical networks. The target user group is internal stakeholders who need to conduct developmental evaluation to inform key decisions along their network's developmental pathway.

  12. Does social marketing provide a framework for changing healthcare practice?

    PubMed

    Morris, Zoë Slote; Clarkson, Peter John

    2009-07-01

    We argue that social marketing can be used as a generic framework for analysing barriers to the take-up of clinical guidelines, and planning interventions which seek to enable this change. We reviewed the literature on take-up of clinical guidelines, in particular barriers and enablers to change; social marketing principles and social marketing applied to healthcare. We then applied the social marketing framework to analyse the literature and to consider implications for future guideline policy to assess its feasibility and accessibility. There is sizeable extant literature on healthcare practitioners' non-compliance with clinical guidelines. This is an international problem common to a number of settings. The reasons for poor levels of take up appear to be well understood, but not addressed adequately in practice. Applying a social marketing framework brings new insights to the problem." We show that a social marketing framework provides a useful solution-focused framework for systematically understanding barriers to individual behaviour change and designing interventions accordingly. Whether the social marketing framework provides an effective means of bringing about behaviour change remains an empirical question which has still to be tested in practice. The analysis presented here provides strong motivation to begin such testing.

  13. Networks of equities in financial markets

    NASA Astrophysics Data System (ADS)

    Bonanno, G.; Caldarelli, G.; Lillo, F.; Miccichè, S.; Vandewalle, N.; Mantegna, R. N.

    2004-03-01

    We review the recent approach of correlation based networks of financial equities. We investigate portfolio of stocks at different time horizons, financial indices and volatility time series and we show that meaningful economic information can be extracted from noise dressed correlation matrices. We show that the method can be used to falsify widespread market models by directly comparing the topological properties of networks of real and artificial markets.

  14. Tactical Network Integration Test Framework

    DTIC Science & Technology

    2011-05-16

    Wood Street, Lexington, MA 02420 Patrick Boehm ( OPNET ), Edward Kuczynski (DAG), John Cain (Akibia) Thomas Mak (PM WIN-T, US Army) Abstract- Mobile...tier" of the network is a Wideband Networking Waveform (WNW) cloud that is represented in an OPNET model. The difference between the three test...with an OPNET model that is representative of a higher tier backbone network. In the high fidelity emulation environment, a smaD number of upper

  15. [Marketing in veterinary practice; a theoretical framework].

    PubMed

    Schuurmans, A J; Smidts, A

    1990-03-15

    An increase in the number of veterinarians, while at the same time the number of animals has remained constant, has resulted in growing competition. By extending the range of products and by enlarging the veterinarians' scope of activities this competition can be decreased. A marketing-orientation will be helpful in this respect. This article indicates in which way marketing concepts can be used in a veterinary practice. The services of the veterinarian will be looked at by means of the Abell approach. This focuses on the functions performed by the services and examines, per function performed, for whom this might be interesting and which alternatives there might be. Next the concept of market segmentation is filled in for a veterinary practice by means of a hypothetical example. The marketing mix (product, place, price, promotion and personnel) is given considerable attention. The last element of marketing in a veterinary practice that is discussed here is the marketing information system. In a next article the question will be answered how marketing-directed the Dutch veterinarian works nowadays. To find this out research has been done; 166 vets were interviewed by telephone for approximately 40 minutes each.

  16. A Framework for Curriculum Development in Marketing Education.

    ERIC Educational Resources Information Center

    Everett, Donna R.

    This book is designed to show marketing education teachers how Missouri's Show-Me Knowledge and Performance Standards can be reflected in the Marketing Education Framework. It is organized to present each of the nine competency strands (instructional units) by learner outcome and competencies. The instructional units are as follows: communications…

  17. Stock market index prediction using neural networks

    NASA Astrophysics Data System (ADS)

    Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

    1994-03-01

    A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

  18. Labour Market Outcomes of National Qualifications Frameworks in Six Countries

    ERIC Educational Resources Information Center

    Allais, Stephanie

    2017-01-01

    This article presents the major findings of an international study that attempted to investigate the labour market outcomes of qualifications frameworks in six countries--Belize, France, Ireland, Jamaica, Sri Lanka, and Tunisia, as well as the regional framework in the Caribbean. It finds limited evidence of success, but fairly strong support for…

  19. Information transfer network of global market indices

    NASA Astrophysics Data System (ADS)

    Kim, Yup; Kim, Jinho; Yook, Soon-Hyung

    2015-07-01

    We study the topological properties of the information transfer networks (ITN) of the global financial market indices for six different periods. ITN is a directed weighted network, in which the direction and weight are determined by the transfer entropy between market indices. By applying the threshold method, it is found that ITN undergoes a crossover from the complete graph to a small-world (SW) network. SW regime of ITN for a global crisis is found to be much more enhanced than that for ordinary periods. Furthermore, when ITN is in SW regime, the average clustering coefficient is found to be synchronized with average volatility of markets. We also compare the results with the topological properties of correlation networks.

  20. The stability of financial market networks

    NASA Astrophysics Data System (ADS)

    Yan, Xin-Guo; Xie, Chi; Wang, Gang-Jin

    2014-08-01

    We investigate the stability of a financial market network by measuring its topological robustness, namely the ability of the network to resist structural or topological changes. The closing prices of 710 stocks in the Shanghai Stock Exchange (SSE) from 2005 to 2011 are chosen as the empirical data. We divide the period into three sub-periods: before, during, and after the US sub-prime crisis. By monitoring the size of the clusters which fall apart from the network after removing the nodes (i.e., the listed companies in the SSE), we find that: i) the SSE network is sensitive to the nodes' failure, which implies that the network is unstable. ii) the SSE network before the financial crisis has the strongest robustness against the intentional topological damage; iii) the hubs (i.e., highly connected nodes) connect with each other directly and play a vital important role in maintaining SSE network's stability.

  1. A framework for marketing image management.

    PubMed

    Barich, H; Kotler, P

    1991-01-01

    Managers know that the customer's impression of an organization is important. And sometimes companies attempt to determine just what that impression is. They conduct ad hoc surveys and focus groups. But too often the data is insubstantial, or difficult to analyze, or even inaccurate. Barich and Kotler introduce the concept of "marketing image" and describe a system of image management: designing a study, collecting data, analyzing image problems, modifying the image, and tracking responses to that image. They argue that only a systematic approach will yield useful and accurate information that a company can translate into action.

  2. A network model of the interbank market

    NASA Astrophysics Data System (ADS)

    Li, Shouwei; He, Jianmin; Zhuang, Yaming

    2010-12-01

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

  3. Influence network in the Chinese stock market

    NASA Astrophysics Data System (ADS)

    Gao, Ya-Chun; Zeng, Yong; Cai, Shi-Min

    2015-03-01

    In a stock market, the price fluctuations are interactive, that is, one listed company can influence others. In this paper, we seek to study the influence relationships among listed companies by constructing a directed network on the basis of the Chinese stock market. This influence network shows distinct topological properties. In particular, a few large companies that can lead the tendency of the stock market are recognized. Furthermore, by analyzing the subnetworks of listed companies distributed in several significant economic sectors, it is found that the influence relationships are totally different from one economic sector to another, of which three types of connectivity as well as hub-like listed companies are identified. In addition, the rankings of listed companies obtained from the centrality metrics of the influence network are compared with those according to the assets, which gives inspiration to uncover and understand the importance of listed companies on the stock market. These empirical results are meaningful in providing these topological properties of the Chinese stock market and economic sectors as well as revealing the interactive influence relationships among listed companies.

  4. Developing Functional Networks of Frontier Capital Markets

    DTIC Science & Technology

    2011-10-01

    development in countries where social capital can be as important as financial capital. As Stiglitz and Gallegati (2011) note, “Some network designs may be...entities enabling classification of capital market structure and functions in innovative ways. 3 Stiglitz , Joseph E. and Mauro Gallegati

  5. Characteristics of networks in financial markets

    NASA Astrophysics Data System (ADS)

    Kim, Kyungsik; Kim, Soo Yong; Ha, Deock-Ho

    2007-07-01

    We investigate the financial network of the Korea Stock Exchange (KSE) using numerical simulations and scaling arguments. The frequency of degree and the edge density for a real stock market graph are mainly discussed from a numerical point of view. In particular, our frequency of degree follows approximately the power law distribution.

  6. Framework for State-Level Renewable Energy Market Potential Studies

    SciTech Connect

    Kreycik, C.; Vimmerstedt, L.; Doris, E.

    2010-01-01

    State-level policymakers are relying on estimates of the market potential for renewable energy resources as they set goals and develop policies to accelerate the development of these resources. Therefore, accuracy of such estimates should be understood and possibly improved to appropriately support these decisions. This document provides a framework and next steps for state officials who require estimates of renewable energy market potential. The report gives insight into how to conduct a market potential study, including what supporting data are needed and what types of assumptions need to be made. The report distinguishes between goal-oriented studies and other types of studies, and explains the benefits of each.

  7. A compositional framework for reaction networks

    NASA Astrophysics Data System (ADS)

    Baez, John C.; Pollard, Blake S.

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

  8. Contagions across networks: colds and markets

    NASA Astrophysics Data System (ADS)

    Berryman, Matthew J.; Johnson, Neil F.; Abbott, Derek

    2005-12-01

    We explore a variety of network models describing transmission across a network. In particular we focus on transmission across composite networks, or "networks of networks", in which a finite number of networked objects are then themselves connected together into a network. In a disease context we introduce two interrelated viruses to hosts on a network, to model the infection of hosts in a classroom situation, with high rates of infection within a classroom, and lower rates of infection between classrooms. The hosts can be either susceptible to infection, infected, or recovering from each virus. During the infection stage and recovery stage there is some level of cross-immunity to related viruses. We explore the effects of immunizing sections of the community on transmission through social networks. In a stock market context we introduce memes, or virus-like ideas into a virtual agent-based model of a stock exchange. By varying the parameters of the individual traders and the way in which they are connected we are able to show emergent behaviour, including boom and bust cycles.

  9. Framework for Network Co-Simulation

    SciTech Connect

    Daily, Jeff; Ciraci, PNNL Selim; Fuller, PNNL Jason; Marinovici, PNNL Laurentiu; Fisher, PNNL Andrew; Lo, PNNL Chaomei; Hauer, PNNL Matthew

    2014-01-09

    The Framework for Network Co-Simulation (FNCS) uses a federated approach to integrate simulations which may have differing time scales. Special consideration is given to integration with a communication network simulation such that inter-simulation messages may be optionally routed through and delayed by such a simulation. In addition, FNCS uses novel time synchronization algorithms to accelerate co-simulation including the application of speculative multithreading. FNCS accomplishes all of these improvements with minimal end user intervention. Simulations can be integrated using FNCS while maintaining their original model input files simply by linking with the FNCS library and making appropriate calls into the FNCS API.

  10. Social Network Analysis in Frontier Capital Markets

    DTIC Science & Technology

    2012-06-01

    their interactions, and the consequences of network activity [Kir10]. Stiglitz and Gallegati [SG11] introduced new heterogeneous agent models to enhance...foster capital market development in countries where social capital can be as important as financial capital. As Stiglitz and Gallegati [SG11] note, “Some...10-27-2011. [ORA] ORA. http://www.casos.cs.cmu.edu/projects/ora/, Accessed 10-05-2011. [SG11] Joseph E Stiglitz and Mauro Gallegati. Heterogeneous

  11. A General Framework of Human Trust in Networks

    DTIC Science & Technology

    2009-06-01

    information dominance and complete mission objectives. Soldiers must possess a sufficient amount of trust in networks for adequate mission performance. We are investigating human trust in tactical networks by establishing a theoretical framework for analysis and an approach for validation of the framework. We identify reliability and availability as network parameters that define the relationship between quality of service performance and human trust in networks. A general framework is being developed for human trust in networks, which combines singular elements of trust

  12. Market-Based Decision Guidance Framework for Power and Alternative Energy Collaboration

    NASA Astrophysics Data System (ADS)

    Altaleb, Hesham

    With the introduction of power energy markets deregulation, innovations have transformed once a static network into a more flexible grid. Microgrids have also been deployed to serve various purposes (e.g., reliability, sustainability, etc.). With the rapid deployment of smart grid technologies, it has become possible to measure and record both, the quantity and time of the consumption of electrical power. In addition, capabilities for controlling distributed supply and demand have resulted in complex systems where inefficiencies are possible and where improvements can be made. Electric power like other volatile resources cannot be stored efficiently, therefore, managing such resource requires considerable attention. Such complex systems present a need for decisions that can streamline consumption, delay infrastructure investments, and reduce costs. When renewable power resources and the need for limiting harmful emissions are added to the equation, the search space for decisions becomes increasingly complex. As a result, the need for a comprehensive decision guidance system for electrical power resources consumption and productions becomes evident. In this dissertation, I formulate and implement a comprehensive framework that addresses different aspect of the electrical power generation and consumption using optimization models and utilizing collaboration concepts. Our solution presents a two-prong approach: managing interaction in real-time for the short-term immediate consumption of already allocated resources; and managing the operational planning for the long-run consumption. More specifically, in real-time, we present and implement a model of how to organize a secondary market for peak-demand allocation and describe the properties of the market that guarantees efficient execution and a method for the fair distribution of collaboration gains. We also propose and implement a primary market for peak demand bounds determination problem with the assumption that

  13. An object-oriented network-transparent data transportation framework

    NASA Astrophysics Data System (ADS)

    Steinbeck, T. M.; Lindenstruth, V.; Schulz, M. W.

    2002-04-01

    An object-oriented data transportation framework based upon the publisher-subscriber (producer-consumer) principle has been developed that transparently incorporates a network transport mechanism independently of the underlying network technology and protocol.

  14. Communication and marketing as tools to cultivate the public's health: a proposed "people and places" framework.

    PubMed

    Maibach, Edward W; Abroms, Lorien C; Marosits, Mark

    2007-05-22

    Communication and marketing are rapidly becoming recognized as core functions, or core competencies, in the field of public health. Although these disciplines have fostered considerable academic inquiry, a coherent sense of precisely how these disciplines can inform the practice of public health has been slower to emerge. In this article we propose a framework--based on contemporary ecological models of health--to explain how communication and marketing can be used to advance public health objectives. The framework identifies the attributes of people (as individuals, as social networks, and as communities or populations) and places that influence health behaviors and health. Communication, i.e., the provision of information, can be used in a variety of ways to foster beneficial change among both people (e.g., activating social support for smoking cessation among peers) and places (e.g., convincing city officials to ban smoking in public venues). Similarly, marketing, i.e., the development, distribution and promotion of products and services, can be used to foster beneficial change among both people (e.g., by making nicotine replacement therapy more accessible and affordable) and places (e.g., by providing city officials with model anti-tobacco legislation that can be adapted for use in their jurisdiction). Public health agencies that use their communication and marketing resources effectively to support people in making healthful decisions and to foster health-promoting environments have considerable opportunity to advance the public's health, even within the constraints of their current resource base.

  15. Assessing citation networks for dissemination and implementation research frameworks.

    PubMed

    Skolarus, Ted A; Lehmann, Todd; Tabak, Rachel G; Harris, Jenine; Lecy, Jesse; Sales, Anne E

    2017-07-28

    A recent review of frameworks used in dissemination and implementation (D&I) science described 61 judged to be related either to dissemination, implementation, or both. The current use of these frameworks and their contributions to D&I science more broadly has yet to be reviewed. For these reasons, our objective was to determine the role of these frameworks in the development of D&I science. We used the Web of Science™ Core Collection and Google Scholar™ to conduct a citation network analysis for the key frameworks described in a recent systematic review of D&I frameworks (Am J Prev Med 43(3):337-350, 2012). From January to August 2016, we collected framework data including title, reference, publication year, and citations per year and conducted descriptive and main path network analyses to identify those most important in holding the current citation network for D&I frameworks together. The source article contained 119 cited references, with 50 published articles and 11 documents identified as a primary framework reference. The average citations per year for the 61 frameworks reviewed ranged from 0.7 to 103.3 among articles published from 1985 to 2012. Citation rates from all frameworks are reported with citation network analyses for the framework review article and ten highly cited framework seed articles. The main path for the D&I framework citation network is presented. We examined citation rates and the main paths through the citation network to delineate the current landscape of D&I framework research, and opportunities for advancing framework development and use. Dissemination and implementation researchers and practitioners may consider frequency of framework citation and our network findings when planning implementation efforts to build upon this foundation and promote systematic advances in D&I science.

  16. A multi-regional framework for China's electric power market

    NASA Astrophysics Data System (ADS)

    Xie, Zhijun

    2000-10-01

    China faces the challenging task of substantially increasing its generation of electricity within economic, energy, political, and environmental constraints. Some of the most immediate issues are the decentralization of the utility decision making process, the creation of electricity markets over large geographic areas, the establishment of pricing mechanisms, and the maintenance of reasonable levels of air quality. This research develops a multi-regional optimization model to analyze these issues in the North China Power System. The model minimizes the total delivery cost of electric power for a multi-regional grid subject to the constraints of power flow, generation and transmission capacities and emission limits. Benders' decomposition is used to decompose the model into a two-level structure an inter-regional Power exchange module and an intra-regional power dispatch and transmission module The intra-regional market contains the least cost power dispatch based on bids from generators bidding and on users, insurance of their power consumption, all within local emission limits. The inter-regional market balances power exchange among regions. The results indicate that a decentralized, competitive electricity market has significant economic and environmental benefits to the entire region. Regional competition brings more cheap electric power from remote regions to urban demand centers. The expansion of power exchange reduces sulfur emissions without significantly increasing cost. The results demonstrate that without congestion, the nodal and transmission prices can be effectively separated, an important criterion for decentralization and competition. Transmission prices are related only to the wire and topological characteristics of the network. The effects of electricity loop flow on transmission prices are reflected through a multiplier that is specific to each loop. A priori calculation of the congestion and loop multipliers under congestion is possible with

  17. A Framework for State-Level Renewable Energy Market Potential Studies

    EPA Pesticide Factsheets

    This document provides a framework/next steps for state officials who require estimates of renewable energy market potential, shows how to conduct a market potential study, and distinguishes between goal-oriented studies and other types of studies.

  18. Network Centric Operations Conceptual Framework Version 1.0

    DTIC Science & Technology

    2003-11-01

    Network Centric Operations Conceptual Framework Version 1.0 Prepared for: John Garstka Office of Force Transformation Prepared by...COVERED 00-11-2003 to 00-11-2003 4. TITLE AND SUBTITLE Network Centric Operations Conceptual Framework Version 1.0 5a. CONTRACT NUMBER 5b. GRANT... Conceptual Framework Version 1.0 Table of Contents 1.0 Introduction and Background

  19. Simulating market dynamics: interactions between consumer psychology and social networks.

    PubMed

    Janssen, Marco A; Jager, Wander

    2003-01-01

    Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).

  20. A distributed framework for inter-domain virtual network embedding

    NASA Astrophysics Data System (ADS)

    Wang, Zihua; Han, Yanni; Lin, Tao; Tang, Hui

    2013-03-01

    Network virtualization has been a promising technology for overcoming the Internet impasse. A main challenge in network virtualization is the efficient assignment of virtual resources. Existing work focused on intra-domain solutions whereas inter-domain situation is more practical in realistic setting. In this paper, we present a distributed inter-domain framework for mapping virtual networks to physical networks which can ameliorate the performance of the virtual network embedding. The distributed framework is based on a Multi-agent approach. A set of messages for information exchange is defined. We design different operations and IPTV use scenarios to validate the advantages of our framework. Use cases shows that our framework can solve the inter-domain problem efficiently.

  1. Universal framework for edge controllability of complex networks.

    PubMed

    Pang, Shao-Peng; Wang, Wen-Xu; Hao, Fei; Lai, Ying-Cheng

    2017-06-26

    Dynamical processes occurring on the edges in complex networks are relevant to a variety of real-world situations. Despite recent advances, a framework for edge controllability is still required for complex networks of arbitrary structure and interaction strength. Generalizing a previously introduced class of processes for edge dynamics, the switchboard dynamics, and exploit- ing the exact controllability theory, we develop a universal framework in which the controllability of any node is exclusively determined by its local weighted structure. This framework enables us to identify a unique set of critical nodes for control, to derive analytic formulas and articulate efficient algorithms to determine the exact upper and lower controllability bounds, and to evaluate strongly structural controllability of any given network. Applying our framework to a large number of model and real-world networks, we find that the interaction strength plays a more significant role in edge controllability than the network structure does, due to a vast range between the bounds determined mainly by the interaction strength. Moreover, transcriptional regulatory networks and electronic circuits are much more strongly structurally controllable (SSC) than other types of real-world networks, directed networks are more SSC than undirected networks, and sparse networks are typically more SSC than dense networks.

  2. An Evolutionary Optimization Framework for Neural Networks and Neuromorphic Architectures

    SciTech Connect

    Schuman, Catherine D; Plank, James; Disney, Adam; Reynolds, John

    2016-01-01

    As new neural network and neuromorphic architectures are being developed, new training methods that operate within the constraints of the new architectures are required. Evolutionary optimization (EO) is a convenient training method for new architectures. In this work, we review a spiking neural network architecture and a neuromorphic architecture, and we describe an EO training framework for these architectures. We present the results of this training framework on four classification data sets and compare those results to other neural network and neuromorphic implementations. We also discuss how this EO framework may be extended to other architectures.

  3. Changes of hierarchical network in local and world stock market

    NASA Astrophysics Data System (ADS)

    Patwary, Enayet Ullah; Lee, Jong Youl; Nobi, Ashadun; Kim, Doo Hwan; Lee, Jae Woo

    2017-10-01

    We consider the cross-correlation coefficients of the daily returns in the local and global stock markets. We generate the minimal spanning tree (MST) using the correlation matrix. We observe that the MSTs change their structure from chain-like networks to star-like networks during periods of market uncertainty. We quantify the measure of the hierarchical network utilizing the value of the hierarchy measured by the hierarchical path. The hierarchy and betweenness centrality characterize the state of the market regarding the impact of crises. During crises, the non-financial company is established as the central node of the MST. However, before the crisis and during stable periods, the financial company is occupying the central node of the MST in the Korean and the U.S. stock markets. The changes in the network structure and the central node are good indicators of an upcoming crisis.

  4. Granger causality stock market networks: Temporal proximity and preferential attachment

    NASA Astrophysics Data System (ADS)

    Výrost, Tomáš; Lyócsa, Štefan; Baumöhl, Eduard

    2015-06-01

    The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, a preferential attachment between stock markets exists, i.e. the probability of the presence of spillover effects between any given two markets increases with their degree of connectedness to others.

  5. Multiple crossbar network: Integrated supercomputing framework

    SciTech Connect

    Hoebelheinrich, R. )

    1989-01-01

    At Los Alamos National Laboratory, site of one of the world's most powerful scientific supercomputing facilities, a prototype network for an environment that links supercomputers and workstations is being developed. Driven by a need to provide graphics data at movie rates across a network from a Cray supercomputer to a Sun scientific workstation, the network is called the Multiple Crossbar Network (MCN). It is intended to be coarsely grained, loosely coupled, general-purpose interconnection network that will vastly increase the speed at which supercomputers communicate with each other in large networks. The components of the network are described, as well as work done in collaboration with vendors who are interested in providing commercial products. 9 refs.

  6. Communication and marketing as tools to cultivate the public's health: a proposed "people and places" framework

    PubMed Central

    Maibach, Edward W; Abroms, Lorien C; Marosits, Mark

    2007-01-01

    Background Communication and marketing are rapidly becoming recognized as core functions, or core competencies, in the field of public health. Although these disciplines have fostered considerable academic inquiry, a coherent sense of precisely how these disciplines can inform the practice of public health has been slower to emerge. Discussion In this article we propose a framework – based on contemporary ecological models of health – to explain how communication and marketing can be used to advance public health objectives. The framework identifies the attributes of people (as individuals, as social networks, and as communities or populations) and places that influence health behaviors and health. Communication, i.e., the provision of information, can be used in a variety of ways to foster beneficial change among both people (e.g., activating social support for smoking cessation among peers) and places (e.g., convincing city officials to ban smoking in public venues). Similarly, marketing, i.e., the development, distribution and promotion of products and services, can be used to foster beneficial change among both people (e.g., by making nicotine replacement therapy more accessible and affordable) and places (e.g., by providing city officials with model anti-tobacco legislation that can be adapted for use in their jurisdiction). Summary Public health agencies that use their communication and marketing resources effectively to support people in making healthful decisions and to foster health-promoting environments have considerable opportunity to advance the public's health, even within the constraints of their current resource base. PMID:17519027

  7. Novel indexes based on network structure to indicate financial market

    NASA Astrophysics Data System (ADS)

    Zhong, Tao; Peng, Qinke; Wang, Xiao; Zhang, Jing

    2016-02-01

    There have been various achievements to understand and to analyze the financial market by complex network model. However, current studies analyze the financial network model but seldom present quantified indexes to indicate or forecast the price action of market. In this paper, the stock market is modeled as a dynamic network, in which the vertices refer to listed companies and edges refer to their rank-based correlation based on price series. Characteristics of the network are analyzed and then novel indexes are introduced into market analysis, which are calculated from maximum and fully-connected subnets. The indexes are compared with existing ones and the results confirm that our indexes perform better to indicate the daily trend of market composite index in advance. Via investment simulation, the performance of our indexes is analyzed in detail. The results indicate that the dynamic complex network model could not only serve as a structural description of the financial market, but also work to predict the market and guide investment by indexes.

  8. Cointegration-based financial networks study in Chinese stock market

    NASA Astrophysics Data System (ADS)

    Tu, Chengyi

    2014-05-01

    We propose a method based on cointegration instead of correlation to construct financial complex network in Chinese stock market. The network is obtained starting from the matrix of p-value calculated by Engle-Granger cointegration test between all pairs of stocks. Then some tools for filtering information in complex network are implemented to prune the complete graph described by the above matrix, such as setting a level of statistical significance as a threshold and Planar Maximally Filtered Graph. We also calculate Partial Correlation Planar Graph of these stocks to compare the above networks. Last, we analyze these directed, weighted and non-symmetric networks by using standard methods of network analysis, including degree centrality, PageRank, HITS, local clustering coefficient, K-shell and strongly and weakly connected components. The results shed a new light on the underlying mechanisms and driving forces in a financial market and deepen our understanding of financial complex network.

  9. Weighted Networks at the Polish Market

    NASA Astrophysics Data System (ADS)

    Chmiel, A. M.; Sienkiewicz, J.; Suchecki, K.; Hołyst, J. A.

    During the last few years various models of networks [1,2] have become a powerful tool for analysis of complex systems in such distant fields as Internet [3], biology [4], social groups [5], ecology [6] and public transport [7]. Modeling behavior of economical agents is a challenging issue that has also been studied from a network point of view. The examples of such studies are models of financial networks [8], supply chains [9, 10], production networks [11], investment networks [12] or collective bank bankrupcies [13, 14]. Relations between different companies have been already analyzed using several methods: as networks of shareholders [15], networks of correlations between stock prices [16] or networks of board directors [17]. In several cases scaling laws for network characteristics have been observed.

  10. Agent-Based Framework for Personalized Service Provisioning in Converged IP Networks

    NASA Astrophysics Data System (ADS)

    Podobnik, Vedran; Matijasevic, Maja; Lovrek, Ignac; Skorin-Kapov, Lea; Desic, Sasa

    In a global multi-service and multi-provider market, the Internet Service Providers will increasingly need to differentiate in the service quality they offer and base their operation on new, consumer-centric business models. In this paper, we propose an agent-based framework for the Business-to-Consumer (B2C) electronic market, comprising the Consumer Agents, Broker Agents and Content Agents, which enable Internet consumers to select a content provider in an automated manner. We also discuss how to dynamically allocate network resources to provide end-to-end Quality of Service (QoS) for a given consumer and content provider.

  11. Instantiating a Global Network Measurement Framework

    SciTech Connect

    Tierney, Brian L.; Boote, Jeff; Boyd, Eric; Brown, Aaron; Grigoriev, Maxim; Metzger, Joe; Swany, Martin; Zekauskas, Matt; Zurawski, Jason

    2008-12-15

    perfSONAR is a web services-based infrastructure for collecting and publishing network performance monitoring. A primary goal of perfSONAR is making it easier to solve end-to-end performance problems on paths crossing several networks. It contains a set of services delivering performance measurements in a federated environment. These services act as an intermediate layer, between the performance measurement tools and the diagnostic or visualization applications. This layer is aimed at making and exchanging performance measurements across multiple networks and multiple user communities, using well-defined protocols. This paper summarizes the key perfSONAR components, and describes how they are deployed by the US-LHC community to monitor the networks distributing LHC data from CERN. All monitoring data described herein is publicly available, and we hope the availability of this data via a standard schema will inspire others to contribute to the effort by building network data analysis applications that use perfSONAR.

  12. Design framework for entanglement-distribution switching networks

    NASA Astrophysics Data System (ADS)

    Drost, Robert J.; Brodsky, Michael

    2016-09-01

    The distribution of quantum entanglement appears to be an important component of applications of quantum communications and networks. The ability to centralize the sourcing of entanglement in a quantum network can provide for improved efficiency and enable a variety of network structures. A necessary feature of an entanglement-sourcing network node comprising several sources of entangled photons is the ability to reconfigurably route the generated pairs of photons to network neighbors depending on the desired entanglement sharing of the network users at a given time. One approach to such routing is the use of a photonic switching network. The requirements for an entanglement distribution switching network are less restrictive than for typical conventional applications, leading to design freedom that can be leveraged to optimize additional criteria. In this paper, we present a mathematical framework defining the requirements of an entanglement-distribution switching network. We then consider the design of such a switching network using a number of 2 × 2 crossbar switches, addressing the interconnection of these switches and efficient routing algorithms. In particular, we define a worst-case loss metric and consider 6 × 6, 8 × 8, and 10 × 10 network designs that optimize both this metric and the number of crossbar switches composing the network. We pay particular attention to the 10 × 10 network, detailing novel results proving the optimality of the proposed design. These optimized network designs have great potential for use in practical quantum networks, thus advancing the concept of quantum networks toward reality.

  13. An Organizational Framework of Personal Health Records for Social Networks

    ERIC Educational Resources Information Center

    Hasan, Syed Omair

    2009-01-01

    This work proposes an organizational framework for creating a community to share personal health record (PHR) information in the form of a Health Records Social Network (HRSN). The work builds upon existing social network community concepts as well as the existing Systemized Nomenclature of Medicine (SNOMED) model used by the medical community and…

  14. An Organizational Framework of Personal Health Records for Social Networks

    ERIC Educational Resources Information Center

    Hasan, Syed Omair

    2009-01-01

    This work proposes an organizational framework for creating a community to share personal health record (PHR) information in the form of a Health Records Social Network (HRSN). The work builds upon existing social network community concepts as well as the existing Systemized Nomenclature of Medicine (SNOMED) model used by the medical community and…

  15. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

    PubMed Central

    2011-01-01

    Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis. PMID:22784571

  16. A framework for analyzing the impact of data integrity/quality on electricity market operations

    NASA Astrophysics Data System (ADS)

    Choi, Dae Hyun

    a circuit breaker) do network topology estimate, thus leading to the distortion of LMP. We present an analytical framework to quantify real-time LMP sensitivity subject to continuous and discrete data corruption via state estimation. The proposed framework offers system operators an analytical tool to identify economically sensitive buses and transmission lines to data corruption as well as find sensors that impact LMP changes significantly. This dissertation serves as a first step towards rigorous understanding of the fundamental coupling among cyber, physical and economical layers of operations in future smart grid.

  17. Establishing a Network Framework for TRADOC Scenarios

    DTIC Science & Technology

    2010-09-30

    Optimized Network Evaluation Tool ( OPNET ), Net Warfare Simulation (NETWARS) and the Joint...Electronic Maintenance Training Department OFF officer OneSAF One Semi Automated Force OPCON operational control OPLAN operational plan OPNET Optimized

  18. A Framework for Imperfectly Observed Networks

    NASA Astrophysics Data System (ADS)

    Aldous, David; Li, Xiang

    2017-07-01

    Model a network as an edge-weighted graph, where the (unknown) weight w_e of edge e indicates the frequency of observed interactions, and over time t we observe a Poisson (t w_e ) number of interactions across edges e. How should we estimate some given statistic of the underlying network? This leads to wide-ranging and challenging problems, on which this article makes only partial progress.

  19. Social Network Analysis in Frontier Capital Markets

    DTIC Science & Technology

    2012-06-01

    incorporate the network of interacting individuals, the structure of their interactions, and the consequences of network activity [Kir10]. Stiglitz and...as financial capital. As Stiglitz and Gallegati [SG11] note, “Some network designs may be good at absorbing small shocks, when there can be systemic...2011. [SG11] Joseph E Stiglitz and Mauro Gallegati. Heterogeneous interacting agent models for understanding monetary economies. Eastern Economic

  20. U.S. stock market interaction network as learned by the Boltzmann machine

    SciTech Connect

    Borysov, Stanislav S.; Roudi, Yasser; Balatsky, Alexander V.

    2015-12-07

    Here, we study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented results show that binarization preserves the correlation structure of the market. Properties of distributions of external fields and couplings as well as the market interaction network and industry sector clustering structure are studied for different historical dates and moving window sizes. We demonstrate that the observed positive heavy tail in distribution of couplings is related to the sparse clustering structure of the market. We also show that discrepancies between the model’s parameters might be used as a precursor of financial instabilities.

  1. U.S. stock market interaction network as learned by the Boltzmann machine

    NASA Astrophysics Data System (ADS)

    Borysov, Stanislav S.; Roudi, Yasser; Balatsky, Alexander V.

    2015-12-01

    We study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented results show that binarization preserves the correlation structure of the market. Properties of distributions of external fields and couplings as well as the market interaction network and industry sector clustering structure are studied for different historical dates and moving window sizes. We demonstrate that the observed positive heavy tail in distribution of couplings is related to the sparse clustering structure of the market. We also show that discrepancies between the model's parameters might be used as a precursor of financial instabilities.

  2. STOCK Market Differences in Correlation-Based Weighted Network

    NASA Astrophysics Data System (ADS)

    Youn, Janghyuk; Lee, Junghoon; Chang, Woojin

    We examined the sector dynamics of Korean stock market in relation to the market volatility. The daily price data of 360 stocks for 5019 trading days (from January, 1990 to August, 2008) in Korean stock market are used. We performed the weighted network analysis and employed four measures: the average, the variance, the intensity, and the coherence of network weights (absolute values of stock return correlations) to investigate the network structure of Korean stock market. We performed regression analysis using the four measures in the seven major industry sectors and the market (seven sectors combined). We found that the average, the intensity, and the coherence of sector (subnetwork) weights increase as market becomes volatile. Except for the "Financials" sector, the variance of sector weights also grows as market volatility increases. Based on the four measures, we can categorize "Financials," "Information Technology" and "Industrials" sectors into one group, and "Materials" and "Consumer Discretionary" sectors into another group. We investigated the distributions of intrasector and intersector weights for each sector and found the differences in "Financials" sector are most distinct.

  3. Networks in financial markets based on the mutual information rate.

    PubMed

    Fiedor, Paweł

    2014-05-01

    In the last few years there have been many efforts in econophysics studying how network theory can facilitate understanding of complex financial markets. These efforts consist mainly of the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financial markets are that they are complex systems and thus behave in a nonlinear manner, which is confirmed by numerous studies, making the use of correlations which are inherently dealing with linear dependencies only baffling. In this paper we introduce a way to incorporate nonlinear dynamics and dependencies into hierarchical networks to study financial markets using mutual information and its dynamical extension: the mutual information rate. We show that this approach leads to different results than the correlation-based approach used in most studies, on the basis of 91 companies listed on the New York Stock Exchange 100 between 2003 and 2013, using minimal spanning trees and planar maximally filtered graphs.

  4. Networks in financial markets based on the mutual information rate

    NASA Astrophysics Data System (ADS)

    Fiedor, Paweł

    2014-05-01

    In the last few years there have been many efforts in econophysics studying how network theory can facilitate understanding of complex financial markets. These efforts consist mainly of the study of correlation-based hierarchical networks. This is somewhat surprising as the underlying assumptions of research looking at financial markets are that they are complex systems and thus behave in a nonlinear manner, which is confirmed by numerous studies, making the use of correlations which are inherently dealing with linear dependencies only baffling. In this paper we introduce a way to incorporate nonlinear dynamics and dependencies into hierarchical networks to study financial markets using mutual information and its dynamical extension: the mutual information rate. We show that this approach leads to different results than the correlation-based approach used in most studies, on the basis of 91 companies listed on the New York Stock Exchange 100 between 2003 and 2013, using minimal spanning trees and planar maximally filtered graphs.

  5. On effectiveness of network sensor-based defense framework

    NASA Astrophysics Data System (ADS)

    Zhang, Difan; Zhang, Hanlin; Ge, Linqiang; Yu, Wei; Lu, Chao; Chen, Genshe; Pham, Khanh

    2012-06-01

    Cyber attacks are increasing in frequency, impact, and complexity, which demonstrate extensive network vulnerabilities with the potential for serious damage. Defending against cyber attacks calls for the distributed collaborative monitoring, detection, and mitigation. To this end, we develop a network sensor-based defense framework, with the aim of handling network security awareness, mitigation, and prediction. We implement the prototypical system and show its effectiveness on detecting known attacks, such as port-scanning and distributed denial-of-service (DDoS). Based on this framework, we also implement the statistical-based detection and sequential testing-based detection techniques and compare their respective detection performance. The future implementation of defensive algorithms can be provisioned in our proposed framework for combating cyber attacks.

  6. The structure and resilience of financial market networks

    NASA Astrophysics Data System (ADS)

    Kauê Dal'Maso Peron, Thomas; da Fontoura Costa, Luciano; Rodrigues, Francisco A.

    2012-03-01

    Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.

  7. The structure and resilience of financial market networks.

    PubMed

    Peron, Thomas Kaue Dal'Maso; Costa, Luciano da Fontoura; Rodrigues, Francisco A

    2012-03-01

    Financial markets can be viewed as a highly complex evolving system that is very sensitive to economic instabilities. The complex organization of the market can be represented in a suitable fashion in terms of complex networks, which can be constructed from stock prices such that each pair of stocks is connected by a weighted edge that encodes the distance between them. In this work, we propose an approach to analyze the topological and dynamic evolution of financial networks based on the stock correlation matrices. An entropy-related measurement is adopted to quantify the robustness of the evolving financial market organization. It is verified that the network topological organization suffers strong variation during financial instabilities and the networks in such periods become less robust. A statistical robust regression model is proposed to quantity the relationship between the network structure and resilience. The obtained coefficients of such model indicate that the average shortest path length is the measurement most related to network resilience coefficient. This result indicates that a collective behavior is observed between stocks during financial crisis. More specifically, stocks tend to synchronize their price evolution, leading to a high correlation between pair of stock prices, which contributes to the increase in distance between them and, consequently, decrease the network resilience.

  8. Cyber Security Research Frameworks For Coevolutionary Network Defense

    SciTech Connect

    Rush, George D.; Tauritz, Daniel Remy

    2015-12-03

    Several architectures have been created for developing and testing systems used in network security, but most are meant to provide a platform for running cyber security experiments as opposed to automating experiment processes. In the first paper, we propose a framework termed Distributed Cyber Security Automation Framework for Experiments (DCAFE) that enables experiment automation and control in a distributed environment. Predictive analysis of adversaries is another thorny issue in cyber security. Game theory can be used to mathematically analyze adversary models, but its scalability limitations restrict its use. Computational game theory allows us to scale classical game theory to larger, more complex systems. In the second paper, we propose a framework termed Coevolutionary Agent-based Network Defense Lightweight Event System (CANDLES) that can coevolve attacker and defender agent strategies and capabilities and evaluate potential solutions with a custom network defense simulation. The third paper is a continuation of the CANDLES project in which we rewrote key parts of the framework. Attackers and defenders have been redesigned to evolve pure strategy, and a new network security simulation is devised which specifies network architecture and adds a temporal aspect. We also add a hill climber algorithm to evaluate the search space and justify the use of a coevolutionary algorithm.

  9. Towards A Network-of-Networks Framework for Cyber Security

    SciTech Connect

    Halappanavar, Mahantesh; Choudhury, Sutanay; Hogan, Emilie A.; Hui, Peter SY; Johnson, John R.; Ray, Indrajit; Holder, Lawrence B.

    2013-06-07

    Networks-of-networks (NoN) is a graph-theoretic model of interdependent networks that have distinct dynamics at each network (layer). By adding special edges to represent relationships between nodes in different layers, NoN provides a unified mechanism to study interdependent systems intertwined in a complex relationship. While NoN based models have been proposed for cyber-physical systems, in this paper we build towards a three-layer NoN model for an enterprise cyber system. Each layer captures a different facet of a cyber system. We then discuss the potential benefits of graph-theoretic analysis enabled from such a model. Our goal is to provide a novel and powerful tool for modeling and analyzing problems in cyber security.

  10. Conceptual Framework for Curriculum Decisions in Education for Marketing and Distribution Careers.

    ERIC Educational Resources Information Center

    Gordon, Alice K.; And Others

    Developed to provide bases for curriculum decisions in education for marketing and distribution careers, the conceptual framework presented here contains the following elements: Identification of social, economic and educational trends which affect employment and education in marketing and distribution; an assessment of current education practice;…

  11. An autocatalytic network model for stock markets

    NASA Astrophysics Data System (ADS)

    Caetano, Marco Antonio Leonel; Yoneyama, Takashi

    2015-02-01

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

  12. Online Monitor Framework for Network Distributed Data Acquisition Systems

    NASA Astrophysics Data System (ADS)

    Konno, Tomoyuki; Cabrera, Anatael; Ishitsuka, Masaki; Kuze, Masahiro; Sakamoto, Yasunobu; the Double Chooz Collaboration

    Data acquisition (DAQ) systems for recent high energy physics experiments consist of lots of subsystems distributed in the local area network. Therefore, scalability for the number of connections from subsystems and availability of access via the Internet are required. "Online monitor framework" is a general software framework for online data monitoring, which provides a way to collect monitoring information distributed in the network and pass them though the firewalls. The framework consists of two subsystems; "Monitor Sever" and "Monitor Viewer". Monitor Server is a core system of the framework. The server collects monitoring information from the DAQ subsystems to provide them to Monitor Viewer. Monitor Viewer is a graphical user interface of the monitor framework, which displays plots in itself. We adapted two types of technologies; Java and HTML5 with Google Web Toolkit, which are independent of operating systems or plugin-libraries like ROOT and contain some functionalities of communicating via the Internet and drawing graphics. The monitoring framework was developed for the Double Chooz reactor neutrino oscillation experiment but is general enough for other experiments. This document reports the structure of the online monitor framework with some examples from the adaption to the Double Chooz experiment.

  13. SERA Scenarios of Early Market Fuel Cell Electric Vehicle Introductions: Modeling Framework, Regional Markets, and Station Clustering

    SciTech Connect

    Bush, B.; Melaina, M.; Penev, M.; Daniel, W.

    2013-09-01

    This report describes the development and analysis of detailed temporal and spatial scenarios for early market hydrogen fueling infrastructure clustering and fuel cell electric vehicle rollout using the Scenario Evaluation, Regionalization and Analysis (SERA) model. The report provides an overview of the SERA scenario development framework and discusses the approach used to develop the nationwidescenario.

  14. A Framework for Dimensioning VDL-2 Air-Ground Networks

    NASA Technical Reports Server (NTRS)

    Ribeiro, Leila Z.; Monticone, Leone C.; Snow, Richard E.; Box, Frank; Apaza, Rafel; Bretmersky, Steven

    2014-01-01

    This paper describes a framework developed at MITRE for dimensioning a Very High Frequency (VHF) Digital Link Mode 2 (VDL-2) Air-to-Ground network. This framework was developed to support the FAA's Data Communications (Data Comm) program by providing estimates of expected capacity required for the air-ground network services that will support Controller-Pilot-Data-Link Communications (CPDLC), as well as the spectrum needed to operate the system at required levels of performance. The Data Comm program is part of the FAA's NextGen initiative to implement advanced communication capabilities in the National Airspace System (NAS). The first component of the framework is the radio-frequency (RF) coverage design for the network ground stations. Then we proceed to describe the approach used to assess the aircraft geographical distribution and the data traffic demand expected in the network. The next step is the resource allocation utilizing optimization algorithms developed in MITRE's Spectrum ProspectorTM tool to propose frequency assignment solutions, and a NASA-developed VDL-2 tool to perform simulations and determine whether a proposed plan meets the desired performance requirements. The framework presented is capable of providing quantitative estimates of multiple variables related to the air-ground network, in order to satisfy established coverage, capacity and latency performance requirements. Outputs include: coverage provided at different altitudes; data capacity required in the network, aggregated or on a per ground station basis; spectrum (pool of frequencies) needed for the system to meet a target performance; optimized frequency plan for a given scenario; expected performance given spectrum available; and, estimates of throughput distributions for a given scenario. We conclude with a discussion aimed at providing insight into the tradeoffs and challenges identified with respect to radio resource management for VDL-2 air-ground networks.

  15. A local area computer network expert system framework

    NASA Technical Reports Server (NTRS)

    Dominy, Robert

    1987-01-01

    Over the past years an expert system called LANES designed to detect and isolate faults in the Goddard-wide Hybrid Local Area Computer Network (LACN) was developed. As a result, the need for developing a more generic LACN fault isolation expert system has become apparent. An object oriented approach was explored to create a set of generic classes, objects, rules, and methods that would be necessary to meet this need. The object classes provide a convenient mechanism for separating high level information from low level network specific information. This approach yeilds a framework which can be applied to different network configurations and be easily expanded to meet new needs.

  16. A Framework for Event Prioritization in Cyber Network Defense

    DTIC Science & Technology

    2014-07-15

    myong H. KAng Jim Z. Luo ALex VeLAZqueZ Center for High Assurance Computer Systems Information Technology Division July 15, 2014 Approved for public...OF ABSTRACT A Framework for Event Prioritization in Cyber Network Defense Anya Kim, Myong H. Kang, Jim Z. Luo, and Alex Velazquez Naval Research

  17. Framework for adaptive content delivery in heterogeneous network environments

    NASA Astrophysics Data System (ADS)

    Ma, Wei-Ying; Bedner, Ilja; Chang, Grace; Kuchinsky, Allan; Zhang, HongJiang

    1999-12-01

    The explosive growth of the Internet has come with increasing diversity and heterogeneity in terms of client device capability, network bandwidth, and user preferences. To date, most Web content has been designed with desktop computers in mind, and often contains rich media such as images, audio, and video. In many cases, this content is not suitable for devices like netTVs, handheld computers, personal digital assistants, and smart phones with relatively limited display capability, storage, processing power, and network access. Thus, Internet access is still constrained on these devices and there is a need to develop alternative approaches for information delivery. In this paper, we propose a framework for adaptive content delivery in heterogeneous environments. The goal is to improve content accessibility and perceived quality of service for information access under changing network and viewer conditions. The framework includes content adaptation algorithms, client capability and network bandwidth discovery methods, and a Decision Engine for determining when and how to adapt content. We describe this framework, initial system implementations based upon this framework, and the issues associated with the deployment of such systems based on different architectures.

  18. A framework for analyzing contagion in assortative banking networks.

    PubMed

    Hurd, Thomas R; Gleeson, James P; Melnik, Sergey

    2017-01-01

    We introduce a probabilistic framework that represents stylized banking networks with the aim of predicting the size of contagion events. Most previous work on random financial networks assumes independent connections between banks, whereas our framework explicitly allows for (dis)assortative edge probabilities (i.e., a tendency for small banks to link to large banks). We analyze default cascades triggered by shocking the network and find that the cascade can be understood as an explicit iterated mapping on a set of edge probabilities that converges to a fixed point. We derive a cascade condition, analogous to the basic reproduction number R0 in epidemic modelling, that characterizes whether or not a single initially defaulted bank can trigger a cascade that extends to a finite fraction of the infinite network. This cascade condition is an easily computed measure of the systemic risk inherent in a given banking network topology. We use percolation theory for random networks to derive a formula for the frequency of global cascades. These analytical results are shown to provide limited quantitative agreement with Monte Carlo simulation studies of finite-sized networks. We show that edge-assortativity, the propensity of nodes to connect to similar nodes, can have a strong effect on the level of systemic risk as measured by the cascade condition. However, the effect of assortativity on systemic risk is subtle, and we propose a simple graph theoretic quantity, which we call the graph-assortativity coefficient, that can be used to assess systemic risk.

  19. A framework for analyzing contagion in assortative banking networks

    PubMed Central

    Hurd, Thomas R.; Gleeson, James P.; Melnik, Sergey

    2017-01-01

    We introduce a probabilistic framework that represents stylized banking networks with the aim of predicting the size of contagion events. Most previous work on random financial networks assumes independent connections between banks, whereas our framework explicitly allows for (dis)assortative edge probabilities (i.e., a tendency for small banks to link to large banks). We analyze default cascades triggered by shocking the network and find that the cascade can be understood as an explicit iterated mapping on a set of edge probabilities that converges to a fixed point. We derive a cascade condition, analogous to the basic reproduction number R0 in epidemic modelling, that characterizes whether or not a single initially defaulted bank can trigger a cascade that extends to a finite fraction of the infinite network. This cascade condition is an easily computed measure of the systemic risk inherent in a given banking network topology. We use percolation theory for random networks to derive a formula for the frequency of global cascades. These analytical results are shown to provide limited quantitative agreement with Monte Carlo simulation studies of finite-sized networks. We show that edge-assortativity, the propensity of nodes to connect to similar nodes, can have a strong effect on the level of systemic risk as measured by the cascade condition. However, the effect of assortativity on systemic risk is subtle, and we propose a simple graph theoretic quantity, which we call the graph-assortativity coefficient, that can be used to assess systemic risk. PMID:28231324

  20. Mathematical Frameworks for Oscillatory Network Dynamics in Neuroscience.

    PubMed

    Ashwin, Peter; Coombes, Stephen; Nicks, Rachel

    2016-12-01

    The tools of weakly coupled phase oscillator theory have had a profound impact on the neuroscience community, providing insight into a variety of network behaviours ranging from central pattern generation to synchronisation, as well as predicting novel network states such as chimeras. However, there are many instances where this theory is expected to break down, say in the presence of strong coupling, or must be carefully interpreted, as in the presence of stochastic forcing. There are also surprises in the dynamical complexity of the attractors that can robustly appear-for example, heteroclinic network attractors. In this review we present a set of mathematical tools that are suitable for addressing the dynamics of oscillatory neural networks, broadening from a standard phase oscillator perspective to provide a practical framework for further successful applications of mathematics to understanding network dynamics in neuroscience.

  1. Pythoscape: a framework for generation of large protein similarity networks.

    PubMed

    Barber, Alan E; Babbitt, Patricia C

    2012-11-01

    Pythoscape is a framework implemented in Python for processing large protein similarity networks for visualization in other software packages. Protein similarity networks are graphical representations of sequence, structural and other similarities among proteins for which pairwise all-by-all similarity connections have been calculated. Mapping of biological and other information to network nodes or edges enables hypothesis creation about sequence-structure-function relationships across sets of related proteins. Pythoscape provides several options to calculate pairwise similarities for input sequences or structures, applies filters to network edges and defines sets of similar nodes and their associated data as single nodes (termed representative nodes) for compression of network information and output data or formatted files for visualization.

  2. Practical use of a framework for network science experimentation

    NASA Astrophysics Data System (ADS)

    Toth, Andrew; Bergamaschi, Flavio

    2014-06-01

    In 2006, the US Army Research Laboratory (ARL) and the UK Ministry of Defence (MoD) established a collaborative research alliance with academia and industry, called the International Technology Alliance (ITA)1 In Network and Information Sciences, to address fundamental issues concerning Network and Information Sciences that will enhance decision making for coalition operations and enable rapid, secure formation of ad hoc teams in coalition environments and enhance US and UK capabilities to conduct coalition warfare. Research conducted under the ITA was extended through collaboration between ARL and IBM UK to characterize and dene a software stack and tooling that has become the reference framework for network science experimentation in support for validation of theoretical research. This paper discusses the composition of the reference framework for experimentation resulting from the ARL/IBM UK collaboration and its use, by the Network Science Collaborative Technology Alliance (NS CTA)2 , in a recent network science experiment conducted at ARL. It also discusses how the experiment was modeled using the reference framework, the integration of two new components, the Apollo Fact-Finder3 tool and the Medusa Crowd Sensing4 application, the limitations identified and how they shall be addressed in future work.

  3. Topological isomorphisms of human brain and financial market networks.

    PubMed

    Vértes, Petra E; Nicol, Ruth M; Chapman, Sandra C; Watkins, Nicholas W; Robertson, Duncan A; Bullmore, Edward T

    2011-01-01

    Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets - the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular - more highly optimized for information processing - than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets.

  4. Topological Isomorphisms of Human Brain and Financial Market Networks

    PubMed Central

    Vértes, Petra E.; Nicol, Ruth M.; Chapman, Sandra C.; Watkins, Nicholas W.; Robertson, Duncan A.; Bullmore, Edward T.

    2011-01-01

    Although metaphorical and conceptual connections between the human brain and the financial markets have often been drawn, rigorous physical or mathematical underpinnings of this analogy remain largely unexplored. Here, we apply a statistical and graph theoretic approach to the study of two datasets – the time series of 90 stocks from the New York stock exchange over a 3-year period, and the fMRI-derived time series acquired from 90 brain regions over the course of a 10-min-long functional MRI scan of resting brain function in healthy volunteers. Despite the many obvious substantive differences between these two datasets, graphical analysis demonstrated striking commonalities in terms of global network topological properties. Both the human brain and the market networks were non-random, small-world, modular, hierarchical systems with fat-tailed degree distributions indicating the presence of highly connected hubs. These properties could not be trivially explained by the univariate time series statistics of stock price returns. This degree of topological isomorphism suggests that brains and markets can be regarded broadly as members of the same family of networks. The two systems, however, were not topologically identical. The financial market was more efficient and more modular – more highly optimized for information processing – than the brain networks; but also less robust to systemic disintegration as a result of hub deletion. We conclude that the conceptual connections between brains and markets are not merely metaphorical; rather these two information processing systems can be rigorously compared in the same mathematical language and turn out often to share important topological properties in common to some degree. There will be interesting scientific arbitrage opportunities in further work at the graph-theoretically mediated interface between systems neuroscience and the statistical physics of financial markets. PMID:22007161

  5. A network analysis of the Chinese stock market

    NASA Astrophysics Data System (ADS)

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

    2009-07-01

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

  6. Conceptual Framework for Developing a Diabetes Information Network.

    PubMed

    Riazi, Hossein; Langarizadeh, Mostafa; Larijani, Bagher; Shahmoradi, Leila

    2016-06-01

    To provide a conceptual framework for managing diabetic patient care, and creating an information network for clinical research. A wide range of information technology (IT) based interventions such as distance learning, diabetes registries, personal or electronic health record systems, clinical information systems, and clinical decision support systems have so far been used in supporting diabetic care. Previous studies demonstrated that IT could improve diabetes care at its different aspects. There is however no comprehensive conceptual framework that defines how different IT applications can support diverse aspects of this care. Therefore, a conceptual framework that combines different IT solutions into a wide information network for improving care processes and for research purposes is widely lacking. In this study we describe the theoretical underpin of a big project aiming at building a wide diabetic information network namely DIANET. A literature review and a survey of national programs and existing regulations for diabetes management was conducted in order to define different aspects of diabetic care that should be supported by IT solutions. Both qualitative and quantitative research methods were used in this study. In addition to the results of a previous systematic literature review, two brainstorming and three expert panel sessions were conducted to identify requirements of a comprehensive information technology solution. Based on these inputs, the requirements for creating a diabetes information network were identified and used to create a questionnaire based on 9-point Likert scale. The questionnaire was finalized after removing some items based on calculated content validity ratio and content validity index coefficients. Cronbach's alpha reliability coefficient was also calculated (αTotal= 0.98, P<0.05, CI=0.95). The final questionnaire was containing 45 items. It was sent to 13 clinicians at two diabetes clinics of endocrine and metabolism research

  7. Conceptual Framework for Developing a Diabetes Information Network

    PubMed Central

    Riazi, Hossein; Langarizadeh, Mostafa; Larijani, Bagher; Shahmoradi, Leila

    2016-01-01

    Objective: To provide a conceptual framework for managing diabetic patient care, and creating an information network for clinical research. Background: A wide range of information technology (IT) based interventions such as distance learning, diabetes registries, personal or electronic health record systems, clinical information systems, and clinical decision support systems have so far been used in supporting diabetic care. Previous studies demonstrated that IT could improve diabetes care at its different aspects. There is however no comprehensive conceptual framework that defines how different IT applications can support diverse aspects of this care. Therefore, a conceptual framework that combines different IT solutions into a wide information network for improving care processes and for research purposes is widely lacking. In this study we describe the theoretical underpin of a big project aiming at building a wide diabetic information network namely DIANET. Research design and methods: A literature review and a survey of national programs and existing regulations for diabetes management was conducted in order to define different aspects of diabetic care that should be supported by IT solutions. Both qualitative and quantitative research methods were used in this study. In addition to the results of a previous systematic literature review, two brainstorming and three expert panel sessions were conducted to identify requirements of a comprehensive information technology solution. Based on these inputs, the requirements for creating a diabetes information network were identified and used to create a questionnaire based on 9-point Likert scale. The questionnaire was finalized after removing some items based on calculated content validity ratio and content validity index coefficients. Cronbach’s alpha reliability coefficient was also calculated (αTotal= 0.98, P<0.05, CI=0.95). The final questionnaire was containing 45 items. It was sent to 13 clinicians at two

  8. Dynamics of cluster structures in a financial market network

    NASA Astrophysics Data System (ADS)

    Kocheturov, Anton; Batsyn, Mikhail; Pardalos, Panos M.

    2014-11-01

    In the course of recent fifteen years the network analysis has become a powerful tool for studying financial markets. In this work we analyze stock markets of the USA and Sweden. We study cluster structures of a market network constructed from a correlation matrix of returns of the stocks traded in each of these markets. Such cluster structures are obtained by means of the P-Median Problem (PMP) whose objective is to maximize the total correlation between a set of stocks called medians of size p and other stocks. Every cluster structure is an undirected disconnected weighted graph in which every connected component (cluster) is a star, or a tree with one central node (called a median) and several leaf nodes connected with the median by weighted edges. Our main observation is that in non-crisis periods of time cluster structures change more chaotically, while during crises they show more stable behavior and fewer changes. Thus an increasing stability of a market graph cluster structure obtained via the PMP could be used as an indicator of a coming crisis.

  9. Organizing product innovation: hierarchy, market or triple-helix networks?

    PubMed

    Fitjar, Rune Dahl; Gjelsvik, Martin; Rodríguez-Pose, Andrés

    This paper assesses the extent to which the organization of the innovation effort in firms, as well as the geographical scale at which this effort is pursued, affects the capacity to benefit from product innovations. Three alternative modes of organization are studied: hierarchy, market and triple-helix-type networks. Furthermore, we consider triple-helix networks at three geographical scales: local, national and international. These relationships are tested on a random sample of 763 firms located in five urban regions of Norway which reported having introduced new products or services during the preceding 3 years. The analysis shows that firms exploiting internal hierarchy or triple-helix networks with a wide range of partners managed to derive a significantly higher share of their income from new products, compared to those that mainly relied on outsourcing within the market. In addition, the analysis shows that the geographical scale of cooperation in networks, as well as the type of partner used, matters for the capacity of firms to benefit from product innovation. In particular, firms that collaborate in international triple-helix-type networks involving suppliers, customers and R&D institutions extract a higher share of their income from product innovations, regardless of whether they organize the processes internally or through the network.

  10. An integrated network visualization framework towards metabolic engineering applications.

    PubMed

    Noronha, Alberto; Vilaça, Paulo; Rocha, Miguel

    2014-12-30

    Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential. In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks. The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

  11. Neural network based load and price forecasting and confidence interval estimation in deregulated power markets

    NASA Astrophysics Data System (ADS)

    Zhang, Li

    With the deregulation of the electric power market in New England, an independent system operator (ISO) has been separated from the New England Power Pool (NEPOOL). The ISO provides a regional spot market, with bids on various electricity-related products and services submitted by utilities and independent power producers. A utility can bid on the spot market and buy or sell electricity via bilateral transactions. Good estimation of market clearing prices (MCP) will help utilities and independent power producers determine bidding and transaction strategies with low risks, and this is crucial for utilities to compete in the deregulated environment. MCP prediction, however, is difficult since bidding strategies used by participants are complicated and MCP is a non-stationary process. The main objective of this research is to provide efficient short-term load and MCP forecasting and corresponding confidence interval estimation methodologies. In this research, the complexity of load and MCP with other factors is investigated, and neural networks are used to model the complex relationship between input and output. With improved learning algorithm and on-line update features for load forecasting, a neural network based load forecaster was developed, and has been in daily industry use since summer 1998 with good performance. MCP is volatile because of the complexity of market behaviors. In practice, neural network based MCP predictors usually have a cascaded structure, as several key input factors need to be estimated first. In this research, the uncertainties involved in a cascaded neural network structure for MCP prediction are analyzed, and prediction distribution under the Bayesian framework is developed. A fast algorithm to evaluate the confidence intervals by using the memoryless Quasi-Newton method is also developed. The traditional back-propagation algorithm for neural network learning needs to be improved since MCP is a non-stationary process. The extended Kalman

  12. An integrated text mining framework for metabolic interaction network reconstruction.

    PubMed

    Patumcharoenpol, Preecha; Doungpan, Narumol; Meechai, Asawin; Shen, Bairong; Chan, Jonathan H; Vongsangnak, Wanwipa

    2016-01-01

    Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module-MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module-MINR). The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme-metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source code, and virtual

  13. Introducing FNCS: Framework for Network Co-Simulation

    ScienceCinema

    None

    2016-07-12

    This video provides a basic overview of the PNNL Future Power Grid Initiative-developed Framework for Network Co-Simulation (FNCS). It discusses the increasing amounts of data coming from the power grid, and the need for a tool like FNCS that brings together data, transmission and distribution simulators. Included is a description of the FNCS architecture, and the advantages this new open source tool can bring to grid research and development efforts.

  14. Introducing FNCS: Framework for Network Co-Simulation

    SciTech Connect

    2014-10-23

    This video provides a basic overview of the PNNL Future Power Grid Initiative-developed Framework for Network Co-Simulation (FNCS). It discusses the increasing amounts of data coming from the power grid, and the need for a tool like FNCS that brings together data, transmission and distribution simulators. Included is a description of the FNCS architecture, and the advantages this new open source tool can bring to grid research and development efforts.

  15. A framework for network-wide semantic event correlation

    NASA Astrophysics Data System (ADS)

    Hall, Robert T.; Taylor, Joshua

    2013-05-01

    An increasing need for situational awareness within network-deployed Systems Under Test has increased desire for frameworks that facilitate system-wide data correlation and analysis. Massive event streams are generated from heterogeneous sensors which require tedious manual analysis. We present a framework for sensor data integration and event correlation based on Linked Data principles, Semantic Web reasoning technology, complex event processing, and blackboard architectures. Sensor data are encoded as RDF models, then processed by complex event processing agents (which incorporate domain specific reasoners, as well as general purpose Semantic Web reasoning techniques). Agents can publish inferences on shared blackboards and generate new semantic events that are fed back into the system. We present AIS, Inc.'s Cyber Battlefield Training and Effectiveness Environment to demonstrate use of the framework.

  16. E-Services quality assessment framework for collaborative networks

    NASA Astrophysics Data System (ADS)

    Stegaru, Georgiana; Danila, Cristian; Sacala, Ioan Stefan; Moisescu, Mihnea; Mihai Stanescu, Aurelian

    2015-08-01

    In a globalised networked economy, collaborative networks (CNs) are formed to take advantage of new business opportunities. Collaboration involves shared resources and capabilities, such as e-Services that can be dynamically composed to automate CN participants' business processes. Quality is essential for the success of business process automation. Current approaches mostly focus on quality of service (QoS)-based service selection and ranking algorithms, overlooking the process of service composition which requires interoperable, adaptable and secure e-Services to ensure seamless collaboration, data confidentiality and integrity. Lack of assessment of these quality attributes can result in e-Service composition failure. The quality of e-Service composition relies on the quality of each e-Service and on the quality of the composition process. Therefore, there is the need for a framework that addresses quality from both views: product and process. We propose a quality of e-Service composition (QoESC) framework for quality assessment of e-Service composition for CNs which comprises of a quality model for e-Service evaluation and guidelines for quality of e-Service composition process. We implemented a prototype considering a simplified telemedicine use case which involves a CN in e-Healthcare domain. To validate the proposed quality-driven framework, we analysed service composition reliability with and without using the proposed framework.

  17. Intoxigenic digital spaces? Youth, social networking sites and alcohol marketing.

    PubMed

    Griffiths, Richard; Casswell, Sally

    2010-09-01

    To examine how young people in New Zealand engage with alcohol and reproduce alcohol marketing messages and alcohol-related branding in 'Bebo', a popular social networking site (SNS) on the Internet. Data are drawn from information posted on approximately 150 Bebo Web pages and analysed by way of textual analysis and cyberspace ethnography. Social networking sites, such as Bebo, provide young people with a digital space in which to share a range of alcohol marketing messages via peer-to-peer transmission. Bebo also enables youth to communicate to one another how they consume alcohol and their views of alcohol marketing messages. The information being shared by young people who use Bebo is openly provided in the form of personal information, forum comments, digital photographs and answering quizzes about their engagement with alcohol. Through this sharing of information in the digital Internet environment, young people are creating 'intoxigenic social identities' as well as 'intoxigenic digital spaces' that further contribute towards the normalisation of youth consumption of alcohol. A better understanding of how youth are using the Internet to share their experiences with alcohol and engagement with alcohol-related messages is crucial to public health research as alcohol marketing practices rapidly evolve.

  18. A Study on Market-based Strategic Procurement Planning in Convergent Supply Networks

    NASA Astrophysics Data System (ADS)

    Opadiji, Jayeola Femi; Kaihara, Toshiya

    We present a market-based decentralized approach which uses a market-oriented programming algorithm to obtain Pareto-optimal allocation of resources traded among agents which represent enterprise units in a supply network. The proposed method divides the network into a series of Walrsian markets in order to obtain procurement budgets for enterprises in the network. An interaction protocol based on market value propagation is constructed to coordinate the flow of resources across the network layers. The method mitigates the effect of product complementarity in convergent network by allowing for enterprises to hold private valuations of resources in the markets.

  19. A Scalable Distribution Network Risk Evaluation Framework via Symbolic Dynamics

    PubMed Central

    Yuan, Kai; Liu, Jian; Liu, Kaipei; Tan, Tianyuan

    2015-01-01

    Background Evaluations of electric power distribution network risks must address the problems of incomplete information and changing dynamics. A risk evaluation framework should be adaptable to a specific situation and an evolving understanding of risk. Methods This study investigates the use of symbolic dynamics to abstract raw data. After introducing symbolic dynamics operators, Kolmogorov-Sinai entropy and Kullback-Leibler relative entropy are used to quantitatively evaluate relationships between risk sub-factors and main factors. For layered risk indicators, where the factors are categorized into four main factors – device, structure, load and special operation – a merging algorithm using operators to calculate the risk factors is discussed. Finally, an example from the Sanya Power Company is given to demonstrate the feasibility of the proposed method. Conclusion Distribution networks are exposed and can be affected by many things. The topology and the operating mode of a distribution network are dynamic, so the faults and their consequences are probabilistic. PMID:25789859

  20. Developing a marketing function in public healthcare systems: a framework for action.

    PubMed

    Lega, Federico

    2006-10-01

    The scope of this paper is to analyse the contribution that a marketing function can bring to the wide variety of healthcare organizations operating in public health systems (PHSs). While extensive research on marketing applied to healthcare services has been elaborated in competitive and managed care contexts, marketing is a rather new issue in PHSs and little research has been conducted to assess its relevance and benefits in these environments. This study tackles that gap and is based on a review of the current literature in order to provide answers to the following points: - definition of the scopes of marketing and of the elements that affect its incorporation in the healthcare sector; - conceptualization of the possible approaches to marketing by health organizations operating in PHSs; - discussion of the resulting framework for action.

  1. Selling Innovations Like Soap: The Interactive Systems Framework and Social Marketing.

    PubMed

    McAlindon, Kathryn

    2017-09-01

    Despite the popularity and noted utility of Wandersman and colleagues' (2008) Interactive Systems Framework, the literature currently provides a primary focus on delivery organizations' and supportive stakeholders' capacities and strategies to implement innovations, presenting a critical gap in understanding. Unfortunately, reflective of a larger void in community dissemination and implementation efforts, there is a more limited focus on the dissemination of innovations. This paper presents the social marketing literature as a supplement to the Prevention Synthesis and Translation System (PSTS), the system responsible for dissemination. The study and practice of innovation synthesis and translation is examined in the literature; and based on the conclusions drawn, social marketing theory is used to provide a systematic approach to improving dissemination within the Interactive Systems Framework. Specifically, three gaps related to the PSTS are identified in the literature that align with and can be filled using social marketing. Social marketing is defined and presented as a supplement by providing theory and practices, within a systems context, for effectively communicating and influencing change. By blending social marketing with the Interactive Systems Framework, the aim is to improve the understanding of strategic communication and its role in the effective dissemination, and subsequent implementation, of innovations. © Society for Community Research and Action 2017.

  2. OWL reasoning framework over big biological knowledge network.

    PubMed

    Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

    2014-01-01

    Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

  3. OWL Reasoning Framework over Big Biological Knowledge Network

    PubMed Central

    Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

    2014-01-01

    Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity. PMID:24877076

  4. Analytical framework for recurrence network analysis of time series

    NASA Astrophysics Data System (ADS)

    Donges, Jonathan F.; Heitzig, Jobst; Donner, Reik V.; Kurths, Jürgen

    2012-04-01

    Recurrence networks are a powerful nonlinear tool for time series analysis of complex dynamical systems. While there are already many successful applications ranging from medicine to paleoclimatology, a solid theoretical foundation of the method has still been missing so far. Here, we interpret an ɛ-recurrence network as a discrete subnetwork of a “continuous” graph with uncountably many vertices and edges corresponding to the system's attractor. This step allows us to show that various statistical measures commonly used in complex network analysis can be seen as discrete estimators of newly defined continuous measures of certain complex geometric properties of the attractor on the scale given by ɛ. In particular, we introduce local measures such as the ɛ-clustering coefficient, mesoscopic measures such as ɛ-motif density, path-based measures such as ɛ-betweennesses, and global measures such as ɛ-efficiency. This new analytical basis for the so far heuristically motivated network measures also provides an objective criterion for the choice of ɛ via a percolation threshold, and it shows that estimation can be improved by so-called node splitting invariant versions of the measures. We finally illustrate the framework for a number of archetypical chaotic attractors such as those of the Bernoulli and logistic maps, periodic and two-dimensional quasiperiodic motions, and for hyperballs and hypercubes by deriving analytical expressions for the novel measures and comparing them with data from numerical experiments. More generally, the theoretical framework put forward in this work describes random geometric graphs and other networks with spatial constraints, which appear frequently in disciplines ranging from biology to climate science.

  5. Analytical framework for recurrence network analysis of time series.

    PubMed

    Donges, Jonathan F; Heitzig, Jobst; Donner, Reik V; Kurths, Jürgen

    2012-04-01

    Recurrence networks are a powerful nonlinear tool for time series analysis of complex dynamical systems. While there are already many successful applications ranging from medicine to paleoclimatology, a solid theoretical foundation of the method has still been missing so far. Here, we interpret an ɛ-recurrence network as a discrete subnetwork of a "continuous" graph with uncountably many vertices and edges corresponding to the system's attractor. This step allows us to show that various statistical measures commonly used in complex network analysis can be seen as discrete estimators of newly defined continuous measures of certain complex geometric properties of the attractor on the scale given by ɛ. In particular, we introduce local measures such as the ɛ-clustering coefficient, mesoscopic measures such as ɛ-motif density, path-based measures such as ɛ-betweennesses, and global measures such as ɛ-efficiency. This new analytical basis for the so far heuristically motivated network measures also provides an objective criterion for the choice of ɛ via a percolation threshold, and it shows that estimation can be improved by so-called node splitting invariant versions of the measures. We finally illustrate the framework for a number of archetypical chaotic attractors such as those of the Bernoulli and logistic maps, periodic and two-dimensional quasiperiodic motions, and for hyperballs and hypercubes by deriving analytical expressions for the novel measures and comparing them with data from numerical experiments. More generally, the theoretical framework put forward in this work describes random geometric graphs and other networks with spatial constraints, which appear frequently in disciplines ranging from biology to climate science.

  6. Movie Piracy Networks at Alaba International Market, Lagos, Nigeria.

    PubMed

    Tade, Oludayo; Mmahi, Okoro Paul

    2017-02-01

    This study examined the veiled chain of film piracy, a major crime in the Nigeria entertainment industry. Studies on film piracy in Nigeria have focused on its economic implications, both on the copyright owners and on the Nigerian economy. The organization of the activities of the film pirates has, however, been neglected. Narratives were extracted through in-depth interviews with pirates, "marketers," and "producers." Data indicated that pirates were insiders in the film marketing industry and included importers of foreign movies, registered and nonregistered retailers of Nigerian films, as well as marketers appointed by copyright owners to distribute their films. With the connivance of sales girls working with the copyright owners and dubbing companies, original copies of films (white face) are "procured." Pirates distributed pirated copies, also secretly known as "green face," without issuing receipts or putting the logo of the company on it. For security reasons, pirated films are sold only to buyers introduced by a member in the piracy network. Efforts aimed at fighting piracy must take into account this veiled network to effectively combat intellectual theft via aggressive ban on the public sale of such products.

  7. Knowledge Discovery Using Bayesian Network Framework for Intelligent Telecommunication Network Management

    NASA Astrophysics Data System (ADS)

    Bashar, Abul; Parr, Gerard; McClean, Sally; Scotney, Bryan; Nauck, Detlef

    The ever-evolving nature of telecommunication networks has put enormous pressure on contemporary Network Management Systems (NMSs) to come up with improved functionalities for efficient monitoring, control and management. In such a context, the rapid deployments of Next Generation Networks (NGN) and their management requires intelligent, autonomic and resilient mechanisms to guarantee Quality of Service (QoS) to the end users and at the same time to maximize revenue for the service/network providers. We present a framework for evaluating a Bayesian Networks (BN) based Decision Support System (DSS) for assisting and improving the performance of a Simple Network Management Protocol (SNMP) based NMS. More specifically, we describe our methodology through a case study which implements the function of Call Admission Control (CAC) in a multi-class video conferencing service scenario. Simulation results are presented for a proof of concept, followed by a critical analysis of our proposed approach and its application.

  8. Teaching Students How to Integrate and Assess Social Networking Tools in Marketing Communications

    ERIC Educational Resources Information Center

    Schlee, Regina Pefanis; Harich, Katrin R.

    2013-01-01

    This research is based on two studies that focus on teaching students how to integrate and assess social networking tools in marketing communications. Study 1 examines how students in marketing classes utilize social networking tools and explores their attitudes regarding the use of such tools for marketing communications. Study 2 focuses on an…

  9. Teaching Students How to Integrate and Assess Social Networking Tools in Marketing Communications

    ERIC Educational Resources Information Center

    Schlee, Regina Pefanis; Harich, Katrin R.

    2013-01-01

    This research is based on two studies that focus on teaching students how to integrate and assess social networking tools in marketing communications. Study 1 examines how students in marketing classes utilize social networking tools and explores their attitudes regarding the use of such tools for marketing communications. Study 2 focuses on an…

  10. A Multi Agent-Based Framework for Simulating Household PHEV Distribution and Electric Distribution Network Impact

    SciTech Connect

    Cui, Xiaohui; Liu, Cheng; Kim, Hoe Kyoung; Kao, Shih-Chieh; Tuttle, Mark A; Bhaduri, Budhendra L

    2011-01-01

    The variation of household attributes such as income, travel distance, age, household member, and education for different residential areas may generate different market penetration rates for plug-in hybrid electric vehicle (PHEV). Residential areas with higher PHEV ownership could increase peak electric demand locally and require utilities to upgrade the electric distribution infrastructure even though the capacity of the regional power grid is under-utilized. Estimating the future PHEV ownership distribution at the residential household level can help us understand the impact of PHEV fleet on power line congestion, transformer overload and other unforeseen problems at the local residential distribution network level. It can also help utilities manage the timing of recharging demand to maximize load factors and utilization of existing distribution resources. This paper presents a multi agent-based simulation framework for 1) modeling spatial distribution of PHEV ownership at local residential household level, 2) discovering PHEV hot zones where PHEV ownership may quickly increase in the near future, and 3) estimating the impacts of the increasing PHEV ownership on the local electric distribution network with different charging strategies. In this paper, we use Knox County, TN as a case study to show the simulation results of the agent-based model (ABM) framework. However, the framework can be easily applied to other local areas in the US.

  11. Functional Network Analysis: A New Way to Compare Frontier and Emerging Markets

    DTIC Science & Technology

    2012-06-01

    intersection of human behavior and economics. The individual motivations, information availability, transaction systems , and cultural realities in...and information flows between people, organizations, and functions enabling us to describe capital market structure and function in innovative ways...This study involves developing methodologies to classify capital market networks by comparing the capital markets in three frontier markets ( Ghana

  12. Framework for engineering finite state machines in gene regulatory networks.

    PubMed

    Oishi, Kevin; Klavins, Eric

    2014-09-19

    Finite state machines are fundamental computing devices at the core of many models of computation. In biology, finite state machines are commonly used as models of development in multicellular organisms. However, it remains unclear to what extent cells can remember state, how they can transition from one state to another reliably, and whether the existing parts available to the synthetic biologist are sufficient to implement specified finite state machines in living cells. Furthermore, how complex multicellular behaviors can be realized by multiple cells coordinating their states with signaling, growth, and division is not well understood. Here, we describe a method by which any finite state machine can be built using nothing more than a suitably engineered network of readily available repressing transcription factors. In particular, we show the mathematical equivalence of finite state machines with a Boolean model of gene regulatory networks. We describe how such networks can be realized with a small class of promoters and transcription factors. To demonstrate the effectiveness of our approach, we show that the behavior of the coarse grained ideal Boolean network model approximates a fine grained delay differential equation model of gene expression. Finally, we explore a framework for the design of more complex systems via an example, synthetic bacterial microcolony edge detection, that illustrates how finite state machines could be used together with cell signaling to construct novel multicellular behaviors.

  13. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection.

    PubMed

    Delaney, Declan T; O'Hare, Gregory M P

    2016-12-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

  14. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection †

    PubMed Central

    Delaney, Declan T.; O’Hare, Gregory M. P.

    2016-01-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks. PMID:27916929

  15. U.S. stock market interaction network as learned by the Boltzmann machine

    DOE PAGES

    Borysov, Stanislav S.; Roudi, Yasser; Balatsky, Alexander V.

    2015-12-07

    Here, we study historical dynamics of joint equilibrium distribution of stock returns in the U.S. stock market using the Boltzmann distribution model being parametrized by external fields and pairwise couplings. Within Boltzmann learning framework for statistical inference, we analyze historical behavior of the parameters inferred using exact and approximate learning algorithms. Since the model and inference methods require use of binary variables, effect of this mapping of continuous returns to the discrete domain is studied. The presented results show that binarization preserves the correlation structure of the market. Properties of distributions of external fields and couplings as well as themore » market interaction network and industry sector clustering structure are studied for different historical dates and moving window sizes. We demonstrate that the observed positive heavy tail in distribution of couplings is related to the sparse clustering structure of the market. We also show that discrepancies between the model’s parameters might be used as a precursor of financial instabilities.« less

  16. Social marketing as a framework for recruitment: illustrations from the REACH study.

    PubMed

    Nichols, Linda; Martindale-Adams, Jennifer; Burns, Robert; Coon, David; Ory, Marcia; Mahoney, Diane; Tarlow, Barbara; Burgio, Louis; Gallagher-Thompson, Dolores; Guy, Delois; Arguelles, Trinidad; Winter, Laraine

    2004-11-01

    Recruitment is often the most challenging aspect of research with older persons. Social marketing--applying marketing techniques to influence the behavior of target audiences to improve their welfare--can help researchers identify factors that influence recruitment. Illustrations of social marketing principles are provided from the Resources for Enhancing Alzheimer's Caregiver Health project, a national Alzheimer's caregivers study that targeted ethnic and racial minorities. Social marketing principles--the six Ps of participants, product, price, place, promotion, and partners--provide a theoretical framework for organizing and planning recruitment activities, including developing varying strategies to define the target audience (participants), develop the intervention (product), manage time and trouble (price), target the audience, improve accessibility (place), promote the study, and develop and work with partners. Strategies to enhance recruitment are often undertaken without a comprehensive plan. A social marketing plan provides a framework to map out the steps in recruitment that will be needed and to plan for allocations of time, staff, and resources.

  17. A Framework for Integration of Heterogeneous Medical Imaging Networks

    PubMed Central

    Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

    2014-01-01

    Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS. PMID:25279021

  18. A framework for integration of heterogeneous medical imaging networks.

    PubMed

    Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

    2014-01-01

    Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS.

  19. Statistical properties of the stock and credit market: RMT and network topology

    NASA Astrophysics Data System (ADS)

    Lim, Kyuseong; Kim, Min Jae; Kim, Sehyun; Kim, Soo Yong

    We analyzed the dependence structure of the credit and stock market using random matrix theory and network topology. The dynamics of both markets have been spotlighted throughout the subprime crisis. In this study, we compared these two markets in view of the market-wide effect from random matrix theory and eigenvalue analysis. We found that the largest eigenvalue of the credit market as a whole preceded that of the stock market in the beginning of the financial crisis and that of two markets tended to be synchronized after the crisis. The correlation between the companies of both markets became considerably stronger after the crisis as well.

  20. A rigorous framework for multiscale simulation of stochastic cellular networks

    PubMed Central

    Chevalier, Michael W.; El-Samad, Hana

    2009-01-01

    Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-cell variability even in clonal populations. Stochastic biochemical networks are modeled as continuous time discrete state Markov processes whose probability density functions evolve according to a chemical master equation (CME). The CME is not solvable but for the simplest cases, and one has to resort to kinetic Monte Carlo techniques to simulate the stochastic trajectories of the biochemical network under study. A commonly used such algorithm is the stochastic simulation algorithm (SSA). Because it tracks every biochemical reaction that occurs in a given system, the SSA presents computational difficulties especially when there is a vast disparity in the timescales of the reactions or in the number of molecules involved in these reactions. This is common in cellular networks, and many approximation algorithms have evolved to alleviate the computational burdens of the SSA. Here, we present a rigorously derived modified CME framework based on the partition of a biochemically reacting system into restricted and unrestricted reactions. Although this modified CME decomposition is as analytically difficult as the original CME, it can be naturally used to generate a hierarchy of approximations at different levels of accuracy. Most importantly, some previously derived algorithms are demonstrated to be limiting cases of our formulation. We apply our methods to biologically relevant test systems to demonstrate their accuracy and efficiency. PMID:19673546

  1. A network access control framework for 6LoWPAN networks.

    PubMed

    Oliveira, Luís M L; Rodrigues, Joel J P C; de Sousa, Amaro F; Lloret, Jaime

    2013-01-18

    Low power over wireless personal area networks (LoWPAN), in particular wireless sensor networks, represent an emerging technology with high potential to be employed in critical situations like security surveillance, battlefields, smart-grids, and in e-health applications. The support of security services in LoWPAN is considered a challenge. First, this type of networks is usually deployed in unattended environments, making them vulnerable to security attacks. Second, the constraints inherent to LoWPAN, such as scarce resources and limited battery capacity, impose a careful planning on how and where the security services should be deployed. Besides protecting the network from some well-known threats, it is important that security mechanisms be able to withstand attacks that have not been identified before. One way of reaching this goal is to control, at the network access level, which nodes can be attached to the network and to enforce their security compliance. This paper presents a network access security framework that can be used to control the nodes that have access to the network, based on administrative approval, and to enforce security compliance to the authorized nodes.

  2. A Network Access Control Framework for 6LoWPAN Networks

    PubMed Central

    Oliveira, Luís M. L.; Rodrigues, Joel J. P. C.; de Sousa, Amaro F.; Lloret, Jaime

    2013-01-01

    Low power over wireless personal area networks (LoWPAN), in particular wireless sensor networks, represent an emerging technology with high potential to be employed in critical situations like security surveillance, battlefields, smart-grids, and in e-health applications. The support of security services in LoWPAN is considered a challenge. First, this type of networks is usually deployed in unattended environments, making them vulnerable to security attacks. Second, the constraints inherent to LoWPAN, such as scarce resources and limited battery capacity, impose a careful planning on how and where the security services should be deployed. Besides protecting the network from some well-known threats, it is important that security mechanisms be able to withstand attacks that have not been identified before. One way of reaching this goal is to control, at the network access level, which nodes can be attached to the network and to enforce their security compliance. This paper presents a network access security framework that can be used to control the nodes that have access to the network, based on administrative approval, and to enforce security compliance to the authorized nodes. PMID:23334610

  3. Multiple Hub Network Choice in the Liberalized European Market

    NASA Technical Reports Server (NTRS)

    Berechman, Joseph; deWit, Jaap

    1997-01-01

    . In the meantime, open skies agreements have been concluded between the USA and most of the EU member states to facilitate strategic alliances between airlines of the states involved. As a result of this on-going liberalization the model of the single 'national' carrier using the national home base as its single hub for the designated third, fourth and sixth freedom operations will stepwise disappear. Within the EU the concept of the national carrier has already been replaced by that of the community carrier. State ownership in more and more European carriers is reduced. On the longer run mergers or even bankruptcy will further undermine the "single national carrier - single national hub" model in Europe. In the meantime, strategic alliances between national carriers in Europe will already reduce the airlines' loyalty to a single airport. Profit maximization and accountability to share holders will supersede the loyalty of these newly emerging alliances, probably looking for the opportunities of a multiple hub network to adequately cover the whole European market. As a consequence, some European airports might see a substantial decline in arriving, departing and transfer traffic, thus in revenues and financial solvency, as well as in their connection to other inter-continental and intra-European destinations. At the same time, other airports might realize a significant increase in traffic as they will be sought after by the profit maximizing airlines as their major gateway hubs. Which will be the losing airports and which will be the winning ones? Can airports anticipate the actions of airlines in deregulated markets and utilize policies which will improve their relative position? If so, what should be these anticipatory policies? These questions become the more urgent, since an increasing number of major European airports will be privatized in the near future. Although increasing airport congestion in Europe will also be reflected in a growing demand pressure for

  4. Social Marketing as a Framework for Recruitment: Illustrations From the REACH Study

    PubMed Central

    Nichols, Linda; Martindale-Adams, Jennifer; Burns, Robert; Coon, David; Ory, Marcia; Tarlow, Diane Mahoney Barbara; Burgio, Louis; Gallagher-Thompson, Dolores; Guy, Delois; Arguelles, Trinidad; Winter, Laraine

    2008-01-01

    Objectives Recruitment is often the most challenging aspect of research with older persons. Social marketing—applying marketing techniques to influence the behavior of target audiences to improve their welfare—can help researchers identify factors Methods Illustrations of social marketing principles are provided from the Resources for Enhancing Alzheimer’s Caregiver Health project, a national Alzheimer’s caregivers study that targeted ethnic and racial minorities. Results Social marketing principles—the six Ps of participants, product, price, place, promotion, and partners—provide a theoretical framework for organizing and planning recruitment activities, including developing varying strategies to define the target audience (participants), develop the intervention (product), manage time and trouble (price), target the audience, improve accessibility (place), promote the study, and develop and work with partners. Discussion Strategies to enhance recruitment are often undertaken without a comprehensive plan. A social marketing plan provides a framework to map out the steps in recruitment that will be needed and to plan for allocations of time, staff, and resources. PMID:15448292

  5. A study of the spreading scheme for viral marketing based on a complex network model

    NASA Astrophysics Data System (ADS)

    Yang, Jianmei; Yao, Canzhong; Ma, Weicheng; Chen, Guanrong

    2010-02-01

    Buzzword-based viral marketing, known also as digital word-of-mouth marketing, is a marketing mode attached to some carriers on the Internet, which can rapidly copy marketing information at a low cost. Viral marketing actually uses a pre-existing social network where, however, the scale of the pre-existing network is believed to be so large and so random, so that its theoretical analysis is intractable and unmanageable. There are very few reports in the literature on how to design a spreading scheme for viral marketing on real social networks according to the traditional marketing theory or the relatively new network marketing theory. Complex network theory provides a new model for the study of large-scale complex systems, using the latest developments of graph theory and computing techniques. From this perspective, the present paper extends the complex network theory and modeling into the research of general viral marketing and develops a specific spreading scheme for viral marking and an approach to design the scheme based on a real complex network on the QQ instant messaging system. This approach is shown to be rather universal and can be further extended to the design of various spreading schemes for viral marketing based on different instant messaging systems.

  6. A Framework of Hyperspectral Image Compression using Neural Networks

    SciTech Connect

    Masalmah, Yahya M.; Martínez Nieves, Christian; Rivera Soto, Rafael; Velez, Carlos; Gonzalez, Jenipher

    2015-01-01

    Hyperspectral image analysis has gained great attention due to its wide range of applications. Hyperspectral images provide a vast amount of information about underlying objects in an image by using a large range of the electromagnetic spectrum for each pixel. However, since the same image is taken multiple times using distinct electromagnetic bands, the size of such images tend to be significant, which leads to greater processing requirements. The aim of this paper is to present a proposed framework for image compression and to study the possible effects of spatial compression on quality of unmixing results. Image compression allows us to reduce the dimensionality of an image while still preserving most of the original information, which could lead to faster image processing. Lastly, this paper presents preliminary results of different training techniques used in Artificial Neural Network (ANN) based compression algorithm.

  7. A Framework of Hyperspectral Image Compression using Neural Networks

    DOE PAGES

    Masalmah, Yahya M.; Martínez Nieves, Christian; Rivera Soto, Rafael; ...

    2015-01-01

    Hyperspectral image analysis has gained great attention due to its wide range of applications. Hyperspectral images provide a vast amount of information about underlying objects in an image by using a large range of the electromagnetic spectrum for each pixel. However, since the same image is taken multiple times using distinct electromagnetic bands, the size of such images tend to be significant, which leads to greater processing requirements. The aim of this paper is to present a proposed framework for image compression and to study the possible effects of spatial compression on quality of unmixing results. Image compression allows usmore » to reduce the dimensionality of an image while still preserving most of the original information, which could lead to faster image processing. Lastly, this paper presents preliminary results of different training techniques used in Artificial Neural Network (ANN) based compression algorithm.« less

  8. A Generic Framework of Performance Measurement in Networked Enterprises

    NASA Astrophysics Data System (ADS)

    Kim, Duk-Hyun; Kim, Cheolhan

    Performance measurement (PM) is essential for managing networked enterprises (NEs) because it greatly affects the effectiveness of collaboration among members of NE.PM in NE requires somewhat different approaches from PM in a single enterprise because of heterogeneity, dynamism, and complexity of NE’s. This paper introduces a generic framework of PM in NE (we call it NEPM) based on the Balanced Scorecard (BSC) approach. In NEPM key performance indicators and cause-and-effect relationships among them are defined in a generic strategy map. NEPM could be applied to various types of NEs after specializing KPIs and relationships among them. Effectiveness of NEPM is shown through a case study of some Korean NEs.

  9. An Integrated Dynamic Online Management Framework for QoS-Sensitive Multimedia Overlay Networks

    NASA Astrophysics Data System (ADS)

    Kim, Sungwook; Choi, Myungwhan; Kim, Sungchun

    New multimedia services over cellular/WLAN overlay networks require different Quality of Service (QoS) levels. Therefore, an efficient network management system is necessary in order to realize QoS sensitive multimedia services while enhancing network performance. In this paper, we propose a new online network management framework for overlay networks. Our online approach to network management exhibits dynamic adaptability, flexibility, and responsiveness to the traffic conditions in multimedia networks. Simulation results indicate that our proposed framework can strike the appropriate balance between performance criteria under widely varying diverse traffic loads.

  10. A cognitive information processing framework for distributed sensor networks

    NASA Astrophysics Data System (ADS)

    Wang, Feiyi; Qi, Hairong

    2004-09-01

    In this paper, we present a cognitive agent framework (CAF) based on swarm intelligence and self-organization principles, and demonstrate it through collaborative processing for target classification in sensor networks. The framework involves integrated designs to provide both cognitive behavior at the organization level to conquer complexity and reactive behavior at the individual agent level to retain simplicity. The design tackles various problems in the current information processing systems, including overly complex systems, maintenance difficulties, increasing vulnerability to attack, lack of capability to tolerate faults, and inability to identify and cope with low-frequency patterns. An important and distinguishing point of the presented work from classical AI research is that the acquired intelligence does not pertain to distinct individuals but to groups. It also deviates from multi-agent systems (MAS) due to sheer quantity of extremely simple agents we are able to accommodate, to the degree that some loss of coordination messages and behavior of faulty/compromised agents will not affect the collective decision made by the group.

  11. A computational molecular design framework for crosslinked polymer networks

    PubMed Central

    Eslick, J.C.; Ye, Q.; Park, J.; Topp, E.M.; Spencer, P.; Camarda, K.V.

    2013-01-01

    Crosslinked polymers are important in a very wide range of applications including dental restorative materials. However, currently used polymeric materials experience limited durability in the clinical oral environment. Researchers in the dental polymer field have generally used a time-consuming experimental trial-and-error approach to the design of new materials. The application of computational molecular design (CMD) to crosslinked polymer networks has the potential to facilitate development of improved polymethacrylate dental materials. CMD uses quantitative structure property relations (QSPRs) and optimization techniques to design molecules possessing desired properties. This paper describes a mathematical framework which provides tools necessary for the application of CMD to crosslinked polymer systems. The novel parts of the system include the data structures used, which allow for simple calculation of structural descriptors, and the formulation of the optimization problem. A heuristic optimization method, Tabu Search, is used to determine candidate monomers. Use of a heuristic optimization algorithm makes the system more independent of the types of QSPRs used, and more efficient when applied to combinatorial problems. A software package has been created which provides polymer researchers access to the design framework. A complete example of the methodology is provided for polymethacrylate dental materials. PMID:23904665

  12. RUASN: A Robust User Authentication Framework for Wireless Sensor Networks

    PubMed Central

    Kumar, Pardeep; Choudhury, Amlan Jyoti; Sain, Mangal; Lee, Sang-Gon; Lee, Hoon-Jae

    2011-01-01

    In recent years, wireless sensor networks (WSNs) have been considered as a potential solution for real-time monitoring applications and these WSNs have potential practical impact on next generation technology too. However, WSNs could become a threat if suitable security is not considered before the deployment and if there are any loopholes in their security, which might open the door for an attacker and hence, endanger the application. User authentication is one of the most important security services to protect WSN data access from unauthorized users; it should provide both mutual authentication and session key establishment services. This paper proposes a robust user authentication framework for wireless sensor networks, based on a two-factor (password and smart card) concept. This scheme facilitates many services to the users such as user anonymity, mutual authentication, secure session key establishment and it allows users to choose/update their password regularly, whenever needed. Furthermore, we have provided the formal verification using Rubin logic and compare RUASN with many existing schemes. As a result, we found that the proposed scheme possesses many advantages against popular attacks, and achieves better efficiency at low computation cost. PMID:22163888

  13. Cournot games with network effects for electric power markets

    NASA Astrophysics Data System (ADS)

    Spezia, Carl John

    The electric utility industry is moving from regulated monopolies with protected service areas to an open market with many wholesale suppliers competing for consumer load. This market is typically modeled by a Cournot game oligopoly where suppliers compete by selecting profit maximizing quantities. The classical Cournot model can produce multiple solutions when the problem includes typical power system constraints. This work presents a mathematical programming formulation of oligopoly that produces unique solutions when constraints limit the supplier outputs. The formulation casts the game as a supply maximization problem with power system physical limits and supplier incremental profit functions as constraints. The formulation gives Cournot solutions identical to other commonly used algorithms when suppliers operate within the constraints. Numerical examples demonstrate the feasibility of the theory. The results show that the maximization formulation will give system operators more transmission capacity when compared to the actions of suppliers in a classical constrained Cournot game. The results also show that the profitability of suppliers in constrained networks depends on their location relative to the consumers' load concentration.

  14. Social Networks in the Labour Market--The Sociology of Job Search.

    ERIC Educational Resources Information Center

    Carson, Edgar

    1989-01-01

    Reviews literature on nature of social networks in labor market and their implications for job search strategies of dislocated workers. Suggests issues for further research: (1) how the job search changes as unemployment increases; (2) the role of social networks in the labor market; and (3) claims about security and conditions of jobs found…

  15. A Semiquantitative Framework for Gene Regulatory Networks: Increasing the Time and Quantitative Resolution of Boolean Networks

    PubMed Central

    Kerkhofs, Johan; Geris, Liesbet

    2015-01-01

    Boolean models have been instrumental in predicting general features of gene networks and more recently also as explorative tools in specific biological applications. In this study we introduce a basic quantitative and a limited time resolution to a discrete (Boolean) framework. Quantitative resolution is improved through the employ of normalized variables in unison with an additive approach. Increased time resolution stems from the introduction of two distinct priority classes. Through the implementation of a previously published chondrocyte network and T helper cell network, we show that this addition of quantitative and time resolution broadens the scope of biological behaviour that can be captured by the models. Specifically, the quantitative resolution readily allows models to discern qualitative differences in dosage response to growth factors. The limited time resolution, in turn, can influence the reachability of attractors, delineating the likely long term system behaviour. Importantly, the information required for implementation of these features, such as the nature of an interaction, is typically obtainable from the literature. Nonetheless, a trade-off is always present between additional computational cost of this approach and the likelihood of extending the model’s scope. Indeed, in some cases the inclusion of these features does not yield additional insight. This framework, incorporating increased and readily available time and semi-quantitative resolution, can help in substantiating the litmus test of dynamics for gene networks, firstly by excluding unlikely dynamics and secondly by refining falsifiable predictions on qualitative behaviour. PMID:26067297

  16. Using a research framework to identify knowledge gaps in research on food marketing to children in Australia.

    PubMed

    Chapman, Kathy; Kelly, Bridget; King, Lesley

    2009-06-01

    Research in the field of food marketing to children requires a better understanding of the research gaps in order to inform policy development. The purpose of this paper was to propose a framework for classifying food marketing research, using Australian research on food marketing to children to demonstrate how this framework can be used to determine knowledge gaps. A literature review of research databases and 'grey' material was conducted to identify research from the previous 10 years. Studies were classified according to their research focus, and media type, as either: exposure, including content analyses; effects of exposure, including opinions, attitudes and actions resulting from food marketing exposure; regulations, including the type and level of regulation that applies to food marketing; or breaches of regulations, including instances where marketing regulations have been violated. The majority of Australian research on food marketing to children has focused on television advertising and exposure research. Research has consistently shown that the content of food marketing directed at children is predominately for unhealthy foods. There is a lack of research on the effects of food marketing, which would be valuable to inform policy. The development of a logical framework for food marketing research allows for the identification of research gaps and enables research priorities to be identified.

  17. A complex network for studying the transmission mechanisms in stock market

    NASA Astrophysics Data System (ADS)

    Long, Wen; Guan, Lijing; Shen, Jiangjian; Song, Linqiu; Cui, Lingxiao

    2017-10-01

    This paper introduces a new complex network to describe the volatility transmission mechanisms in stock market. The network can not only endogenize stock market's volatility but also figure out the direction of volatility spillover. In this model, we first use BEKK-GARCH to estimate the volatility spillover effects among Chinese 18 industry sectors. Then, based on the ARCH coefficients and GARCH coefficients, the directional shock networks and variance networks in different stages are constructed separately. We find that the spillover effects and network structures changes in different stages. The results of the topological stability test demonstrate that the connectivity of networks becomes more fragile to selective attacks than stochastic attacks.

  18. Global regulatory framework for production and marketing of crops biofortified with vitamins and minerals.

    PubMed

    Mejia, Luis A; Dary, Omar; Boukerdenna, Hala

    2017-02-01

    Biofortification of crops is being introduced in several countries as a strategy to reduce micronutrient deficiencies. Biofortified products, with increased contents of micronutrients, are currently produced by conventional plant breeding, genetic modification, or nutrient-enhanced fertilization. Corn, rice, wheat, beans, pearl millet, sweet potato, and cassava have been biofortified with increased contents of provitamin A carotenoids, iron, or zinc. However, regulatory considerations are rare or nonexistent. The objective of this paper is to review the regulatory framework for production and marketing of biofortified crops in countries that have adopted this strategy. The information was identified using Internet search engines and websites of health and nutrition organizations and nongovernmental organizations and by consulting scientists and government authorities. Thus far, biofortified products introduced in Latin America, Africa, and Asia have been produced only by conventional breeding. Cultivars using other techniques are still under testing. The production and marketing of these products have been conducted without regulatory framework and under limited government control or regulatory guidance. Nevertheless, some countries have integrated biofortified crops into their nutrition agendas. Although improvements by conventional breeding have not been subject to regulations, when biofortification becomes expanded by including other techniques, an appropriate regulatory framework will be necessary. © 2016 New York Academy of Sciences.

  19. Switching benefits and costs in competitive health insurance markets: A conceptual framework and empirical evidence from the Netherlands.

    PubMed

    Duijmelinck, Daniëlle M I D; Mosca, Ilaria; van de Ven, Wynand P M M

    2015-05-01

    Competitive health insurance markets will only enhance cost-containment, efficiency, quality, and consumer responsiveness if all consumers feel free to easily switch insurer. Consumers will switch insurer if their perceived switching benefits outweigh their perceived switching costs. We developed a conceptual framework with potential switching benefits and costs in competitive health insurance markets. Moreover, we used a questionnaire among Dutch consumers (1091 respondents) to empirically examine the relevance of the different switching benefits and costs in consumers' decision to (not) switch insurer. Price, insurers' service quality, insurers' contracted provider network, the benefits of supplementary insurance, and welcome gifts are potential switching benefits. Transaction costs, learning costs, 'benefit loss' costs, uncertainty costs, the costs of (not) switching provider, and sunk costs are potential switching costs. In 2013 most Dutch consumers switched insurer because of (1) price and (2) benefits of supplementary insurance. Nearly half of the non-switchers - and particularly unhealthy consumers - mentioned one of the switching costs as their main reason for not switching. Because unhealthy consumers feel not free to easily switch insurer, insurers have reduced incentives to invest in high-quality care for them. Therefore, policymakers should develop strategies to increase consumer choice. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  20. An efficient automated parameter tuning framework for spiking neural networks

    PubMed Central

    Carlson, Kristofor D.; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L.

    2014-01-01

    As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier. PMID:24550771

  1. An efficient automated parameter tuning framework for spiking neural networks.

    PubMed

    Carlson, Kristofor D; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L

    2014-01-01

    As the desire for biologically realistic spiking neural networks (SNNs) increases, tuning the enormous number of open parameters in these models becomes a difficult challenge. SNNs have been used to successfully model complex neural circuits that explore various neural phenomena such as neural plasticity, vision systems, auditory systems, neural oscillations, and many other important topics of neural function. Additionally, SNNs are particularly well-adapted to run on neuromorphic hardware that will support biological brain-scale architectures. Although the inclusion of realistic plasticity equations, neural dynamics, and recurrent topologies has increased the descriptive power of SNNs, it has also made the task of tuning these biologically realistic SNNs difficult. To meet this challenge, we present an automated parameter tuning framework capable of tuning SNNs quickly and efficiently using evolutionary algorithms (EA) and inexpensive, readily accessible graphics processing units (GPUs). A sample SNN with 4104 neurons was tuned to give V1 simple cell-like tuning curve responses and produce self-organizing receptive fields (SORFs) when presented with a random sequence of counterphase sinusoidal grating stimuli. A performance analysis comparing the GPU-accelerated implementation to a single-threaded central processing unit (CPU) implementation was carried out and showed a speedup of 65× of the GPU implementation over the CPU implementation, or 0.35 h per generation for GPU vs. 23.5 h per generation for CPU. Additionally, the parameter value solutions found in the tuned SNN were studied and found to be stable and repeatable. The automated parameter tuning framework presented here will be of use to both the computational neuroscience and neuromorphic engineering communities, making the process of constructing and tuning large-scale SNNs much quicker and easier.

  2. Geotube: a network based framework for Goescience dissemination

    NASA Astrophysics Data System (ADS)

    Grieco, Giovanni; Porta, Marina; Merlini, Anna Elisabetta; Caironi, Valeria; Reggiori, Donatella

    2016-04-01

    Geotube is a project promoted by Il Geco cultural association for the dissemination of Geoscience education in schools by open multimedia environments. The approach is based on the following keystones: • A deep and permanent epistemological reflection supported by confrontation within the International Scientific Community • A close link with the territory • A local to global inductive approach to basic concepts in Geosciences • The construction of an open framework to stimulate creativity The project has been developed as an educational activity for secondary schools (11 to 18 years old students). It provides for the creation of a network of institutions to be involved in order to ensure the required diversified expertise. They can comprise: Universities, Natural Parks, Mountain Communities, Municipalities, schools, private companies working in the sector, and so on. A single project lasts for one school year (October to June) and requires 8-12 work hours at school, one or two half day or full day excursions and a final event of presentation of outputs. The possible outputs comprise a pdf or ppt guidebook, a script and a video completely shooted and edited by the students. The framework is open in order to adapt to the single class or workgroup needs, the level and type of school, the time available and different subjects in Geosciences. In the last two years the two parts of the project have been successfully tested separately, while the full project will be presented at schools in in its full form in April 2016, in collaboration with University of Milan, Campo dei Fiori Natural Park, Piambello Mountain Community and Cunardo Municipality. The production of geotube outputs has been tested in a high school for three consecutive years. Students produced scripts and videos on the following subjects: geologic hazards, volcanoes and earthquakes, and climate change. The excursions have been tested with two different high schools. Firstly two areas have been

  3. Network Centric Operations Conceptual Framework Air-to-Ground Case Study

    DTIC Science & Technology

    2004-06-01

    Network Centric Operations Conceptual Framework Air-to-Ground Case Study Final Brief 17 June 2004 Prepared by SAIC for: Evidence Based Research...JUN 2004 2. REPORT TYPE 3. DATES COVERED 00-00-2004 to 00-00-2004 4. TITLE AND SUBTITLE Network Centric Operations Conceptual Framework Air-to... conceptual framework which drove approach Cognitive Social Interviews to provide insights into cognitive process Assumptions OEF and OIF would

  4. Characterizing emerging European stock markets through complex networks: From local properties to self-similar characteristics

    NASA Astrophysics Data System (ADS)

    Caraiani, Petre

    2012-07-01

    We investigate the properties of the returns of the main emerging stock markets from Europe by means of complex networks. We transform the series of daily returns into complex networks, and analyze the local properties of these networks with respect to degree distributions, clustering, or average line length. We further use the clustering coefficients as quantities describing the local structure of the network, and approach them by using multifractal analysis. We find evidence of scale-free networks and multifractality of clustering coefficients.

  5. A Modified Artifitial Neural Network Ensemble Framework for Drought Estimation

    NASA Astrophysics Data System (ADS)

    Alobaidi, M. H.; Marpu, P. R.; Ouarda, T.

    2014-12-01

    Drought estimation at ungauged sites is a difficult task due to various challenges such as scale and limited availability and information about hydrologic neighborhoods. Ensemble regression has been recently utilized in modeling various hydrologic systems and showed advantage over classical regression approaches to such studies. A challenging task in ensemble modeling is the proper training of the ensemble's individual learners and the ensemble combiners. In this work, an ensemble framework is proposed to enhance the generalization ability of the sub-ensemble models and its combiner. Information mixtures between the subsamples are introduced. Such measure is dedicated to the ensemble members and ensemble combiners. Controlled homogeneity magnitudes are then stimulated and induced in the proposed model via a two-stage resampling algorithm. Artificial neural networks (ANNs) were used as ensemble members in addition to different ensemble integration plans. The model provided superior results when compared to previous models applied to the case study in this work. The root mean squared error (RMSE) in the testing phase for the drought quantiles improved by 67% - 76%. The bias error (BIAS) also showed 61% - 95% improvement.

  6. Fluvial networks of the Iberian Peninsula: a chronological framework

    NASA Astrophysics Data System (ADS)

    Santisteban, Juan I.; Schulte, Lothar

    2007-11-01

    Knowledge of the evolution of Spanish fluvial networks has improved during recent years as more river systems have been studied and more geochronological data has become available. However, the chronological framework is a major issue as the range of applications is limited by methodological constraints and spatial coverage is sparse. Integration of 'absolute' dating methods with biostratigraphy and palaeomagnetism permits the recent evolution of these river systems to be reviewed. The timing of incision from the Late Neogene to the present varies between the major Iberian fluvial systems, depending on the substrata and tectonic settings. Early Pleistocene and older fluvial sequences in the core areas of the Iberian Peninsula provide a more extensive record of fluvial evolution and are better preserved than the terrace flights in the coastal lowlands. Middle Pleistocene sequences are well developed in most of the major river systems in Iberia, particularly those of the Tajo, Guadalquivir and Aguas River, and frequently represent the principal climatic cycles of that period, although tectonic and sea-level effects can also be seen. For Late Pleistocene to Holocene times, the scheme becomes more complex. Our review suggests that each river system has responded differently to local and regional climate control, glacial and periglacial processes in headwaters in high mountain areas, glacio-eustatic sea-level changes and local and regional tectonic patterns.

  7. NetSim-Steer: A Runtime Steering Framework for Network Simulators

    SciTech Connect

    Ciraci, Selim; Akyol, Bora A.

    2012-08-07

    This paper presents NetSim-Steer, a runtime steering framework for network simulators. With this framework, users can specify execution constraints for the network protocols and network models. In addition to this, users can implement steering rules to be executed when a constraint is violated. These rules allows users to alter the parameters of the protocols/models during the simulation so that they stay within the boundaries of the constraints.

  8. NetSim Steer: A Runtime Steering Framework for Network Simulators

    SciTech Connect

    Ciraci, Selim; Akyol, Bora A.

    2012-08-09

    This paper presents NetSim-Steer, a runtime steering framework for network simulators. With this framework, users can specify execution constraints for the network protocols and network models. In addition to this, users can implement steering rules to be executed when a constraint is violated. These rules allows users to alter the parameters of the protocols/models during the simulation so that they stay within the boundaries of the constraints.

  9. Using LinkedIn in the Marketing Classroom: Exploratory Insights and Recommendations for Teaching Social Media/Networking

    ERIC Educational Resources Information Center

    McCorkle, Denny E.; McCorkle, Yuhua Li

    2012-01-01

    With the rapid growth of social networking and media comes their consideration for use in the marketing classroom. Social networking skills are becoming essential for personal branding (e.g., networking, self-marketing) and corporate/product branding (e.g., marketing communication). This paper addresses the use of LinkedIn (i.e., an online…

  10. Using LinkedIn in the Marketing Classroom: Exploratory Insights and Recommendations for Teaching Social Media/Networking

    ERIC Educational Resources Information Center

    McCorkle, Denny E.; McCorkle, Yuhua Li

    2012-01-01

    With the rapid growth of social networking and media comes their consideration for use in the marketing classroom. Social networking skills are becoming essential for personal branding (e.g., networking, self-marketing) and corporate/product branding (e.g., marketing communication). This paper addresses the use of LinkedIn (i.e., an online…

  11. An Agent-Based Optimization Framework for Engineered Complex Adaptive Systems with Application to Demand Response in Electricity Markets

    NASA Astrophysics Data System (ADS)

    Haghnevis, Moeed

    The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation.

  12. Customer social network affects marketing strategy: A simulation analysis based on competitive diffusion model

    NASA Astrophysics Data System (ADS)

    Hou, Rui; Wu, Jiawen; Du, Helen S.

    2017-03-01

    To explain the competition phenomenon and results between QQ and MSN (China) in the Chinese instant messaging software market, this paper developed a new population competition model based on customer social network. The simulation results show that the firm whose product with greater network externality effect will gain more market share than its rival when the same marketing strategy is used. The firm with the advantage of time, derived from the initial scale effect will become more competitive than its rival when facing a group of common penguin customers within a social network, verifying the winner-take-all phenomenon in this case.

  13. Making Network Markets in Education: The Development of Data Infrastructure in Australian Schooling

    ERIC Educational Resources Information Center

    Sellar, Sam

    2017-01-01

    This paper examines the development of data infrastructure in Australian schooling with a specific focus on interoperability standards that help to make new markets for education data. The conceptual framework combines insights from studies of infrastructure, economic markets and digital data. The case of the Australian National Schools…

  14. Sustainability for behaviour change in the fight against antibiotic resistance: a social marketing framework.

    PubMed

    Edgar, Timothy; Boyd, Stephanie D; Palamé, Megan J

    2009-02-01

    Antibiotic resistance is one of today's most urgent public health problems, threatening to undermine the effectiveness of infectious disease treatment in every country of the world. Specific individual behaviours such as not taking the entire antibiotic regimen and skipping doses contribute to resistance development as does the taking of antibiotics for colds and other illnesses that antibiotics cannot treat. Antibiotic resistance is as much a societal problem as it is an individual one; if mass behaviour change across the population does not occur, the problem of resistance cannot be mitigated at community levels. The problem is one that potentially can be solved if both providers and patients become sufficiently aware of the issue and if they engage in appropriate behaviours. Although a number of initiatives have been implemented in various parts of the world to elicit behaviour change, results have been mixed, and there is little evidence that trial programmes with positive outcomes serve as models of sustainability. In recent years, several scholars have suggested social marketing as the framework for behaviour change that has the greatest chance of sustained success, but the antibiotic resistance literature provides no specifics for how the principles of social marketing should be applied. This paper provides an overview of previous communication-based initiatives and offers a detailed approach to social marketing to guide future efforts.

  15. Cointegration analysis and influence rank—A network approach to global stock markets

    NASA Astrophysics Data System (ADS)

    Yang, Chunxia; Chen, Yanhua; Niu, Lei; Li, Qian

    2014-04-01

    In this paper, cointegration relationships among 26 global stock market indices over the periods of sub-prime and European debt crisis and their influence rank are investigated by constructing and analyzing directed and weighted cointegration networks. The obtained results are shown as follows: the crises have changed cointegration relationships among stock market indices, their cointegration relationship increased after the Lehman Brothers collapse, while the degree of cointegration gradually decreased from the sub-prime to European debt crisis. The influence of US, Japan and China market indices are entirely distinguished over different periods. Before European debt crisis US stock market is a ‘global factor’ which leads the developed and emerging markets, while the influence of US stock market decreased evidently during the European debt crisis. Before sub-prime crisis, there is no significant evidence to show that other stock markets co-move with China stock market, while it becomes more integrated with other markets during the sub-prime and European debt crisis. Among developed and emerging stock markets, the developed stock markets lead the world stock markets before European debt crisis, while due to the shock of sub-prime and European debt crisis, their influences decreased and emerging stock markets replaced them to lead global stock markets.

  16. Driving factors of interactions between the exchange rate market and the commodity market: A wavelet-based complex network perspective

    NASA Astrophysics Data System (ADS)

    Wen, Shaobo; An, Haizhong; Chen, Zhihua; Liu, Xueyong

    2017-08-01

    In traditional econometrics, a time series must be in a stationary sequence. However, it usually shows time-varying fluctuations, and it remains a challenge to execute a multiscale analysis of the data and discover the topological characteristics of conduction in different scales. Wavelet analysis and complex networks in physical statistics have special advantages in solving these problems. We select the exchange rate variable from the Chinese market and the commodity price index variable from the world market as the time series of our study. We explore the driving factors behind the behavior of the two markets and their topological characteristics in three steps. First, we use the Kalman filter to find the optimal estimation of the relationship between the two markets. Second, wavelet analysis is used to extract the scales of the relationship that are driven by different frequency wavelets. Meanwhile, we search for the actual economic variables corresponding to different frequency wavelets. Finally, a complex network is used to search for the transfer characteristics of the combination of states driven by different frequency wavelets. The results show that statistical physics have a unique advantage over traditional econometrics. The Chinese market has time-varying impacts on the world market: it has greater influence when the world economy is stable and less influence in times of turmoil. The process of forming the state combination is random. Transitions between state combinations have a clustering feature. Based on these characteristics, we can effectively reduce the information burden on investors and correctly respond to the government's policy mix.

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

    PubMed

    Laubichler, Manfred D; Renn, Jürgen

    2015-11-01

    This paper introduces a conceptual framework for the evolution of complex systems based on the integration of regulatory network and niche construction theories. It is designed to apply equally to cases of biological, social and cultural evolution. Within the conceptual framework we focus especially on the transformation of complex networks through the linked processes of externalization and internalization of causal factors between regulatory networks and their corresponding niches and argue that these are an important part of evolutionary explanations. This conceptual framework extends previous evolutionary models and focuses on several challenges, such as the path-dependent nature of evolutionary change, the dynamics of evolutionary innovation and the expansion of inheritance systems.

  18. Network of listed companies based on common shareholders and the prediction of market volatility

    NASA Astrophysics Data System (ADS)

    Li, Jie; Ren, Da; Feng, Xu; Zhang, Yongjie

    2016-11-01

    In this paper, we build a network of listed companies in the Chinese stock market based on common shareholding data from 2003 to 2013. We analyze the evolution of topological characteristics of the network (e.g., average degree, diameter, average path length and clustering coefficient) with respect to the time sequence. Additionally, we consider the economic implications of topological characteristic changes on market volatility and use them to make future predictions. Our study finds that the network diameter significantly predicts volatility. After adding control variables used in traditional financial studies (volume, turnover and previous volatility), network topology still significantly influences volatility and improves the predictive ability of the model.

  19. Marketing Career Speed Networking: A Classroom Event to Foster Career Awareness

    ERIC Educational Resources Information Center

    Buff, Cheryl L.; O'Connor, Suzanne

    2012-01-01

    This paper describes the design, implementation, and evaluation of a marketing career speed networking event held during class time in two sections of the consumer behavior class. The event was coordinated through a partnering effort with marketing faculty and the college's Career Center. A total of 57 students participated in the event, providing…

  20. A social network model of investment behaviour in the stock market

    NASA Astrophysics Data System (ADS)

    Bakker, L.; Hare, W.; Khosravi, H.; Ramadanovic, B.

    2010-03-01

    To consider the psychological factors that impact market valuation, a model is formulated for investment behaviour of traders whose decisions are influenced by their trusted peers’ behaviour. The model is implemented and several different “trust networks” are tested. Simulation results demonstrate that real life trust networks can significantly delay the stabilisation of a market.

  1. Marketing of Academic Library Services through Social Networking Sites: Implications of Electronic Word-of-Mouth

    ERIC Educational Resources Information Center

    Siddike, Md. Abul Kalam; Kiran, K.

    2015-01-01

    The main objective of this study is to investigate the perceptions of academic librarians towards the marketing of library services through social networking sites (SNSs) and their understanding of using electronic word-of-mouth (eWOM) as a marketing tool in academic libraries. This study follows a qualitative data-gathering approach of structured…

  2. Marketing Career Speed Networking: A Classroom Event to Foster Career Awareness

    ERIC Educational Resources Information Center

    Buff, Cheryl L.; O'Connor, Suzanne

    2012-01-01

    This paper describes the design, implementation, and evaluation of a marketing career speed networking event held during class time in two sections of the consumer behavior class. The event was coordinated through a partnering effort with marketing faculty and the college's Career Center. A total of 57 students participated in the event, providing…

  3. Marketing of Academic Library Services through Social Networking Sites: Implications of Electronic Word-of-Mouth

    ERIC Educational Resources Information Center

    Siddike, Md. Abul Kalam; Kiran, K.

    2015-01-01

    The main objective of this study is to investigate the perceptions of academic librarians towards the marketing of library services through social networking sites (SNSs) and their understanding of using electronic word-of-mouth (eWOM) as a marketing tool in academic libraries. This study follows a qualitative data-gathering approach of structured…

  4. Identifying emergent social networks at a federally qualified health center-based farmers' market.

    PubMed

    Alia, Kassandra A; Freedman, Darcy A; Brandt, Heather M; Browne, Teri

    2014-06-01

    Identifying potential mechanisms connecting farmers' market interventions with health, economic, and community outcomes could inform strategies for addressing health disparities. The present study used social network theory to guide the in-depth examination of naturally occurring social interactions at a farmers' market located at a federally qualified health center located in a rural, low-income community. Trained observers recorded 61 observation logs at the market over 18 weeks. Thematic analysis revealed a range of actors and nonhuman facilitators instrumental to the farmers' market context. These actors connected with one another for communication and relationship development, economic and financial exchange, education, resource sharing, community ownership of the farmers' market, and conflict resolution. These interactions provided opportunities for social networks to develop among attendees, which may have facilitated the acquisition of social supports related to improved health, economic and community outcomes. Results provide insight into the role social networks may play in mediating the relationship between a farmers' market intervention and individual benefits. Findings also contribute to defining the typology of social networks, which may further disentangle the complex relationships between social networks and health outcomes. Future research should identify strategies for purposefully targeting social networks as a way to reduce diet-related health disparities.

  5. Integrated network management framework for ATM-over-ADSL service

    NASA Astrophysics Data System (ADS)

    Hong, Won-Kyu; Yoon, Sung-Sook; Hong, Seong-Ik; Kim, Dong-Il; Jung, Mun-Jo; Song, Joong-Goo

    2001-11-01

    In this paper, we describe an integrated network management system for ATM over ADSL service provisioning. There are two distinct networks of ATM and Internet. Most of routers in Internet connected with WDM. The Network Access Server (NAS) in the Internet provides the Internet access service for the ATM over ADSL subscriber. The ATM network takes the roles of backbone network for the pure ATM PVC and SVC services and the access network for the ATM over ADSL service. In order to define the generic network model that can be commonly applicable for the backbone network for pure ATM service and the access network for ATM over ADSL service taking into account the scalability, we suggest two fragments of the topological fragment and connectivity fragment to maximize the scalability in accordance with the ITU-T G.805 layering and partitioning concepts and the RM-ODP information viewpoint. In addition, we propose the distributed computational model of the ATM over ADSL network management system using the RM-ODP computational viewpoint and TMN functional decomposition of FCAPS taking into account the functional distribution and the modularity. Lastly, we describe the scenario for providing the integrated ADSL service.

  6. Forecasting of Market Clearing Price by Using GA Based Neural Network

    NASA Astrophysics Data System (ADS)

    Yang, Bo; Chen, Yun-Ping; Zhao, Zun-Lian; Han, Qi-Ye

    Forecasting of Market Clearing Price (MCP) is important to economic benefits of electricity market participants. To accurately forecast MCP, a novel two-stage GA-based neural network model (GA-NN) is proposed. In the first stage, GA chromosome is designed into two parts: boolean coding part for neural network topology and real coding part for connection weights. By hybrid genetic operation of selection, crossover and mutation under the criterion of error minimization between the actual output and the desired output, optimal architecture of neural network is obtained. In the second stage, gradient learning algorithm with momentum rate is imposed on neural network with optimal architecture. After learning process, optimal connection weights are obtained. The proposed model is tested on MCP forecasting in California electricity market. The test results show that GA-NN has self-adaptive ability in its topology and connection weights and can obtain more accurate MCP forecasting values than BP neural network.

  7. New Frameworks for Detecting and Minimizing Information Leakage in Anonymized Network Data

    DTIC Science & Technology

    2011-10-01

    FORCE RESEARCH LABORATORY INFORMATION DIRECTORATE NEW FRAMEWORKS FOR DETECTING AND MINIMIZING INFORMATION LEAKAGE IN ANONYMIZED NETWORK...FRAMEWORKS FOR DETECTING AND MINIMIZING INFORMATION LEAKAGE IN ANONYMIZED NETWORK DATA 5a. CONTRACT NUMBER FA8750-08-2-0147 5b. GRANT NUMBER N/A...risk, high-value data is that of trace anonymization - a process of sanitizing data before release so that information of concern cannot be extracted

  8. A modelling and reasoning framework for social networks policies

    NASA Astrophysics Data System (ADS)

    Governatori, Guido; Iannella, Renato

    2011-02-01

    Policy languages (such as privacy and rights) have had little impact on the wider community. Now that social networks have taken off, the need to revisit policy languages and realign them towards social networks requirements has become more apparent. One such language is explored as to its applicability to the social networks masses. We also argue that policy languages alone are not sufficient and thus they should be paired with reasoning mechanisms to provide precise and unambiguous execution models of the policies. To this end, we propose a computationally oriented model to represent, reason with and execute policies for social networks.

  9. Internationalization, Globalization and Relationship Networks as an Epistemological Framework Based on Comparative Studies in Education

    ERIC Educational Resources Information Center

    García, Amelia Molina; Lara, José Luis Horacio Andrade

    2016-01-01

    In this paper we present some thoughts on the epistemological framework of comparative studies in education. We present some concepts on the internationalization, globalization and inter-relation networks, based on Jürgen Schriewer, Immanuel Wallerstein, and Norbert Elias's theoretical concepts. These reflections were built within the framework of…

  10. Visibility graph network analysis of natural gas price: The case of North American market

    NASA Astrophysics Data System (ADS)

    Sun, Mei; Wang, Yaqi; Gao, Cuixia

    2016-11-01

    Fluctuations in prices of natural gas significantly affect global economy. Therefore, the research on the characteristics of natural gas price fluctuations, turning points and its influencing cycle on the subsequent price series is of great significance. Global natural gas trade concentrates on three regional markets: the North American market, the European market and the Asia-Pacific market, with North America having the most developed natural gas financial market. In addition, perfect legal supervision and coordinated regulations make the North American market more open and more competitive. This paper focuses on the North American natural gas market specifically. The Henry Hub natural gas spot price time series is converted to a visibility graph network which provides a new direction for macro analysis of time series, and several indicators are investigated: degree and degree distribution, the average shortest path length and community structure. The internal mechanisms underlying price fluctuations are explored through the indicators. The results show that the natural gas prices visibility graph network (NGP-VGN) is of small-world and scale-free properties simultaneously. After random rearrangement of original price time series, the degree distribution of network becomes exponential distribution, different from the original ones. This means that, the original price time series is of long-range negative correlation fractal characteristic. In addition, nodes with large degree correspond to significant geopolitical or economic events. Communities correspond to time cycles in visibility graph network. The cycles of time series and the impact scope of hubs can be found by community structure partition.

  11. Applying temporal network analysis to the venture capital market

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Feng, Ling; Zhu, Rongqian; Stanley, H. Eugene

    2015-10-01

    Using complex network theory to study the investment relationships of venture capital firms has produced a number of significant results. However, previous studies have often neglected the temporal properties of those relationships, which in real-world scenarios play a pivotal role. Here we examine the time-evolving dynamics of venture capital investment in China by constructing temporal networks to represent (i) investment relationships between venture capital firms and portfolio companies and (ii) the syndication ties between venture capital investors. The evolution of the networks exhibits rich variations in centrality, connectivity and local topology. We demonstrate that a temporal network approach provides a dynamic and comprehensive analysis of real-world networks.

  12. Implementation of a Framework for Collaborative Social Networks in E-Learning

    ERIC Educational Resources Information Center

    Maglajlic, Seid

    2016-01-01

    This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…

  13. Implementation of a Framework for Collaborative Social Networks in E-Learning

    ERIC Educational Resources Information Center

    Maglajlic, Seid

    2016-01-01

    This paper describes the implementation of a framework for the construction and utilization of social networks in ELearning. These social networks aim to enhance collaboration between all E-Learning participants (i.e. both traineeto-trainee and trainee-to-tutor communication are targeted). E-Learning systems that include a so-called "social…

  14. A multiobjective optimization framework for multicontaminant industrial water network design.

    PubMed

    Boix, Marianne; Montastruc, Ludovic; Pibouleau, Luc; Azzaro-Pantel, Catherine; Domenech, Serge

    2011-07-01

    The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view.

  15. A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network

    PubMed Central

    Jadamba, Erkhembayar

    2016-01-01

    Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment. PMID:28127549

  16. A Systematic Framework for Drug Repositioning from Integrated Omics and Drug Phenotype Profiles Using Pathway-Drug Network.

    PubMed

    Jadamba, Erkhembayar; Shin, Miyoung

    2016-01-01

    Drug repositioning offers new clinical indications for old drugs. Recently, many computational approaches have been developed to repurpose marketed drugs in human diseases by mining various of biological data including disease expression profiles, pathways, drug phenotype expression profiles, and chemical structure data. However, despite encouraging results, a comprehensive and efficient computational drug repositioning approach is needed that includes the high-level integration of available resources. In this study, we propose a systematic framework employing experimental genomic knowledge and pharmaceutical knowledge to reposition drugs for a specific disease. Specifically, we first obtain experimental genomic knowledge from disease gene expression profiles and pharmaceutical knowledge from drug phenotype expression profiles and construct a pathway-drug network representing a priori known associations between drugs and pathways. To discover promising candidates for drug repositioning, we initialize node labels for the pathway-drug network using identified disease pathways and known drugs associated with the phenotype of interest and perform network propagation in a semisupervised manner. To evaluate our method, we conducted some experiments to reposition 1309 drugs based on four different breast cancer datasets and verified the results of promising candidate drugs for breast cancer by a two-step validation procedure. Consequently, our experimental results showed that the proposed framework is quite useful approach to discover promising candidates for breast cancer treatment.

  17. A framework for visualization of battlefield network behavior

    NASA Astrophysics Data System (ADS)

    Perzov, Yury; Yurcik, William

    2006-05-01

    An extensible network simulation application was developed to study wireless battlefield communications. The application monitors node mobility and depicts broadcast and unicast traffic as expanding rings and directed links. The network simulation was specially designed to support fault injection to show the impact of air strikes on disabling nodes. The application takes standard ns-2 trace files as an input and provides for performance data output in different graphical forms (histograms and x/y plots). Network visualization via animation of simulation output can be saved in AVI format that may serve as a basis for a real-time battlefield awareness system.

  18. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Tradeoff on Phenotype Robustness in Biological Networks Part II: Ecological Networks.

    PubMed

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    In ecological networks, network robustness should be large enough to confer intrinsic robustness for tolerating intrinsic parameter fluctuations, as well as environmental robustness for resisting environmental disturbances, so that the phenotype stability of ecological networks can be maintained, thus guaranteeing phenotype robustness. However, it is difficult to analyze the network robustness of ecological systems because they are complex nonlinear partial differential stochastic systems. This paper develops a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance sensitivity in ecological networks. We found that the phenotype robustness criterion for ecological networks is that if intrinsic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations and environmental disturbances. These results in robust ecological networks are similar to that in robust gene regulatory networks and evolutionary networks even they have different spatial-time scales.

  19. The Evolution of ICT Markets: An Agent-Based Model on Complex Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Liangjie; Wu, Bangtao; Chen, Zhong; Li, Li

    Information and communication technology (ICT) products exhibit positive network effects.The dynamic process of ICT markets evolution has two intrinsic characteristics: (1) customers are influenced by each others’ purchasing decision; (2) customers are intelligent agents with bounded rationality.Guided by complex systems theory, we construct an agent-based model and simulate on complex networks to examine how the evolution can arise from the interaction of customers, which occur when they make expectations about the future installed base of a product by the fraction of neighbors who are using the same product in his personal network.We demonstrate that network effects play an important role in the evolution of markets share, which make even an inferior product can dominate the whole market.We also find that the intensity of customers’ communication can influence whether the best initial strategy for firms is to improve product quality or expand their installed base.

  20. Mississippi Curriculum Framework for Fashion Marketing Technology (Program CIP: 08.0101--Apparel and Accessories Mkt. Op., Gen.). Postsecondary Programs.

    ERIC Educational Resources Information Center

    Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

    This document, which is intended for use by community and junior colleges throughout Mississippi, contains curriculum frameworks for the course sequences in the fashion marketing technology programs cluster. Presented in the introductory section are a description of the program and suggested course sequence. Section I lists baseline competencies,…

  1. Mississippi Curriculum Framework for Marketing Management Technology (Program CIP: 52.1401--Business Mkt. & Mkt. Mgmt.). Postsecondary Programs.

    ERIC Educational Resources Information Center

    Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

    This document, which is intended for use by community and junior colleges throughout Mississippi, contains curriculum frameworks for the course sequences in the state's marketing management technology program. Presented in the introduction are a program description and suggested course sequence. Section I lists baseline competencies for the…

  2. Developing a Framework for Effective Network Capacity Planning

    NASA Technical Reports Server (NTRS)

    Yaprak, Ece

    2005-01-01

    As Internet traffic continues to grow exponentially, developing a clearer understanding of, and appropriately measuring, network's performance is becoming ever more critical. An important challenge faced by the Information Resources Directorate (IRD) at the Johnson Space Center in this context remains not only monitoring and maintaining a secure network, but also better understanding the capacity and future growth potential boundaries of its network. This requires capacity planning which involves modeling and simulating different network alternatives, and incorporating changes in design as technologies, components, configurations, and applications change, to determine optimal solutions in light of IRD's goals, objectives and strategies. My primary task this summer was to address this need. I evaluated network-modeling tools from OPNET Technologies Inc. and Compuware Corporation. I generated a baseline model for Building 45 using both tools by importing "real" topology/traffic information using IRD's various network management tools. I compared each tool against the other in terms of the advantages and disadvantages of both tools to accomplish IRD's goals. I also prepared step-by-step "how to design a baseline model" tutorial for both OPNET and Compuware products.

  3. A new framework to integrate wireless sensor networks with cloud computing

    NASA Astrophysics Data System (ADS)

    Shah, Sajjad Hussain; Khan, Fazle Kabeer; Ali, Wajid; Khan, Jamshed

    Wireless sensors networks have several applications of their own. These applications can further enhanced by integrating a local wireless sensor network to internet, which can be used in real time applications where the results of sensors are stored on the cloud. We propose an architecture that integrates a wireless sensor network to the internet using cloud technology. The resultant system is proved to be reliable, available and extensible. In this paper a new framework is proposed for WSN integration with Cloud computing model, existing WSN will be connected to the proposed framework. Three deployment layer are used to serve user request (IaaS, PaaS, SaaS) either from the library which is made from data collected from data centric DC by WSN periodically. The integration controller unit of the proposed framework integrates the sensor network and cloud computing technology which offers reliability, availability and extensibility.

  4. The use of social network methodological framework in nursing care to breastfeeding women.

    PubMed

    Souza, Maria Helena do Nascimento; Souza, Ivis Emília de Oliveira; Tocantins, Florence Romijn

    2009-01-01

    This study aimed to discuss the contribution of the social network methodological framework in nursing care delivered to women who breastfeed their children up to six months of age. This qualitative study aimed to elaborate the social network map of 20 women through tape-recorded interview. Social network analysis evidenced a 'strong' bond between these women and members from their primary network, especially friends, neighbors, mothers or with the child's father, who were reported as the people most involved in the breastfeeding period. The contribution of this framework to nursing practice is discussed, especially in care and research processes. We believe that nurses' appropriation of this framework can be an important support for efficacious actions, as well as to favor a broader perspective on the social context people experience.

  5. Unraveling chaotic attractors by complex networks and measurements of stock market complexity

    NASA Astrophysics Data System (ADS)

    Cao, Hongduo; Li, Ying

    2014-03-01

    We present a novel method for measuring the complexity of a time series by unraveling a chaotic attractor modeled on complex networks. The complexity index R, which can potentially be exploited for prediction, has a similar meaning to the Kolmogorov complexity (calculated from the Lempel-Ziv complexity), and is an appropriate measure of a series' complexity. The proposed method is used to research the complexity of the world's major capital markets. None of these markets are completely random, and they have different degrees of complexity, both over the entire length of their time series and at a level of detail. However, developing markets differ significantly from mature markets. Specifically, the complexity of mature stock markets is stronger and more stable over time, whereas developing markets exhibit relatively low and unstable complexity over certain time periods, implying a stronger long-term price memory process.

  6. Unraveling chaotic attractors by complex networks and measurements of stock market complexity

    SciTech Connect

    Cao, Hongduo; Li, Ying

    2014-03-15

    We present a novel method for measuring the complexity of a time series by unraveling a chaotic attractor modeled on complex networks. The complexity index R, which can potentially be exploited for prediction, has a similar meaning to the Kolmogorov complexity (calculated from the Lempel–Ziv complexity), and is an appropriate measure of a series' complexity. The proposed method is used to research the complexity of the world's major capital markets. None of these markets are completely random, and they have different degrees of complexity, both over the entire length of their time series and at a level of detail. However, developing markets differ significantly from mature markets. Specifically, the complexity of mature stock markets is stronger and more stable over time, whereas developing markets exhibit relatively low and unstable complexity over certain time periods, implying a stronger long-term price memory process.

  7. Unraveling chaotic attractors by complex networks and measurements of stock market complexity.

    PubMed

    Cao, Hongduo; Li, Ying

    2014-03-01

    We present a novel method for measuring the complexity of a time series by unraveling a chaotic attractor modeled on complex networks. The complexity index R, which can potentially be exploited for prediction, has a similar meaning to the Kolmogorov complexity (calculated from the Lempel-Ziv complexity), and is an appropriate measure of a series' complexity. The proposed method is used to research the complexity of the world's major capital markets. None of these markets are completely random, and they have different degrees of complexity, both over the entire length of their time series and at a level of detail. However, developing markets differ significantly from mature markets. Specifically, the complexity of mature stock markets is stronger and more stable over time, whereas developing markets exhibit relatively low and unstable complexity over certain time periods, implying a stronger long-term price memory process.

  8. Fiber Access Networks: Reliability Analysis and Swedish Broadband Market

    NASA Astrophysics Data System (ADS)

    Wosinska, Lena; Chen, Jiajia; Larsen, Claus Popp

    Fiber access network architectures such as active optical networks (AONs) and passive optical networks (PONs) have been developed to support the growing bandwidth demand. Whereas particularly Swedish operators prefer AON, this may not be the case for operators in other countries. The choice depends on a combination of technical requirements, practical constraints, business models, and cost. Due to the increasing importance of reliable access to the network services, connection availability is becoming one of the most crucial issues for access networks, which should be reflected in the network owner's architecture decision. In many cases protection against failures is realized by adding backup resources. However, there is a trade off between the cost of protection and the level of service reliability since improving reliability performance by duplication of network resources (and capital expenditures CAPEX) may be too expensive. In this paper we present the evolution of fiber access networks and compare reliability performance in relation to investment and management cost for some representative cases. We consider both standard and novel architectures for deployment in both sparsely and densely populated areas. While some recent works focused on PON protection schemes with reduced CAPEX the current and future effort should be put on minimizing the operational expenditures (OPEX) during the access network lifetime.

  9. Automated iterative reclustering framework for determining hierarchical functional networks in resting state fMRI.

    PubMed

    Shams, Seyed-Mohammad; Afshin-Pour, Babak; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali; Strother, Stephen C

    2015-09-01

    To spatially cluster resting state-functional magnetic resonance imaging (rs-fMRI) data into potential networks, there are only a few general approaches that determine the number of networks/clusters, despite a wide variety of techniques proposed for clustering. For individual subjects, extraction of a large number of spatially disjoint clusters results in multiple small networks that are spatio-temporally homogeneous but irreproducible across subjects. Alternatively, extraction of a small number of clusters creates spatially large networks that are temporally heterogeneous but spatially reproducible across subjects. We propose a fully automatic, iterative reclustering framework in which a small number of spatially large, heterogeneous networks are initially extracted to maximize spatial reproducibility. Subsequently, the large networks are iteratively subdivided to create spatially reproducible subnetworks until the overall within-network homogeneity does not increase substantially. The proposed approach discovers a rich network hierarchy in the brain while simultaneously optimizing spatial reproducibility of networks across subjects and individual network homogeneity. We also propose a novel metric to measure the connectivity of brain regions, and in a simulation study show that our connectivity metric and framework perform well in the face of low signal to noise and initial segmentation errors. Experimental results generated using real fMRI data show that the proposed metric improves stability of network clusters across subjects, and generates a meaningful pattern for spatially hierarchical structure of the brain. © 2015 Wiley Periodicals, Inc.

  10. Security Frameworks for Machine-to-Machine Devices and Networks

    NASA Astrophysics Data System (ADS)

    Demblewski, Michael

    Attacks against mobile systems have escalated over the past decade. There have been increases of fraud, platform attacks, and malware. The Internet of Things (IoT) offers a new attack vector for Cybercriminals. M2M contributes to the growing number of devices that use wireless systems for Internet connection. As new applications and platforms are created, old vulnerabilities are transferred to next-generation systems. There is a research gap that exists between the current approaches for security framework development and the understanding of how these new technologies are different and how they are similar. This gap exists because system designers, security architects, and users are not fully aware of security risks and how next-generation devices can jeopardize safety and personal privacy. Current techniques, for developing security requirements, do not adequately consider the use of new technologies, and this weakens countermeasure implementations. These techniques rely on security frameworks for requirements development. These frameworks lack a method for identifying next generation security concerns and processes for comparing, contrasting and evaluating non-human device security protections. This research presents a solution for this problem by offering a novel security framework that is focused on the study of the "functions and capabilities" of M2M devices and improves the systems development life cycle for the overall IoT ecosystem.

  11. Measuring performance in off-patent drug markets: a methodological framework and empirical evidence from twelve EU Member States.

    PubMed

    Kanavos, Panos

    2014-11-01

    This paper develops a methodological framework to help evaluate the performance of generic pharmaceutical policies post-patent expiry or after loss of exclusivity in non-tendering settings, comprising five indicators (generic availability, time delay to and speed of generic entry, number of generic competitors, price developments, and generic volume share evolution) and proposes a series of metrics to evaluate performance. The paper subsequently tests this framework across twelve EU Member States (MS) by using IMS data on 101 patent expired molecules over the 1998-2010 period. Results indicate that significant variation exists in generic market entry, price competition and generic penetration across the study countries. Size of a geographical market is not a predictor of generic market entry intensity or price decline. Regardless of geographic or product market size, many off patent molecules lack generic competitors two years after loss of exclusivity. The ranges in each of the five proposed indicators suggest, first, that there are numerous factors--including institutional ones--contributing to the success of generic entry, price decline and market penetration and, second, MS should seek a combination of supply and demand-side policies in order to maximise cost-savings from generics. Overall, there seems to be considerable potential for faster generic entry, uptake and greater generic competition, particularly for molecules at the lower end of the market.

  12. Framework to study dynamic dependencies in networks of interacting processes

    NASA Astrophysics Data System (ADS)

    Chicharro, Daniel; Ledberg, Anders

    2012-10-01

    The analysis of dynamic dependencies in complex systems such as the brain helps to understand how emerging properties arise from interactions. Here we propose an information-theoretic framework to analyze the dynamic dependencies in multivariate time-evolving systems. This framework constitutes a fully multivariate extension and unification of previous approaches based on bivariate or conditional mutual information and Granger causality or transfer entropy. We define multi-information measures that allow us to study the global statistical structure of the system as a whole, the total dependence between subsystems, and the temporal statistical structure of each subsystem. We develop a stationary and a nonstationary formulation of the framework. We then examine different decompositions of these multi-information measures. The transfer entropy naturally appears as a term in some of these decompositions. This allows us to examine its properties not as an isolated measure of interdependence but in the context of the complete framework. More generally we use causal graphs to study the specificity and sensitivity of all the measures appearing in these decompositions to different sources of statistical dependence arising from the causal connections between the subsystems. We illustrate that there is no straightforward relation between the strength of specific connections and specific terms in the decompositions. Furthermore, causal and noncausal statistical dependencies are not separable. In particular, the transfer entropy can be nonmonotonic in dependence on the connectivity strength between subsystems and is also sensitive to internal changes of the subsystems, so it should not be interpreted as a measure of connectivity strength. Altogether, in comparison to an analysis based on single isolated measures of interdependence, this framework is more powerful to analyze emergent properties in multivariate systems and to characterize functionally relevant changes in the

  13. Framework to study dynamic dependencies in networks of interacting processes.

    PubMed

    Chicharro, Daniel; Ledberg, Anders

    2012-10-01

    The analysis of dynamic dependencies in complex systems such as the brain helps to understand how emerging properties arise from interactions. Here we propose an information-theoretic framework to analyze the dynamic dependencies in multivariate time-evolving systems. This framework constitutes a fully multivariate extension and unification of previous approaches based on bivariate or conditional mutual information and Granger causality or transfer entropy. We define multi-information measures that allow us to study the global statistical structure of the system as a whole, the total dependence between subsystems, and the temporal statistical structure of each subsystem. We develop a stationary and a nonstationary formulation of the framework. We then examine different decompositions of these multi-information measures. The transfer entropy naturally appears as a term in some of these decompositions. This allows us to examine its properties not as an isolated measure of interdependence but in the context of the complete framework. More generally we use causal graphs to study the specificity and sensitivity of all the measures appearing in these decompositions to different sources of statistical dependence arising from the causal connections between the subsystems. We illustrate that there is no straightforward relation between the strength of specific connections and specific terms in the decompositions. Furthermore, causal and noncausal statistical dependencies are not separable. In particular, the transfer entropy can be nonmonotonic in dependence on the connectivity strength between subsystems and is also sensitive to internal changes of the subsystems, so it should not be interpreted as a measure of connectivity strength. Altogether, in comparison to an analysis based on single isolated measures of interdependence, this framework is more powerful to analyze emergent properties in multivariate systems and to characterize functionally relevant changes in the

  14. Complex network analysis of conventional and Islamic stock market in Indonesia

    NASA Astrophysics Data System (ADS)

    Rahmadhani, Andri; Purqon, Acep; Kim, Sehyun; Kim, Soo Yong

    2015-09-01

    The rising popularity of Islamic financial products in Indonesia has become a new interesting topic to be analyzed recently. We introduce a complex network analysis to compare conventional and Islamic stock market in Indonesia. Additionally, Random Matrix Theory (RMT) has been added as a part of reference to expand the analysis of the result. Both of them are based on the cross correlation matrix of logarithmic price returns. Closing price data, which is taken from June 2011 to July 2012, is used to construct logarithmic price returns. We also introduce the threshold value using winner-take-all approach to obtain scale-free property of the network. This means that the nodes of the network that has a cross correlation coefficient below the threshold value should not be connected with an edge. As a result, we obtain 0.5 as the threshold value for all of the stock market. From the RMT analysis, we found that there is only market wide effect on both stock market and no clustering effect has been found yet. From the network analysis, both of stock market networks are dominated by the mining sector. The length of time series of closing price data must be expanded to get more valuable results, even different behaviors of the system.

  15. 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.

  16. Network Community Detection based on the Physarum-inspired Computational Framework.

    PubMed

    Gao, Chao; Liang, Mingxin; Li, Xianghua; Zhang, Zili; Wang, Zhen; Zhou, Zhili

    2016-12-13

    Community detection is a crucial and essential problem in the structure analytics of complex networks, which can help us understand and predict the characteristics and functions of complex networks. Many methods, ranging from the optimization-based algorithms to the heuristic-based algorithms, have been proposed for solving such a problem. Due to the inherent complexity of identifying network structure, how to design an effective algorithm with a higher accuracy and a lower computational cost still remains an open problem. Inspired by the computational capability and positive feedback mechanism in the wake of foraging process of Physarum, which is a large amoeba-like cell consisting of a dendritic network of tube-like pseudopodia, a general Physarum-based computational framework for community detection is proposed in this paper. Based on the proposed framework, the inter-community edges can be identified from the intra-community edges in a network and the positive feedback of solving process in an algorithm can be further enhanced, which are used to improve the efficiency of original optimization-based and heuristic-based community detection algorithms, respectively. Some typical algorithms (e.g., genetic algorithm, ant colony optimization algorithm, and Markov clustering algorithm) and real-world datasets have been used to estimate the efficiency of our proposed computational framework. Experiments show that the algorithms optimized by Physarum-inspired computational framework perform better than the original ones, in terms of accuracy and computational cost. Moreover, a computational complexity analysis verifies the scalability of our framework.

  17. Towards a matrix mechanics framework for dynamic protein network

    PubMed Central

    2010-01-01

    Protein–protein interaction networks are currently visualized by software generated interaction webs based upon static experimental data. Current state is limited to static, mostly non-compartmental network and non time resolved protein interactions. A satisfactory mathematical foundation for particle interactions within a viscous liquid state (situation within the cytoplasm) does not exist nor do current computer programs enable building dynamic interaction networks for time resolved interactions. Building mathematical foundation for intracellular protein interactions can be achieved in two increments (a) trigger and capture the dynamic molecular changes for a select subset of proteins using several model systems and high throughput time resolved proteomics and, (b) use this information to build the mathematical foundation and computational algorithm for a compartmentalized and dynamic protein interaction network. Such a foundation is expected to provide benefit in at least two spheres: (a) understanding physiology enabling explanation of phenomenon such as incomplete penetrance in genetic disorders and (b) enabling several fold increase in biopharmaceutical production using impure starting materials. PMID:20805933

  18. Social Networking Framework for Universities in Saudi Arabia

    ERIC Educational Resources Information Center

    Alqahtani, Sulaiman

    2016-01-01

    The interactive capacities of social networking instruments have unleashed a number of possibilities for enhancing teaching and learning in the higher education sector and many universities are engaged in harnessing the capabilities of these tools. While much valuable research has been conducted on this theme, scholarship has tended to be oriented…

  19. Modeling dynamics of adaptive complex systems: From gene regulatory networks to financial markets

    NASA Astrophysics Data System (ADS)

    Liu, Min

    This dissertation aims to model the dynamics of two types of adaptive complex systems: gene regulatory networks and financial markets. In modeling gene regulatory networks, a dynamics-driven rewiring mechanism is introduced to Boolean networks and it is found that a critical state emerges spontaneously resulting from the interplay between topology and dynamics during evolution. For biologically realized network sizes, significant finite-size effects are observed. In networks of competing Boolean nodes, we find that in small networks, the evolutionary dynamics selects for input inverting functions rather than canalizing functions in infinitely large networks. It is found that finite sizes can cause symmetry breaking in the evolutionary dynamics. Using the Polya theorem, we show the number of the function classes increases to 46, in contrast to 14 in infinitely large networks, due to the reduced symmetry which matches our simulation results well. In addition, we find that an optimum amount of stochastic noise in the signals exchanged between nodes can result in maximum excess canalization. In modeling financial markets, we simulate a double-auction virtual market by utilizing reaction-diffusion processes to describe the dynamics of limit orders. We find that the log-returns produced have a dynamical scaling exponent of 1/4 and nonstationary, negatively autocorrelated increments. By investigating the microstructure of the virtual market, we find that the mean interarrival time between transactions satisfies an increasing power-law function of time. We propose an inhomogeneous compound Poisson process with a decreasing power-law intensity rate function and demonstrate that this purely jump process captures the essential macroscopic dynamics of the virtual market.

  20. Network structure of cross-correlations among the world market indices

    NASA Astrophysics Data System (ADS)

    Eryiğit, Mehmet; Eryiğit, Resul

    2009-09-01

    We report the results of an investigation of the properties of the networks formed by the cross-correlations of the daily and weekly index changes of 143 stock market indices from 59 different countries. Analysis of the asset graphs, minimum spanning trees (MST) and planar maximally filtered graphs (PMFG) of the afermentioned networks confirms that globalization has been increasing in recent years. North American and European markets are observed to be much more strongly connected among themselves compared to the integration with the other geographical regions. Surprisingly, the integration of East Asian markets among themselves as well as to the Western markets is found to be rather weak. MST and PMFG of both daily and weekly return correlations indicates that the clustering of the indices is mostly geographical. The French fsbf250 index is found to be most important node of the MST and PMFG based on several graph centrality measures.

  1. Analysis of a Real Online Social Network Using Semantic Web Frameworks

    NASA Astrophysics Data System (ADS)

    Erétéo, Guillaume; Buffa, Michel; Gandon, Fabien; Corby, Olivier

    Social Network Analysis (SNA) provides graph algorithms to characterize the structure of social networks, strategic positions in these networks, specific sub-networks and decompositions of people and activities. Online social platforms like Facebook form huge social networks, enabling people to connect, interact and share their online activities across several social applications. We extended SNA operators using semantic web frameworks to include the semantics of these graph-based representations when analyzing such social networks and to deal with the diversity of their relations and interactions. We present here the results of this approach when it was used to analyze a real social network with 60,000 users connecting, interacting and sharing content.

  2. Market Related System Analysis of Satellite Communication Networks

    NASA Astrophysics Data System (ADS)

    Malyshev, V. V.; Panasenkova, M. V.

    2002-01-01

    The report is devoted to the technique of effectiveness analysis of communication space system with satellites in geostationary orbit using market models. The technique is worked out in order to choose the most optimal alternative of communication space system design. The alternative considered optimal and the system effective when the maximum profit from the system with limited system costs is achieved. The key point of the technique is a wide use of market models and application of market related parameters as an integral part of the design technique in order to secure the high commercial output of the communication space system. A range of models for decisive characteristics of communication space system is synthesized in terms of the technique. Flexible market model with detailed insight into the structure of the given market sector and its trends is created. The technique enables to choose the image and key parameters of the future system such as payload and ground sector characteristics so as to make the system most cost-effective and profitable. It is shown that such factors as the choice of launch vehicle can influence the system effectiveness rather dramatically. In particular, it is shown that under certain conditions delivering the small (five hundred kg) satellite to the geostationary orbit with the help of light-weight launch vehicle and the satellite's own electro-rocket thrusters is forty per cent more cost- effective than when the satellite is delivered with the help of the medium-size launch vehicle. The latter case can lead to the significant losses due to high launch costs that are nearly two times higher for the medium size launch vehicle than for the light launce vehicle. The technique is applicable both for designing a wide range of communication space systems and is recommended for those dealing with designing commercial systems. It can also be used to update and improve the systems that are already in operation.

  3. Multi-agent modelling framework for water, energy and other resource networks

    NASA Astrophysics Data System (ADS)

    Knox, S.; Selby, P. D.; Meier, P.; Harou, J. J.; Yoon, J.; Lachaut, T.; Klassert, C. J. A.; Avisse, N.; Mohamed, K.; Tomlinson, J.; Khadem, M.; Tilmant, A.; Gorelick, S.

    2015-12-01

    Bespoke modelling tools are often needed when planning future engineered interventions in the context of various climate, socio-economic and geopolitical futures. Such tools can help improve system operating policies or assess infrastructure upgrades and their risks. A frequently used approach is to simulate and/or optimise the impact of interventions in engineered systems. Modelling complex infrastructure systems can involve incorporating multiple aspects into a single model, for example physical, economic and political. This presents the challenge of combining research from diverse areas into a single system effectively. We present the Pynsim 'Python Network Simulator' framework, a library for building simulation models capable of representing, the physical, institutional and economic aspects of an engineered resources system. Pynsim is an open source, object oriented code aiming to promote integration of different modelling processes through a single code library. We present two case studies that demonstrate important features of Pynsim's design. The first is a large interdisciplinary project of a national water system in the Middle East with modellers from fields including water resources, economics, hydrology and geography each considering different facets of a multi agent system. It includes: modelling water supply and demand for households and farms; a water tanker market with transfer of water between farms and households, and policy decisions made by government institutions at district, national and international level. This study demonstrates that a well-structured library of code can provide a hub for development and act as a catalyst for integrating models. The second focuses on optimising the location of new run-of-river hydropower plants. Using a multi-objective evolutionary algorithm, this study analyses different network configurations to identify the optimal placement of new power plants within a river network. This demonstrates that Pynsim can be

  4. A Framework for Supporting Survivability, Network Planning and Cross-Layer Optimization in Future Multi-Domain Terabit Networks

    SciTech Connect

    Baldin, Ilya; Huang, Shu; Gopidi, Rajesh

    2015-01-28

    This final project report describes the accomplishments, products and publications from the award. It includes the overview of the project goals to devise a framework for managing resources in multi-domain, multi-layer networks, as well the details of the mathematical problem formulation and the description of the prototype built to prove the concept.

  5. Effects of global financial crisis on network structure in a local stock market

    NASA Astrophysics Data System (ADS)

    Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

    2014-08-01

    This study considers the effects of the 2008 global financial crisis on threshold networks of a local Korean financial market around the time of the crisis. Prices of individual stocks belonging to KOSPI 200 (Korea Composite Stock Price Index 200) are considered for three time periods, namely before, during, and after the crisis. Threshold networks are constructed from fully connected cross-correlation networks, and thresholds of cross-correlation coefficients are assigned to obtain threshold networks. At the high threshold, only one large cluster consisting of firms in the financial sector, heavy industry, and construction is observed during the crisis. However, before and after the crisis, there are several fragmented clusters belonging to various sectors. The power law of the degree distribution in threshold networks is observed within the limited range of thresholds. Threshold networks are fatter during the crisis than before or after the crisis. The clustering coefficient of the threshold network follows the power law in the scaling range.

  6. Quantitative social science. A network framework of cultural history.

    PubMed

    Schich, Maximilian; Song, Chaoming; Ahn, Yong-Yeol; Mirsky, Alexander; Martino, Mauro; Barabási, Albert-László; Helbing, Dirk

    2014-08-01

    The emergent processes driving cultural history are a product of complex interactions among large numbers of individuals, determined by difficult-to-quantify historical conditions. To characterize these processes, we have reconstructed aggregate intellectual mobility over two millennia through the birth and death locations of more than 150,000 notable individuals. The tools of network and complexity theory were then used to identify characteristic statistical patterns and determine the cultural and historical relevance of deviations. The resulting network of locations provides a macroscopic perspective of cultural history, which helps us to retrace cultural narratives of Europe and North America using large-scale visualization and quantitative dynamical tools and to derive historical trends of cultural centers beyond the scope of specific events or narrow time intervals.

  7. Discovering the influential users oriented to viral marketing based on online social networks

    NASA Astrophysics Data System (ADS)

    Zhu, Zhiguo

    2013-08-01

    The target of viral marketing on the platform of popular online social networks is to rapidly propagate marketing information at lower cost and increase sales, in which a key problem is how to precisely discover the most influential users in the process of information diffusion. A novel method is proposed in this paper for helping companies to identify such users as seeds to maximize information diffusion in the viral marketing. Firstly, the user trust network oriented to viral marketing and users’ combined interest degree in the network including isolated users are extensively defined. Next, we construct a model considering the time factor to simulate the process of information diffusion in viral marketing and propose a dynamic algorithm description. Finally, experiments are conducted with a real dataset extracted from the famous SNS website Epinions. The experimental results indicate that the proposed algorithm has better scalability and is less time-consuming. Compared with the classical model, the proposed algorithm achieved a better performance than does the classical method on the two aspects of network coverage rate and time-consumption in our four sub-datasets.

  8. Network analysis of returns and volume trading in stock markets: The Euro Stoxx case

    NASA Astrophysics Data System (ADS)

    Brida, Juan Gabriel; Matesanz, David; Seijas, Maria Nela

    2016-02-01

    This study applies network analysis to analyze the structure of the Euro Stoxx market during the long period from 2002 up to 2014. The paper generalizes previous research on stock market networks by including asset returns and volume trading as the main variables to study the financial market. A multidimensional generalization of the minimal spanning tree (MST) concept is introduced, by adding the role of trading volume to the traditional approach which only includes price returns. Additionally, we use symbolization methods to the raw data to study the behavior of the market structure in different, normal and critical, situations. The hierarchical organization of the network is derived, and the MST for different sub-periods of 2002-2014 is created to illustrate how the structure of the market evolves over time. From the structural topologies of these trees, different clusters of companies are identified and analyzed according to their geographical and economic links. Two important results are achieved. Firstly, as other studies have highlighted, at the time of the financial crisis after 2008 the network becomes a more centralized one. Secondly and most important, during our second period of analysis, 2008-2014, we observe that hierarchy becomes more country-specific where different sub-clusters of stocks belonging to France, Germany, Spain or Italy are found apart from their business sector group. This result may suggest that during this period of time financial investors seem to be worried most about country specific economic circumstances.

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

    PubMed Central

    Renn, Jürgen

    2015-01-01

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

  10. Security framework for networked storage system based on artificial immune system

    NASA Astrophysics Data System (ADS)

    Huang, Jianzhong; Xie, Changsheng; Zhang, Chengfeng; Zhan, Ling

    2007-11-01

    This paper proposed a theoretical framework for the networked storage system addressing the storage security. The immune system is an adaptive learning system, which can recognize, classify and eliminate 'non-self' such as foreign pathogens. Thus, we introduced the artificial immune technique to the storage security research, and proposed a full theoretical framework for storage security system. Under this framework, it is possible to carry out the quantitative evaluation for the storage security system using modeling language of artificial immune system (AIS), and the evaluation can offer security consideration for the deployment of networked storage system. Meanwhile, it is potential to obtain the active defense technique suitable for networked storage system via exploring the principle of AIS and achieve a highly secure storage system with immune characteristic.

  11. Wireless industrial sensor networks: framework for QoS assessment and QoS management.

    PubMed

    Howitt, Ivan; Manges, Wayne W; Kuruganti, Phani Teja; Allgood, Glenn; Gutierrez, José A; Conrad, James M

    2006-07-01

    This paper presents a framework that addresses Quality of Service (QoS) for industrial wireless sensor networks as a real-time measurable set of parameters within the context of feedback control, thereby facilitating QoS management. This framework is based on examining the interaction between the industrial control processes and the wireless network. Control theory is used to evaluate the impact of the control/communication interaction, providing a methodology for defining, measuring, and quantifying QoS requirements. An example is presented illustrating the wireless industrial sensor network (WISN) QoS management framework for providing dynamic QoS control within WISN. The example focuses on WISN operating in a time-varying RF interference environment in order to manage application-driven QoS latency constraints.

  12. Social networks, market transactions, and reputation as a central resource. The Mercado del Mar, a fish market in central Mexico.

    PubMed

    Pedroza-Gutiérrez, Carmen; Hernández, Juan M

    2017-01-01

    Fish consumption in Mexico is considered low (around 12 kg per person per year) and non-homogeneously distributed across the country. One of the reasons for this situation is the scarcity of wholesale selling sites. In this context, the Mercado del Mar (MM), located in Guadalajara city, Jalisco, is the second biggest wholesale fish market in Mexico, with a distribution of about 500 tons per day and a variety of about 350 different species of fish. In this paper, we argue that MM has accumulated social capital, which is formed from two main resources: buyer and seller relationships, and reputation. Specifically, the MM manages a broad and intensive interaction among business actors and the already achieved reputation allows the MM to adapt to market changes. To validate our hypotheses, an empirical study was conducted in 2015 by means of interviews to fish wholesalers in the MM and a sample of their suppliers and buyers. For simplicity we have only considered fresh water fish. We have followed snow-ball sampling as the survey strategy. Results show that the MM has responded to fish market dynamics organizing a complex network of buyers and suppliers whose relationships can be explained in the form of strong and weak ties. At the same time, reputation has been the central resource to build this social capital and also gives place to market transactions. Additionally, the strategic position of Guadalajara city and the well-connected routes have facilitated fish bulking and distribution in the region.

  13. Development of a Neural Network-Based Renewable Energy Forecasting Framework for Process Industries

    SciTech Connect

    Lee, Soobin; Ryu, Jun-Hyung; Hodge, Bri-Mathias; Lee, In-Beum

    2016-06-25

    This paper presents a neural network-based forecasting framework for photovoltaic power (PV) generation as a decision-supporting tool to employ renewable energies in the process industry. The applicability of the proposed framework is illustrated by comparing its performance against other methodologies such as linear and nonlinear time series modelling approaches. A case study of an actual PV power plant in South Korea is presented.

  14. Stress Averaging for a Beam Network for Use in a Hierarchical Multiscale Framework

    DTIC Science & Technology

    2015-03-01

    Framework by Richard Becker and Adam Sokolow Approved for public release; distribution is unlimited. NOTICES...Averaging for a Beam Network for Use in a Hierarchical Multiscale Framework by Richard Becker and Adam Sokolow Weapons and Materials Research...5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Richard Becker and Adam Sokolow 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7

  15. A QoS Framework with Traffic Request in Wireless Mesh Network

    NASA Astrophysics Data System (ADS)

    Fu, Bo; Huang, Hejiao

    In this paper, we consider major issues in ensuring greater Quality-of-Service (QoS) in Wireless Mesh Networks (WMNs), specifically with regard to reliability and delay. To this end, we use traffic request to record QoS requirements of data flows. In order to achieve required QoS for all data flows efficiently and with high portability, we develop Network State Update Algorithm. All assumptions, definitions, and algorithms are made exclusively with WMNs in mind, guaranteeing the portability of our framework to various environments in WMNs. The simulation results in proof that our framework is correct.

  16. Threshold network of a financial market using the P-value of correlation coefficients

    NASA Astrophysics Data System (ADS)

    Ha, Gyeong-Gyun; Lee, Jae Woo; Nobi, Ashadun

    2015-06-01

    Threshold methods in financial networks are important tools for obtaining important information about the financial state of a market. Previously, absolute thresholds of correlation coefficients have been used; however, they have no relation to the length of time. We assign a threshold value depending on the size of the time window by using the P-value concept of statistics. We construct a threshold network (TN) at the same threshold value for two different time window sizes in the Korean Composite Stock Price Index (KOSPI). We measure network properties, such as the edge density, clustering coefficient, assortativity coefficient, and modularity. We determine that a significant difference exists between the network properties of the two time windows at the same threshold, especially during crises. This implies that the market information depends on the length of the time window when constructing the TN. We apply the same technique to Standard and Poor's 500 (S&P500) and observe similar results.

  17. Collaborative Procurement within Enterprise Networks: A Literature Review, a Reference Framework and a Case Study

    NASA Astrophysics Data System (ADS)

    Cagnazzo, Luca; Taticchi, Paolo; Bidini, Gianni; Sameh, Mohamed

    Collaboration among companies is nowadays a success leverage from those involved, especially for SMEs. The networking advantages are several and among them, reducing costs is a critical one. Costs reduction due to the possibility of Collaborative Procurement (CP) among partners is one of the most important achievements in a network. While the literature available offers good bases for managing single contractor procurement issues, little research addresses the case of CP within Enterprise Networks (ENs). This paper explore the mentioned issue and proposes a general framework for managing CP in ENs, those with the Virtual Development Office (VDO) structure. The findings from the application of the framework proposed in an Italian network are highlighted so as to provide preliminary results and drive future research.

  18. A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies.

    PubMed

    Ochs, Christopher; Geller, James; Perl, Yehoshua; Musen, Mark A

    2016-08-01

    Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT's relational format). A Protégé plugin for deriving "live partial-area taxonomies" is demonstrated.

  19. Towards a Bio-inspired Security Framework for Mission-Critical Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Ren, Wei; Song, Jun; Ma, Zhao; Huang, Shiyong

    Mission-critical wireless sensor networks (WSNs) have been found in numerous promising applications in civil and military fields. However, the functionality of WSNs extensively relies on its security capability for detecting and defending sophisticated adversaries, such as Sybil, worm hole and mobile adversaries. In this paper, we propose a bio-inspired security framework to provide intelligence-enabled security mechanisms. This scheme is composed of a middleware, multiple agents and mobile agents. The agents monitor the network packets, host activities, make decisions and launch corresponding responses. Middleware performs an infrastructure for the communication between various agents and corresponding mobility. Certain cognitive models and intelligent algorithms such as Layered Reference Model of Brain and Self-Organizing Neural Network with Competitive Learning are explored in the context of sensor networks that have resource constraints. The security framework and implementation are also described in details.

  20. A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.

    PubMed

    Hui, David Shui Wing; Chen, Yi-Chao; Zhang, Gong; Wu, Weijie; Chen, Guanrong; Lui, John C S; Li, Yingtao

    2017-06-16

    This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the "trichotomy" observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical models. Implications of the model to various applications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.

  1. Hough transform network: learning conoidal structures in a connectionist framework.

    PubMed

    Basak, J; Das, A

    2002-01-01

    A two-layer neural-network model is designed which accepts image coordinates as the input and learns the parametric form of conoidal shapes (lines/circles/ellipses) adaptively. It provides an efficient representation of visual information embedded in the connection weights and the parameters of the processing elements. It not only reduces the large space requirements of the classical Hough transform (HT), but also represents parameters with a higher precision. The performance of the methodology is compared with other existing algorithms and has been found to excel over those algorithms in many cases.

  2. Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework

    NASA Astrophysics Data System (ADS)

    Aydin, Orhun; Caers, Jef Karel

    2017-08-01

    Faults are one of the building-blocks for subsurface modeling studies. Incomplete observations of subsurface fault networks lead to uncertainty pertaining to location, geometry and existence of faults. In practice, gaps in incomplete fault network observations are filled based on tectonic knowledge and interpreter's intuition pertaining to fault relationships. Modeling fault network uncertainty with realistic models that represent tectonic knowledge is still a challenge. Although methods that address specific sources of fault network uncertainty and complexities of fault modeling exists, a unifying framework is still lacking. In this paper, we propose a rigorous approach to quantify fault network uncertainty. Fault pattern and intensity information are expressed by means of a marked point process, marked Strauss point process. Fault network information is constrained to fault surface observations (complete or partial) within a Bayesian framework. A structural prior model is defined to quantitatively express fault patterns, geometries and relationships within the Bayesian framework. Structural relationships between faults, in particular fault abutting relations, are represented with a level-set based approach. A Markov Chain Monte Carlo sampler is used to sample posterior fault network realizations that reflect tectonic knowledge and honor fault observations. We apply the methodology to a field study from Nankai Trough & Kumano Basin. The target for uncertainty quantification is a deep site with attenuated seismic data with only partially visible faults and many faults missing from the survey or interpretation. A structural prior model is built from shallow analog sites that are believed to have undergone similar tectonics compared to the site of study. Fault network uncertainty for the field is quantified with fault network realizations that are conditioned to structural rules, tectonic information and partially observed fault surfaces. We show the proposed

  3. The discipline of hospital development: a conceptual framework incorporating marketing, managerial, consumer behavior, and adult learning theories.

    PubMed

    Shirley, S; Stampfl, R

    1997-12-01

    The purpose of this explanatory and prescriptive article is to identify interdisciplinary theories used by hospital development to direct its practice. The article explores, explains, and applies theories and principles from behavioral, social, and managerial disciplines. Learning, motivational, organizational, marketing, and attitudinal theories are incorporated and transformed into the fundamental components of a conceptual framework that provides an overview of the practice of hospital development. How this discipline incorporates these theories to design, explain, and prescribe the focus of its own practice is demonstrated. This interdisciplinary approach results in a framework for practice that is adaptable to changing social, cultural, economic, political, and technological environments.

  4. Some characteristics of supernetworks based on unified hybrid network theory framework

    NASA Astrophysics Data System (ADS)

    Liu, Qiang; Fang, Jin-Qing; Li, Yong

    Comparing with single complex networks, supernetworks are more close to the real world in some ways, and have become the newest research hot spot in the network science recently. Some progresses have been made in the research of supernetworks, but the theoretical research method and complex network characteristics of supernetwork models are still needed to further explore. In this paper, we propose three kinds of supernetwork models with three layers based on the unified hybrid network theory framework (UHNTF), and introduce preferential and random linking, respectively, between the upper and lower layers. Then we compared the topological characteristics of the single networks with the supernetwork models. In order to analyze the influence of the interlayer edges on network characteristics, the cross-degree is defined as a new important parameter. Then some interesting new phenomena are found, the results imply this supernetwork model has reference value and application potential.

  5. A Framework for Managing Inter-Site Storage Area Networks using Grid Technologies

    NASA Technical Reports Server (NTRS)

    Kobler, Ben; McCall, Fritz; Smorul, Mike

    2006-01-01

    The NASA Goddard Space Flight Center and the University of Maryland Institute for Advanced Computer Studies are studying mechanisms for installing and managing Storage Area Networks (SANs) that span multiple independent collaborating institutions using Storage Area Network Routers (SAN Routers). We present a framework for managing inter-site distributed SANs that uses Grid Technologies to balance the competing needs to control local resources, share information, delegate administrative access, and manage the complex trust relationships between the participating sites.

  6. Dynamic and scalable audio classification by collective network of binary classifiers framework: an evolutionary approach.

    PubMed

    Kiranyaz, Serkan; Mäkinen, Toni; Gabbouj, Moncef

    2012-10-01

    In this paper, we propose a novel framework based on a collective network of evolutionary binary classifiers (CNBC) to address the problems of feature and class scalability. The main goal of the proposed framework is to achieve a high classification performance over dynamic audio and video repositories. The proposed framework adopts a "Divide and Conquer" approach in which an individual network of binary classifiers (NBC) is allocated to discriminate each audio class. An evolutionary search is applied to find the best binary classifier in each NBC with respect to a given criterion. Through the incremental evolution sessions, the CNBC framework can dynamically adapt to each new incoming class or feature set without resorting to a full-scale re-training or re-configuration. Therefore, the CNBC framework is particularly designed for dynamically varying databases where no conventional static classifiers can adapt to such changes. In short, it is entirely a novel topology, an unprecedented approach for dynamic, content/data adaptive and scalable audio classification. A large set of audio features can be effectively used in the framework, where the CNBCs make appropriate selections and combinations so as to achieve the highest discrimination among individual audio classes. Experiments demonstrate a high classification accuracy (above 90%) and efficiency of the proposed framework over large and dynamic audio databases. Copyright © 2012 Elsevier Ltd. All rights reserved.

  7. Coupling infectious diseases, human preventive behavior, and networks--a conceptual framework for epidemic modeling.

    PubMed

    Mao, Liang; Yang, Yan

    2012-01-01

    Human-disease interactions involve the transmission of infectious diseases among individuals and the practice of preventive behavior by individuals. Both infectious diseases and preventive behavior diffuse simultaneously through human networks and interact with one another, but few existing models have coupled them together. This article proposes a conceptual framework to fill this knowledge gap and illustrates the model establishment. The conceptual model consists of two networks and two diffusion processes. The two networks include: an infection network that transmits diseases and a communication network that channels inter-personal influence regarding preventive behavior. Both networks are composed of same individuals but different types of interactions. This article further introduces modeling approaches to formulize such a framework, including the individual-based modeling approach, network theory, disease transmission models and behavioral models. An illustrative model was implemented to simulate a coupled-diffusion process during an influenza epidemic. The simulation outcomes suggest that the transmission probability of a disease and the structure of infection network have profound effects on the dynamics of coupled-diffusion. The results imply that current models may underestimate disease transmissibility parameters, because human preventive behavior has not been considered. This issue calls for a new interdisciplinary study that incorporates theories from epidemiology, social science, behavioral science, and health psychology. Copyright © 2011 Elsevier Ltd. All rights reserved.

  8. Evaluating communities of practice and knowledge networks: a systematic scoping review of evaluation frameworks.

    PubMed

    McKellar, Kaileah A; Pitzul, Kristen B; Yi, Juliana Y; Cole, Donald C

    2014-09-01

    Communities of Practice (CoPs) are increasingly considered a part of ecohealth and other sectors such as health care, education, and business. However, there is little agreement on approaches to evaluate the influence and effectiveness of CoPs. The purpose of this review was to understand what frameworks and methods have been proposed or used to evaluate CoPs and/or knowledge networks. The review searched electronic databases in interdisciplinary, health, education, and business fields, and further collected references and forward citations from relevant articles. Nineteen articles with 16 frameworks were included in the synthesis. The purposes of the evaluation frameworks varied; while some focused on assessing the performance of CoPs, several frameworks sought to learn about CoPs and their critical success factors. Nine of the frameworks had been applied or tested in some way, most frequently to guide a case study. With limited applications of the frameworks, strong claims about generalizability could not be made. The review results can inform the development of tailored frameworks. However, there is a need for more detailed and targeted CoP evaluation frameworks, as many imperative CoP evaluation needs would be unmet by the available frameworks.

  9. Optimality problem of network topology in stocks market analysis

    NASA Astrophysics Data System (ADS)

    Djauhari, Maman Abdurachman; Gan, Siew Lee

    2015-02-01

    Since its introduction fifteen years ago, minimal spanning tree has become an indispensible tool in econophysics. It is to filter the important economic information contained in a complex system of financial markets' commodities. Here we show that, in general, that tool is not optimal in terms of topological properties. Consequently, the economic interpretation of the filtered information might be misleading. To overcome that non-optimality problem, a set of criteria and a selection procedure of an optimal minimal spanning tree will be developed. By using New York Stock Exchange data, the advantages of the proposed method will be illustrated in terms of the power-law of degree distribution.

  10. Market analyses of livestock trade networks to inform the prevention of joint economic and epidemiological risks.

    PubMed

    Moslonka-Lefebvre, Mathieu; Gilligan, Christopher A; Monod, Hervé; Belloc, Catherine; Ezanno, Pauline; Filipe, João A N; Vergu, Elisabeta

    2016-03-01

    Conventional epidemiological studies of infections spreading through trade networks, e.g., via livestock movements, generally show that central large-size holdings (hubs) should be preferentially surveyed and controlled in order to reduce epidemic spread. However, epidemiological strategies alone may not be economically optimal when costs of control are factored in together with risks of market disruption from targeting core holdings in a supply chain. Using extensive data on animal movements in supply chains for cattle and swine in France, we introduce a method to identify effective strategies for preventing outbreaks with limited budgets while minimizing the risk of market disruptions. Our method involves the categorization of holdings based on position along the supply chain and degree of market share. Our analyses suggest that trade has a higher risk of propagating epidemics through cattle networks, which are dominated by exchanges involving wholesalers, than for swine. We assess the effectiveness of contrasting interventions from the perspectives of regulators and the market, using percolation analysis. We show that preferentially targeting minor, non-central agents can outperform targeting of hubs when the costs to stakeholders and the risks of market disturbance are considered. Our study highlights the importance of assessing joint economic-epidemiological risks in networks underlying pathogen propagation and trade.

  11. Market analyses of livestock trade networks to inform the prevention of joint economic and epidemiological risks

    PubMed Central

    Gilligan, Christopher A.; Belloc, Catherine; Filipe, João A. N.; Vergu, Elisabeta

    2016-01-01

    Conventional epidemiological studies of infections spreading through trade networks, e.g. via livestock movements, generally show that central large-size holdings (hubs) should be preferentially surveyed and controlled in order to reduce epidemic spread. However, epidemiological strategies alone may not be economically optimal when costs of control are factored in together with risks of market disruption from targeting core holdings in a supply chain. Using extensive data on animal movements in supply chains for cattle and swine in France, we introduce a method to identify effective strategies for preventing outbreaks with limited budgets while minimizing the risk of market disruptions. Our method involves the categorization of holdings based on position along the supply chain and degree of market share. Our analyses suggest that trade has a higher risk of propagating epidemics through cattle networks, which are dominated by exchanges involving wholesalers, than for swine. We assess the effectiveness of contrasting interventions from the perspectives of regulators and the market, using percolation analysis. We show that preferentially targeting minor, non-central agents can outperform targeting of hubs when the costs to stakeholders and the risks of market disturbance are considered. Our study highlights the importance of assessing joint economic–epidemiological risks in networks underlying pathogen propagation and trade. PMID:26984191

  12. Theoretical framework for the histone modification network: modifications in the unstructured histone tails form a robust scale-free network.

    PubMed

    Hayashi, Yohei; Senda, Toshiya; Sano, Norihiko; Horikoshi, Masami

    2009-07-01

    A rapid increase in research on the relationship between histone modifications and their subsequent reactions in the nucleus has revealed that the histone modification system is complex, and robust against point mutations. The prevailing theoretical framework (the histone code hypothesis) is inadequate to explain either the complexity or robustness, making the formulation of a new theoretical framework both necessary and desirable. Here, we develop a model of the regulatory network of histone modifications in which we encode histone modifications as nodes and regulatory interactions between histone modifications as links. This network has scale-free properties and subnetworks with a pseudo-mirror symmetry structure, which supports the robustness of the histone modification network. In addition, we show that the unstructured tail regions of histones are suitable for the acquisition of this scale-free property. Our model and related insights provide the first framework for an overall architecture of a histone modification network system, particularly with regard to the structural and functional roles of the unstructured histone tail region. In general, the post-translational "modification webs" of natively unfolded regions (proteins) may function as signal routers for the robust processing of the large amounts of signaling information.

  13. A network engineering framework for upgrading service availability of optical mesh networks

    NASA Astrophysics Data System (ADS)

    Chang, Hung-Yi; Wang, Pi-Chung

    2015-06-01

    We present a network engineering process to improve service availability of optical mesh networks by employing protection links. In the existing optical mesh networks, high service availability is usually achieved by network planning with an appropriate routing and wavelength assignment (RWA) algorithm. However, network planning cannot provide upgrade paths for unpredictable new demands. To meet the requirements of practical operations, a network engineering process is developed to improve service availability in an upgrade manner. A model of integer linear programming (ILP) is proposed to minimize the upgrade cost. In addition, heuristic methods for calculating the sequence of incrementally installing protection links are presented. Our experimental results show that the proposed network engineering process can improve service availability with low cost. They also demonstrate that the network engineering process can exclude the necessity of a routing algorithm optimized for service availability because such an algorithm usually results in higher upgrade cost than those optimized for path length or link load.

  14. A framework on the emergence and effectiveness of global health networks.

    PubMed

    Shiffman, Jeremy; Quissell, Kathryn; Schmitz, Hans Peter; Pelletier, David L; Smith, Stephanie L; Berlan, David; Gneiting, Uwe; Van Slyke, David; Mergel, Ines; Rodriguez, Mariela; Walt, Gill

    2016-04-01

    Since 1990 mortality and morbidity decline has been more extensive for some conditions prevalent in low- and middle-income countries than for others. One reason may be differences in the effectiveness of global health networks, which have proliferated in recent years. Some may be more capable than others in attracting attention to a condition, in generating funding, in developing interventions and in convincing national governments to adopt policies. This article introduces a supplement on the emergence and effectiveness of global health networks. The supplement examines networks concerned with six global health problems: tuberculosis (TB), pneumonia, tobacco use, alcohol harm, maternal mortality and newborn deaths. This article presents a conceptual framework delineating factors that may shape why networks crystallize more easily surrounding some issues than others, and once formed, why some are better able than others to shape policy and public health outcomes. All supplement papers draw on this framework. The framework consists of 10 factors in three categories: (1) features of the networks and actors that comprise them, including leadership, governance arrangements, network composition and framing strategies; (2) conditions in the global policy environment, including potential allies and opponents, funding availability and global expectations concerning which issues should be prioritized; (3) and characteristics of the issue, including severity, tractability and affected groups. The article also explains the design of the project, which is grounded in comparison of networks surrounding three matched issues: TB and pneumonia, tobacco use and alcohol harm, and maternal and newborn survival. Despite similar burden and issue characteristics, there has been considerably greater policy traction for the first in each pair. The supplement articles aim to explain the role of networks in shaping these differences, and collectively represent the first comparative effort

  15. A framework on the emergence and effectiveness of global health networks

    PubMed Central

    Shiffman, Jeremy; Quissell, Kathryn; Schmitz, Hans Peter; Pelletier, David L; Smith, Stephanie L; Berlan, David; Gneiting, Uwe; Van Slyke, David; Mergel, Ines; Rodriguez, Mariela; Walt, Gill

    2016-01-01

    Since 1990 mortality and morbidity decline has been more extensive for some conditions prevalent in low- and middle-income countries than for others. One reason may be differences in the effectiveness of global health networks, which have proliferated in recent years. Some may be more capable than others in attracting attention to a condition, in generating funding, in developing interventions and in convincing national governments to adopt policies. This article introduces a supplement on the emergence and effectiveness of global health networks. The supplement examines networks concerned with six global health problems: tuberculosis (TB), pneumonia, tobacco use, alcohol harm, maternal mortality and newborn deaths. This article presents a conceptual framework delineating factors that may shape why networks crystallize more easily surrounding some issues than others, and once formed, why some are better able than others to shape policy and public health outcomes. All supplement papers draw on this framework. The framework consists of 10 factors in three categories: (1) features of the networks and actors that comprise them, including leadership, governance arrangements, network composition and framing strategies; (2) conditions in the global policy environment, including potential allies and opponents, funding availability and global expectations concerning which issues should be prioritized; (3) and characteristics of the issue, including severity, tractability and affected groups. The article also explains the design of the project, which is grounded in comparison of networks surrounding three matched issues: TB and pneumonia, tobacco use and alcohol harm, and maternal and newborn survival. Despite similar burden and issue characteristics, there has been considerably greater policy traction for the first in each pair. The supplement articles aim to explain the role of networks in shaping these differences, and collectively represent the first comparative effort

  16. Beyond the Labor Market Paradigm: A Social Network Perspective on Teacher Recruitment and Retention

    ERIC Educational Resources Information Center

    Baker-Doyle, Kira

    2010-01-01

    This article identifies limits of the dominant labor market perspective (LMP) in research on teacher recruitment and retention and describes how research that incorporates a social network perspective (SNP) can contribute to the knowledge base and development of teacher education, staffing, and professional development approaches. A discussion of…

  17. Functional Network Analysis: A New Way to Compare Frontier and Emerging Markets

    DTIC Science & Technology

    2012-06-01

    countries where social capital can be as important as financial capital. As Stiglitz and Gallegati (2011) note, “Some network designs may be good at...1 Levine, Ross and Sara Zervos (1996), “Stock Market Development and Long Run Growth,” World Bank Economic Review, Vol. 10, No. 2. 2 Stiglitz

  18. Multilayer network of language: A unified framework for structural analysis of linguistic subsystems

    NASA Astrophysics Data System (ADS)

    Martinčić-Ipšić, Sanda; Margan, Domagoj; Meštrović, Ana

    2016-09-01

    Recently, the focus of complex networks' research has shifted from the analysis of isolated properties of a system toward a more realistic modeling of multiple phenomena - multilayer networks. Motivated by the prosperity of multilayer approach in social, transport or trade systems, we introduce the multilayer networks for language. The multilayer network of language is a unified framework for modeling linguistic subsystems and their structural properties enabling the exploration of their mutual interactions. Various aspects of natural language systems can be represented as complex networks, whose vertices depict linguistic units, while links model their relations. The multilayer network of language is defined by three aspects: the network construction principle, the linguistic subsystem and the language of interest. More precisely, we construct a word-level (syntax and co-occurrence) and a subword-level (syllables and graphemes) network layers, from four variations of original text (in the modeled language). The analysis and comparison of layers at the word and subword-levels are employed in order to determine the mechanism of the structural influences between linguistic units and subsystems. The obtained results suggest that there are substantial differences between the networks' structures of different language subsystems, which are hidden during the exploration of an isolated layer. The word-level layers share structural properties regardless of the language (e.g. Croatian or English), while the syllabic subword-level expresses more language dependent structural properties. The preserved weighted overlap quantifies the similarity of word-level layers in weighted and directed networks. Moreover, the analysis of motifs reveals a close topological structure of the syntactic and syllabic layers for both languages. The findings corroborate that the multilayer network framework is a powerful, consistent and systematic approach to model several linguistic subsystems

  19. Heat-Passing Framework for Robust Interpretation of Data in Networks

    PubMed Central

    Fang, Yi; Sun, Mengtian; Ramani, Karthik

    2015-01-01

    Researchers are regularly interested in interpreting the multipartite structure of data entities according to their functional relationships. Data is often heterogeneous with intricately hidden inner structure. With limited prior knowledge, researchers are likely to confront the problem of transforming this data into knowledge. We develop a new framework, called heat-passing, which exploits intrinsic similarity relationships within noisy and incomplete raw data, and constructs a meaningful map of the data. The proposed framework is able to rank, cluster, and visualize the data all at once. The novelty of this framework is derived from an analogy between the process of data interpretation and that of heat transfer, in which all data points contribute simultaneously and globally to reveal intrinsic similarities between regions of data, meaningful coordinates for embedding the data, and exemplar data points that lie at optimal positions for heat transfer. We demonstrate the effectiveness of the heat-passing framework for robustly partitioning the complex networks, analyzing the globin family of proteins and determining conformational states of macromolecules in the presence of high levels of noise. The results indicate that the methodology is able to reveal functionally consistent relationships in a robust fashion with no reference to prior knowledge. The heat-passing framework is very general and has the potential for applications to a broad range of research fields, for example, biological networks, social networks and semantic analysis of documents. PMID:25668316

  20. MODELING FRAMEWORK FOR EVALUATING SEDIMENTATION IN STREAM NETWORKS: FOR USE IN SEDIMENT TMDL ANALYSIS

    EPA Science Inventory

    A modeling framework that can be used to evaluate sedimentation in stream networks is described. This methodology can be used to determine sediment Total Maximum Daily Loads (TMDLs) in sediment impaired waters, and provide the necessary hydrodynamic and sediment-related data t...

  1. A Theoretical Framework for Building Online Communities of Practice with Social Networking Tools

    ERIC Educational Resources Information Center

    Gunawardena, Charlotte N.; Hermans, Mary Beth; Sanchez, Damien; Richmond, Carol; Bohley, Maribeth; Tuttle, Rebekah

    2009-01-01

    This paper proposes a theoretical framework as a foundation for building online communities of practice when a suite of social networking applications referred to as collective intelligence tools are utilized to develop a product or solutions to a problem. Drawing on recent developments in Web 2.0 tools, research on communities of practice and…

  2. Mobile Applications and 4G Wireless Networks: A Framework for Analysis

    ERIC Educational Resources Information Center

    Yang, Samuel C.

    2012-01-01

    Purpose: The use of mobile wireless data services continues to increase worldwide. New fourth-generation (4G) wireless networks can deliver data rates exceeding 2 Mbps. The purpose of this paper is to develop a framework of 4G mobile applications that utilize such high data rates and run on small form-factor devices. Design/methodology/approach:…

  3. MODELING FRAMEWORK FOR EVALUATING SEDIMENTATION IN STREAM NETWORKS: FOR USE IN SEDIMENT TMDL ANALYSIS

    EPA Science Inventory

    A modeling framework that can be used to evaluate sedimentation in stream networks is described. This methodology can be used to determine sediment Total Maximum Daily Loads (TMDLs) in sediment impaired waters, and provide the necessary hydrodynamic and sediment-related data t...

  4. Mobile Applications and 4G Wireless Networks: A Framework for Analysis

    ERIC Educational Resources Information Center

    Yang, Samuel C.

    2012-01-01

    Purpose: The use of mobile wireless data services continues to increase worldwide. New fourth-generation (4G) wireless networks can deliver data rates exceeding 2 Mbps. The purpose of this paper is to develop a framework of 4G mobile applications that utilize such high data rates and run on small form-factor devices. Design/methodology/approach:…

  5. Emerging markets in the global economic network: Real(ly) decoupling?

    NASA Astrophysics Data System (ADS)

    Trancoso, Tiago

    2014-02-01

    We evaluate the degree of business cycle interdependence in the global economic network, focusing on the hypothesis that emergent market (EM) economies have decoupled from advanced economies in the recent period of globalization. We employ a novel methodological approach to the study of business cycles synchronization that combines network analysis and dynamic correlations. We find a process of increasing transnational interdependence within and across all economic development groups. Our results suggest that EM do not form a cohesive group and support the view of an increasingly multipolar and interdependent global economic network.

  6. Analysis of network clustering behavior of the Chinese stock market

    NASA Astrophysics Data System (ADS)

    Chen, Huan; Mai, Yong; Li, Sai-Ping

    2014-11-01

    Random Matrix Theory (RMT) and the decomposition of correlation matrix method are employed to analyze spatial structure of stocks interactions and collective behavior in the Shanghai and Shenzhen stock markets in China. The result shows that there exists prominent sector structures, with subsectors including the Real Estate (RE), Commercial Banks (CB), Pharmaceuticals (PH), Distillers&Vintners (DV) and Steel (ST) industries. Furthermore, the RE and CB subsectors are mostly anti-correlated. We further study the temporal behavior of the dataset and find that while the sector structures are relatively stable from 2007 through 2013, the correlation between the real estate and commercial bank stocks shows large variations. By employing the ensemble empirical mode decomposition (EEMD) method, we show that this anti-correlation behavior is closely related to the monetary and austerity policies of the Chinese government during the period of study.

  7. Microbiome Networks: A Systems Framework for Identifying Candidate Microbial Assemblages for Disease Management.

    PubMed

    Poudel, R; Jumpponen, A; Schlatter, D C; Paulitz, T C; Gardener, B B McSpadden; Kinkel, L L; Garrett, K A

    2016-10-01

    Network models of soil and plant microbiomes provide new opportunities for enhancing disease management, but also challenges for interpretation. We present a framework for interpreting microbiome networks, illustrating how observed network structures can be used to generate testable hypotheses about candidate microbes affecting plant health. The framework includes four types of network analyses. "General network analysis" identifies candidate taxa for maintaining an existing microbial community. "Host-focused analysis" includes a node representing a plant response such as yield, identifying taxa with direct or indirect associations with that node. "Pathogen-focused analysis" identifies taxa with direct or indirect associations with taxa known a priori as pathogens. "Disease-focused analysis" identifies taxa associated with disease. Positive direct or indirect associations with desirable outcomes, or negative associations with undesirable outcomes, indicate candidate taxa. Network analysis provides characterization not only of taxa with direct associations with important outcomes such as disease suppression, biofertilization, or expression of plant host resistance, but also taxa with indirect associations via their association with other key taxa. We illustrate the interpretation of network structure with analyses of microbiomes in the oak phyllosphere, and in wheat rhizosphere and bulk soil associated with the presence or absence of infection by Rhizoctonia solani.

  8. Development of Network Interface Cards for TRIDAQ systems with the NaNet framework

    NASA Astrophysics Data System (ADS)

    Ammendola, R.; Biagioni, A.; Cretaro, P.; Di Lorenzo, S.; Fiorini, M.; Frezza, O.; Lamanna, G.; Lo Cicero, F.; Lonardo, A.; Martinelli, M.; Neri, I.; Paolucci, P. S.; Pastorelli, E.; Piandani, R.; Pontisso, L.; Rossetti, D.; Simula, F.; Sozzi, M.; Valente, P.; Vicini, P.

    2017-03-01

    NaNet is a framework for the development of FPGA-based PCI Express (PCIe) Network Interface Cards (NICs) with real-time data transport architecture that can be effectively employed in TRIDAQ systems. Key features of the architecture are the flexibility in the configuration of the number and kind of the I/O channels, the hardware offloading of the network protocol stack, the stream processing capability, and the zero-copy CPU and GPU Remote Direct Memory Access (RDMA). Three NIC designs have been developed with the NaNet framework: NaNet-1 and NaNet-10 for the CERN NA62 low level trigger and NaNet3 for the KM3NeT-IT underwater neutrino telescope DAQ system. We will focus our description on the NaNet-10 design, as it is the most complete of the three in terms of capabilities and integrated IPs of the framework.

  9. Experimental analysis of chaotic neural network models for combinatorial optimization under a unifying framework.

    PubMed

    Kwok, T; Smith, K A

    2000-09-01

    The aim of this paper is to study both the theoretical and experimental properties of chaotic neural network (CNN) models for solving combinatorial optimization problems. Previously we have proposed a unifying framework which encompasses the three main model types, namely, Chen and Aihara's chaotic simulated annealing (CSA) with decaying self-coupling, Wang and Smith's CSA with decaying timestep, and the Hopfield network with chaotic noise. Each of these models can be represented as a special case under the framework for certain conditions. This paper combines the framework with experimental results to provide new insights into the effect of the chaotic neurodynamics of each model. By solving the N-queen problem of various sizes with computer simulations, the CNN models are compared in different parameter spaces, with optimization performance measured in terms of feasibility, efficiency, robustness and scalability. Furthermore, characteristic chaotic neurodynamics crucial to effective optimization are identified, together with a guide to choosing the corresponding model parameters.

  10. Measuring youth exposure to alcohol marketing on social networking sites: challenges and prospects.

    PubMed

    Jernigan, David H; Rushman, Anne E

    2014-02-01

    Youth exposure to alcohol marketing has been linked to increased alcohol consumption and problems. On relatively new and highly interactive social networking sites (SNS) that are popular with youth, tools for measuring youth exposure to alcohol marketing in traditional media are inadequate. We critically review the existing policies of Facebook, Twitter, and YouTube designed to keep branded alcohol content away from underage youth. Looking at brand and user activity on Facebook for the 15 alcohol brands most popular among US youth, we found activity has grown dramatically in the past 3 years, and underage users may be accounting for some of this activity. Surveys of youth and adult participation in alcohol marketing on SNS will be needed to inform debate over these marketing practices.

  11. Features of spillover networks in international financial markets: Evidence from the G20 countries

    NASA Astrophysics Data System (ADS)

    Liu, Xueyong; An, Haizhong; Li, Huajiao; Chen, Zhihua; Feng, Sida; Wen, Shaobo

    2017-08-01

    The objective of this study is to investigate volatility spillover transmission systematically in stock markets across the G20 countries. To achieve this objective, we combined GARCH-BEKK model with complex network theory using the linkages of spillovers. GARCH-BEKK model was used to capture volatility spillover between stock markets. Then, an information spillover network was built. The data encompass the main stock indexes from 19 individual countries in the G20. To consider the dynamic spillover, the full data set was divided into several sub-periods. The main contribution of this paper is considering the volatility spillover relationships as the edges of a complex network, which can capture the propagation path of volatility spillovers. The results indicate that the volatility spillovers among the stock markets of the G20 countries constitute a holistic associated network, another finding is that Korea acts a role of largest sender in long-term, while Brazil is the largest long-term recipient in the G20 spillover network.

  12. Is there any connection between the network morphology and the fluctuations of the stock market index?

    NASA Astrophysics Data System (ADS)

    Stefan, F. M.; Atman, A. P. F.

    2015-02-01

    Models which consider behavioral aspects of the investors have attracted increasing interest in the Finance and Econophysics literature in the last years. Different behavioral profiles (imitation, anti-imitation, indifference) were proposed for the investors, which take their decision based on their trust network (neighborhood). Results from agent-based models have shown that most of the features observed in actual stock market indices can be replicated in simulations. Here, we present a deeper investigation of an agent based model considering different network morphologies (regular, random, small-world) for the investors' trust network, in an attempt to answer the question raised in the title. We study the model by considering four scenarios for the investors and different initial conditions to analyze their influence in the stock market fluctuations. We have characterized the stationary limit for each scenario tested, focusing on the changes introduced when complex networks were used, and calculated the Hurst exponent in some cases. Simulations showed interesting results suggesting that the fluctuations of the stock market index are strongly affected by the network morphology, a remarkable result which we believe was never reported or predicted before.

  13. A comprehensive review on adaptability of network forensics frameworks for mobile cloud computing.

    PubMed

    Khan, Suleman; Shiraz, Muhammad; Wahab, Ainuddin Wahid Abdul; Gani, Abdullah; Han, Qi; Rahman, Zulkanain Bin Abdul

    2014-01-01

    Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC.

  14. A Comprehensive Review on Adaptability of Network Forensics Frameworks for Mobile Cloud Computing

    PubMed Central

    Abdul Wahab, Ainuddin Wahid; Han, Qi; Bin Abdul Rahman, Zulkanain

    2014-01-01

    Network forensics enables investigation and identification of network attacks through the retrieved digital content. The proliferation of smartphones and the cost-effective universal data access through cloud has made Mobile Cloud Computing (MCC) a congenital target for network attacks. However, confines in carrying out forensics in MCC is interrelated with the autonomous cloud hosting companies and their policies for restricted access to the digital content in the back-end cloud platforms. It implies that existing Network Forensic Frameworks (NFFs) have limited impact in the MCC paradigm. To this end, we qualitatively analyze the adaptability of existing NFFs when applied to the MCC. Explicitly, the fundamental mechanisms of NFFs are highlighted and then analyzed using the most relevant parameters. A classification is proposed to help understand the anatomy of existing NFFs. Subsequently, a comparison is given that explores the functional similarities and deviations among NFFs. The paper concludes by discussing research challenges for progressive network forensics in MCC. PMID:25097880

  15. Dynamic Graph Analytic Framework (DYGRAF): greater situation awareness through layered multi-modal network analysis

    NASA Astrophysics Data System (ADS)

    Margitus, Michael R.; Tagliaferri, William A., Jr.; Sudit, Moises; LaMonica, Peter M.

    2012-06-01

    Understanding the structure and dynamics of networks are of vital importance to winning the global war on terror. To fully comprehend the network environment, analysts must be able to investigate interconnected relationships of many diverse network types simultaneously as they evolve both spatially and temporally. To remove the burden from the analyst of making mental correlations of observations and conclusions from multiple domains, we introduce the Dynamic Graph Analytic Framework (DYGRAF). DYGRAF provides the infrastructure which facilitates a layered multi-modal network analysis (LMMNA) approach that enables analysts to assemble previously disconnected, yet related, networks in a common battle space picture. In doing so, DYGRAF provides the analyst with timely situation awareness, understanding and anticipation of threats, and support for effective decision-making in diverse environments.

  16. Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market.

    PubMed

    Qiao, Haishu; Xia, Yue; Li, Ying

    2016-01-01

    This study used the dynamic conditional correlations (DCC) method to identify the linkage effects of Chinese stock market, and further detected the influence of network linkage effects on magnitude of security returns across different industries. Applying two physics-derived techniques, the minimum spanning tree and the hierarchical tree, we analyzed the stock interdependence within the network of the China Securities Index (CSI) industry index basket. We observed that that obvious linkage effects existed among stock networks. CII and CCE, CAG and ITH as well as COU, CHA and REI were confirmed as the core nodes in the three different networks respectively. We also investigated the stability of linkage effects by estimating the mean correlations and mean distances, as well as the normalized tree length of these indices. In addition, using the GMM model approach, we found inter-node influence within the stock network had a pronounced effect on stock returns. Our results generally suggested that there appeared to be greater clustering effect among the indexes belonging to related industrial sectors than those of diverse sectors, and network comovement was significantly affected by impactive financial events in the reality. Besides, stocks that were more central within the network of stock market usually had higher returns for compensation because they endured greater exposure to correlation risk.

  17. Can Network Linkage Effects Determine Return? Evidence from Chinese Stock Market

    PubMed Central

    Qiao, Haishu; Xia, Yue; Li, Ying

    2016-01-01

    This study used the dynamic conditional correlations (DCC) method to identify the linkage effects of Chinese stock market, and further detected the influence of network linkage effects on magnitude of security returns across different industries. Applying two physics-derived techniques, the minimum spanning tree and the hierarchical tree, we analyzed the stock interdependence within the network of the China Securities Index (CSI) industry index basket. We observed that that obvious linkage effects existed among stock networks. CII and CCE, CAG and ITH as well as COU, CHA and REI were confirmed as the core nodes in the three different networks respectively. We also investigated the stability of linkage effects by estimating the mean correlations and mean distances, as well as the normalized tree length of these indices. In addition, using the GMM model approach, we found inter-node influence within the stock network had a pronounced effect on stock returns. Our results generally suggested that there appeared to be greater clustering effect among the indexes belonging to related industrial sectors than those of diverse sectors, and network comovement was significantly affected by impactive financial events in the reality. Besides, stocks that were more central within the network of stock market usually had higher returns for compensation because they endured greater exposure to correlation risk. PMID:27257816

  18. A biologically inspired sensor network framework for autonomous structural health monitoring

    NASA Astrophysics Data System (ADS)

    Chen, Bo

    2009-03-01

    This paper presents a biologically inspired sensor network framework for autonomous structural health monitoring (SHM). The presented sensor network framework transforms desirable characteristics and effective defense mechanisms of the natural immune system to wireless sensor networks for SHM. The autonomous structural health monitoring is achieved through an integrated sensor network framework consisting of high computational power sensors, a mobileagent- based sensor network middleware, and artificial immune pattern recognition (AIPR) methodology for structure damage detection and classification. An AIPR-based structure damage classifier (AIPR-SDC) has been developed, which incorporates several novel characteristics of the natural immune system. The performance of the AIPR-SDC has been validated using a benchmark structure proposed by the IASC-ASCE (International Association for Structural Control - American Society of Civil Engineers) SHM Task Group. The validation results show a better classification success rate comparing to some of other classification algorithms. The further study of unsupervised structure damage classification is also conducted by integrating data clustering techniques and the AIPR method.

  19. Design and architecture of the Mars relay network planning and analysis framework

    NASA Technical Reports Server (NTRS)

    Cheung, K. M.; Lee, C. H.

    2002-01-01

    In this paper we describe the design and architecture of the Mars Network planning and analysis framework that supports generation and validation of efficient planning and scheduling strategy. The goals are to minimize the transmitting time, minimize the delaying time, and/or maximize the network throughputs. The proposed framework would require (1) a client-server architecture to support interactive, batch, WEB, and distributed analysis and planning applications for the relay network analysis scheme, (2) a high-fidelity modeling and simulation environment that expresses link capabilities between spacecraft to spacecraft and spacecraft to Earth stations as time-varying resources, and spacecraft activities, link priority, Solar System dynamic events, the laws of orbital mechanics, and other limiting factors as spacecraft power and thermal constraints, (3) an optimization methodology that casts the resource and constraint models into a standard linear and nonlinear constrained optimization problem that lends itself to commercial off-the-shelf (COTS)planning and scheduling algorithms.

  20. Design and architecture of the Mars relay network planning and analysis framework

    NASA Technical Reports Server (NTRS)

    Cheung, K. M.; Lee, C. H.

    2002-01-01

    In this paper we describe the design and architecture of the Mars Network planning and analysis framework that supports generation and validation of efficient planning and scheduling strategy. The goals are to minimize the transmitting time, minimize the delaying time, and/or maximize the network throughputs. The proposed framework would require (1) a client-server architecture to support interactive, batch, WEB, and distributed analysis and planning applications for the relay network analysis scheme, (2) a high-fidelity modeling and simulation environment that expresses link capabilities between spacecraft to spacecraft and spacecraft to Earth stations as time-varying resources, and spacecraft activities, link priority, Solar System dynamic events, the laws of orbital mechanics, and other limiting factors as spacecraft power and thermal constraints, (3) an optimization methodology that casts the resource and constraint models into a standard linear and nonlinear constrained optimization problem that lends itself to commercial off-the-shelf (COTS)planning and scheduling algorithms.

  1. Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.

    PubMed

    Wang, Edwin; Zaman, Naif; Mcgee, Shauna; Milanese, Jean-Sébastien; Masoudi-Nejad, Ali; O'Connor-McCourt, Maureen

    2015-02-01

    Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have

  2. Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty.

    PubMed

    Casnici, Niccolò; Dondio, Pierpaolo; Casarin, Roberto; Squazzoni, Flaminio

    2015-01-01

    This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors' network structure by measuring the investors' communication patterns. Our results showed that the investors' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum's reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks.

  3. Decrypting Financial Markets through E-Joint Attention Efforts: On-Line Adaptive Networks of Investors in Periods of Market Uncertainty

    PubMed Central

    Casnici, Niccolò; Dondio, Pierpaolo; Casarin, Roberto; Squazzoni, Flaminio

    2015-01-01

    This paper looks at 800,000 messages on the Unicredit stock, exchanged by 7,500 investors in the Finanzaonline.com forum, between 2005 and 2012 and measured collective interpretations of stock market trends. We examined the correlation patterns between market uncertainty, bad news and investors' network structure by measuring the investors' communication patterns. Our results showed that the investors' network reacted to market trends in different ways: While less turbulent market phases implied less communication, higher market volatility generated more complex communication patterns. While the information content of messages was less technical in situations of uncertainty, bad news caused more informative messages only when market volatility was lower. This meant that bad news had a different impact on network behaviour, depending on market uncertainty. By measuring the investors' expertise, we found that their behaviour could help predict changes in daily stock returns. We also found that expert investors were more influential in communication processes during high volatility market phases, whereas they had less influence on the real-time forum's reaction after bad news. Our findings confirm the crucial role of e-communication platforms. However, they also show the need to reconsider the fragility of these collective intelligence systems when under external shocks. PMID:26244550

  4. Curation-Based Network Marketing: Strategies for Network Growth and Electronic Word-of-Mouth Diffusion

    ERIC Educational Resources Information Center

    Church, Earnie Mitchell, Jr.

    2013-01-01

    In the last couple of years, a new aspect of online social networking has emerged, in which the strength of social network connections is based not on social ties but mutually shared interests. This dissertation studies these "curation-based" online social networks (CBN) and their suitability for the diffusion of electronic word-of-mouth…

  5. Curation-Based Network Marketing: Strategies for Network Growth and Electronic Word-of-Mouth Diffusion

    ERIC Educational Resources Information Center

    Church, Earnie Mitchell, Jr.

    2013-01-01

    In the last couple of years, a new aspect of online social networking has emerged, in which the strength of social network connections is based not on social ties but mutually shared interests. This dissertation studies these "curation-based" online social networks (CBN) and their suitability for the diffusion of electronic word-of-mouth…

  6. Spatiotemporal Dynamics and Fitness Analysis of Global Oil Market: Based on Complex Network.

    PubMed

    Du, Ruijin; Dong, Gaogao; Tian, Lixin; Wang, Minggang; Fang, Guochang; Shao, Shuai

    2016-01-01

    We study the overall topological structure properties of global oil trade network, such as degree, strength, cumulative distribution, information entropy and weight clustering. The structural evolution of the network is investigated as well. We find the global oil import and export networks do not show typical scale-free distribution, but display disassortative property. Furthermore, based on the monthly data of oil import values during 2005.01-2014.12, by applying random matrix theory, we investigate the complex spatiotemporal dynamic from the country level and fitness evolution of the global oil market from a demand-side analysis. Abundant information about global oil market can be obtained from deviating eigenvalues. The result shows that the oil market has experienced five different periods, which is consistent with the evolution of country clusters. Moreover, we find the changing trend of fitness function agrees with that of gross domestic product (GDP), and suggest that the fitness evolution of oil market can be predicted by forecasting GDP values. To conclude, some suggestions are provided according to the results.

  7. Spatiotemporal Dynamics and Fitness Analysis of Global Oil Market: Based on Complex Network

    PubMed Central

    Wang, Minggang; Fang, Guochang; Shao, Shuai

    2016-01-01

    We study the overall topological structure properties of global oil trade network, such as degree, strength, cumulative distribution, information entropy and weight clustering. The structural evolution of the network is investigated as well. We find the global oil import and export networks do not show typical scale-free distribution, but display disassortative property. Furthermore, based on the monthly data of oil import values during 2005.01–2014.12, by applying random matrix theory, we investigate the complex spatiotemporal dynamic from the country level and fitness evolution of the global oil market from a demand-side analysis. Abundant information about global oil market can be obtained from deviating eigenvalues. The result shows that the oil market has experienced five different periods, which is consistent with the evolution of country clusters. Moreover, we find the changing trend of fitness function agrees with that of gross domestic product (GDP), and suggest that the fitness evolution of oil market can be predicted by forecasting GDP values. To conclude, some suggestions are provided according to the results. PMID:27706147

  8. Toward a management framework for networks of protected areas in the face of climate change.

    PubMed

    Hole, David G; Huntley, Brian; Arinaitwe, Julius; Butchart, Stuart H M; Collingham, Yvonne C; Fishpool, Lincoln D C; Pain, Deborah J; Willis, Stephen G

    2011-04-01

    Networks of sites of high importance for conservation of biological diversity are a cornerstone of current conservation strategies but are fixed in space and time. As climate change progresses, substantial shifts in species' ranges may transform the ecological community that can be supported at a given site. Thus, some species in an existing network may not be protected in the future or may be protected only if they can move to sites that in future provide suitable conditions. We developed an approach to determine appropriate climate-change adaptation strategies for individual sites within a network that was based on projections of future changes in the relative proportions of emigrants (species for which a site becomes climatically unsuitable), colonists (species for which a site becomes climatically suitable), and persistent species (species able to remain within a site despite the climatic change). Our approach also identifies key regions where additions to a network could enhance its future effectiveness. Using the sub-Saharan African Important Bird Area (IBA) network as a case study, we found that appropriate conservation strategies for individual sites varied widely across sub-Saharan Africa, and key regions where new sites could help increase network robustness varied in space and time. Although these results highlight the potential difficulties within any planning framework that seeks to address climate-change adaptation needs, they demonstrate that such planning frameworks are necessary, if current conservation strategies are to be adapted effectively, and feasible, if applied judiciously.

  9. High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: HPC Data Reduction Framework

    DTIC Science & Technology

    2015-09-01

    HPC ) Environment: HPC Data Reduction Framework prepared by Brian Panneton Technical and Project Engineering, LLC Alexandria, VA James...High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing ( HPC ) Environment: HPC Data Reduction Framework prepared by...SUBTITLE High-Bandwidth Tactical-Network Data Analysis in a High-Performance- Computing ( HPC ) Environment: HPC Data Reduction Framework 5a. CONTRACT NUMBER

  10. Marketing for health-care organizations: an introduction to network management.

    PubMed

    Boonekamp, L C

    1994-01-01

    The introduction of regulated competition in health care in several Western countries confronts health care providing organizations with changing relationships, with their environment and a need for knowledge and skills to analyse and improve their market position. Marketing receives more and more attention, as recent developments in this field of study provide a specific perspective on the relationships between an organization and external and internal parties. In doing so, a basis is offered for network management. A problem is that the existing marketing literature is not entirely appropriate for the specific characteristics of health care. After a description of the developments in marketing and its most recent key concepts, the applicability of these concepts in health-care organizations is discussed. States that for the health-care sector, dominated by complex networks of interorganizational relationships, the strategic marketing vision on relationships can be very useful. At the same time however, the operationalization of these concepts requires special attention and a distinct role of the management of health-care organizations, because of the characteristics of such organizations and the specific type of their service delivery.

  11. An efficient and adaptive mutual authentication framework for heterogeneous wireless sensor network-based applications.

    PubMed

    Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

    2014-02-11

    Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications.

  12. An Efficient and Adaptive Mutual Authentication Framework for Heterogeneous Wireless Sensor Network-Based Applications

    PubMed Central

    Kumar, Pardeep; Ylianttila, Mika; Gurtov, Andrei; Lee, Sang-Gon; Lee, Hoon-Jae

    2014-01-01

    Robust security is highly coveted in real wireless sensor network (WSN) applications since wireless sensors' sense critical data from the application environment. This article presents an efficient and adaptive mutual authentication framework that suits real heterogeneous WSN-based applications (such as smart homes, industrial environments, smart grids, and healthcare monitoring). The proposed framework offers: (i) key initialization; (ii) secure network (cluster) formation (i.e., mutual authentication and dynamic key establishment); (iii) key revocation; and (iv) new node addition into the network. The correctness of the proposed scheme is formally verified. An extensive analysis shows the proposed scheme coupled with message confidentiality, mutual authentication and dynamic session key establishment, node privacy, and message freshness. Moreover, the preliminary study also reveals the proposed framework is secure against popular types of attacks, such as impersonation attacks, man-in-the-middle attacks, replay attacks, and information-leakage attacks. As a result, we believe the proposed framework achieves efficiency at reasonable computation and communication costs and it can be a safeguard to real heterogeneous WSN applications. PMID:24521942

  13. Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks.

    PubMed

    Beber, Moritz Emanuel; Fretter, Christoph; Jain, Shubham; Sonnenschein, Nikolaus; Müller-Hannemann, Matthias; Hütt, Marc-Thorsten

    2012-12-07

    Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.

  14. Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

    PubMed Central

    Beber, Moritz Emanuel; Fretter, Christoph; Jain, Shubham; Sonnenschein, Nikolaus; Müller-Hannemann, Matthias; Hütt, Marc-Thorsten

    2012-01-01

    Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges. PMID:22896565

  15. A uniform instrumentation, event, and adaptation framework for network-aware middleware and advanced network applications

    SciTech Connect

    Reed, Daniel A.

    2003-03-14

    Developers of advanced network applications such as remote instrument control, distributed data management, tele-immersion and collaboration, and distributed computing face a daunting challenge: sustaining robust application performance despite time-varying resource demands and dynamically changing resource availability. It is widely recognized that network-aware middleware is key to achieving performance robustness.

  16. Marketing.

    PubMed

    Chambers, David W

    2010-01-01

    There is not enough marketing of dentistry; but there certainly is too much selling of poor quality service that is being passed off as dentistry. The marketing concept makes the patient and the patients' needs the ultimate criteria of marketing efforts. Myths and good practices for effective marketing that will promote oral health are described under the traditional four "Ps" categories of "product" (best dental care), "place" (availability), "promotion" (advertising and other forms of making patients aware of available services and how to use them), and "price" (the total cost to patients of receiving care).

  17. Networks for improving care in patients with acute coronary syndrome: A framework.

    PubMed

    Radke, Peter W; Halvorsen, Sigrun; Jukema, J Wouter; Kolh, Philippe; Annemans, Lieven; Postma, Maarten J; Ardissino, Diego; Kristensen, Steen D; Bassand, Jean-Pierre; Collet, Jean-Philippe; Morais, João; Tuñón, José; Halcox, Julian

    2014-06-01

    In recent years, it has become evident that the level of guideline adherence in patients presenting with acute coronary syndrome (ACS) is highly correlated with patient outcomes. Unfortunately, guideline adherence is low in some geographic areas and especially in those patients at high-risk. Regional networks including ambulance systems and hospitals with catheterization laboratories are able to increase guideline adherence and patient outcomes by streamlining the critical pre- and intra-hospital processes as well as improving timely access to invasive procedures and recommended medication. Successful organization of an ACS network requires engagement of multiple stakeholders to create effective solutions for the specific local setting. There is no 'one-size-fits all' strategy to set-up and successfully run an ACS network. We present a framework for how to set up and organize an effective ACS network, delivering guideline-based care to improve patient outcomes.

  18. Green Power Grids: How Energy from Renewable Sources Affects Networks and Markets

    PubMed Central

    Mureddu, Mario; Caldarelli, Guido; Chessa, Alessandro; Scala, Antonio; Damiano, Alfonso

    2015-01-01

    The increasing attention to environmental issues is forcing the implementation of novel energy models based on renewable sources. This is fundamentally changing the configuration of energy management and is introducing new problems that are only partly understood. In particular, renewable energies introduce fluctuations which cause an increased request for conventional energy sources to balance energy requests at short notice. In order to develop an effective usage of low-carbon sources, such fluctuations must be understood and tamed. In this paper we present a microscopic model for the description and for the forecast of short time fluctuations related to renewable sources in order to estimate their effects on the electricity market. To account for the inter-dependencies in the energy market and the physical power dispatch network, we use a statistical mechanics approach to sample stochastic perturbations in the power system and an agent based approach for the prediction of the market players’ behavior. Our model is data-driven; it builds on one-day-ahead real market transactions in order to train agents’ behaviour and allows us to deduce the market share of different energy sources. We benchmarked our approach on the Italian market, finding a good accordance with real data. PMID:26335705

  19. Green Power Grids: How Energy from Renewable Sources Affects Networks and Markets.

    PubMed

    Mureddu, Mario; Caldarelli, Guido; Chessa, Alessandro; Scala, Antonio; Damiano, Alfonso

    2015-01-01

    The increasing attention to environmental issues is forcing the implementation of novel energy models based on renewable sources. This is fundamentally changing the configuration of energy management and is introducing new problems that are only partly understood. In particular, renewable energies introduce fluctuations which cause an increased request for conventional energy sources to balance energy requests at short notice. In order to develop an effective usage of low-carbon sources, such fluctuations must be understood and tamed. In this paper we present a microscopic model for the description and for the forecast of short time fluctuations related to renewable sources in order to estimate their effects on the electricity market. To account for the inter-dependencies in the energy market and the physical power dispatch network, we use a statistical mechanics approach to sample stochastic perturbations in the power system and an agent based approach for the prediction of the market players' behavior. Our model is data-driven; it builds on one-day-ahead real market transactions in order to train agents' behaviour and allows us to deduce the market share of different energy sources. We benchmarked our approach on the Italian market, finding a good accordance with real data.

  20. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

    PubMed

    Qiu, Mingyue; Song, Yu

    2016-01-01

    In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.

  1. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market

    PubMed Central

    2017-01-01

    Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load. PMID:28426739

  2. Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market.

    PubMed

    Bozkurt, Ömer Özgür; Biricik, Göksel; Tayşi, Ziya Cihan

    2017-01-01

    Load information plays an important role in deregulated electricity markets, since it is the primary factor to make critical decisions on production planning, day-to-day operations, unit commitment and economic dispatch. Being able to predict the load for a short term, which covers one hour to a few days, equips power generation facilities and traders with an advantage. With the deregulation of electricity markets, a variety of short term load forecasting models are developed. Deregulation in Turkish Electricity Market has started in 2001 and liberalization is still in progress with rules being effective in its predefined schedule. However, there is a very limited number of studies for Turkish Market. In this study, we introduce two different models for current Turkish Market using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) and present their comparative performances. Building models that cope with the dynamic nature of deregulated market and are able to run in real-time is the main contribution of this study. We also use our ANN based model to evaluate the effect of several factors, which are claimed to have effect on electrical load.

  3. Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model

    PubMed Central

    Qiu, Mingyue; Song, Yu

    2016-01-01

    In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders’ expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day’s price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately. PMID:27196055

  4. Building oceanographic and atmospheric observation networks by composition: unmanned vehicles, communication networks, and planning and execution control frameworks

    NASA Astrophysics Data System (ADS)

    Sousa, J. T.; Pinto, J.; Martins, R.; Costa, M.; Ferreira, F.; Gomes, R.

    2014-12-01

    The problem of developing mobile oceanographic and atmospheric observation networks (MOAO) with coordinated air and ocean vehicles is discussed in the framework of the communications and control software tool chain developed at Underwater Systems and Technologies Laboratory (LSTS) from Porto University. This is done with reference to field experiments to illustrate key capabilities and to assess future MOAO operations. First, the motivation for building MOAO by "composition" of air and ocean vehicles, communication networks, and planning and execution control frameworks is discussed - in networked vehicle systems information and commands are exchanged among multiple vehicles and operators, and the roles, relative positions, and dependencies of these vehicles and operators change during operations. Second, the planning and execution control framework developed at LSTS for multi-vehicle systems is discussed with reference to key concepts such as autonomy, mixed-initiative interactions, and layered organization. Third, the LSTS tool software tool chain is presented to show how to develop MOAO by composition. The tool chain comprises the Neptus command and control framework for mixed initiative interactions, the underlying IMC messaging protocol, and the DUNE on-board software. Fourth, selected LSTS operational deployments illustrate MOAO capability building. In 2012 we demonstrated the use of UAS to "ferry" data from UUVs located beyond line of sight (BLOS). In 2013 we demonstrated coordinated observations of coastal fronts with small UAS and UUVs, "bent" BLOS through the use of UAS as communication relays, and UAS tracking of juvenile hammer-head sharks. In 2014 we demonstrated UUV adaptive sampling with the closed loop controller of the UUV residing on a UAS; this was done with the help of a Wave Glider ASV with a communications gateway. The results from these experiments provide a background for assessing potential future UAS operations in a compositional MOAO.

  5. Marketing.

    ERIC Educational Resources Information Center

    Appel, David L.

    This booklet suggests ways in which institutions--Catholic schools in particular--can move beyond public relations and advertising to engage in the broader arena of marketing with its focus on consumer satisfaction. The first of the book's three chapters reviews the concept of marketing, providing definitions of key terms, clarification of…

  6. Marketing.

    ERIC Educational Resources Information Center

    Maust, Robert N.

    1985-01-01

    Although college administrators may be committed to the concept and need for institutional marketing, even a well-developed marketing plan may not work if it is not clearly organized to address special needs. This article reviews management fads, how to make jargon operational, organizational dynamics, and monitoring fads. (MSE)

  7. The structural role of weak and strong links in a financial market network

    NASA Astrophysics Data System (ADS)

    Garas, A.; Argyrakis, P.; Havlin, S.

    2008-05-01

    We investigate the properties of correlation based networks originating from economic complex systems, such as the network of stocks traded at the New York Stock Exchange (NYSE). The weaker links (low correlation) of the system are found to contribute to the overall connectivity of the network significantly more than the strong links (high correlation). We find that nodes connected through strong links form well defined communities. These communities are clustered together in more complex ways compared to the widely used classification according to the economic activity. We find that some companies, such as General Electric (GE), Coca Cola (KO), and others, can be involved in different communities. The communities are found to be quite stable over time. Similar results were obtained by investigating markets completely different in size and properties, such as the Athens Stock Exchange (ASE). The present method may be also useful for other networks generated through correlations.

  8. Comparing administered and market-based water allocation systems through a consistent agent-based modeling framework.

    PubMed

    Zhao, Jianshi; Cai, Ximing; Wang, Zhongjing

    2013-07-15

    Water allocation can be undertaken through administered systems (AS), market-based systems (MS), or a combination of the two. The debate on the performance of the two systems has lasted for decades but still calls for attention in both research and practice. This paper compares water users' behavior under AS and MS through a consistent agent-based modeling framework for water allocation analysis that incorporates variables particular to both MS (e.g., water trade and trading prices) and AS (water use violations and penalties/subsidies). Analogous to the economic theory of water markets under MS, the theory of rational violation justifies the exchange of entitled water under AS through the use of cross-subsidies. Under water stress conditions, a unique water allocation equilibrium can be achieved by following a simple bargaining rule that does not depend upon initial market prices under MS, or initial economic incentives under AS. The modeling analysis shows that the behavior of water users (agents) depends on transaction, or administrative, costs, as well as their autonomy. Reducing transaction costs under MS or administrative costs under AS will mitigate the effect that equity constraints (originating with primary water allocation) have on the system's total net economic benefits. Moreover, hydrologic uncertainty is shown to increase market prices under MS and penalties/subsidies under AS and, in most cases, also increases transaction, or administrative, costs. Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. A Framework of Algorithms: Computing the Bias and Prestige of Nodes in Trust Networks

    PubMed Central

    Li, Rong-Hua; Yu, Jeffrey Xu; Huang, Xin; Cheng, Hong

    2012-01-01

    A trust network is a social network in which edges represent the trust relationship between two nodes in the network. In a trust network, a fundamental question is how to assess and compute the bias and prestige of the nodes, where the bias of a node measures the trustworthiness of a node and the prestige of a node measures the importance of the node. The larger bias of a node implies the lower trustworthiness of the node, and the larger prestige of a node implies the higher importance of the node. In this paper, we define a vector-valued contractive function to characterize the bias vector which results in a rich family of bias measurements, and we propose a framework of algorithms for computing the bias and prestige of nodes in trust networks. Based on our framework, we develop four algorithms that can calculate the bias and prestige of nodes effectively and robustly. The time and space complexities of all our algorithms are linear with respect to the size of the graph, thus our algorithms are scalable to handle large datasets. We evaluate our algorithms using five real datasets. The experimental results demonstrate the effectiveness, robustness, and scalability of our algorithms. PMID:23239990

  10. A framework of algorithms: computing the bias and prestige of nodes in trust networks.

    PubMed

    Li, Rong-Hua; Yu, Jeffrey Xu; Huang, Xin; Cheng, Hong

    2012-01-01

    A trust network is a social network in which edges represent the trust relationship between two nodes in the network. In a trust network, a fundamental question is how to assess and compute the bias and prestige of the nodes, where the bias of a node measures the trustworthiness of a node and the prestige of a node measures the importance of the node. The larger bias of a node implies the lower trustworthiness of the node, and the larger prestige of a node implies the higher importance of the node. In this paper, we define a vector-valued contractive function to characterize the bias vector which results in a rich family of bias measurements, and we propose a framework of algorithms for computing the bias and prestige of nodes in trust networks. Based on our framework, we develop four algorithms that can calculate the bias and prestige of nodes effectively and robustly. The time and space complexities of all our algorithms are linear with respect to the size of the graph, thus our algorithms are scalable to handle large datasets. We evaluate our algorithms using five real datasets. The experimental results demonstrate the effectiveness, robustness, and scalability of our algorithms.

  11. Shareholding relationships in the Euro Area banking market: A network perspective

    NASA Astrophysics Data System (ADS)

    Pecora, Nicolò; Spelta, Alessandro

    2015-09-01

    In this paper we analyze the topological properties of the network of the Euro Area banking market network, with the primary aim of assessing the importance of a bank in the financial system with respect to ownership and control of other credit institutions. The network displays power law distributions in both binary and weighted degree metrics indicating a robust yet fragile structure and a direct link between an increase of control diversification and a rise in the market power. Therefore while in good time the network is seemingly robust, in bad times many banks can simultaneously go into distress. This behavior paves the way for Central bank's actions. In particular we investigate whether the Single Supervisory Mechanism introduced by the European Central Banks and based on banks' total asset is a good proxy to quantify their systemic importance. Results indicate that not all the financial institutions with high valued total assets are systemically important but only few of them. Moreover the network structure reveals that control is highly concentrated, with few important shareholders approximately controlling a separate subset of banks.

  12. Chemical structure, network topology, and porosity effects on the mechanical properties of Zeolitic Imidazolate Frameworks

    PubMed Central

    Tan, Jin Chong; Bennett, Thomas D.; Cheetham, Anthony K.

    2010-01-01

    The mechanical properties of seven zeolitic imidazolate frameworks (ZIFs) based on five unique network topologies have been systematically characterized by single-crystal nanoindentation studies. We demonstrate that the elastic properties of ZIF crystal structures are strongly correlated to the framework density and the underlying porosity. For the systems considered here, the elastic modulus was found to range from 3 to 10 GPa, whereas the hardness property lies between 300 MPa and 1.1 GPa. Notably, these properties are superior to those of other metal–organic frameworks (MOFs), such as MOF-5. In substituted imidazolate frameworks, our results show that their mechanical properties are mainly governed by the rigidity and bulkiness of the substituted organic linkages. The framework topology and the intricate pore morphology can also influence the degree of mechanical anisotropy. Our findings present the previously undescribed structure-mechanical property relationships pertaining to hybrid open frameworks that are important for the design and application of new MOF materials. PMID:20479264

  13. Framework for a hydrologic climate-response network in New England

    USGS Publications Warehouse

    Lent, Robert M.; Hodgkins, Glenn A.; Dudley, Robert W.; Schalk, Luther F.

    2015-01-01

    Many climate-related hydrologic variables in New England have changed in the past century, and many are expected to change during the next century. It is important to understand and monitor these changes because they can affect human water supply, hydroelectric power generation, transportation infrastructure, and stream and riparian ecology. This report describes a framework for hydrologic monitoring in New England by means of a climate-response network. The framework identifies specific inland hydrologic variables that are sensitive to climate variation; identifies geographic regions with similar hydrologic responses; proposes a fixed-station monitoring network composed of existing streamflow, groundwater, lake ice, snowpack, and meteorological data-collection stations for evaluation of hydrologic response to climate variation; and identifies streamflow basins for intensive, process-based studies and for estimates of future hydrologic conditions.

  14. A Graph-Based Computational Framework for Simulation and Optimization of Coupled Infrastructure Network

    SciTech Connect

    Jalving, Jordan; Abhyankar, Shrirang; Kim, Kibaek; Hereld, Mark; Zavala, Victor M.

    2017-01-01

    We present a computational framework that facilitates the construction, instantiation, and analysis of large-scale optimization and simulation applications of coupled energy networks. The framework integrates the optimization modeling package PLASMO and the simulation package DMNetwork (built around PETSc). These tools use a common graphbased abstraction that enables us to achieve compatibility between data structures and to build applications that use network models of different physical fidelity. We also describe how to embed these tools within complex computational workflows using SWIFT, which is a tool that facilitates parallel execution of multiple simulation runs and management of input and output data. We discuss how to use these capabilities to target coupled natural gas and electricity systems.

  15. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems.

    PubMed

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-06-02

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.

  16. Comparative Study of Neural Network Frameworks for the Next Generation of Adaptive Optics Systems

    PubMed Central

    González-Gutiérrez, Carlos; Santos, Jesús Daniel; Martínez-Zarzuela, Mario; Basden, Alistair G.; Osborn, James; Díaz-Pernas, Francisco Javier; De Cos Juez, Francisco Javier

    2017-01-01

    Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named “CARMEN” are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances. PMID:28574426

  17. A Graph-Based Computational Framework for Simulation and Optimization of Coupled Infrastructure Network

    DOE PAGES

    Jalving, Jordan; Abhyankar, Shrirang; Kim, Kibaek; ...

    2017-01-01

    We present a computational framework that facilitates the construction, instantiation, and analysis of large-scale optimization and simulation applications of coupled energy networks. The framework integrates the optimization modeling package PLASMO and the simulation package DMNetwork (built around PETSc). These tools use a common graphbased abstraction that enables us to achieve compatibility between data structures and to build applications that use network models of different physical fidelity. We also describe how to embed these tools within complex computational workflows using SWIFT, which is a tool that facilitates parallel execution of multiple simulation runs and management of input and output data. Wemore » discuss how to use these capabilities to target coupled natural gas and electricity systems.« less

  18. A graph-based computational framework for simulation and optimisation of coupled infrastructure networks

    DOE PAGES

    Jalving, Jordan; Abhyankar, Shrirang; Kim, Kibaek; ...

    2017-04-24

    Here, we present a computational framework that facilitates the construction, instantiation, and analysis of large-scale optimization and simulation applications of coupled energy networks. The framework integrates the optimization modeling package PLASMO and the simulation package DMNetwork (built around PETSc). These tools use a common graphbased abstraction that enables us to achieve compatibility between data structures and to build applications that use network models of different physical fidelity. We also describe how to embed these tools within complex computational workflows using SWIFT, which is a tool that facilitates parallel execution of multiple simulation runs and management of input and output data.more » We discuss how to use these capabilities to target coupled natural gas and electricity systems.« less

  19. Strategic planning for health care markets: a framework and case study in analyzing diagnosis related groups.

    PubMed

    Wood, V R; Singh, J

    1986-09-01

    The sweeping changes in the health care industry, of which implementation of the prospective payment system (PPS) is one, put heavy demands on hospital administrators to "manage" their portfolio of health care products and services. The authors discuss the implications of PPS and other changes in the industry for strategic planning and present a framework based on an efficiency/profitability matrix. The framework can assist hospital managers in gaining strategic insight into their current portfolio and can guide their efforts in determining future product/service portfolios. A case study demonstrates the application of the proposed framework.

  20. Short-term load forecasting using generalized regression and probabilistic neural networks in the electricity market

    SciTech Connect

    Tripathi, M.M.; Upadhyay, K.G.; Singh, S.N.

    2008-11-15

    For the economic and secure operation of power systems, a precise short-term load forecasting technique is essential. Modern load forecasting techniques - especially artificial neural network methods - are particularly attractive, as they have the ability to handle the non-linear relationships between load, weather temperature, and the factors affecting them directly. A test of two different ANN models on data from Australia's Victoria market is promising. (author)

  1. Anomaly Detection Framework Based on Matching Pursuit for Network Security Enhancement

    DTIC Science & Technology

    2010-11-01

    RTO-MP-IST-091 P11 - 1 Anomaly Detection Framework Based on Matching Pursuit for Network Security Enhancement Rafał Renk, Witold...Detection Systems can be classified as belonging to two main groups depending on the detection technique employed: anomaly detection and signature...based detection. Anomaly detection techniques, that we focus on in our work, rely on the existence of a reliable characterization of what is normal and

  2. MASM: a market architecture for sensor management in distributed sensor networks

    NASA Astrophysics Data System (ADS)

    Viswanath, Avasarala; Mullen, Tracy; Hall, David; Garga, Amulya

    2005-03-01

    Rapid developments in sensor technology and its applications have energized research efforts towards devising a firm theoretical foundation for sensor management. Ubiquitous sensing, wide bandwidth communications and distributed processing provide both opportunities and challenges for sensor and process control and optimization. Traditional optimization techniques do not have the ability to simultaneously consider the wildly non-commensurate measures involved in sensor management in a single optimization routine. Market-oriented programming provides a valuable and principled paradigm to designing systems to solve this dynamic and distributed resource allocation problem. We have modeled the sensor management scenario as a competitive market, wherein the sensor manager holds a combinatorial auction to sell the various items produced by the sensors and the communication channels. However, standard auction mechanisms have been found not to be directly applicable to the sensor management domain. For this purpose, we have developed a specialized market architecture MASM (Market architecture for Sensor Management). In MASM, the mission manager is responsible for deciding task allocations to the consumers and their corresponding budgets and the sensor manager is responsible for resource allocation to the various consumers. In addition to having a modified combinatorial winner determination algorithm, MASM has specialized sensor network modules that address commensurability issues between consumers and producers in the sensor network domain. A preliminary multi-sensor, multi-target simulation environment has been implemented to test the performance of the proposed system. MASM outperformed the information theoretic sensor manager in meeting the mission objectives in the simulation experiments.

  3. A unified framework for chaotic neural-network approaches to combinatorial optimization.

    PubMed

    Kwok, T; Smith, K A

    1999-01-01

    As an attempt to provide an organized way to study the chaotic structures and their effects in solving combinatorial optimization with chaotic neural networks (CNN's), a unifying framework is proposed to serve as a basis where the existing CNN models can be placed and compared. The key of this proposed framework is the introduction of an extra energy term into the computational energy of the Hopfield model, which takes on different forms for different CNN models, and modifies the original Hopfield energy landscape in various manners. Three CNN models, namely the Chen and Aihara model with self-feedback chaotic simulated annealing (CSA), the Wang and Smith model with timestep CSA, and the chaotic noise model, are chosen as examples to show how they can be classified and compared within the proposed framework.

  4. Disruptive innovation, labor markets, and Big Valley STEM School: network analysis in STEM education

    NASA Astrophysics Data System (ADS)

    Ellison, Scott; Allen, Ben

    2016-11-01

    A defining characteristic of contemporary trends in global education policy is the promotion of STEM learning in the primary, secondary, and tertiary sectors of education as a means to generate innovation and prosperity in the economy. Intertwined with common sensical assumptions about future labor markets and the transformative potential of technology in education, STEM has become a hegemonic discourse informing policy formation and educational practice. In Gramscian terms, the struggle over STEM as a discursive practice, between proponents of instrumental learning of marketable economic skills and those of education towards humanistic goals, reveals insights about the ideological characteristics of the push for STEM learning. This article explores the power dynamics behind the push for STEM learning as an ideological discourse propagated by global networks of elite policy actors and enacted by non-elite policy actors at the school level. The findings point toward a disjuncture between the discourse of elite policy actors in the US, the realities of STEM labor markets, and the actualization of this policy discourse into classroom practice. The implications of this study indicate that analyses of vertical power relations in network governance in STEM education should attend to the semiotics, materiality, and mutability of networked spaces.

  5. A perimotor framework reveals functional segmentation in the motoneuronal network controlling locomotion in Caenorhabditis elegans.

    PubMed

    Haspel, Gal; O'Donovan, Michael J

    2011-10-12

    The neuronal connectivity dataset of the nematode Caenorhabditis elegans attracts wide attention from computational neuroscientists and experimentalists. However, the dataset is incomplete. The ventral and dorsal nerve cords of a single nematode were reconstructed halfway along the body and the posterior data are missing, leaving 21 of 75 motoneurons of the locomotor network with partial or no connectivity data. Using a new framework for network analysis, the perimotor space, we identified rules of connectivity that allowed us to approximate the missing data by extrapolation. Motoneurons were mapped into perimotor space in which each motoneuron is located according to the muscle cells it innervates. In this framework, a pattern of iterative connections emerges which includes most (0.90) of the connections. We identified a repeating unit consisting of 12 motoneurons and 12 muscle cells. The cell bodies of the motoneurons of such a unit are not necessarily anatomical neighbors and there is no obvious anatomical segmentation. A connectivity model, composed of six repeating units, is a description of the network that is both simplified (modular and without noniterative connections) and more complete (includes the posterior part) than the original dataset. The perimotor framework of observed connectivity and the segmented connectivity model give insights and advance the study of the neuronal infrastructure underlying locomotion in C. elegans. Furthermore, we suggest that the tools used herein may be useful to interpret, simplify, and represent connectivity data of other motor systems.

  6. Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

    NASA Astrophysics Data System (ADS)

    Hargrave, C.; Moores, M.; Deegan, T.; Gibbs, A.; Poulsen, M.; Harden, F.; Mengersen, K.

    2014-03-01

    A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific subregions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

  7. Application of a Framework to Implement Trauma-Informed Care Throughout a Pediatric Health Care Network.

    PubMed

    Weiss, Danielle; Kassam-Adams, Nancy; Murray, Carol; Kohser, Kristen L; Fein, Joel A; Winston, Flaura K; Marsac, Meghan L

    2017-01-01

    To evaluate the initial application of a recently published three-step framework for implementing trauma-informed care (TIC) in a pediatric health care network by applying Framework for Spread. In steps 1 and 2 of the framework, we established commitment from the health care network leadership and initial interest in TIC among clinical providers (step 1), set evidence-based training goals and created the associated TIC training content (step 2). In step 3, 440 health care professionals (from 27 health care teams) participated in single-session, 1-hour training that covered the psychological impact of injury- and illness-related trauma, identification of traumatic stress symptoms, and how to respond to children exposed to potentially traumatic events. A concomitant quality improvement project allowed us to assess potential changes in training participants' favorable attitudes toward the integration of TIC and confidence in delivering TIC. Compared with pretraining, participants demonstrated increases in attitude toward TIC, t(293) = 5.8, P < .001, Cohen's d = 0.32, and confidence in delivering TIC, t(293) = 20.9, P < .001, Cohen's d = 1.09. Trainings were effective in achieving proximal goals targeting attitudes and confidence, thereby demonstrating the feasibility and clinical relevance of TIC training when implemented according to the three-step framework. Future research should examine methods of training to reach wide audiences to promote systematic change and evaluate changes in patient outcomes associated with providers' implementation of TIC.

  8. Temporal Information Partition Networks (TIPNets): A Process Network Framework to Reveal Eco-Hydrologic Feedbacks, Responses and Shifts

    NASA Astrophysics Data System (ADS)

    Goodwell, A. E.; Kumar, P.

    2016-12-01

    Within an ecosystem, components of the atmosphere, vegetation, and the root-soil system participate in forcing and feedback reactions at varying time scales and intensities. These interactions constitute a complex network that exhibits behavioral shifts due to perturbations ranging from weather events to long-term drought or land use change. However, it is challenging to characterize this shifting network due to multiple drivers, non-linear interactions, and feedback-induced synchronization. To overcome these issues, we implement a process network approach where eco-hydrologic time-series variables are nodes and information measures are links. We introduce a Temporal Information Partition Network (TIPNet) framework in which multivariate lagged mutual information between pairs of source nodes and target nodes is decomposed into synergistic, redundant, and unique components, each of which reveals different aspects of interactions within the network. We use methods to compute information measures given as few as 200 data points to construct TIPNets based on 1-minute weather station data (radiation Rg, air temperature Ta, wind speed WS, relative humidity RH, precipitation PPT, and leaf wetness LWet) from the Intensively Managed Critical Zone Observatory (IML-CZO) during the growing season of 2015. We find that the source node pairs (Ta, Rg) and (Ta, RH) tend to be synchronized sources that provide dominantly redundant information to target nodes during the day and night, respectively. Meanwhile, source node pairs that include WS often provide synergistic information, indicating the presence of multiple non-redundant influences. We assess temporal shifts in network behavior for weather conditions including rainy periods, dew, and accumulating drought to show how process networks reveal ecosystem behaviors and responses.

  9. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework.

    PubMed

    Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

    2016-02-01

    The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, "trained" networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale's principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns

  10. From field notes to data portal - An operational QA/QC framework for tower networks

    NASA Astrophysics Data System (ADS)

    Sturtevant, C.; Hackley, S.; Meehan, T.; Roberti, J. A.; Holling, G.; Bonarrigo, S.

    2016-12-01

    Quality assurance and control (QA/QC) is one of the most important yet challenging aspects of producing research-quality data. This is especially so for environmental sensor networks collecting numerous high-frequency measurement streams at distributed sites. Here, the quality issues are multi-faceted, including sensor malfunctions, unmet theoretical assumptions, and measurement interference from the natural environment. To complicate matters, there are often multiple personnel managing different sites or different steps in the data flow. For large, centrally managed sensor networks such as NEON, the separation of field and processing duties is in the extreme. Tower networks such as Ameriflux, ICOS, and NEON continue to grow in size and sophistication, yet tools for robust, efficient, scalable QA/QC have lagged. Quality control remains a largely manual process relying on visual inspection of the data. In addition, notes of observed measurement interference or visible problems are often recorded on paper without an explicit pathway to data flagging during processing. As such, an increase in network size requires a near-proportional increase in personnel devoted to QA/QC, quickly stressing the human resources available. There is a need for a scalable, operational QA/QC framework that combines the efficiency and standardization of automated tests with the power and flexibility of visual checks, and includes an efficient communication pathway from field personnel to data processors to end users. Here we propose such a framework and an accompanying set of tools in development, including a mobile application template for recording tower maintenance and an R/shiny application for efficiently monitoring and synthesizing data quality issues. This framework seeks to incorporate lessons learned from the Ameriflux community and provide tools to aid continued network advancements.

  11. Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

    PubMed Central

    Wang, Xiao-Jing

    2016-01-01

    The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity

  12. The Privacy Jungle:On the Market for Data Protection in Social Networks

    NASA Astrophysics Data System (ADS)

    Bonneau, Joseph; Preibusch, Sören

    We have conducted the first thorough analysis of the market for privacy practices and policies in online social networks. From an evaluation of 45 social networking sites using 260 criteria we find that many popular assumptions regarding privacy and social networking need to be revisited when considering the entire ecosystem instead of only a handful of well-known sites. Contrary to the common perception of an oligopolistic market, we find evidence of vigorous competition for new users. Despite observing many poor security practices, there is evidence that social network providers are making efforts to implement privacy enhancing technologies with substantial diversity in the amount of privacy control offered. However, privacy is rarely used as a selling point, even then only as auxiliary, nondecisive feature. Sites also failed to promote their existing privacy controls within the site. We similarly found great diversity in the length and content of formal privacy policies, but found an opposite promotional trend: though almost all policies are not accessible to ordinary users due to obfuscating legal jargon, they conspicuously vaunt the sites' privacy practices. We conclude that the market for privacy in social networks is dysfunctional in that there is significant variation in sites' privacy controls, data collection requirements, and legal privacy policies, but this is not effectively conveyed to users. Our empirical findings motivate us to introduce the novel model of a privacy communication game, where the economically rational choice for a site operator is to make privacy control available to evade criticism from privacy fundamentalists, while hiding the privacy control interface and privacy policy to maximize sign-up numbers and encourage data sharing from the pragmatic majority of users.

  13. Interventions for avian influenza A (H5N1) risk management in live bird market networks.

    PubMed

    Fournié, Guillaume; Guitian, Javier; Desvaux, Stéphanie; Cuong, Vu Chi; Dung, Do Huu; Pfeiffer, Dirk Udo; Mangtani, Punam; Ghani, Azra C

    2013-05-28

    Highly pathogenic avian influenza virus subtype H5N1 is endemic in Asia, with live bird trade as a major disease transmission pathway. A cross-sectional survey was undertaken in northern Vietnam to investigate the structure of the live bird market (LBM) contact network and the implications for virus spread. Based on the movements of traders between LBMs, weighted and directed networks were constructed and used for social network analysis and individual-based modeling. Most LBMs were connected to one another, suggesting that the LBM network may support large-scale disease spread. Because of cross-border trade, it also may promote transboundary virus circulation. However, opportunities for disease control do exist. The implementation of thorough, daily disinfection of the market environment as well as of traders' vehicles and equipment in only a small number of hubs can disconnect the network dramatically, preventing disease spread. These targeted interventions would be an effective alternative to the current policy of a complete ban of LBMs in some areas. Some LBMs that have been banned still are very active, and they likely have a substantial impact on disease dynamics, exhibiting the highest levels of susceptibility and infectiousness. The number of trader visits to markets, information that can be collected quickly and easily, may be used to identify LBMs suitable for implementing interventions. This would not require prior knowledge of the force of infection, for which laboratory-confirmed surveillance would be necessary. These findings are of particular relevance for policy development in resource-scarce settings.

  14. Interventions for avian influenza A (H5N1) risk management in live bird market networks

    PubMed Central

    Fournié, Guillaume; Guitian, Javier; Desvaux, Stéphanie; Cuong, Vu Chi; Dung, Do Huu; Pfeiffer, Dirk Udo; Mangtani, Punam; Ghani, Azra C.

    2013-01-01

    Highly pathogenic avian influenza virus subtype H5N1 is endemic in Asia, with live bird trade as a major disease transmission pathway. A cross-sectional survey was undertaken in northern Vietnam to investigate the structure of the live bird market (LBM) contact network and the implications for virus spread. Based on the movements of traders between LBMs, weighted and directed networks were constructed and used for social network analysis and individual-based modeling. Most LBMs were connected to one another, suggesting that the LBM network may support large-scale disease spread. Because of cross-border trade, it also may promote transboundary virus circulation. However, opportunities for disease control do exist. The implementation of thorough, daily disinfection of the market environment as well as of traders’ vehicles and equipment in only a small number of hubs can disconnect the network dramatically, preventing disease spread. These targeted interventions would be an effective alternative to the current policy of a complete ban of LBMs in some areas. Some LBMs that have been banned still are very active, and they likely have a substantial impact on disease dynamics, exhibiting the highest levels of susceptibility and infectiousness. The number of trader visits to markets, information that can be collected quickly and easily, may be used to identify LBMs suitable for implementing interventions. This would not require prior knowledge of the force of infection, for which laboratory-confirmed surveillance would be necessary. These findings are of particular relevance for policy development in resource-scarce settings. PMID:23650388

  15. On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks.

    PubMed

    Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon

    2015-08-11

    Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting-both in terms of data and energy-data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios.

  16. On the Optimization of a Probabilistic Data Aggregation Framework for Energy Efficiency in Wireless Sensor Networks

    PubMed Central

    Kafetzoglou, Stella; Aristomenopoulos, Giorgos; Papavassiliou, Symeon

    2015-01-01

    Among the key aspects of the Internet of Things (IoT) is the integration of heterogeneous sensors in a distributed system that performs actions on the physical world based on environmental information gathered by sensors and application-related constraints and requirements. Numerous applications of Wireless Sensor Networks (WSNs) have appeared in various fields, from environmental monitoring, to tactical fields, and healthcare at home, promising to change our quality of life and facilitating the vision of sensor network enabled smart cities. Given the enormous requirements that emerge in such a setting—both in terms of data and energy—data aggregation appears as a key element in reducing the amount of traffic in wireless sensor networks and achieving energy conservation. Probabilistic frameworks have been introduced as operational efficient and performance effective solutions for data aggregation in distributed sensor networks. In this work, we introduce an overall optimization approach that improves and complements such frameworks towards identifying the optimal probability for a node to aggregate packets as well as the optimal aggregation period that a node should wait for performing aggregation, so as to minimize the overall energy consumption, while satisfying certain imposed delay constraints. Primal dual decomposition is employed to solve the corresponding optimization problem while simulation results demonstrate the operational efficiency of the proposed approach under different traffic and topology scenarios. PMID:26270664

  17. A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies

    PubMed Central

    Ochs, Christopher; Geller, James; Perl, Yehoshua; Musen, Mark A.

    2016-01-01

    Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT's relational format). A Protégé plugin for deriving “live partial-area taxonomies” is demonstrated. PMID:27345947

  18. A novel framework of classical and quantum prisoner’s dilemma games on coupled networks

    NASA Astrophysics Data System (ADS)

    Deng, Xinyang; Zhang, Qi; Deng, Yong; Wang, Zhen

    2016-03-01

    Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner’s dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner’s dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner’s dilemma is greatly impacted by the combined effect of entanglement and coupling.

  19. Rainfall and streamflow sensor network design: a review of applications, classification, and a proposed framework

    NASA Astrophysics Data System (ADS)

    Chacon-Hurtado, Juan C.; Alfonso, Leonardo; Solomatine, Dimitri P.

    2017-06-01

    Sensors and sensor networks play an important role in decision-making related to water quality, operational streamflow forecasting, flood early warning systems, and other areas. In this paper we review a number of existing applications and analyse a variety of evaluation and design procedures for sensor networks with respect to various criteria. Most of the existing approaches focus on maximising the observability and information content of a variable of interest. From the context of hydrological modelling only a few studies use the performance of the hydrological simulation in terms of output discharge as a design criterion. In addition to the review, we propose a framework for classifying the existing design methods, and a generalised procedure for an optimal network design in the context of rainfall-runoff hydrological modelling.

  20. A novel framework of classical and quantum prisoner's dilemma games on coupled networks.

    PubMed

    Deng, Xinyang; Zhang, Qi; Deng, Yong; Wang, Zhen

    2016-03-15

    Evolutionary games on multilayer networks are attracting growing interest. While among previous studies, the role of quantum games in such a infrastructure is still virgin and may become a fascinating issue across a myriad of research realms. To mimick two kinds of different interactive environments and mechanisms, in this paper a new framework of classical and quantum prisoner's dilemma games on two-layer coupled networks is considered. Within the proposed model, the impact of coupling factor of networks and entanglement degree in quantum games on the evolutionary process has been studied. Simulation results show that the entanglement has no impact on the evolution of the classical prisoner's dilemma, while the rise of the coupling factor obviously impedes cooperation in this game, and the evolution of quantum prisoner's dilemma is greatly impacted by the combined effect of entanglement and coupling.

  1. A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

    PubMed

    Birlutiu, Adriana; d'Alché-Buc, Florence; Heskes, Tom

    2015-01-01

    Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

  2. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations.

    PubMed

    Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

    2015-01-01

    Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology.

  3. Using a two-phase evolutionary framework to select multiple network spreaders based on community structure

    NASA Astrophysics Data System (ADS)

    Fu, Yu-Hsiang; Huang, Chung-Yuan; Sun, Chuen-Tsai

    2016-11-01

    Using network community structures to identify multiple influential spreaders is an appropriate method for analyzing the dissemination of information, ideas and infectious diseases. For example, data on spreaders selected from groups of customers who make similar purchases may be used to advertise products and to optimize limited resource allocation. Other examples include community detection approaches aimed at identifying structures and groups in social or complex networks. However, determining the number of communities in a network remains a challenge. In this paper we describe our proposal for a two-phase evolutionary framework (TPEF) for determining community numbers and maximizing community modularity. Lancichinetti-Fortunato-Radicchi benchmark networks were used to test our proposed method and to analyze execution time, community structure quality, convergence, and the network spreading effect. Results indicate that our proposed TPEF generates satisfactory levels of community quality and convergence. They also suggest a need for an index, mechanism or sampling technique to determine whether a community detection approach should be used for selecting multiple network spreaders.

  4. A unified framework for spiking and gap-junction interactions in distributed neuronal network simulations

    PubMed Central

    Hahne, Jan; Helias, Moritz; Kunkel, Susanne; Igarashi, Jun; Bolten, Matthias; Frommer, Andreas; Diesmann, Markus

    2015-01-01

    Contemporary simulators for networks of point and few-compartment model neurons come with a plethora of ready-to-use neuron and synapse models and support complex network topologies. Recent technological advancements have broadened the spectrum of application further to the efficient simulation of brain-scale networks on supercomputers. In distributed network simulations the amount of spike data that accrues per millisecond and process is typically low, such that a common optimization strategy is to communicate spikes at relatively long intervals, where the upper limit is given by the shortest synaptic transmission delay in the network. This approach is well-suited for simulations that employ only chemical synapses but it has so far impeded the incorporation of gap-junction models, which require instantaneous neuronal interactions. Here, we present a numerical algorithm based on a waveform-relaxation technique which allows for network simulations with gap junctions in a way that is compatible with the delayed communication strategy. Using a reference implementation in the NEST simulator, we demonstrate that the algorithm and the required data structures can be smoothly integrated with existing code such that they complement the infrastructure for spiking connections. To show that the unified framework for gap-junction and spiking interactions achieves high performance and delivers high accuracy in the presence of gap junctions, we present benchmarks for workstations, clusters, and supercomputers. Finally, we discuss limitations of the novel technology. PMID:26441628

  5. Network-Based Inference Framework for Identifying Cancer Genes from Gene Expression Data

    PubMed Central

    Yang, Bo; Zhang, Junying; Yin, Yaling; Zhang, Yuanyuan

    2013-01-01

    Great efforts have been devoted to alleviate uncertainty of detected cancer genes as accurate identification of oncogenes is of tremendous significance and helps unravel the biological behavior of tumors. In this paper, we present a differential network-based framework to detect biologically meaningful cancer-related genes. Firstly, a gene regulatory network construction algorithm is proposed, in which a boosting regression based on likelihood score and informative prior is employed for improving accuracy of identification. Secondly, with the algorithm, two gene regulatory networks are constructed from case and control samples independently. Thirdly, by subtracting the two networks, a differential-network model is obtained and then used to rank differentially expressed hub genes for identification of cancer biomarkers. Compared with two existing gene-based methods (t-test and lasso), the method has a significant improvement in accuracy both on synthetic datasets and two real breast cancer datasets. Furthermore, identified six genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1) susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Among these oncogenes, TSPYL5 and CCNE2 have been already known as prognostic biomarkers in breast cancer, CD55 has been suspected of playing an important role in breast cancer prognosis from literature evidence, and other three genes are newly discovered breast cancer biomarkers. More generally, the differential-network schema can be extended to other complex diseases for detection of disease associated-genes. PMID:24073403

  6. Segment-based Mass Customization: An Exploration of a New Conceptual Marketing Framework.

    ERIC Educational Resources Information Center

    Jiang, Pingjun

    2000-01-01

    Suggests that the concept of mass customization should be seen as an integral part of market segmentation theory which offers the best way to satisfy consumers' unique needs and wants while yielding profits to companies. Proposes a new concept of "segment-based based mass customization," and offers a series of propositions which are…

  7. Segment-based Mass Customization: An Exploration of a New Conceptual Marketing Framework.

    ERIC Educational Resources Information Center

    Jiang, Pingjun

    2000-01-01

    Suggests that the concept of mass customization should be seen as an integral part of market segmentation theory which offers the best way to satisfy consumers' unique needs and wants while yielding profits to companies. Proposes a new concept of "segment-based based mass customization," and offers a series of propositions which are…

  8. Forecasting the portuguese stock market time series by using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Isfan, Monica; Menezes, Rui; Mendes, Diana A.

    2010-04-01

    In this paper, we show that neural networks can be used to uncover the non-linearity that exists in the financial data. First, we follow a traditional approach by analysing the deterministic/stochastic characteristics of the Portuguese stock market data and some typical features are studied, like the Hurst exponents, among others. We also simulate a BDS test to investigate nonlinearities and the results are as expected: the financial time series do not exhibit linear dependence. Secondly, we trained four types of neural networks for the stock markets and used the models to make forecasts. The artificial neural networks were obtained using a three-layer feed-forward topology and the back-propagation learning algorithm. The quite large number of parameters that must be selected to develop a neural network forecasting model involves some trial and as a consequence the error is not small enough. In order to improve this we use a nonlinear optimization algorithm to minimize the error. Finally, the output of the 4 models is quite similar, leading to a qualitative forecast that we compare with the results of the application of k-nearest-neighbor for the same time series.

  9. [Market and public policy network failures: challenges and possibilities for the Brazilian Unified Health System].

    PubMed

    Pinheiro Filho, Francisco Percival; Sarti, Flávia Mori

    2012-11-01

    The principles and guidelines of the Brazilian Unified Health System (SUS) impose a healthcare service structure based on public policy networks which, combined with the financing model adopted, leads to market failings. This imposes barriers to the management of the public health system and the enactment of SUS objectives. The institutional characteristics and the heterogeneity of players, allied to the existence of different healthcare approaches, generate analytical complexity in the study of the global dynamics of the SUS network. There are limitations in the use of quantitative methods based on static analysis of retrospective SUS data. Thus, an approach taking SUS as a complex system using innovative quantitative methodology based on computational simulation is proposed. This paper sought to analyze challenges and possibilities of the combined application of cellular automata modeling and agent-based modeling for simulation of the evolution of the SUS healthcare service network. This approach should permit better understanding of the organization, heterogeneity and structural dynamics of the SUS service network and a minimization of the effects of market failings on the Brazilian health system.

  10. Using the social structure of markets as a framework for analyzing vaccination debates: The case of emergency polio vaccination.

    PubMed

    Connelly, Yaron; Ziv, Arnona; Goren, Uri; Tal, Orna; Kaplan, Giora; Velan, Baruch

    2016-07-02

    The framework of the social structure of markets was used to analyze an online debate revolving around an emergency poliovirus vaccination campaign in Israel. Examination of a representative sample of 200 discussions revealed the activity of three parties: authoritative agents promoting vaccinations, alternative agents promoting anti-vaccination, both representing sellers, and the impartial agents, representing the customers-the general public deliberating whether to comply with vaccination or not. Both sellers interacted with consumers using mechanisms of luring and convincing. The authoritative agents conveyed their message by exhibiting professionalism, building trust and offering to share information. The alternative agents spread doubts and evoked negative emotions of distrust and fear. Among themselves, the alternative agents strived to discredit the authoritative agents, while the latter preferred to ignore the former. Content analysis of discussions conducted by the general public reveal reiteration of the messages conveyed by the sellers, implying that the transaction of pro and anti-vaccination ideas indeed took place. We suggest that the framework of the market as a social structure can be applied to the analysis of other vaccination debates, and thereby provide additional insights into vaccination polemics.

  11. Using the social structure of markets as a framework for analyzing vaccination debates: The case of emergency polio vaccination

    PubMed Central

    Connelly, Yaron; Ziv, Arnona; Goren, Uri; Tal, Orna; Kaplan, Giora; Velan, Baruch

    2016-01-01

    ABSTRACT The framework of the social structure of markets was used to analyze an online debate revolving around an emergency poliovirus vaccination campaign in Israel. Examination of a representative sample of 200 discussions revealed the activity of three parties: authoritative agents promoting vaccinations, alternative agents promoting anti-vaccination, both representing sellers, and the impartial agents, representing the customers—the general public deliberating whether to comply with vaccination or not. Both sellers interacted with consumers using mechanisms of luring and convincing. The authoritative agents conveyed their message by exhibiting professionalism, building trust and offering to share information. The alternative agents spread doubts and evoked negative emotions of distrust and fear. Among themselves, the alternative agents strived to discredit the authoritative agents, while the latter preferred to ignore the former. Content analysis of discussions conducted by the general public reveal reiteration of the messages conveyed by the sellers, implying that the transaction of pro and anti-vaccination ideas indeed took place. We suggest that the framework of the market as a social structure can be applied to the analysis of other vaccination debates, and thereby provide additional insights into vaccination polemics. PMID:27058586

  12. A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks

    PubMed Central

    Khammash, Mustafa

    2014-01-01

    Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed. PMID:24968191

  13. A scalable computational framework for establishing long-term behavior of stochastic reaction networks.

    PubMed

    Gupta, Ankit; Briat, Corentin; Khammash, Mustafa

    2014-06-01

    Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed.

  14. Market scenarios and alternative administrative frameworks for US educational satellite systems

    NASA Technical Reports Server (NTRS)

    Walkmeyer, J. E., Jr.; Morgan, R. P.; Singh, J. P.

    1975-01-01

    Costs and benefits of developing an operational educational satellite system in the U.S. are analyzed. Scenarios are developed for each educational submarket and satellite channel and ground terminal requirements for a large-scale educational telecommunications system are estimated. Alternative organizational frameworks for such a system are described.

  15. ENcentive: A Framework for Intelligent Marketing in Mobile Peer-To-Peer Environments

    DTIC Science & Technology

    2005-01-01

    circulation since businesses that originally created the promotions re- ward the active distributors with additional promotions and other compensations...trade and commu- nication strategies, mobile electronic marketing, intelligent agents, collaborative eCommerce 1. INTRODUCTION With the explosion of...gadgets, applications and services, comes a set of new business models that address the needs of such mobile users. Mobile commerce is a new way to do

  16. Time and frequency structure of causal correlation networks in the China bond market

    NASA Astrophysics Data System (ADS)

    Wang, Zhongxing; Yan, Yan; Chen, Xiaosong

    2017-07-01

    There are more than eight hundred interest rates published in the China bond market every day. Identifying the benchmark interest rates that have broad influences on most other interest rates is a major concern for economists. In this paper, a multi-variable Granger causality test is developed and applied to construct a directed network of interest rates, whose important nodes, regarded as key interest rates, are evaluated with CheiRank scores. The results indicate that repo rates are the benchmark of short-term rates, the central bank bill rates are in the core position of mid-term interest rates network, and treasury bond rates lead the long-term bond rates. The evolution of benchmark interest rates from 2008 to 2014 is also studied, and it is found that SHIBOR has generally become the benchmark interest rate in China. In the frequency domain we identify the properties of information flows between interest rates, and the result confirms the existence of market segmentation in the China bond market.

  17. Physics of transportation: Towards optimal capacity using the multilayer network framework

    PubMed Central

    Du, Wen-Bo; Zhou, Xing-Lian; Jusup, Marko; Wang, Zhen

    2016-01-01

    Because of the critical role of transportation in modern times, one of the most successful application areas of statistical physics of complex networks is the study of traffic dynamics. However, the vast majority of works treat transportation networks as an isolated system, which is inconsistent with the fact that many complex networks are interrelated in a nontrivial way. To mimic a realistic scenario, we use the framework of multilayer networks to construct a two-layered traffic model, whereby the upper layer provides higher transport speed than the lower layer. Moreover, passengers are guided to travel along the path of minimal travelling time and with the additional cost they can transfer from one layer to another to avoid congestion and/or reach the final destination faster. By means of numerical simulations, we show that a degree distribution-based strategy, although facilitating the cooperation between both layers, can be further improved by enhancing the critical generating rate of passengers using a particle swarm optimisation (PSO) algorithm. If initialised with the prior knowledge from the degree distribution-based strategy, the PSO algorithm converges considerably faster. Our work exemplifies how statistical physics of complex networks can positively affect daily life. PMID:26791580

  18. Physics of transportation: Towards optimal capacity using the multilayer network framework.

    PubMed

    Du, Wen-Bo; Zhou, Xing-Lian; Jusup, Marko; Wang, Zhen

    2016-01-21

    Because of the critical role of transportation in modern times, one of the most successful application areas of statistical physics of complex networks is the study of traffic dynamics. However, the vast majority of works treat transportation networks as an isolated system, which is inconsistent with the fact that many complex networks are interrelated in a nontrivial way. To mimic a realistic scenario, we use the framework of multilayer networks to construct a two-layered traffic model, whereby the upper layer provides higher transport speed than the lower layer. Moreover, passengers are guided to travel along the path of minimal travelling time and with the additional cost they can transfer from one layer to another to avoid congestion and/or reach the final destination faster. By means of numerical simulations, we show that a degree distribution-based strategy, although facilitating the cooperation between both layers, can be further improved by enhancing the critical generating rate of passengers using a particle swarm optimisation (PSO) algorithm. If initialised with the prior knowledge from the degree distribution-based strategy, the PSO algorithm converges considerably faster. Our work exemplifies how statistical physics of complex networks can positively affect daily life.

  19. Physics of transportation: Towards optimal capacity using the multilayer network framework

    NASA Astrophysics Data System (ADS)

    Du, Wen-Bo; Zhou, Xing-Lian; Jusup, Marko; Wang, Zhen

    2016-01-01

    Because of the critical role of transportation in modern times, one of the most successful application areas of statistical physics of complex networks is the study of traffic dynamics. However, the vast majority of works treat transportation networks as an isolated system, which is inconsistent with the fact that many complex networks are interrelated in a nontrivial way. To mimic a realistic scenario, we use the framework of multilayer networks to construct a two-layered traffic model, whereby the upper layer provides higher transport speed than the lower layer. Moreover, passengers are guided to travel along the path of minimal travelling time and with the additional cost they can transfer from one layer to another to avoid congestion and/or reach the final destination faster. By means of numerical simulations, we show that a degree distribution-based strategy, although facilitating the cooperation between both layers, can be further improved by enhancing the critical generating rate of passengers using a particle swarm optimisation (PSO) algorithm. If initialised with the prior knowledge from the degree distribution-based strategy, the PSO algorithm converges considerably faster. Our work exemplifies how statistical physics of complex networks can positively affect daily life.

  20. A minimum resource neural network framework for solving multiconstraint shortest path problems.

    PubMed

    Zhang, Junying; Zhao, Xiaoxue; He, Xiaotao

    2014-08-01

    Characterized by using minimum hard (structural) and soft (computational) resources, a novel parameter-free minimal resource neural network (MRNN) framework is proposed for solving a wide range of single-source shortest path (SP) problems for various graph types. The problems are the k-shortest time path problems with any combination of three constraints: time, hop, and label constraints, and the graphs can be directed, undirected, or bidirected with symmetric and/or asymmetric traversal time, which can be real and time dependent. Isomorphic to the graph where the SP is to be sought, the network is activated by generating autowave at source neuron and the autowave travels automatically along the paths with the speed of a hop in an iteration. Properties of the network are studied, algorithms are presented, and computation complexity is analyzed. The framework guarantees globally optimal solutions of a series of problems during the iteration process of the network, which provides insight into why even the SP is still too long to be satisfied. The network facilitates very large scale integrated circuit implementation and adapt to very large scale problems due to its massively parallel processing and minimum resource utilization. When implemented in a sequentially processing computer, experiments on synthetic graphs, road maps of cities of the USA, and vehicle routing with time windows indicate that the MRNN is especially efficient for large scale sparse graphs and even dense graphs with some constraints, e.g., the CPU time taken and the iteration number used for the road maps of cities of the USA is even less than  ∼ 2% and 0.5% that of the Dijkstra's algorithm.

  1. Development of a privacy and security policy framework for a multistate comparative effectiveness research network.

    PubMed

    Kim, Katherine K; McGraw, Deven; Mamo, Laura; Ohno-Machado, Lucila

    2013-08-01

    Comparative effectiveness research (CER) conducted in distributed research networks (DRNs) is subject to different state laws and regulations as well as institution-specific policies intended to protect privacy and security of health information. The goal of the Scalable National Network for Effectiveness Research (SCANNER) project is to develop and demonstrate a scalable, flexible technical infrastructure for DRNs that enables near real-time CER consistent with privacy and security laws and best practices. This investigation began with an analysis of privacy and security laws and state health information exchange (HIE) guidelines applicable to SCANNER participants from California, Illinois, Massachusetts, and the Federal Veteran's Administration. A 7-member expert panel of policy and technical experts reviewed the analysis and gave input into the framework during 5 meetings held in 2011-2012. The state/federal guidelines were applied to 3 CER use cases: safety of new oral hematologic medications; medication therapy management for patients with diabetes and hypertension; and informational interventions for providers in the treatment of acute respiratory infections. The policy framework provides flexibility, beginning with a use-case approach rather than a one-size-fits-all approach. The policies may vary depending on the type of patient data shared (aggregate counts, deidentified, limited, and fully identified datasets) and the flow of data. The types of agreements necessary for a DRN may include a network-level and data use agreements. The need for flexibility in the development and implementation of policies must be balanced with responsibilities of data stewardship.

  2. On Governance, Embedding and Marketing: Reflections on the Construction of Alternative Sustainable Food Networks.

    PubMed

    Roep, Dirk; Wiskerke, Johannes S C

    Based on the reconstruction of the development of 14 food supply chain initiatives in 7 European countries, we developed a conceptual framework that demonstrates that the process of increasing the sustainability of food supply chains is rooted in strategic choices regarding governance, embedding, and marketing and in the coordination of these three dimensions that are inextricably interrelated. The framework also shows that when seeking to further develop an initiative (e.g., through scaling up or product diversification) these interrelations need continuous rebalancing. We argue that the framework can serve different purposes: it can be used as an analytical tool by researchers studying food supply chain dynamics, as a policy tool by policymakers that want to support the development of sustainable food supply chains, and as a reflexive tool by practitioners and their advisors to help them to position themselves, develop a clear strategy, find the right allies, develop their skills, and build the capacities that they need. In this paper, we elaborate upon the latter function of the framework and illustrate this briefly with empirical evidence from three of the initiatives that we studied.

  3. Markets

    Treesearch

    David N. Wear; Jeffrey Prestemon; Robert Huggett; Douglas Carter

    2013-01-01

    Key FindingsAlthough timber production in the South more than doubled from the 1960s to the late 1990s, output levels have declined over the last 10 years, signaling structural changes in timber markets.For softwood products, production declines are most clearly related to demand issues. Demand for softwood solid wood products...

  4. 76 FR 53443 - ENBALA Power Networks (USA), Inc.; Supplemental Notice That Initial Market-Based Rate Filing...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2011-08-26

    ... Federal Energy Regulatory Commission ENBALA Power Networks (USA), Inc.; Supplemental Notice That Initial Market-Based Rate Filing Includes Request for Blanket Section 204 Authorization This is a supplemental notice in the above-referenced proceeding of ENBALA Power Networks (USA), Inc.'s application for...

  5. A Framework for Using Rural Markets to Analyze Local Food Shortage Resilience and Mitigation Potential in sub-Saharan Africa based on Evidence from Zambia

    NASA Astrophysics Data System (ADS)

    Montgomery, M. J.; Baylis, K.; Evans, T. P.

    2016-12-01

    Climate change is predicted to have negative impacts on agriculture and food security in many parts of sub-Saharan Africa. Regional and temporal climate variability will disburse these effects, creating opportunities to mitigate food shortages through well-studied international, regional, and national food flows and associated food prices. However, most food products consumed and traded by rural smallhold farmers rely on local market exchanges that take place outside the scope of prevalent regional and national market analysis. There is little empirical evidence on these rural markets outside of their potential for smallholder agribusiness. However, they offer an unopened window into local food supply and the nuances of food movements in rural areas. Our research explores how to analyze the cost and availability of food products in rural markets and their connection with each other, as well as with nearby households' food security. This new approach of using food markets as a unit of analysis necessitates a new framework that groups markets based on a hierarchy of variables relevant to their role as food movers and suppliers. In our research, we collected price and source data for 22 commodities bought and sold within 52 rural markets in 12 districts spatially distributed throughout Zambia. We continue to collect data via phone interviews with 206 traders and market managers within these markets each month. We used this data to develop a typology of stationary rural food markets based on their size in terms of traders and buyers, the diversity of commodities available year-round and seasonally, their price transmission with other markets, and their trading scheme and governance. The result is a dynamic framework with varying weights on each variable that classifies which characteristic of markets under which conditions increase their potential for local food shortage resilience and mitigation. We also allocate for commodity-specific scenarios to allow for modeling

  6. A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks

    PubMed Central

    Ye, Dayon; Zhang, Minji; Yang, Yu

    2015-01-01

    Wireless sensor networks (WSNs) have been widely investigated in recent years. One of the fundamental issues in WSNs is packet routing, because in many application domains, packets have to be routed from source nodes to destination nodes as soon and as energy efficiently as possible. To address this issue, a large number of routing approaches have been proposed. Although every existing routing approach has advantages, they also have some disadvantages. In this paper, a multi-agent framework is proposed that can assist existing routing approaches to improve their routing performance. This framework enables each sensor node to build a cooperative neighbour set based on past routing experience. Such cooperative neighbours, in turn, can help the sensor to effectively relay packets in the future. This framework is independent of existing routing approaches and can be used to assist many existing routing approaches. Simulation results demonstrate the good performance of this framework in terms of four metrics: average delivery latency, successful delivery ratio, number of live nodes and total sensing coverage. PMID:25928063

  7. Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning

    PubMed Central

    Zhang, Yue; Song, Bin; Zhang, Ying; Du, Xiaojiang; Guizani, Mohsen

    2016-01-01

    Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resources are limited. By viewing ESNs as multi-agent environments, we model them with an agent-based modelling (ABM) method and deal with resource allocation problems with market models, after describing users’ patterns. Reinforcement learning methods are introduced to estimate users’ patterns and verify the outcomes in our market models. Experimental results show the efficiency of our methods, which are also capable of guiding topology management. PMID:27916841

  8. Market Model for Resource Allocation in Emerging Sensor Networks with Reinforcement Learning.

    PubMed

    Zhang, Yue; Song, Bin; Zhang, Ying; Du, Xiaojiang; Guizani, Mohsen

    2016-11-29

    Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resources are limited. By viewing ESNs as multi-agent environments, we model them with an agent-based modelling (ABM) method and deal with resource allocation problems with market models, after describing users' patterns. Reinforcement learning methods are introduced to estimate users' patterns and verify the outcomes in our market models. Experimental results show the efficiency of our methods, which are also capable of guiding topology management.

  9. Pharma Pricing & Market Access Europe 2016--Health Network Communications' Tenth Annual Conference (February 23-25, 2016--London, UK).

    PubMed

    D'Souza, P

    2016-03-01

    Tighter national budgets and escalating drug prices continue to present challenges for pharmaceutical market access strategies and societal cost of care. As pharmaceutical companies and medical governmental advisory organizations enter tougher negotiations, hospital trusts and other dispensary firms face barriers to receiving the best medical treatment, and as a result patient access is limited. The 2016 HealthNetwork Communications' Pharma Pricing & Market Access Europe meeting brought together pharmaceutical, medical governmental advisory and stakeholders and market access/pricing consultants, to encourage discussions and negotiations into how to improve the drug pricing system and consequential market access strategies while achieving the respective reimbursement and affordability objectives.

  10. Study of a market model with conservative exchanges on complex networks

    NASA Astrophysics Data System (ADS)

    Braunstein, Lidia A.; Macri, Pablo A.; Iglesias, J. R.

    2013-04-01

    Many models of market dynamics make use of the idea of conservative wealth exchanges among economic agents. A few years ago an exchange model using extremal dynamics was developed and a very interesting result was obtained: a self-generated minimum wealth or poverty line. On the other hand, the wealth distribution exhibited an exponential shape as a function of the square of the wealth. These results have been obtained both considering exchanges between nearest neighbors or in a mean field scheme. In the present paper we study the effect of distributing the agents on a complex network. We have considered archetypical complex networks: Erdös-Rényi random networks and scale-free networks. The presence of a poverty line with finite wealth is preserved but spatial correlations are important, particularly between the degree of the node and the wealth. We present a detailed study of the correlations, as well as the changes in the Gini coefficient, that measures the inequality, as a function of the type and average degree of the considered networks.

  11. An informatics framework for the standardized collection and analysis of medication data in networked research.

    PubMed

    Richesson, Rachel L

    2014-12-01

    Medication exposure is an important variable in virtually all clinical research, yet there is great variation in how the data are collected, coded, and analyzed. Coding and classification systems for medication data are heterogeneous in structure, and there is little guidance for implementing them, especially in large research networks and multi-site trials. Current practices for handling medication data in clinical trials have emerged from the requirements and limitations of paper-based data collection, but there are now many electronic tools to enable the collection and analysis of medication data. This paper reviews approaches to coding medication data in multi-site research contexts, and proposes a framework for the classification, reporting, and analysis of medication data. The framework can be used to develop tools for classifying medications in coded data sets to support context appropriate, explicit, and reproducible data analyses by researchers and secondary users in virtually all clinical research domains.

  12. Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study.

    PubMed

    Bhattacharjee, Biplab; Shafi, Muhammad; Acharjee, Animesh

    2016-01-01

    The socio-economic systems today possess high levels of both interconnectedness and interdependencies, and such system-level relationships behave very dynamically. In such situations, it is all around perceived that influence is a perplexing power that has an overseeing part in affecting the dynamics and behaviours of involved ones. As a result of the force & direction of influence, the transformative change of one entity has a cogent aftereffect on the other entities in the system. The current study employs directed weighted networks for investigating the influential relationship patterns existent in a typical equity market as an outcome of inter-stock interactions happening at the market level, the sectorial level and the industrial level. The study dataset is derived from 335 constituent stocks of 'Standard & Poor Bombay Stock Exchange 500 index' and study period is 1st June 2005 to 30th June 2015. The study identifies the set of most dynamically influential stocks & their respective temporal pattern at three hierarchical levels: the complete equity market, different sectors, and constituting industry segments of those sectors. A detailed influence relationship analysis is performed for the sectorial level network of the construction sector, and it was found that stocks belonging to the cement industry possessed high influence within this sector. Also, the detailed network analysis of construction sector revealed that it follows scale-free characteristics and power law distribution. In the industry specific influence relationship analysis for cement industry, methods based on threshold filtering and minimum spanning tree were employed to derive a set of sub-graphs having temporally stable high-correlation structure over this ten years period.

  13. Investigating the Influence Relationship Models for Stocks in Indian Equity Market: A Weighted Network Modelling Study

    PubMed Central

    Acharjee, Animesh

    2016-01-01

    The socio-economic systems today possess high levels of both interconnectedness and interdependencies, and such system-level relationships behave very dynamically. In such situations, it is all around perceived that influence is a perplexing power that has an overseeing part in affecting the dynamics and behaviours of involved ones. As a result of the force & direction of influence, the transformative change of one entity has a cogent aftereffect on the other entities in the system. The current study employs directed weighted networks for investigating the influential relationship patterns existent in a typical equity market as an outcome of inter-stock interactions happening at the market level, the sectorial level and the industrial level. The study dataset is derived from 335 constituent stocks of ‘Standard & Poor Bombay Stock Exchange 500 index’ and study period is 1st June 2005 to 30th June 2015. The study identifies the set of most dynamically influential stocks & their respective temporal pattern at three hierarchical levels: the complete equity market, different sectors, and constituting industry segments of those sectors. A detailed influence relationship analysis is performed for the sectorial level network of the construction sector, and it was found that stocks belonging to the cement industry possessed high influence within this sector. Also, the detailed network analysis of construction sector revealed that it follows scale-free characteristics and power law distribution. In the industry specific influence relationship analysis for cement industry, methods based on threshold filtering and minimum spanning tree were employed to derive a set of sub-graphs having temporally stable high-correlation structure over this ten years period. PMID:27846251

  14. Evaluation of Metal-Organic Frameworks and Porous Polymer Networks for CO2 -Capture Applications.

    PubMed

    Verdegaal, Wolfgang M; Wang, Kecheng; Sculley, Julian P; Wriedt, Mario; Zhou, Hong-Cai

    2016-03-21

    This manuscript presents experimental data for 20 adsorption materials (metal-organic frameworks, porous polymer networks, and Zeolite-5A), including CO2 and N2 isotherms and heat capacities. With input from only experimental data, working capacities per energy for each material were calculated. Furthermore, by running seven different carbon-capture scenarios in which the initial flue-gas composition and process temperature was systematically changed, we present a range of performances for each material and quantify how sensitive each is to these varying parameters. The presented calculations provide researchers with a tool to investigate promising carbon-capture materials more easily and completely.

  15. JCell--a Java-based framework for inferring regulatory networks from time series data.

    PubMed

    Spieth, C; Supper, J; Streichert, F; Speer, N; Zell, A

    2006-08-15

    JCell is a Java-based application for reconstructing gene regulatory networks from experimental data. The framework provides several algorithms to identify genetic and metabolic dependencies based on experimental data conjoint with mathematical models to describe and simulate regulatory systems. Owing to the modular structure, researchers can easily implement new methods. JCell is a pure Java application with additional scripting capabilities and thus widely usable, e.g. on parallel or cluster computers. The software is freely available for download at http://www-ra.informatik.uni-tuebingen.de/software/JCell.

  16. Framework and Method for Controlling a Robotic System Using a Distributed Computer Network

    NASA Technical Reports Server (NTRS)

    Sanders, Adam M. (Inventor); Barajas, Leandro G. (Inventor); Permenter, Frank Noble (Inventor); Strawser, Philip A. (Inventor)

    2015-01-01

    A robotic system for performing an autonomous task includes a humanoid robot having a plurality of compliant robotic joints, actuators, and other integrated system devices that are controllable in response to control data from various control points, and having sensors for measuring feedback data at the control points. The system includes a multi-level distributed control framework (DCF) for controlling the integrated system components over multiple high-speed communication networks. The DCF has a plurality of first controllers each embedded in a respective one of the integrated system components, e.g., the robotic joints, a second controller coordinating the components via the first controllers, and a third controller for transmitting a signal commanding performance of the autonomous task to the second controller. The DCF virtually centralizes all of the control data and the feedback data in a single location to facilitate control of the robot across the multiple communication networks.

  17. FNCS: A Framework for Power System and Communication Networks Co-Simulation

    SciTech Connect

    Ciraci, Selim; Daily, Jeffrey A.; Fuller, Jason C.; Fisher, Andrew R.; Marinovici, Laurentiu D.; Agarwal, Khushbu

    2014-04-13

    This paper describes the Fenix framework that uses a federated approach for integrating power grid and communication network simulators. Compared existing approaches, Fenix al- lows co-simulation of both transmission and distribution level power grid simulators with the communication network sim- ulator. To reduce the performance overhead of time synchro- nization, Fenix utilizes optimistic synchronization strategies that make speculative decisions about when the simulators are going to exchange messages. GridLAB-D (a distribution simulator), PowerFlow (a transmission simulator), and ns-3 (a telecommunication simulator) are integrated with the frame- work and are used to illustrate the enhanced performance pro- vided by speculative multi-threading on a smart grid applica- tion. Our speculative multi-threading approach achieved on average 20% improvement over the existing synchronization methods

  18. A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths

    NASA Astrophysics Data System (ADS)

    Passalacqua, Paola; Do Trung, Tien; Foufoula-Georgiou, Efi; Sapiro, Guillermo; Dietrich, William E.

    2010-03-01

    A geometric framework for the automatic extraction of channels and channel networks from high-resolution digital elevation data is introduced in this paper. The proposed approach incorporates nonlinear diffusion for the preprocessing of the data, both to remove noise and to enhance features that are critical to the network extraction. Following this preprocessing, channels are defined as curves of minimal effort, or geodesics, where the effort is measured on the basis of fundamental geomorphological characteristics such as flow accumulation area and isoheight contours curvature. The merits of the proposed methodology, and especially the computational efficiency and accurate localization of the extracted channels, are demonstrated using light detection and ranging (lidar) data of the Skunk Creek, a tributary of the South Fork Eel River basin in northern California.

  19. Explicit synchronisation of heterogeneous dynamics networks via three-layer communication framework

    NASA Astrophysics Data System (ADS)

    Wang, Bohui; Wang, Jingcheng; Zhang, Langwen; Zhang, Bin

    2016-06-01

    This paper addresses the explicit synchronisation of heterogeneous dynamics networks via three-layer communication framework. The main contribution is to propose an explicit synchronisation algorithm, in which the synchronisation errors of all the agents are decoupled. By constructing a three-layer node model, the proposed algorithm removes the assumptions that the topology is fixed and the synchronisation process is coupled. By introducing appropriate assumptions, the algorithm leads to a class of explicit synchronisation protocols based on the states of agents in different layers. It is proved in the sense of Lyapunov that, if the dwell time is larger than a threshold, the explicit synchronisation can be achieved for closed-loop heterogeneous dynamics networks under switching topologies. The results are further extended to the cases in which the switching topologies are only frequently but not always connected. Simulation results are presented with four single-link manipulators to verify the theoretical analysis.

  20. A framework for performance measurement in university using extended network data envelopment analysis (DEA) structures

    NASA Astrophysics Data System (ADS)

    Kashim, Rosmaini; Kasim, Maznah Mat; Rahman, Rosshairy Abd

    2015-12-01

    Measuring university performance is essential for efficient allocation and utilization of educational resources. In most of the previous studies, performance measurement in universities emphasized the operational efficiency and resource utilization without investigating the university's ability to fulfill the needs of its stakeholders and society. Therefore, assessment of the performance of university should be separated into two stages namely efficiency and effectiveness. In conventional DEA analysis, a decision making unit (DMU) or in this context, a university is generally treated as a black-box which ignores the operation and interdependence of the internal processes. When this happens, the results obtained would be misleading. Thus, this paper suggest an alternative framework for measuring the overall performance of a university by incorporating both efficiency and effectiveness and applies network DEA model. The network DEA models are recommended because this approach takes into account the interrelationship between the processes of efficiency and effectiveness in the system. This framework also focuses on the university structure which is expanded from the hierarchical to form a series of horizontal relationship between subordinate units by assuming both intermediate unit and its subordinate units can generate output(s). Three conceptual models are proposed to evaluate the performance of a university. An efficiency model is developed at the first stage by using hierarchical network model. It is followed by an effectiveness model which take output(s) from the hierarchical structure at the first stage as a input(s) at the second stage. As a result, a new overall performance model is proposed by combining both efficiency and effectiveness models. Thus, once this overall model is realized and utilized, the university's top management can determine the overall performance of each unit more accurately and systematically. Besides that, the result from the network

  1. FERN - a Java framework for stochastic simulation and evaluation of reaction networks.

    PubMed

    Erhard, Florian; Friedel, Caroline C; Zimmer, Ralf

    2008-08-29

    Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a) do not provide the most efficient simulation algorithms and are difficult to extend, b) cannot be easily integrated into other applications or c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment. FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new

  2. A computational framework for gene regulatory network inference that combines multiple methods and datasets

    PubMed Central

    2011-01-01

    Background Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective. Results This paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies. Conclusions The approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes. PMID:21489290

  3. Understanding cancer complexome using networks, spectral graph theory and multilayer framework

    NASA Astrophysics Data System (ADS)

    Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K.; Chowdhury, Rajdeep; Jalan, Sarika

    2017-02-01

    Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

  4. A network approach for the scientific collaboration in the European Framework Programs

    NASA Astrophysics Data System (ADS)

    Garas, A.; Argyrakis, P.

    2008-12-01

    We construct the networks of collaboration between partners for projects carried out with the support of European Commission Framework Programs FP5 and FP6. We analyze in detail these networks, not only in terms of total number of projects, but also for the different tools employed, the different geographical partitions, and the different thematic areas. For all cases we find a scale-free behavior, as expected for such social networks, and also reported in the literature. In comparing FP5 to FP6, we show that, despite a decrease in the number of signed contracts, and the total number of unique partners, there is an increase in the average number of collaborative partners per institution. Furthermore, we establish a measure for the central role (hub) for each country, by using the Minimum Spanning Tree (MST), which we construct in detail for each thematic area (e.g. Informatics, Nanoscience, Life Sciences, etc.). The importance of these network hubs is highlighted, as this information can be used by policy planners in designing future research plans regarding the distribution of available funds.

  5. A Network Approach to Environmental Impact in Psychotic Disorder: Brief Theoretical Framework.

    PubMed

    Isvoranu, Adela-Maria; Borsboom, Denny; van Os, Jim; Guloksuz, Sinan

    2016-07-01

    The spectrum of psychotic disorder represents a multifactorial and heterogeneous condition and is thought to result from a complex interplay between genetic and environmental factors. In the current paper, we analyze this interplay using network analysis, which has been recently proposed as a novel psychometric framework for the study of mental disorders. Using general population data, we construct network models for the relation between 3 environmental risk factors (cannabis use, developmental trauma, and urban environment), dimensional measures of psychopathology (anxiety, depression, interpersonal sensitivity, obsessive-compulsive disorder, phobic anxiety, somatizations, and hostility), and a composite measure of psychosis expression. Results indicate the existence of specific paths between environmental factors and symptoms. These paths most often involve cannabis use. In addition, the analyses suggest that symptom networks are more strongly connected for people exposed to environmental risk factors, implying that environmental exposure may lead to less resilient symptom networks. © The Author 2016. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  6. A framework for multi-object tracking over distributed wireless camera networks

    NASA Astrophysics Data System (ADS)

    Gau, Victor; Hwang, Jenq-Neng

    2010-07-01

    In this paper, we propose a unified framework targeting at two important issues in a distributed wireless camera network, i.e., object tracking and network communication, to achieve reliable multi-object tracking over distributed wireless camera networks. In the object tracking part, we propose a fully automated approach for tracking of multiple objects across multiple cameras with overlapping and non-overlapping field of views without initial training. To effectively exchange the tracking information among the distributed cameras, we proposed an idle probability based broadcasting method, iPro, which adaptively adjusts the broadcast probability to improve the broadcast effectiveness in a dense saturated camera network. Experimental results for the multi-object tracking demonstrate the promising performance of our approach on real video sequences for cameras with overlapping and non-overlapping views. The modeling and ns-2 simulation results show that iPro almost approaches the theoretical performance upper bound if cameras are within each other's transmission range. In more general scenarios, e.g., in case of hidden node problems, the simulation results show that iPro significantly outperforms standard IEEE 802.11, especially when the number of competing nodes increases.

  7. Understanding cancer complexome using networks, spectral graph theory and multilayer framework

    PubMed Central

    Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K.; Chowdhury, Rajdeep; Jalan, Sarika

    2017-01-01

    Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine. PMID:28155908

  8. Understanding cancer complexome using networks, spectral graph theory and multilayer framework.

    PubMed

    Rai, Aparna; Pradhan, Priodyuti; Nagraj, Jyothi; Lohitesh, K; Chowdhury, Rajdeep; Jalan, Sarika

    2017-02-03

    Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.

  9. Topology of the South African stock market network across the 2008 financial crisis

    NASA Astrophysics Data System (ADS)

    Majapa, Mohamed; Gossel, Sean Joss

    2016-03-01

    This study uses the cross-correlations in the daily closing prices of the South African Top 100 companies listed on the JSE All share index (ALSI) from June 2003 to June 2013 to compute minimum spanning tree maps. In addition to the full sample, the analysis also uses three sub-periods to investigate the topological evolution before, during, and after the 2008 financial crisis. The findings show that although there is substantial clustering and homogeneity on the JSE, the most connected nodes are in the financial and resources sectors. The sub-sample results further reveal that the JSE network tree shrank in the run-up to, and during the financial crisis, and slowly expanded afterwards. In addition, the different clusters in the network are connected by various nodes that are significantly affected by diversification and credit market dynamics.

  10. Dietary supplements: International legal framework and adulteration profiles, and characteristics of products on the Brazilian clandestine market.

    PubMed

    da Justa Neves, Diana Brito; Caldas, Eloisa Dutra

    2015-10-01

    The objectives of this work were to evaluate current legislation on dietary supplements in the United States, the European Union and Brazil, and the profile of adulterated and/or irregular products on these markets. Due to a less restrictive legal framework, a supplement product that is freely available in the US may be considered a drug or even be proscribed in the EU and Brazil, thus giving rise to a clandestine market based on smuggling. From 2007 to 2014, the United States Food and Drug Administration reported 572 cases of supplement adulterations in the country, mainly products for sexual enhancement (41.6%). Data from the European Union Rapid Alert System for Food and Feed showed 929 adulterations during the same period, over 40% due to unauthorized ingredients or undeclared medicines. From 2007 to 2013, the Brazilian Federal Police Department seized 5470 supplement products, 92.2% with an American-declared origin. Qualitative chemical analyses performed on 2898 products found 180 adulterations, 41.1% due to undeclared drugs, mainly anabolic steroids, anorectics and products for erectile dysfunction, all considered medicines in Brazil. Educating the public regarding the potential risks they are taking when consuming adulterated or irregular products is necessary to protect the health of consumers. Copyright © 2015 Elsevier Inc. All rights reserved.

  11. How can the regulator show evidence of (no) risk selection in health insurance markets? Conceptual framework and empirical evidence.

    PubMed

    van de Ven, Wynand P M M; van Vliet, René C J A; van Kleef, Richard C

    2017-03-01

    If consumers have a choice of health plan, risk selection is often a serious problem (e.g., as in Germany, Israel, the Netherlands, the United States of America, and Switzerland). Risk selection may threaten the quality of care for chronically ill people, and may reduce the affordability and efficiency of healthcare. Therefore, an important question is: how can the regulator show evidence of (no) risk selection? Although this seems easy, showing such evidence is not straightforward. The novelty of this paper is two-fold. First, we provide a conceptual framework for showing evidence of risk selection in competitive health insurance markets. It is not easy to disentangle risk selection and the insurers' efficiency. We suggest two methods to measure risk selection that are not biased by the insurers' efficiency. Because these measures underestimate the true risk selection, we also provide a list of signals of selection that can be measured and that, in particular in combination, can show evidence of risk selection. It is impossible to show the absence of risk selection. Second, we empirically measure risk selection among the switchers, taking into account the insurers' efficiency. Based on 2-year administrative data on healthcare expenses and risk characteristics of nearly all individuals with basic health insurance in the Netherlands (N > 16 million) we find significant risk selection for most health insurers. This is the first publication of hard empirical evidence of risk selection in the Dutch health insurance market.

  12. Development and Interpretation of River Water Isoscapes Using a Stream Network Modeling Framework

    NASA Astrophysics Data System (ADS)

    Smith, R. M.; Bowen, G. J.; Brennan, S.

    2016-12-01

    The isotopic signature of hydrogen and oxygen (2H and 18O) in rivers is a useful tracer of hydrologic processes such as evapotranspiration and recharge at the catchment-scale. A recent model of river isoscapes across the United States using spatially and temporally weighted precipitation consistently under-estimated river isotopes compared with measurements from the USGS NASQAN river-monitoring network. The bias in this model suggests that river isotopes may represent an integrated signal of catchment-scale evaporation. We developed a mixed effects model to examine the role of landscape-scale factors such as vegetation cover and surface water features (i.e. lakes and reservoirs) on residuals of the precipitation-based river isoscape, while taking into account the unique spatial relationships of dendritic river networks (e.g., longitudinal transport of flowing water). This modeling framework allows us to evaluate the role of evaporation vs. transpiration on catchment water balances. Since transpiration does not fractionate source water, catchments with more vegetation cover are less likely to exhibit the signal of evaporated water in rivers compared with those with abundant surface water, bare soil, or extended flow distance. Using the Mississippi River as a test case, we employed a recently developed ArcGIS toolbox (STARS - Spatial Tools for the Analysis of River Systems) and its corresponding R package (SSN - Spatial Stream Networks) to develop a detailed network topology dataset for the Mississippi Basin. This dataset describes how the landscape attributes that are likely to drive variation in the magnitude of evapotranspiration are expressed across river systems, and the unique spatial relationships of dendritic river systems, such as directional flow and the branching network of streams. Employing this new class of geostatistical tools provides a flexible analytical framework, which has the ability to assess the relative importance of landscape processes (e

  13. Sexual networking of market women in Benin City, Edo State, Nigeria.

    PubMed

    Omorodion, F I

    1993-01-01

    The manifestation of acquired immune deficiency syndrome (AIDS) all over the world has increased the need for information on the nature and pattern of sexual networking in Africa, where there is a dearth of such information. In the present study, information on the sexual networking of market women in Benin City, Nigeria, was obtained using a questionnaire instrument. The questionnaire covered the sexual, reproductive, and health behaviors of these women. The data revealed that the levels of both premarital and extramarital sexual networking are high. Such a high degree of sexual networking has exposed a number of these women to sexually transmitted diseases. The manifestation of AIDS in this society will be widespread and devastating to the people and the society because these women are in a polygamous relationship in a society that frowns on the use of contraceptives by couples. In addition, controversies surround the correlation between socioeconomic status (SES) and AIDS, geographical areas of prevalence, and the role of prostitutes in the spread of the disease. For example, studies in Africa show that whereas there is no correlation between SES and AIDS in Kinshasa, the attack rate was higher in educated people in Rwanda and Zambia. Moreover, there are predominantly urban outbreaks in other countries, such as Uganda (Piot & Carael, 1988).

  14. A hybrid framework for reservoir characterization using fuzzy ranking and an artificial neural network

    NASA Astrophysics Data System (ADS)

    Wang, Baijie; Wang, Xin; Chen, Zhangxin

    2013-08-01

    Reservoir characterization refers to the process of quantitatively assigning reservoir properties using all available field data. Artificial neural networks (ANN) have recently been introduced to solve reservoir characterization problems dealing with the complex underlying relationships inherent in well log data. Despite the utility of ANNs, the current limitation is that most existing applications simply focus on directly implementing existing ANN models instead of improving/customizing them to fit the specific reservoir characterization tasks at hand. In this paper, we propose a novel intelligent framework that integrates fuzzy ranking (FR) and multilayer perceptron (MLP) neural networks for reservoir characterization. FR can automatically identify a minimum subset of well log data as neural inputs, and the MLP is trained to learn the complex correlations from the selected well log data to a target reservoir property. FR guarantees the selection of the optimal subset of representative data from the overall well log data set for the characterization of a specific reservoir property; and, this implicitly improves the modeling and predication accuracy of the MLP. In addition, a growing number of industrial agencies are implementing geographic information systems (GIS) in field data management; and, we have designed the GFAR solution (GIS-based FR ANN Reservoir characterization solution) system, which integrates the proposed framework into a GIS system that provides an efficient characterization solution. Three separate petroleum wells from southwestern Alberta, Canada, were used in the presented case study of reservoir porosity characterization. Our experiments demonstrate that our method can generate reliable results.

  15. An Algorithmic Framework for Genome-Wide Modeling and Analysis of Translation Networks

    PubMed Central

    Mehra, Amit; Hatzimanikatis, Vassily

    2006-01-01

    The sequencing of genomes of several organisms and advances in high throughput technologies for transcriptome and proteome analysis has allowed detailed mechanistic studies of transcription and translation using mathematical frameworks that allow integration of both sequence-specific and kinetic properties of these fundamental cellular processes. To understand how perturbations in mRNA levels affect the synthesis of individual proteins within a large protein synthesis network, we consider here a genome-scale codon-wide model of the translation machinery with explicit description of the processes of initiation, elongation, and termination. The mechanistic codon-wide description of the translation process and the large number of mRNAs competing for resources, such as ribosomes, requires the use of novel efficient algorithmic approaches. We have developed such an efficient algorithmic framework for genome-scale models of protein synthesis. The mathematical and computational framework was applied to the analysis of the sensitivity of a translation network to perturbation in the rate constants and in the mRNA levels in the system. Our studies suggest that the highest specific protein synthesis rate (protein synthesis rate per mRNA molecule) is achieved when translation is elongation-limited. We find that the mRNA species with the highest number of actively translating ribosomes exerts maximum control on the synthesis of every protein, and the response of protein synthesis rates to mRNA expression variation is a function of the strength of initiation of translation at different mRNA species. Such quantitative understanding of the sensitivity of protein synthesis to the variation of mRNA expression can provide insights into cellular robustness mechanisms and guide the design of protein production systems. PMID:16299083

  16. Systemic risk in a unifying framework for cascading processes on networks

    NASA Astrophysics Data System (ADS)

    Lorenz, J.; Battiston, S.; Schweitzer, F.

    2009-10-01

    We introduce a general framework for models of cascade and contagion processes on networks, to identify their commonalities and differences. In particular, models of social and financial cascades, as well as the fiber bundle model, the voter model, and models of epidemic spreading are recovered as special cases. To unify their description, we define the net fragility of a node, which is the difference between its fragility and the threshold that determines its failure. Nodes fail if their net fragility grows above zero and their failure increases the fragility of neighbouring nodes, thus possibly triggering a cascade. In this framework, we identify three classes depending on the way the fragility of a node is increased by the failure of a neighbour. At the microscopic level, we illustrate with specific examples how the failure spreading pattern varies with the node triggering the cascade, depending on its position in the network and its degree. At the macroscopic level, systemic risk is measured as the final fraction of failed nodes, X*, and for each of the three classes we derive a recursive equation to compute its value. The phase diagram of X* as a function of the initial conditions, thus allows for a prediction of the systemic risk as well as a comparison of the three different model classes. We could identify which model class leads to a first-order phase transition in systemic risk, i.e. situations where small changes in the initial conditions determine a global failure. Eventually, we generalize our framework to encompass stochastic contagion models. This indicates the potential for further generalizations.

  17. A distributed research network model for post-marketing safety studies: the Meningococcal Vaccine Study.

    PubMed

    Velentgas, Priscilla; Bohn, Rhonda L; Brown, Jeffrey S; Chan, K Arnold; Gladowski, Patricia; Holick, Crystal N; Kramer, Judith M; Nakasato, Cynthia; Spettell, Claire M; Walker, Alexander M; Zhang, Fang; Platt, Richard

    2008-12-01

    We describe a multi-center post-marketing safety study that uses distributed data methods to minimize the need for covered entities to share protected health information (PHI). Implementation has addressed several issues relevant to creation of a large scale post-marketing drug safety surveillance system envisioned by the FDA's Sentinel Initiative. This retrospective cohort study of Guillain-Barré syndrome (GBS) following meningococcal conjugate vaccination incorporates the data and analytic expertise of five research organizations closely affiliated with US health insurers. The study uses administrative claims data, plus review of full text medical records to adjudicate the status of individuals with a diagnosis code for GBS (ICD9 357.0). A distributed network approach is used to create the analysis files and to perform most aspects of the analysis, allowing nearly all of the data to remain behind institutional firewalls. Pooled analysis files transferred to a central site will contain one record per person for approximately 0.2% of the study population, and contain PHI limited to the month and year of GBS onset for cases or the index date for matched controls. The first planned data extraction identified over 9 million eligible adolescents in the target age range of 11-21 years. They contributed an average of 14 months of eligible time on study over 27 months of calendar time. MCV4 vaccination coverage levels exceeded 20% among 17-18-year olds and 16% among 11-13 and 14-16-year-old age groups by the second quarter of 2007. This study demonstrates the feasibility of using a distributed data network approach to perform large scale post-marketing safety analyses and is scalable to include additional organizations and data sources. We believe these results can inform the development of a large national surveillance system. Copyright (c) 2008 John Wiley & Sons, Ltd.

  18. Framework and implementation of a continuous network-wide health monitoring system for roadways

    NASA Astrophysics Data System (ADS)

    Wang, Ming; Birken, Ralf; Shahini Shamsabadi, Salar

    2014-03-01

    According to the 2013 ASCE report card America's infrastructure scores only a D+. There are more than four million miles of roads (grade D) in the U.S. requiring a broad range of maintenance activities. The nation faces a monumental problem of infrastructure management in the scheduling and implementation of maintenance and repair operations, and in the prioritization of expenditures within budgetary constraints. The efficient and effective performance of these operations however is crucial to ensuring roadway safety, preventing catastrophic failures, and promoting economic growth. There is a critical need for technology that can cost-effectively monitor the condition of a network-wide road system and provide accurate, up-to-date information for maintenance activity prioritization. The Versatile Onboard Traffic Embedded Roaming Sensors (VOTERS) project provides a framework and the sensing capability to complement periodical localized inspections to continuous network-wide health monitoring. Research focused on the development of a cost-effective, lightweight package of multi-modal sensor systems compatible with this framework. An innovative software infrastructure is created that collects, processes, and evaluates these large time-lapse multi-modal data streams. A GIS-based control center manages multiple inspection vehicles and the data for further analysis, visualization, and decision making. VOTERS' technology can monitor road conditions at both the surface and sub-surface levels while the vehicle is navigating through daily traffic going about its normal business, thereby allowing for network-wide frequent assessment of roadways. This deterioration process monitoring at unprecedented time and spatial scales provides unique experimental data that can be used to improve life-cycle cost analysis models.

  19. Effects of competition and cooperation interaction between agents on networks in the presence of a market capacity

    NASA Astrophysics Data System (ADS)

    Sonubi, A.; Arcagni, A.; Stefani, S.; Ausloos, M.

    2016-08-01

    A network effect is introduced taking into account competition, cooperation, and mixed-type interaction among agents along a generalized Verhulst-Lotka-Volterra model. It is also argued that the presence of a market capacity undoubtedly enforces a definite limit on the agent's size growth. The state stability of triadic agents, i.e., the most basic network plaquette, is investigated analytically for possible scenarios, through a fixed-point analysis. It is discovered that: (i) market demand is only satisfied for full competition when one agent monopolizes the market; (ii) growth of agent size is encouraged in full cooperation; (iii) collaboration among agents to compete against one single agent may result in the disappearance of this single agent out of the market; and (iv) cooperating with two rivals may become a growth strategy for an intelligent agent.

  20. Effects of competition and cooperation interaction between agents on networks in the presence of a market capacity.

    PubMed

    Sonubi, A; Arcagni, A; Stefani, S; Ausloos, M

    2016-08-01

    A network effect is introduced taking into account competition, cooperation, and mixed-type interaction among agents along a generalized Verhulst-Lotka-Volterra model. It is also argued that the presence of a market capacity undoubtedly enforces a definite limit on the agent's size growth. The state stability of triadic agents, i.e., the most basic network plaquette, is investigated analytically for possible scenarios, through a fixed-point analysis. It is discovered that: (i) market demand is only satisfied for full competition when one agent monopolizes the market; (ii) growth of agent size is encouraged in full cooperation; (iii) collaboration among agents to compete against one single agent may result in the disappearance of this single agent out of the market; and (iv) cooperating with two rivals may become a growth strategy for an intelligent agent.

  1. A model framework for the enhancement of community detection in complex networks

    NASA Astrophysics Data System (ADS)

    He, Dongxiao; Wang, Hongcui; Jin, Di; Liu, Baolin

    2016-11-01

    Community detection is an important data analysis problem in many different areas, and how to enhance the quality of community detection in complicated real applications is still a challenge. Current community detection enhancement methods often take the enhancement as a preprocess of community detection. They mainly focus on how to design the suitable topological similarity of nodes to adjust the original network, but did not consider how to make use of this topological similarity more effectively. In order to better utilize the similarity information, we propose a model framework which integrates the enhancement into the whole community detection procedure. First, we calculate the structural similarity of nodes based on network topology. Second, we present a stochastic model to describe the community memberships of nodes; we then model the strong constraint based on structural similarity, i.e., we make each node have the same community membership distribution with its most similar neighbors; and then we model the weak constraint, i.e., if two nodes have a high similarity we will make their community membership distributions close, otherwise we will make them not close. Finally, we present a nonnegative matrix factorization approach to learn the model parameters. We evaluate our method on both synthetic and real-world networks with ground-truths, and compare it with five comparable methods. The experimental results demonstrate the superior performance of our new method over the competing ones for community detection and enhancement.

  2. A Bayesian Framework That Integrates Heterogeneous Data for Inferring Gene Regulatory Networks

    PubMed Central

    Santra, Tapesh

    2014-01-01

    Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances. PMID:25152886

  3. A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China

    NASA Astrophysics Data System (ADS)

    Zhao, Shuangming; Zhao, Pengxiang; Cui, Yunfan

    2017-07-01

    In this paper, we propose an improved network centrality measure framework that takes into account both the topological characteristics and the geometric properties of a road network in order to analyze urban traffic flow in relation to different modes: intersection, road, and community, which correspond to point mode, line mode, and area mode respectively. Degree, betweenness, and PageRank centralities are selected as the analysis measures, and GPS-enabled taxi trajectory data is used to evaluate urban traffic flow. The results show that the mean value of the correlation coefficients between the modified degree, the betweenness, and the PageRank centralities and the traffic flow in all periods are higher than the mean value of the correlation coefficients between the conventional degree, the betweenness, the PageRank centralities and the traffic flow at different modes; this indicates that the modified measurements, for analyzing traffic flow, are superior to conventional centrality measurements. This study helps to shed light into the understanding of urban traffic flow in relation to different modes from the perspective of complex networks.

  4. Energy-saving framework for passive optical networks with ONU sleep/doze mode.

    PubMed

    Van, Dung Pham; Valcarenghi, Luca; Dias, Maluge Pubuduni Imali; Kondepu, Koteswararao; Castoldi, Piero; Wong, Elaine

    2015-02-09

    This paper proposes an energy-saving passive optical network framework (ESPON) that aims to incorporate optical network unit (ONU) sleep/doze mode into dynamic bandwidth allocation (DBA) algorithms to reduce ONU energy consumption. In the ESPON, the optical line terminal (OLT) schedules both downstream (DS) and upstream (US) transmissions in the same slot in an online and dynamic fashion whereas the ONU enters sleep mode outside the slot. The ONU sleep time is maximized based on both DS and US traffic. Moreover, during the slot, the ONU might enter doze mode when only its transmitter is idle to further improve energy efficiency. The scheduling order of data transmission, control message exchange, sleep period, and doze period defines an energy-efficient scheme under the ESPON. Three schemes are designed and evaluated in an extensive FPGA-based evaluation. Results show that whilst all the schemes significantly save ONU energy for different evaluation scenarios, the scheduling order has great impact on their performance. In addition, the ESPON allows for a scheduling order that saves ONU energy independently of the network reach.

  5. Banknote recognition: investigating processing and cognition framework using competitive neural network.

    PubMed

    Oyedotun, Oyebade K; Khashman, Adnan

    2017-02-01

    Humans are apt at recognizing patterns and discovering even abstract features which are sometimes embedded therein. Our ability to use the banknotes in circulation for business transactions lies in the effortlessness with which we can recognize the different banknote denominations after seeing them over a period of time. More significant is that we can usually recognize these banknote denominations irrespective of what parts of the banknotes are exposed to us visually. Furthermore, our recognition ability is largely unaffected even when these banknotes are partially occluded. In a similar analogy, the robustness of intelligent systems to perform the task of banknote recognition should not collapse under some minimum level of partial occlusion. Artificial neural networks are intelligent systems which from inception have taken many important cues related to structure and learning rules from the human nervous/cognition processing system. Likewise, it has been shown that advances in artificial neural network simulations can help us understand the human nervous/cognition system even furthermore. In this paper, we investigate three cognition hypothetical frameworks to vision-based recognition of banknote denominations using competitive neural networks. In order to make the task more challenging and stress-test the investigated hypotheses, we also consider the recognition of occluded banknotes. The implemented hypothetical systems are tasked to perform fast recognition of banknotes with up to 75 % occlusion. The investigated hypothetical systems are trained on Nigeria's Naira banknotes and several experiments are performed to demonstrate the findings presented within this work.

  6. A Human Sensor Network Framework in Support of Near Real Time Situational Geophysical Modeling

    NASA Astrophysics Data System (ADS)

    Aulov, O.; Price, A.; Smith, J. A.; Halem, M.

    2013-12-01

    The area of Disaster Management is well established among Federal Agencies such as FEMA, EPA, NOAA and NASA. These agencies have well formulated frameworks for response and mitigation based on near real time satellite and conventional observing networks for assimilation into geophysical models. Forecasts from these models are used to communicate with emergency responders and the general public. More recently, agencies have started using social media to broadcast warnings and alerts to potentially affected communities. In this presentation, we demonstrate the added benefits of mining and assimilating the vast amounts of social media data available from heterogeneous hand held devices and social networks into established operational geophysical modeling frameworks as they apply to the five cornerstones of disaster management - Prevention, Mitigation, Preparedness, Response and Recovery. Often, in situations of extreme events, social media provide the earliest notification of adverse extreme events. However, various forms of social media data also can provide useful geolocated and time stamped in situ observations, complementary to directly sensed conventional observations. We use the concept of a Human Sensor Network where one views social media users as carrying field deployed "sensors" whose posts are the remotely "sensed instrument measurements.' These measurements can act as 'station data' providing the resolution and coverage needed for extreme event specific modeling and validation. Here, we explore the use of social media through the use of a Human Sensor Network (HSN) approach as another data input source for assimilation into geophysical models. Employing the HSN paradigm can provide useful feedback in near real-time, but presents software challenges for rapid access, quality filtering and transforming massive social media data into formats consistent with the operational models. As a use case scenario, we demonstrate the value of HSN for disaster management

  7. Determinants of hospital choice of rural hospital patients: the impact of networks, service scopes, and market competition.

    PubMed

    Roh, Chul-Young; Lee, Keon-Hyung; Fottler, Myron D

    2008-08-01

    Among 10,384 rural Colorado female patients who received MDC 14 (obstetric services) from 2000 to 2003, 6,615 (63.7%) were admitted to their local rural hospitals; 1,654 (15.9%) were admitted to other rural hospitals; and 2,115 (20.4%) traveled to urban hospitals for inpatient services. This study is to examine how network participation, service scopes, and market competition influences rural women's choice of hospital for their obstetric care. A conditional logistic regression analysis was used. The network participation (p < 0.01), the number of services offered (p < 0.05), and the hospital market competition had a positive and significant relationship with patients' choice to receive obstetric care. That is, rural patients prefer to receive care from a hospital that participates in a network, that provides more number of services, and that has a greater market share (i.e., a lower level of market competition) in their locality. Rural hospitals could actively increase their competitiveness and market share by increasing the number of health care services provided and seeking to network with other hospitals.

  8. Networking Activities and Perceptions of HIV Risk Among Male Migrant Market Vendors in China

    PubMed Central

    Wang, Wenqing; Muessig, Kathryn E.; Li, Mingqiang; Zhang, Yingxia

    2013-01-01

    HIV research among internal migrants in China has not fully explored the contexts and perceptions of “risk”. In 2011, urban markets in Liuzhou, China were mapped, and sixty male vendors, age 22 to 56, were selected for in-depth interviews on migration, social and family life, and perceptions and practices of sexual risk behavior. Participants were evenly divided among higher income shop and small stall vendors. All men were sexually active. Only the shop vendors reported non-marital sexual partners, including concurrent partners (n=15), commercial partners (n=10), and other sexual relationships (n=11). Shop vendors engaged in networking activities that facilitated commercial and non-commercial high-risk sex. Perceptions of HIV risk from commercial sex led some men to doubt the protective ability of condoms and rely on local (unproven) self-protection techniques. Networking activities played a role in high-risk sex and shaping migrants' risk perceptions and health practices. The networks created through these processes could also be used to facilitate health promotion activities. PMID:23572155

  9. Networking activities and perceptions of HIV risk among male migrant market vendors in China.

    PubMed

    Wang, Wenqing; Muessig, Kathryn E; Li, Mingqiang; Zhang, Ying-Xia; Zhang, Yingxia

    2014-02-01

    HIV research among internal migrants in China has not fully explored the contexts and perceptions of "risk". In 2011, urban markets in Liuzhou, China were mapped, and sixty male vendors, age 22-56, were selected for in-depth interviews on migration, social and family life, and perceptions and practices of sexual risk behavior. Participants were evenly divided among higher income shop and small stall vendors. All men were sexually active. Only the shop vendors reported non-marital sexual partners, including concurrent partners (n = 15), commercial partners (n = 10), and other sexual relationships (n = 11). Shop vendors engaged in networking activities that facilitated commercial and non-commercial high-risk sex. Perceptions of HIV risk from commercial sex led some men to doubt the protective ability of condoms and rely on local (unproven) self-protection techniques. Networking activities played a role in high-risk sex and shaping migrants' risk perceptions and health practices. The networks created through these processes could also be used to facilitate health promotion activities.

  10. Conceptual framework for assessing the response of delta channel networks to Holocene sea level rise

    NASA Astrophysics Data System (ADS)

    Jerolmack, Douglas J.

    2009-08-01

    Recent research has identified two fundamental unit processes that build delta distributary channels. The first is mouth-bar deposition at the shoreline and subsequent channel bifurcation, which is driven by progradation of the shoreline; the second is avulsion to a new channel, a result of aggradation of the delta topset. The former creates relatively small, branching networks such as Wax Lake Delta; the latter generates relatively few, long distributaries such as the Mississippi and Atchafalaya channels on the Mississippi Delta. The relative rate of progradation to aggradation, and hence the creation of accommodation space, emerges as a controlling parameter on channel network form. Field and experimental research has identified sea level as the dominant control on Holocene delta growth worldwide, and has empirically linked channel network changes to changes in the rate of sea level rise. Here I outline a simple modeling framework for distributary network evolution, and use this to explore large-scale changes in Holocene channel pattern that have been observed in deltas such as the Rhine-Meuse and Mississippi. Rapid early- to mid-Holocene sea level rise forced many deltas into an aggradational mode, where I hypothesize that avulsion and the generation of large-scale branches should dominate. Slowing of sea level rise in the last ˜6000 yr allowed partitioning of sediment into progradation, facilitating the growth of smaller-scale distributary trees at the shorelines of some deltas, and a reduction in the number of large-scale branches. Significant antecedent topography modulates delta response; the filling of large incised valleys, for example, caused many deltas to bypass the aggradational phase. Human effects on deltas can be cast in terms of geologic controls affecting accommodation: constriction of channels forces rapid local progradation and mouth-bar bifurcation, while accelerated sea level rise increases aggradation and induces more frequent channel

  11. A framework for parameter estimation and model selection in kernel deep stacking networks.

    PubMed

    Welchowski, Thomas; Schmid, Matthias

    2016-06-01

    Kernel deep stacking networks (KDSNs) are a novel method for supervised learning in biomedical research. Belonging to the class of deep learning techniques, KDSNs are based on artificial neural network architectures that involve multiple nonlinear transformations of the input data. Unlike traditional artificial neural networks, KDSNs do not rely on backpropagation algorithms but on an efficient fitting procedure that is based on a series of kernel ridge regression models with closed-form solutions. Although being computationally advantageous, KDSN modeling remains a challenging task, as it requires the specification of a large number of tuning parameters. We propose a new data-driven framework for parameter estimation, hyperparameter tuning, and model selection in KDSNs. The proposed methodology is based on a combination of model-based optimization and hill climbing approaches that do not require the pre-specification of any of the KDSN tuning parameters. We demonstrate the performance of KDSNs by analyzing three medical data sets on hospital readmission of diabetes patients, coronary artery disease, and hospital costs. Our numerical studies show that the run-time of the proposed KDSN methodology is significantly shorter than the respective run-time of grid search strategies for hyperparameter tuning. They also show that KDSN modeling is competitive in terms of prediction accuracy with other state-of-the-art techniques for statistical learning. KDSNs are a computationally efficient approximation of backpropagation-based artificial neural network techniques. Application of the proposed methodology results in a fast tuning procedure that generates KDSN fits having a similar prediction accuracy as other techniques in the field of deep learning. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. Social network analysis of the movement of poultry to and from live bird markets in Bali and Lombok, Indonesia.

    PubMed

    Kurscheid, J; Stevenson, M; Durr, P A; Toribio, J-A L M L; Kurscheid, S; Ambarawati, I G A A; Abdurrahman, M; Fenwick, S

    2017-02-04

    Highly pathogenic avian influenza H5N1 has resulted in large losses to the Indonesian poultry sector. Evidence suggests that live bird markets (LBMs) play an important role in the epidemiology of the disease. Knowledge of the frequency and type of contact between the various poultry market players should allow animal health authorities to develop a better understanding of factors influencing virus transmission between Indonesian villages. A questionnaire-based cross-sectional survey was conducted in 17 LBMs on the neighbouring Indonesian islands of Bali and Lombok to investigate the movement patterns of poultry to and from markets. Using social network analyses, a network of contacts was created for each island from a total of 413 live poultry traders and 134 customers. Individual nodes with high degree and/or betweenness were identified in each network. The Lombok network was more dense and connected than the Bali network indicating that disease transmission would be more efficient in the Lombok network. Our findings indicate that whilst live poultry are typically transported over relatively short distances of approximately 10 km, it is not uncommon for traders and customers to travel in excess of 100 km to buy or sell poultry, which may facilitate the spread of disease over a large geographical area. This study highlights the different roles markets play in poultry movement networks and their potential for disease dissemination. The identification of highly influential market nodes allows authorities to target HPAI surveillance activities to locations where disease is more likely to be present, which is crucial in low-resource settings. © 2017 Blackwell Verlag GmbH.

  13. Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity.

    PubMed

    Xu, Ronghua; Wong, Wing-Keung; Chen, Guanrong; Huang, Shuo

    2017-02-01

    In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.

  14. Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity

    NASA Astrophysics Data System (ADS)

    Xu, Ronghua; Wong, Wing-Keung; Chen, Guanrong; Huang, Shuo

    2017-02-01

    In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development.

  15. Topological Characteristics of the Hong Kong Stock Market: A Test-based P-threshold Approach to Understanding Network Complexity

    PubMed Central

    Xu, Ronghua; Wong, Wing-Keung; Chen, Guanrong; Huang, Shuo

    2017-01-01

    In this paper, we analyze the relationship among stock networks by focusing on the statistically reliable connectivity between financial time series, which accurately reflects the underlying pure stock structure. To do so, we firstly filter out the effect of market index on the correlations between paired stocks, and then take a t-test based P-threshold approach to lessening the complexity of the stock network based on the P values. We demonstrate the superiority of its performance in understanding network complexity by examining the Hong Kong stock market. By comparing with other filtering methods, we find that the P-threshold approach extracts purely and significantly correlated stock pairs, which reflect the well-defined hierarchical structure of the market. In analyzing the dynamic stock networks with fixed-size moving windows, our results show that three global financial crises, covered by the long-range time series, can be distinguishingly indicated from the network topological and evolutionary perspectives. In addition, we find that the assortativity coefficient can manifest the financial crises and therefore can serve as a good indicator of the financial market development. PMID:28145494

  16. An Efficient Framework for Large Scale Multimedia Content Distribution in P2P Network: I2NC

    PubMed Central

    Anandaraj, M.; Ganeshkumar, P.; Vijayakumar, K. P.; Selvaraj, K.

    2015-01-01

    Network coding (NC) makes content distribution more effective and easier in P2P content distribution network and reduces the burden of the original seeder. It generalizes traditional network routing by allowing the intermediate nodes to generate new coded packet by combining the received packets. The randomization introduced by network coding makes all packets equally important and resolves the problem of locating the rarest block. Further, it reduces traffic in the network. In this paper, we analyze the performance of traditional network coding in P2P content distribution network by using a mathematical model and it is proved that traffic reduction has not been fully achieved in P2P network using traditional network coding. It happens due to the redundant transmission of noninnovative information block among the peers in the network. Hence, we propose a new framework, called I2NC (intelligent-peer selection and incremental-network coding), to eliminate the unnecessary flooding of noninnovative coded packets and thereby to improve the performance of network coding in P2P content distribution further. A comparative study and analysis of the proposed system is made through various related implementations and the results show that 10–15% of traffic reduced and improved the average and maximum download time by reducing original seeder's workload. PMID:26605375

  17. Hospital marketing.

    PubMed

    Carter, Tony

    2003-01-01

    This article looks at a prescribed academic framework for various criteria that serve as a checklist for marketing performance that can be applied to hospital marketing organizations. These guidelines are drawn from some of Dr. Noel Capon of Columbia University's book Marketing Management in the 21st Century and applied to actual practices of hospital marketing organizations. In many ways this checklist can act as a "marketing" balanced scorecard to verify performance effectiveness and develop opportunities for innovation.

  18. Adding Abstraction and Reuse to a Network Modelling Tool Using the Reuseware Composition Framework

    NASA Astrophysics Data System (ADS)

    Johannes, Jendrik; Fernández, Miguel A.

    Domain-specific modelling (DSM) environments enable experts in a certain domain to actively participate in model-driven development. Developing DSM environments need to be cost-efficient, since they are only used by a limited group of domain experts. Different model-driven technologies promise to allow this cost-efficient development. [1] presented experiences in developing a DSM environment for telecommunication network modelling. There, challenges were identified that need to be addressed by other new modelling technologies. In this paper, we now present the results of addressing one of theses challenges - abstraction and reuse support - with the Reuseware Composition Framework. We show how we identified the abstraction and reuse features required in the telecommunication DSM environment in a case study and extended the existing environment with these features using Reuseware. We discuss the advantages of using this technology and propose a process for further improving the abstraction and reuse capabilities of the DSM environment in the future.

  19. An automatic fall detection framework using data fusion of Doppler radar and motion sensor network.

    PubMed

    Liu, Liang; Popescu, Mihail; Skubic, Marjorie; Rantz, Marilyn

    2014-01-01

    This paper describes the ongoing work of detecting falls in independent living senior apartments. We have developed a fall detection system with Doppler radar sensor and implemented ceiling radar in real senior apartments. However, the detection accuracy on real world data is affected by false alarms inherent in the real living environment, such as motions from visitors. To solve this issue, this paper proposes an improved framework by fusing the Doppler radar sensor result with a motion sensor network. As a result, performance is significantly improved after the data fusion by discarding the false alarms generated by visitors. The improvement of this new method is tested on one week of continuous data from an actual elderly person who frequently falls while living in her senior home.

  20. A Study of IEEE 802.15.4 Security Framework for Wireless Body Area Networks

    PubMed Central

    Saleem, Shahnaz; Ullah, Sana; Kwak, Kyung Sup

    2011-01-01

    A Wireless Body Area Network (WBAN) is a collection of low-power and lightweight wireless sensor nodes that are used to monitor the human body functions and the surrounding environment. It supports a number of innovative and interesting applications, including ubiquitous healthcare and Consumer Electronics (CE) applications. Since WBAN nodes are used to collect sensitive (life-critical) information and may operate in hostile environments, they require strict security mechanisms to prevent malicious interaction with the system. In this paper, we first highlight major security requirements and Denial of Service (DoS) attacks in WBAN at Physical, Medium Access Control (MAC), Network, and Transport layers. Then we discuss the IEEE 802.15.4 security framework and identify the security vulnerabilities and major attacks in the context of WBAN. Different types of attacks on the Contention Access Period (CAP) and Contention Free Period (CFP) parts of the superframe are analyzed and discussed. It is observed that a smart attacker can successfully corrupt an increasing number of GTS slots in the CFP period and can considerably affect the Quality of Service (QoS) in WBAN (since most of the data is carried in CFP period). As we increase the number of smart attackers the corrupted GTS slots are eventually increased, which prevents the legitimate nodes to utilize the bandwidth efficiently. This means that the direct adaptation of IEEE 802.15.4 security framework for WBAN is not totally secure for certain WBAN applications. New solutions are required to integrate high level security in WBAN. PMID:22319358

  1. A study of IEEE 802.15.4 security framework for wireless body area networks.

    PubMed

    Saleem, Shahnaz; Ullah, Sana; Kwak, Kyung Sup

    2011-01-01

    A Wireless Body Area Network (WBAN) is a collection of low-power and lightweight wireless sensor nodes that are used to monitor the human body functions and the surrounding environment. It supports a number of innovative and interesting applications, including ubiquitous healthcare and Consumer Electronics (CE) applications. Since WBAN nodes are used to collect sensitive (life-critical) information and may operate in hostile environments, they require strict security mechanisms to prevent malicious interaction with the system. In this paper, we first highlight major security requirements and Denial of Service (DoS) attacks in WBAN at Physical, Medium Access Control (MAC), Network, and Transport layers. Then we discuss the IEEE 802.15.4 security framework and identify the security vulnerabilities and major attacks in the context of WBAN. Different types of attacks on the Contention Access Period (CAP) and Contention Free Period (CFP) parts of the superframe are analyzed and discussed. It is observed that a smart attacker can successfully corrupt an increasing number of GTS slots in the CFP period and can considerably affect the Quality of Service (QoS) in WBAN (since most of the data is carried in CFP period). As we increase the number of smart attackers the corrupted GTS slots are eventually increased, which prevents the legitimate nodes to utilize the bandwidth efficiently. This means that the direct adaptation of IEEE 802.15.4 security framework for WBAN is not totally secure for certain WBAN applications. New solutions are required to integrate high level security in WBAN.

  2. A Web Service-based framework model for people-centric sensing applications applied to social networking.

    PubMed

    Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá

    2012-01-01

    As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users' activities and locations, sharing this information amongst the user's friends within a social networking site. We also present some screenshot results of our experimental prototype.

  3. Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation.

    PubMed

    Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

    2011-01-01

    Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Analysis based on the experience of three telemedicine networks (in operation for 7-10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit organisations to assess the performance of their own networks

  4. Experience with low-cost telemedicine in three different settings. Recommendations based on a proposed framework for network performance evaluation

    PubMed Central

    Wootton, Richard; Vladzymyrskyy, Anton; Zolfo, Maria; Bonnardot, Laurent

    2011-01-01

    Background Telemedicine has been used for many years to support doctors in the developing world. Several networks provide services in different settings and in different ways. However, to draw conclusions about which telemedicine networks are successful requires a method of evaluating them. No general consensus or validated framework exists for this purpose. Objective To define a basic method of performance measurement that can be used to improve and compare teleconsultation networks; to employ the proposed framework in an evaluation of three existing networks; to make recommendations about the future implementation and follow-up of such networks. Methods Analysis based on the experience of three telemedicine networks (in operation for 7–10 years) that provide services to doctors in low-resource settings and which employ the same basic design. Findings Although there are many possible indicators and metrics that might be relevant, five measures for each of the three user groups appear to be sufficient for the proposed framework. In addition, from the societal perspective, information about clinical- and cost-effectiveness is also required. The proposed performance measurement framework was applied to three mature telemedicine networks. Despite their differences in terms of activity, size and objectives, their performance in certain respects is very similar. For example, the time to first reply from an expert is about 24 hours for each network. Although all three networks had systems in place to collect data from the user perspective, none of them collected information about the coordinator's time required or about ease of system usage. They had only limited information about quality and cost. Conclusion Measuring the performance of a telemedicine network is essential in understanding whether the network is working as intended and what effect it is having. Based on long-term field experience, the suggested framework is a practical tool that will permit

  5. Autonomous construction agents: An investigative framework for large sensor network self-management

    SciTech Connect

    Koch, Joshua Bruce

    2008-01-01

    Recent technological advances have made it cost effective to utilize massive, heterogeneous sensor networks. To gain appreciable value from these informational systems, there must be a control scheme that coordinates information flow to produce meaningful results. This paper will focus on tools developed to manage the coordination of autonomous construction agents using stigmergy, in which a set of basic low-level rules are implemented through various environmental cues. Using VE-Suite, an open-source virtual engineering software package, an interactive environment is created to explore various informational configurations for the construction problem. A simple test case is developed within the framework, and construction times are analyzed for possible functional relationships pertaining to performance of a particular set of parameters and a given control process. Initial experiments for the test case show sensor saturation occurs relatively quickly with 5-7 sensors, and construction time is generally independent of sensor range except for small numbers of sensors. Further experiments using this framework are needed to define other aspects of sensor performance. These trends can then be used to help decide what kinds of sensing capabilities are required to simultaneously achieve the most cost-effective solution and provide the required value of information when applied to the development of real world sensor applications.

  6. An efficient semi-supervised community detection framework in social networks.

    PubMed

    Li, Zhen; Gong, Yong; Pan, Zhisong; Hu, Guyu

    2017-01-01

    Community detection is an important tasks across a number of research fields including social science, biology, and physics. In the real world, topology information alone is often inadequate to accurately find out community structure due to its sparsity and noise. The potential useful prior information such as pairwise constraints which contain must-link and cannot-link constraints can be obtained from domain knowledge in many applications. Thus, combining network topology with prior information to improve the community detection accuracy is promising. Previous methods mainly utilize the must-link constraints while cannot make full use of cannot-link constraints. In this paper, we propose a semi-supervised community detection framework which can effectively incorporate two types of pairwise constraints into the detection process. Particularly, must-link and cannot-link constraints are represented as positive and negative links, and we encode them by adding different graph regularization terms to penalize closeness of the nodes. Experiments on multiple real-world datasets show that the proposed framework significantly improves the accuracy of community detection.

  7. A Bayesian framework for cell-level protein network analysis for multivariate proteomics image data

    NASA Astrophysics Data System (ADS)

    Kovacheva, Violet N.; Sirinukunwattana, Korsuk; Rajpoot, Nasir M.

    2014-03-01

    The recent development of multivariate imaging techniques, such as the Toponome Imaging System (TIS), has facilitated the analysis of multiple co-localisation of proteins. This could hold the key to understanding complex phenomena such as protein-protein interaction in cancer. In this paper, we propose a Bayesian framework for cell level network analysis allowing the identification of several protein pairs having significantly higher co-expression levels in cancerous tissue samples when compared to normal colon tissue. It involves segmenting the DAPI-labeled image into cells and determining the cell phenotypes according to their protein-protein dependence profile. The cells are phenotyped using Gaussian Bayesian hierarchical clustering (GBHC) after feature selection is performed. The phenotypes are then analysed using Difference in Sums of Weighted cO-dependence Profiles (DiSWOP), which detects differences in the co-expression patterns of protein pairs. We demonstrate that the pairs highlighted by the proposed framework have high concordance with recent results using a different phenotyping method. This demonstrates that the results are independent of the clustering method used. In addition, the highlighted protein pairs are further analysed via protein interaction pathway databases and by considering the localization of high protein-protein dependence within individual samples. This suggests that the proposed approach could identify potentially functional protein complexes active in cancer progression and cell differentiation.

  8. A mathematical framework for agent based models of complex biological networks.

    PubMed

    Hinkelmann, Franziska; Murrugarra, David; Jarrah, Abdul Salam; Laubenbacher, Reinhard

    2011-07-01

    Agent-based modeling and simulation is a useful method to study biological phenomena in a wide range of fields, from molecular biology to ecology. Since there is currently no agreed-upon standard way to specify such models, it is not always easy to use published models. Also, since model descriptions are not usually given in mathematical terms, it is difficult to bring mathematical analysis tools to bear, so that models are typically studied through simulation. In order to address this issue, Grimm et al. proposed a protocol for model specification, the so-called ODD protocol, which provides a standard way to describe models. This paper proposes an addition to the ODD protocol which allows the description of an agent-based model as a dynamical system, which provides access to computational and theoretical tools for its analysis. The mathematical framework is that of algebraic models, that is, time-discrete dynamical systems with algebraic structure. It is shown by way of several examples how this mathematical specification can help with model analysis. This mathematical framework can also accommodate other model types such as Boolean networks and the more general logical models, as well as Petri nets.

  9. A remote quantitative Fugl-Meyer assessment framework for stroke patients based on wearable sensor networks.

    PubMed

    Yu, Lei; Xiong, Daxi; Guo, Liquan; Wang, Jiping

    2016-05-01

    To extend the use of wearable sensor networks for stroke patients training and assessment in non-clinical settings, this paper proposes a novel remote quantitative Fugl-Meyer assessment (FMA) framework, in which two accelerometer and seven flex sensors were used to monitoring the movement function of upper limb, wrist and fingers. The extreme learning machine based ensemble regression model was established to map the sensor data to clinical FMA scores while the RRelief algorithm was applied to find the optimal features subset. Considering the FMA scale is time-consuming and complicated, seven training exercises were designed to replace the upper limb related 33 items in FMA scale. 24 stroke inpatients participated in the experiments in clinical settings and 5 of them were involved in the experiments in home settings after they left the hospital. Both the experimental results in clinical and home settings showed that the proposed quantitative FMA model can precisely predict the FMA scores based on wearable sensor data, the coefficient of determination can reach as high as 0.917. It also indicated that the proposed framework can provide a potential approach to the remote quantitative rehabilitation training and evaluation.

  10. Networked Virtual Organizations: A Chance for Small and Medium Sized Enterprises on Global Markets

    NASA Astrophysics Data System (ADS)

    Cellary, Wojciech

    Networked Virtual Organizations (NVOs) are a right answer to challenges of globalized, diversified, and dynamic contemporary economy. NVOs need more than e-trade and outsourcing, namely, they need out-tasking and e-collaboration. To out-task, but retain control on the way a task is performed by an external partner, two integrations are required: (1) integration of computer management systems of enterprises cooperating within an NVO; and (2) integration of cooperating representatives of NVO member enterprises into a virtual team. NVOs provide a particular chance to Small and Medium size Enterprises (SMEs) to find their place on global markets and to play a significant role on them. Requirements for SMEs to be able to successfully join an NVO are analyzed in the paper.

  11. The use of dynamical networks to detect the hierarchical organization of financial market sectors

    NASA Astrophysics Data System (ADS)

    Di Matteo, T.; Pozzi, F.; Aste, T.

    2010-01-01

    Two kinds of filtered networks: minimum spanning trees (MSTs) and planar maximally filtered graphs (PMFGs) are constructed from dynamical correlations computed over a moving window. We study the evolution over time of both hierarchical and topological properties of these graphs in relation to market fluctuations. We verify that the dynamical PMFG preserves the same hierarchical structure as the dynamical MST, providing in addition a more significant and richer structure, a stronger robustness and dynamical stability. Central and peripheral stocks are differentiated by using a combination of different topological measures. We find stocks well connected and central; stocks well connected but peripheral; stocks poorly connected but central; stocks poorly connected and peripheral. It results that the Financial sector plays a central role in the entire system. The robustness, stability and persistence of these findings are verified by changing the time window and by performing the computations on different time periods. We discuss these results and the economic meaning of this hierarchical positioning.

  12. Product Design Network Self-contextualization: Enterprise Knowledge-Based Approach and Agent-Based Technological Framework

    NASA Astrophysics Data System (ADS)

    Levashova, Tatiana; Sandkuhl, Kurt; Shilov, Nikolay; Smirnov, Alexander; Tarasov, Vladimir

    The paper introduces self-contextualization in a service infrastructure for product design networks as novel application field for multi-agent technology. The main contributions of this paper are (1) identification of requirements from product design networks to the supporting service infrastructure, (2) the use of enterprise knowledge modelling techniques for the representation of computable context models, (3) a technological framework based on agent technology for self-contextualization based on enterprise knowledge models.

  13. Share2Quit: Online Social Network Peer Marketing of Tobacco Cessation Systems.

    PubMed

    Sadasivam, Rajani S; Cutrona, Sarah L; Luger, Tana M; Volz, Erik; Kinney, Rebecca; Rao, Sowmya R; Allison, Jeroan J; Houston, Thomas K

    2017-03-01

    Although technology-assisted tobacco interventions (TATIs) are effective, they are underused due to recruitment challenges. We tested whether we could successfully recruit smokers to a TATI using peer marketing through a social network (Facebook). We recruited smokers on Facebook using online advertisements. These recruited smokers (seeds) and subsequent waves of smokers (peer recruits) were provided the Share2Quit peer recruitment Facebook app and other tools. Smokers were incentivized for up to seven successful peer recruitments and had 30 days to recruit from date of registration. Successful peer recruitment was defined as a peer recruited smoker completing the registration on the TATI following a referral. Our primary questions were (1) whether smokers would recruit other smokers and (2) whether peer recruitment would extend the reach of the intervention to harder-to-reach groups, including those not ready to quit and minority smokers. Overall, 759 smokers were recruited (seeds: 190; peer recruits: 569). Fifteen percent (n = 117) of smokers successfully recruited their peers (seeds: 24.7%; peer recruits: 7.7%) leading to four recruitment waves. Compared to seeds, peer recruits were less likely to be ready to quit (peer recruits 74.2% vs. seeds 95.1%), more likely to be male (67.1% vs. 32.9%), and more likely to be African American (23.8% vs. 10.8%) (p < .01 for all comparisons). Peer marketing quadrupled our engaged smokers and enriched the sample with not-ready-to-quit and African American smokers. Peer recruitment is promising, and our study uncovered several important challenges for future research. This study demonstrates the successful recruitment of smokers to a TATI using a Facebook-based peer marketing strategy. Smokers on Facebook were willing and able to recruit other smokers to a TATI, yielding a large and diverse population of smokers.

  14. Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index

    NASA Astrophysics Data System (ADS)

    Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki

    Genetic Network Programming (GNP) is an evolutionary computation which represents its solutions using graph structures. Since GNP can create quite compact programs and has an implicit memory function, it has been clarified that GNP works well especially in dynamic environments. In addition, a study on creating trading rules on stock markets using GNP with Importance Index (GNP-IMX) has been done. IMX is a new element which is a criterion for decision making. In this paper, we combined GNP-IMX with Actor-Critic (GNP-IMX&AC) and create trading rules on stock markets. Evolution-based methods evolve their programs after enough period of time because they must calculate fitness values, however reinforcement learning can change programs during the period, therefore the trading rules can be created efficiently. In the simulation, the proposed method is trained using the stock prices of 10 brands in 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. The simulation results show that the proposed method can obtain larger profits than GNP-IMX without AC and Buy&Hold.

  15. Review on the worldwide regulatory framework for biosimilars focusing on the Mexican case as an emerging market in Latin America.

    PubMed

    Ibarra-Cabrera, Ricardo; Mena-Pérez, Sandra Carolina; Bondani-Guasti, Augusto; García-Arrazola, Roeb

    2013-12-01

    The global biopharmaceutical market is worth over $100 billion USD. Nearly 90% of these products will lose their patent in the next ten years, leading to the commercialization of their subsequent versions, known as 'biosimilars'. Biosimilars are much more complex molecules than chemically synthesized generics in terms of size, structure, stability, microheterogeneity, manufacture, etc. Therefore, a specific regulatory framework is needed in order to demonstrate their comparability with innovative products, as well as their quality, safety and efficacy. The EU published the first regulatory pathway in 2005 and has approved 14 biosimilars. Mexico has recently developed a clear regulatory pathway for these products. Their legal basis was established in Article 222 Bis of General Law of Health in 2009, clear specifications in the Regulation for Health Goods in 2011, and further requirements in the Mexican Official Norm NOM-EM-001-SSA1-2012. The aim of this review is to summarize the regulatory pathways for biosimilars in the world with a special focus on Mexican experience, so as contribute to the development of regulations in other countries. Copyright © 2013 Elsevier Inc. All rights reserved.

  16. Polyoxometalate-based eight-connected self-catenated network and fivefold interpenetrating framework

    NASA Astrophysics Data System (ADS)

    Zhang, Zhiming; Liu, Jia; Li, Yangguang; Yao, Shuang; Wang, Enbo; Wang, Xinlong

    2010-01-01

    Two entangled compounds [(bpy) 6Cu I6Cl 3(Mo VW 5O 19)] ( 1) and [(bpy) 7Cu I7Cl 2(BW 12O 40)]·H 2O ( 2) (bpy=4,4'-bipyridine), have been successfully synthesized under hydrothermal conditions and characterized by element analysis, IR spectroscopy, thermal gravimetric analysis, X-ray photoelectron spectroscopy, and single crystal X-ray diffraction analyses. Compound 1 represents the first eight-connected self-penetrating network constructed from cuprous chloride clusters [Cu 6Cl 3] and Lindquist-type polyoxoanions. Compound 2 exhibits an interesting fivefold interpenetrating network consisting of Keggin polyoxoanions and Cu +-metal-organic framework. Crystal data of the two compounds are following: 1, triclinic, P1¯, a=11.502(2) Å, b=13.069(3) Å, c=13.296(3) Å, α=90.55(3)°, β=113.74(3)°, γ=110.48(3)°, Z=1; 2, triclinic, P1¯, a=12.341(3) Å, b=13.119(3) Å, c=15.367(3) Å, α=99.12(3)°, β=90.53(3)°, γ=104.49(3)°, Z=1.

  17. AlleleSeq: analysis of allele-specific expression and binding in a network framework.

    PubMed

    Rozowsky, Joel; Abyzov, Alexej; Wang, Jing; Alves, Pedro; Raha, Debasish; Harmanci, Arif; Leng, Jing; Bjornson, Robert; Kong, Yong; Kitabayashi, Naoki; Bhardwaj, Nitin; Rubin, Mark; Snyder, Michael; Gerstein, Mark

    2011-08-02

    To study allele-specific expression (ASE) and binding (ASB), that is, differences between the maternally and paternally derived alleles, we have developed a computational pipeline (AlleleSeq). Our pipeline initially constructs a diploid personal genome sequence (and corresponding personalized gene annotation) using genomic sequence variants (SNPs, indels, and structural variants), and then identifies allele-specific events with significant differences in the number of mapped reads between maternal and paternal alleles. There are many technical challenges in the construction and alignment of reads to a personal diploid genome sequence that we address, for example, bias of reads mapping to the reference allele. We have applied AlleleSeq to variation data for NA12878 from the 1000 Genomes Project as well as matched, deeply sequenced RNA-Seq and ChIP-Seq data sets generated for this purpose. In addition to observing fairly widespread allele-specific behavior within individual functional genomic data sets (including results consistent with X-chromosome inactivation), we can study the interaction between ASE and ASB. Furthermore, we investigate the coordination between ASE and ASB from multiple transcription factors events using a regulatory network framework. Correlation analyses and network motifs show mostly coordinated ASB and ASE.

  18. Cooperative Control of Heterogeneous Uncertain Dynamical Networks: An Adaptive Explicit Synchronization Framework.

    PubMed

    Wang, Bohui; Wang, Jingcheng; Zhang, Langwen; Zhang, Bin; Li, Xiaocheng

    2017-06-01

    This paper proposes an adaptive explicit synchronization framework to address the cooperative control for heterogeneous uncertain dynamical networks under switching communication topologies. The main contribution is to develop an adaptive explicit synchronization algorithm, in which the synchronization state can be completely tracked by each agent in real time rather than only be measured after the synchronization process of all agents is over. By introducing appropriate assumptions, a class of adaptive explicit synchronization protocols is designed by using a combination of the virtual leader's states, the neighboring agents' relative information, distributed feedback gain, and distributed average weighted parameters. It is proved in the sense of Lyapunov that, if the dwell time is larger than a positive threshold, the cooperative control problem for the closed-loop heterogeneous uncertain dynamical networks under switching of strongly-connected communication topologies can be solved by the proposed adaptive explicit synchronization algorithm. Furthermore, by assuming that the topology is frequently strongly-connected, it shows that intermittent adaptive explicit synchronization can be achieved with well-designed control parameters. Two examples are presented to demonstrate the effectiveness of the proposed theory.

  19. An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

    PubMed Central

    Zhang, Xuejun; Lei, Jiaxing

    2015-01-01

    Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology. PMID:26180840

  20. Mathematical framework for large-scale brain network modeling in The Virtual Brain.

    PubMed

    Sanz-Leon, Paula; Knock, Stuart A; Spiegler, Andreas; Jirsa, Viktor K

    2015-05-01

    In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.

  1. A novel framework for command and control of networked sensor systems

    NASA Astrophysics Data System (ADS)

    Chen, Genshe; Tian, Zhi; Shen, Dan; Blasch, Erik; Pham, Khanh

    2007-04-01

    In this paper, we have proposed a highly innovative advanced command and control framework for sensor networks used for future Integrated Fire Control (IFC). The primary goal is to enable and enhance target detection, validation, and mitigation for future military operations by graphical game theory and advanced knowledge information fusion infrastructures. The problem is approached by representing distributed sensor and weapon systems as generic warfare resources which must be optimized in order to achieve the operational benefits afforded by enabling a system of systems. This paper addresses the importance of achieving a Network Centric Warfare (NCW) foundation of information superiority-shared, accurate, and timely situational awareness upon which advanced automated management aids for IFC can be built. The approach uses the Data Fusion Information Group (DFIG) Fusion hierarchy of Level 0 through Level 4 to fuse the input data into assessments for the enemy target system threats in a battlespace to which military force is being applied. Compact graph models are employed across all levels of the fusion hierarchy to accomplish integrative data fusion and information flow control, as well as cross-layer sensor management. The functional block at each fusion level will have a set of innovative algorithms that not only exploit the corresponding graph model in a computationally efficient manner, but also permit combined functional experiments across levels by virtue of the unifying graphical model approach.

  2. A Secure Multicast Framework in Large and High-Mobility Network Groups

    NASA Astrophysics Data System (ADS)

    Lee, Jung-San; Chang, Chin-Chen

    With the widespread use of Internet applications such as Teleconference, Pay-TV, Collaborate tasks, and Message services, how to construct and distribute the group session key to all group members securely is becoming and more important. Instead of adopting the point-to-point packet delivery, these emerging applications are based upon the mechanism of multicast communication, which allows the group member to communicate with multi-party efficiently. There are two main issues in the mechanism of multicast communication: Key Distribution and Scalability. The first issue is how to distribute the group session key to all group members securely. The second one is how to maintain the high performance in large network groups. Group members in conventional multicast systems have to keep numerous secret keys in databases, which makes it very inconvenient for them. Furthermore, in case that a member joins or leaves the communication group, many involved participants have to change their own secret keys to preserve the forward secrecy and the backward secrecy. We consequently propose a novel version for providing secure multicast communication in large network groups. Our proposed framework not only preserves the forward secrecy and the backward secrecy but also possesses better performance than existing alternatives. Specifically, simulation results demonstrate that our scheme is suitable for high-mobility environments.

  3. Polyoxometalate-based eight-connected self-catenated network and fivefold interpenetrating framework

    SciTech Connect

    Zhang Zhiming; Liu Jia; Li Yangguang; Yao Shuang; Wang Enbo; Wang Xinlong

    2010-01-15

    Two entangled compounds [(bpy){sub 6}Cu{sup I}{sub 6}Cl{sub 3}(Mo{sup V}W{sub 5}O{sub 19})] (1) and [(bpy){sub 7}Cu{sup I}{sub 7}Cl{sub 2}(BW{sub 12}O{sub 40})].H{sub 2}O (2) (bpy=4,4'-bipyridine), have been successfully synthesized under hydrothermal conditions and characterized by element analysis, IR spectroscopy, thermal gravimetric analysis, X-ray photoelectron spectroscopy, and single crystal X-ray diffraction analyses. Compound 1 represents the first eight-connected self-penetrating network constructed from cuprous chloride clusters [Cu{sub 6}Cl{sub 3}] and Lindquist-type polyoxoanions. Compound 2 exhibits an interesting fivefold interpenetrating network consisting of Keggin polyoxoanions and Cu{sup +}-metal-organic framework. Crystal data of the two compounds are following: 1, triclinic, P1-bar, a=11.502(2) A, b=13.069(3) A, c=13.296(3) A, alpha=90.55(3){sup o}, beta=113.74(3){sup o}, gamma=110.48(3){sup o}, Z=1; 2, triclinic, P1-bar, a=12.341(3) A, b=13.119(3) A, c=15.367(3) A, alpha=99.12(3){sup o}, beta=90.53(3){sup o}, gamma=104.49(3){sup o}, Z=1. - Graphical abstract: Compound 1 is the POM-based unprecedented eight-connected self-penetrating organic-inorganic hybrid network constructed from the cuprous chloride clusters [Cu{sub 6}Cl{sub 3}], Lindquist-type polyoxoanions [Mo{sup V}W{sub 5}O{sub 19}], and the 4,4'-bipyridine ligands.

  4. Tissue microstructure estimation using a deep network inspired by a dictionary-based framework.

    PubMed

    Ye, Chuyang

    2017-09-06

    Diffusion magnetic resonance imaging (dMRI) captures the anisotropic pattern of water displacement in the neuronal tissue and allows noninvasive investigation of the complex tissue microstructure. A number of biophysical models have been proposed to relate the tissue organization with the observed diffusion signals, so that the tissue microstructure can be inferred. The Neurite Orientation Dispersion and Density Imaging (NODDI) model has been a popular choice and has been widely used for many neuroscientific studies. It models the diffusion signal with three compartments that are characterized by distinct diffusion properties, and the parameters in the model describe tissue microstructure. In NODDI, these parameters are estimated in a maximum likelihood framework, where the nonlinear model fitting is computationally intensive. Therefore, efforts have been made to develop efficient and accurate algorithms for NODDI microstructure estimation, which is still an open problem. In this work, we propose a deep network based approach that performs end-to-end estimation of NODDI microstructure, which is named Microstructure Estimation using a Deep Network (MEDN). MEDN comprises two cascaded stages and is motivated by the AMICO algorithm, where the NODDI microstructure estimation is formulated in a dictionary-based framework. The first stage computes the coefficients of the dictionary. It resembles the solution to a sparse reconstruction problem, where the iterative process in conventional estimation approaches is unfolded and truncated, and the weights are learned instead of predetermined by the dictionary. In the second stage, microstructure properties are computed from the output of the first stage, which resembles the weighted sum of normalized dictionary coefficients in AMICO, and the weights are also learned. Because spatial consistency of diffusion signals can be used to reduce the effect of noise, we also propose MEDN+, which is an extended version of MEDN. MEDN

  5. National Development Education Network: Framework Plan for the Period from 1 August 1986 to 1 August 1991.

    ERIC Educational Resources Information Center

    Hooghoff, Hans

    In 1982 the Netherlands National Institute for Curriculum Development (SLO), with the support of Parliament, devised the framework for the Development Education Project (EPOS). This project concerns a national development education network, which is to be supported for a new period, 1986 to 1991. EPOS is comprised of national organizations,…

  6. Blogs and Social Network Sites as Activity Systems: Exploring Adult Informal Learning Process through Activity Theory Framework

    ERIC Educational Resources Information Center

    Heo, Gyeong Mi; Lee, Romee

    2013-01-01

    This paper uses an Activity Theory framework to explore adult user activities and informal learning processes as reflected in their blogs and social network sites (SNS). Using the assumption that a web-based space is an activity system in which learning occurs, typical features of the components were investigated and each activity system then…

  7. Intelligent microchip networks: an agent-on-chip synthesis framework for the design of smart and robust sensor networks

    NASA Astrophysics Data System (ADS)

    Bosse, Stefan

    2013-05-01

    Sensorial materials consisting of high-density, miniaturized, and embedded sensor networks require new robust and reliable data processing and communication approaches. Structural health monitoring is one major field of application for sensorial materials. Each sensor node provides some kind of sensor, electronics, data processing, and communication with a strong focus on microchip-level implementation to meet the goals of miniaturization and low-power energy environments, a prerequisite for autonomous behaviour and operation. Reliability requires robustness of the entire system in the presence of node, link, data processing, and communication failures. Interaction between nodes is required to manage and distribute information. One common interaction model is the mobile agent. An agent approach provides stronger autonomy than a traditional object or remote-procedure-call based approach. Agents can decide for themselves, which actions are performed, and they are capable of flexible behaviour, reacting on the environment and other agents, providing some degree of robustness. Traditionally multi-agent systems are abstract programming models which are implemented in software and executed on program controlled computer architectures. This approach does not well scale to micro-chip level and requires full equipped computers and communication structures, and the hardware architecture does not consider and reflect the requirements for agent processing and interaction. We propose and demonstrate a novel design paradigm for reliable distributed data processing systems and a synthesis methodology and framework for multi-agent systems implementable entirely on microchip-level with resource and power constrained digital logic supporting Agent-On-Chip architectures (AoC). The agent behaviour and mobility is fully integrated on the micro-chip using pipelined communicating processes implemented with finite-state machines and register-transfer logic. The agent behaviour

  8. Three-dimensional conductive networks based on stacked SiO2@graphene frameworks for enhanced gas sensing.

    PubMed

    Huang, Da; Yang, Zhi; Li, Xiaolin; Zhang, Liling; Hu, Jing; Su, Yanjie; Hu, Nantao; Yin, Guilin; He, Dannong; Zhang, Yafei

    2017-01-07

    Graphene is an ideal candidate for gas sensing due to its excellent conductivity and large specific surface areas. However, it usually suffers from sheet stacking, which seriously debilitates its sensing performance. Herein, we demonstrate a three-dimensional conductive network based on stacked SiO2@graphene core-shell hybrid frameworks for enhanced gas sensing. SiO2 spheres are uniformly encapsulated by graphene oxide (GO) through an electrostatic self-assembly approach to form SiO2@GO core-shell hybrid frameworks, which are reduced through thermal annealing to establish three-dimensional (3D) conductive sensing networks. The SiO2 supported 3D conductive graphene frameworks reveal superior sensing performance to bare reduced graphene oxide (RGO) films, which can be attributed to their less agglomeration and larger surface area. The response value of the 3D framework based sensor for 50 ppm NH3 and 50 ppm NO2 increased 8 times and 5 times, respectively. Additionally, the sensing performance degradation caused by the stacking of the sensing materials is significantly suppressed because the graphene layers are separated by the SiO2 spheres. The sensing performance decays by 92% for the bare RGO films when the concentration of the sensing material increases 8 times, while there is only a decay of 25% for that of the SiO2@graphene core-shell hybrid frameworks. This work provides an insight into 3D frameworks of hybrid materials for effectively improving gas sensing performance.

  9. A collaborative computing framework of cloud network and WBSN applied to fall detection and 3-D motion reconstruction.

    PubMed

    Lai, Chin-Feng; Chen, Min; Pan, Jeng-Shyang; Youn, Chan-Hyun; Chao, Han-Chieh

    2014-03-01

    As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

  10. A decision-making framework to model environmental flow requirements in oasis areas using Bayesian networks

    NASA Astrophysics Data System (ADS)

    Xue, Jie; Gui, Dongwei; Zhao, Ying; Lei, Jiaqiang; Zeng, Fanjiang; Feng, Xinlong; Mao, Donglei; Shareef, Muhammad

    2016-09-01

    The competition for water resources between agricultural and natural oasis ecosystems has become an increasingly serious problem in oasis areas worldwide. Recently, the intensive extension of oasis farmland has led to excessive exploitation of water discharge, and consequently has resulted in a lack of water supply in natural oasis. To coordinate the conflicts, this paper provides a decision-making framework for modeling environmental flows in oasis areas using Bayesian networks (BNs). Three components are included in the framework: (1) assessment of agricultural economic loss due to meeting environmental flow requirements; (2) decision-making analysis using BNs; and (3) environmental flow decision-making under different water management scenarios. The decision-making criterion is determined based on intersection point analysis between the probability of large-level total agro-economic loss and the ratio of total to maximum agro-economic output by satisfying environmental flows. An application in the Qira oasis area of the Tarim Basin, Northwest China indicates that BNs can model environmental flow decision-making associated with agricultural economic loss effectively, as a powerful tool to coordinate water-use conflicts. In the case study, the environmental flow requirement is determined as 50.24%, 49.71% and 48.73% of the natural river flow in wet, normal and dry years, respectively. Without further agricultural economic loss, 1.93%, 0.66% and 0.43% of more river discharge can be allocated to eco-environmental water demands under the combined strategy in wet, normal and dry years, respectively. This work provides a valuable reference for environmental flow decision-making in any oasis area worldwide.

  11. A theoretical framework for analyzing coupled neuronal networks: Application to the olfactory system.

    PubMed

    Barreiro, Andrea K; Gautam, Shree Hari; Shew, Woodrow L; Ly, Cheng

    2017-10-02

    Determining how synaptic coupling within and between regions is modulated during sensory processing is an important topic in neuroscience. Electrophysiological recordings provide detailed information about neural spiking but have traditionally been confined to a particular region or layer of cortex. Here we develop new theoretical methods to study interactions between and within two brain regions, based on experimental measurements of spiking activity simultaneously recorded from the two regions. By systematically comparing experimentally-obtained spiking statistics to (efficiently computed) model spike rate statistics, we identify regions in model parameter space that are consistent with the experimental data. We apply our new technique to dual micro-electrode array in vivo recordings from two distinct regions: olfactory bulb (OB) and anterior piriform cortex (PC). Our analysis predicts that: i) inhibition within the afferent region (OB) has to be weaker than the inhibition within PC, ii) excitation from PC to OB is generally stronger than excitation from OB to PC, iii) excitation from PC to OB and inhibition within PC have to both be relatively strong compared to presynaptic inputs from OB. These predictions are validated in a spiking neural network model of the OB-PC pathway that satisfies the many constraints from our experimental data. We find when the derived relationships are violated, the spiking statistics no longer satisfy the constraints from the data. In principle this modeling framework can be adapted to other systems and be used to investigate relationships between other neural attributes besides network connection strengths. Thus, this work can serve as a guide to further investigations into the relationships of various neural attributes within and across different regions during sensory processing.

  12. Synthesis and Structural Characterization of Carboxylate-Based Metal-Organic Frameworks and Coordination Networks

    NASA Astrophysics Data System (ADS)

    Calderone, Paul

    Coordination networks (CNs) and metal-organic frameworks (MOFs) are crystalline materials composed of metal ions linked by multifunctional organic ligands. From these connections, infinite arrays of one-, two-, or three-dimensional networks can be formed. Exploratory synthesis and research of novel CNs and MOFs is of current interest because of their many possible industrial applications including gas storage, catalysis, magnetism, and luminescence. A variety of metal centers and organic ligands can be used to synthesize MOFs and CNs under a range of reaction conditions, leading to extraordinary structural diversity. The characteristics of the metals and linkers, such as properties and coordination preferences, play the biggest role in determining the structure and properties of the resulting network. Thus, the choice of metal and linker is dictated by the desired traits of the target network. The pervasive use of transition metal centers in MOF synthesis stems from their well-known coordination behavior with carboxylate-based linkers, thus facilitating design strategies. Conversely, CNs and MOFs based on s-block and lanthanide metals are less studied because each group presents unique challenges to structure prediction. Lanthanide metals have variable coordination spheres capable of accommodating up to twelve atoms, while the bonding in s-block metals takes on a mainly ionic character. In spite of these obstacles, lanthanide and s-block CNs are worthwhile synthetic targets because of their unique properties. Interesting photoluminescent and sensing materials can be developed using lanthanide metals, whereas low atomic weight s-block metals may afford an advantage in gravimetric advantages for gas storage applications. The aim of this research was to expand the current understanding of carboxylate-based CN and MOF synthesis by varying the metals, solvents, and temperatures used. To this end

  13. A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications

    PubMed Central

    Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong

    2015-01-01

    It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs’ route planning for small and medium-scale networks. PMID:26076404

  14. A Space-Time Network-Based Modeling Framework for Dynamic Unmanned Aerial Vehicle Routing in Traffic Incident Monitoring Applications.

    PubMed

    Zhang, Jisheng; Jia, Limin; Niu, Shuyun; Zhang, Fan; Tong, Lu; Zhou, Xuesong

    2015-06-12

    It is essential for transportation management centers to equip and manage a network of fixed and mobile sensors in order to quickly detect traffic incidents and further monitor the related impact areas, especially for high-impact accidents with dramatic traffic congestion propagation. As emerging small Unmanned Aerial Vehicles (UAVs) start to have a more flexible regulation environment, it is critically important to fully explore the potential for of using UAVs for monitoring recurring and non-recurring traffic conditions and special events on transportation networks. This paper presents a space-time network- based modeling framework for integrated fixed and mobile sensor networks, in order to provide a rapid and systematic road traffic monitoring mechanism. By constructing a discretized space-time network to characterize not only the speed for UAVs but also the time-sensitive impact areas of traffic congestion, we formulate the problem as a linear integer programming model to minimize the detection delay cost and operational cost, subject to feasible flying route constraints. A Lagrangian relaxation solution framework is developed to decompose the original complex problem into a series of computationally efficient time-dependent and least cost path finding sub-problems. Several examples are used to demonstrate the results of proposed models in UAVs' route planning for small and medium-scale networks.

  15. A Geographic Information System framework for the dataset visualization in Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Shufean, Md Abu

    In Wireless Sensor Networks (WSNs), collecting data from sensor nodes and analyzing them is a challenging task. A Geographic Information System (GIS) could be a better way to analyze, manage, and represent the dataset collected by sensor nodes. Motivated by these considerations, we proposed a system to visualize the WSN dataset in real time using a software implementation of the GIS framework called ArcGIS. In this research, we have implemented our proposed system where a couple of sensor nodes has been deployed to collect Dissolved Oxygen (DO) and water pH value continuously from the John Gray Center pond of Lamar University. To keep track of each sensor node we have accumulated their precise location, sensing time, and corresponding data in our lab. After analyzing couple of data storing technique, we have decided to use PostGIS (a geospatial database) to store data and developed an ArcGIS centric application. Finally, with the help of that application, a web map was designed combining all different data source layers for end users where all the technical details have been encapsulated behind a base map. This web map has been hosted in a web server so that users sitting in any location can easily access and visualize desired data with a real time update. This research work has been supported by National Science Foundation under Grants CNS-0922888 and CNS-1427838.

  16. Sensor network based solar forecasting using a local vector autoregressive ridge framework

    SciTech Connect

    Xu, J.; Yoo, S.; Heiser, J.; Kalb, P.

    2016-04-04

    The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.

  17. Have artificial neural networks met expectations in drug discovery as implemented in QSAR framework?

    PubMed

    Dobchev, Dimitar; Karelson, Mati

    2016-07-01

    Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery. In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field. The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.

  18. A neural networks framework for real-time unfolding of neutron spectroscopic data at JET

    SciTech Connect

    Ronchi, E.; Conroy, S.; Sunden, E. A.; Ericsson, G.; Hjalmarsson, A.; Hellesen, C.; Johnson, M. G.; Weiszflog, M.

    2008-10-15

    A determination of fast ion population parameters such as intensity and kinetic temperature is important for fusion reactors. This becomes more challenging with finer time resolution of the measurements, since the limited data in each time slice cause increasing statistical variations in the data. This paper describes a framework using Bayesian-regularized neural networks (NNs) designed for such a task. The method is applied to the TOFOR 2.5 MeV fusion neutron spectrometer at JET. NN training data are generated by random sampling of variables in neutron spectroscopy models. Ranges and probability distributions of the parameters are chosen to match the experimental data. Results have shown good performance both on synthetic and experimental data. The latter was assessed by statistical considerations and by examining the robustness and time consistency of the results. The regularization of the training algorithm allowed for higher time resolutions than simple forward methods. The fast execution time makes this approach suitable for real-time analysis with a time resolution limit in the microsecond time scale.

  19. a New Framework for Geospatial Site Selection Using Artificial Neural Networks as Decision Rules: a Case Study on Landfill Sites

    NASA Astrophysics Data System (ADS)

    Abujayyab, S. K. M.; Ahamad, M. A. S.; Yahya, A. S.; Saad, A.-M. H. Y.

    2015-10-01

    This paper briefly introduced the theory and framework of geospatial site selection (GSS) and discussed the application and framework of artificial neural networks (ANNs). The related literature on the use of ANNs as decision rules in GSS is scarce from 2000 till 2015. As this study found, ANNs are not only adaptable to dynamic changes but also capable of improving the objectivity of acquisition in GSS, reducing time consumption, and providing high validation. ANNs make for a powerful tool for solving geospatial decision-making problems by enabling geospatial decision makers to implement their constraints and imprecise concepts. This tool offers a way to represent and handle uncertainty. Specifically, ANNs are decision rules implemented to enhance conventional GSS frameworks. The main assumption in implementing ANNs in GSS is that the current characteristics of existing sites are indicative of the degree of suitability of new locations with similar characteristics. GSS requires several input criteria that embody specific requirements and the desired site characteristics, which could contribute to geospatial sites. In this study, the proposed framework consists of four stages for implementing ANNs in GSS. A multilayer feed-forward network with a backpropagation algorithm was used to train the networks from prior sites to assess, generalize, and evaluate the outputs on the basis of the inputs for the new sites. Two metrics, namely, confusion matrix and receiver operating characteristic tests, were utilized to achieve high accuracy and validation. Results proved that ANNs provide reasonable and efficient results as an accurate and inexpensive quantitative technique for GSS.

  20. The DELTA Network Study of Distributed Automated Post-Market Cardiovascular Device Safety Surveillance

    PubMed Central

    Kumar, Amit; Matheny, Michael E.; Ho, Kalon K.L.; Yeh, Robert W.; Piemonte, Thomas C.; Waldman, Howard; Shah, Pinak B.; Cope, Richard; Normand, Sharon-Lise; Donnelly, Sharon; Robbins, Susan; Resnic, Frederic S.

    2016-01-01

    Background Current approaches for post-market medical device safety surveillance are limited in their ability to produce timely and accurate assessments of adverse event rates. Methods and Results The DELTA (Data Extraction and Longitudinal Trend Analysis) network study was a multicenter prospective observational study designed to evaluate the safety of devices used during percutaneous coronary interventions (PCI). All adult patients undergoing PCI from January 2008 through December 2012 at five participating Massachusetts sites were included. A safety alert was triggered if the cumulative observed adverse event rates for the study device exceeded the upper 95% confidence interval of the event rates of propensity-matched control cohort. Pre-specified sensitivity analyses were developed to validate any identified safety signal. A total of 23,805 consecutive PCI procedures were evaluated. Two out of 24 safety analyses triggered safety alerts. Patients receiving Perclose vascular closure device (VCD) experienced an increased risk of minor vascular complications (relative risk [RR] 4.14; p <0.01) and any vascular complication (RR: 2.06; p = 0.01) as compared with propensity-matched patients receiving alternative VCD; a result primarily driven by relatively high event rates at one participating center. Sensitivity analyses based on alternative risk adjustment methods confirmed the a pattern of increased rate of complications at one of the five participating sites in their use of Perclose VCD. Conclusions The DELTA network study demonstrates that distributed automated prospective safety surveillance has the potential of providing near real-time assessment of safety risks of newly approved medical devices. PMID:25491915

  1. Multi-layer photonics modeling framework for the design, analysis, and optimization of devices, links, and networks

    NASA Astrophysics Data System (ADS)

    Richter, André; Louchet, Hadrien; Arellano, Cristina; Farina, Jim; Koltchanov, Igor

    2011-01-01

    Requirements on photonics modeling vary significantly when aiming to design, analyze and optimize a single device, a complete transmission link or a complex network. Depending on the task at hand, different levels of detail for emulating the underlying physical characteristics and signal interactions are necessary. We present a multi-layer photonics modeling framework that addresses the different design challenges of devices, links and networks. Our discussed methodology is based on flexible optical signal representations, equipment models ranging from very detailed to high-level parametric, sophisticated numerical algorithms and means for automated parameter and technology variation and optimization. We discuss applications such as the detailed modeling on photonics integrated circuit level, the characterization of a high-speed transmission link utilizing multilevel modulation and coherent detection, the parametric analysis of transmission links and network dynamics, and the cost-optimized placement of equipment in moderately complex networks.

  2. Genetic Network Programming-Sarsa with Multi-Subroutines for Trading Rules on Stock Markets

    NASA Astrophysics Data System (ADS)

    Yang, Yang; Gu, Yunqing; Mabu, Shingo; Hirasawa, Kotaro

    In this paper, a stock trading model is proposed using a graph-based evolutionary algorithm named Genetic Network Programming-Sarsa (GNP-Sarsa) with multi-subroutines. The method is developed for discovering the frequent transitions of GNP, which can be seen as the repetitive subgraphs, i.e., building blocks with useful knowledge over the entire graph structure, and modularizing them as subroutines. The important points of the subroutines mechanism are as follows: First, the nodes and node connections discovered in the subroutines are reused to create effective trading rules. Second, the evolution can be achieved so quickly by narrowing the search space with subroutines. Last, as the kinds of subroutines increase, the generalization ability is improved since more generalized frequent transitions of GNP, i.e., building blocks are found instead of precisely modeling the training data, which leads to the overiftting problem. The following two experiments are discussed: 1) varying the number of subroutine nodes in the main GNP and 2) varying the kinds of subroutines to be generated. Simulation results on the stock markets show that the proposed method can generate more efficient and generalized trading models and obtain much higher profits.

  3. Insight into the crystallization of amorphous imine-linked polymer networks to 2D covalent organic frameworks.

    PubMed

    Smith, Brian J; Overholts, Anna C; Hwang, Nicky; Dichtel, William R

    2016-03-04

    We explore the crystallization of a high surface area imine-linked two-dimensional covalent organic framework (2D COF). The growth process reveals rapid initial formation of an amorphous network that subsequently crystallizes into the layered 2D network. The metastable amorphous polymer may be isolated and resubjected to growth conditions to form the COF. These experiments provide the first mechanistic insight into the mechanism of imine-linked 2D COF formation, which is distinct from that of boronate-ester linked COFs.

  4. Mapping, Awareness, and Virtualization Network Administrator Training Tool (MAVNATT) Architecture and Framework

    DTIC Science & Technology

    2015-06-01

    including connected devices, directly from SNMP- managed devices. SNMP is available on all OSs including Windows, Mac OS X, and Linux. It is also available...and to the Office of Management and Budget, Paperwork Reduction Project (0704-0188) Washington, DC 20503. 1. AGENCY USE ONLY (Leave blank) 2...network administrator training, network management , network virtualization, tactical network topology, rapid network design, modeling and simulation

  5. A Bayesian Belief Network framework to predict SOC stock change: the Veneto region (Italy) case study

    NASA Astrophysics Data System (ADS)

    Dal Ferro, Nicola; Quinn, Claire Helen; Morari, Francesco

    2017-04-01

    A key challenge for soil scientists is predicting agricultural management scenarios that combine crop productions with high standards of environmental quality. In this context, reversing the soil organic carbon (SOC) decline in croplands is required for maintaining soil fertility and contributing to mitigate GHGs emissions. Bayesian belief networks (BBN) are probabilistic models able to accommodate uncertainty and variability in the predictions of the impacts of management and environmental changes. By linking multiple qualitative and quantitative variables in a cause-and-effect relationships, BBNs can be used as a decision support system at different spatial scales to find best management strategies in the agroecosystems. In this work we built a BBN to model SOC dynamics (0-30 cm layer) in the low-lying plain of Veneto region, north-eastern Italy, and define best practices leading to SOC accumulation and GHGs (CO2-equivalent) emissions reduction. Regional pedo-climatic, land use and management information were combined with experimental and modelled data on soil C dynamics as natural and anthropic key drivers affecting SOC stock change. Moreover, utility nodes were introduced to determine optimal decisions for mitigating GHGs emissions from croplands considering also three different IPCC climate scenarios. The network was finally validated with real field data in terms of SOC stock change. Results showed that the BBN was able to model real SOC stock changes, since validation slightly overestimated SOC reduction (+5%) at the expenses of its accumulation. At regional level, probability distributions showed 50% of SOC loss, while only 17% of accumulation. However, the greatest losses (34%) were associated with low reduction rates (100-500 kg C ha-1 y-1), followed by 33% of stabilized conditions (-100 < SOC < 100 kg ha-1 y-1). Land use management (especially tillage operations and soil cover) played a primary role to affect SOC stock change, while climate conditions

  6. Data Quality Control of the French Permanent Broadband Network in the RESIF Framework

    NASA Astrophysics Data System (ADS)

    Grunberg, Marc; Lambotte, Sophie; Engels, Fabien; Dretzen, Remi; Hernandez, Alain

    2014-05-01

    In the framework of the RESIF (Réseau Sismologique et géodésique Français) project, a new information system is being setting up, allowing the improvement of the management and the distribution of high quality data from the different elements of RESIF and the associated networks. Within this information system, EOST (in Strasbourg) is in charge of collecting real-time permanent broadband seismic waveform, and performing Quality Control on these data. The real-time and validated data set are pushed to the French National Distribution Center (Isterre/Grenoble) in order to make them publicly available. Furthermore EOST hosts the BCSF-ReNaSS, in charge of the French metropolitan seismic bulletin. This allows to benefit from some high-end quality control based on the national and world-wide seismicity. Here we present first the real-time seismic data flow from the stations of the French National Broad Band Network to EOST, and then, the data Quality Control procedures that were recently installed, including some new developments. The data Quality Control consists in applying a variety of subprocesses to check the consistency of the whole system and process from the stations to the data center. This allows us to verify that instruments and data transmission are operating correctly. Moreover analysis of the ambient noise helps to characterize intrinsic seismic quality of the stations and to identify other kind of disturbances. The deployed Quality Control consist in a pipeline that starts with low-level procedures : check the real-time miniseed data file (file naming convention, data integrity), check for inconsistencies between waveform and meta-data (channel name, sample rate, etc.), compute waveform statistics (data availability, gap/overlap, mean, rms, time quality, spike). It is followed by some high-level procedures such as : power spectral density computation (PSD), STA/LTA computation to be correlated to the seismicity, phases picking and stations magnitudes

  7. Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability.

    PubMed

    van der Linden, Joost H; Narsilio, Guillermo A; Tordesillas, Antoinette

    2016-08-01

    We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro- and mesoscales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.

  8. Machine learning framework for analysis of transport through complex networks in porous, granular media: A focus on permeability

    NASA Astrophysics Data System (ADS)

    van der Linden, Joost H.; Narsilio, Guillermo A.; Tordesillas, Antoinette

    2016-08-01

    We present a data-driven framework to study the relationship between fluid flow at the macroscale and the internal pore structure, across the micro- and mesoscales, in porous, granular media. Sphere packings with varying particle size distribution and confining pressure are generated using the discrete element method. For each sample, a finite element analysis of the fluid flow is performed to compute the permeability. We construct a pore network and a particle contact network to quantify the connectivity of the pores and particles across the mesoscopic spatial scales. Machine learning techniques for feature selection are employed to identify sets of microstructural properties and multiscale complex network features that optimally characterize permeability. We find a linear correlation (in log-log scale) between permeability and the average closeness centrality of the weighted pore network. With the pore network links weighted by the local conductance, the average closeness centrality represents a multiscale measure of efficiency of flow through the pore network in terms of the mean geodesic distance (or shortest path) between all pore bodies in the pore network. Specifically, this study objectively quantifies a hypothesized link between high permeability and efficient shortest paths that thread through relatively large pore bodies connected to each other by high conductance pore throats, embodying connectivity and pore structure.

  9. Data Quality Control of the French Permanent Broadband Network in the RESIF Framework.

    NASA Astrophysics Data System (ADS)

    Grunberg, M.; Lambotte, S.; Engels, F.

    2014-12-01

    In the framework of the RESIF (Réseau Sismologique et géodésique Français) project, a new information system is setting up, allowing the improvement of the management and the distribution of high quality data from the different elements of RESIF. Within this information system, EOST (in Strasbourg) is in charge of collecting real-time permanent broadband seismic waveform, and performing Quality Control on these data. The real-time and validated data set are pushed to the French National Distribution Center (Isterre/Grenoble) to make them publicly available. Furthermore EOST hosts the BCSF-ReNaSS, in charge of the French metropolitan seismic bulletin. This allows to benefit from some high-end quality control based on the national and world-wide seismicity. Here we present the real-time seismic data flow from the stations of the French National Broad Band Network to EOST, and then, the data Quality Control procedures that were recently installed, including some new developments.The data Quality Control consists in applying a variety of processes to check the consistency of the whole system from the stations to the data center. This allows us to verify that instruments and data transmission are operating correctly. Moreover, time quality is critical for most of the scientific data applications. To face this challenge and check the consistency of polarities and amplitudes, we deployed several high-end processes including a noise correlation procedure to check for timing accuracy (intrumental time errors result in a time-shift of the whole cross-correlation, clearly distinct from those due to change in medium physical properties), and a systematic comparison of synthetic and real data for teleseismic earthquakes of magnitude larger than 6.5 to detect timing errors as well as polarity and amplitude problems.

  10. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology.

    PubMed

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

  11. A Unifying Mathematical Framework for Genetic Robustness, Environmental Robustness, Network Robustness and their Trade-offs on Phenotype Robustness in Biological Networks. Part III: Synthetic Gene Networks in Synthetic Biology

    PubMed Central

    Chen, Bor-Sen; Lin, Ying-Po

    2013-01-01

    Robust stabilization and environmental disturbance attenuation are ubiquitous systematic properties that are observed in biological systems at many different levels. The underlying principles for robust stabilization and environmental disturbance attenuation are universal to both complex biological systems and sophisticated engineering systems. In many biological networks, network robustness should be large enough to confer: intrinsic robustness for tolerating intrinsic parameter fluctuations; genetic robustness for buffering genetic variations; and environmental robustness for resisting environmental disturbances. Network robustness is needed so phenotype stability of biological network can be maintained, guaranteeing phenotype robustness. Synthetic biology is foreseen to have important applications in biotechnology and medicine; it is expected to contribute significantly to a better understanding of functioning of complex biological systems. This paper presents a unifying mathematical framework for investigating the principles of both robust stabilization and environmental disturbance attenuation for synthetic gene networks in synthetic biology. Further, from the unifying mathematical framework, we found that the phenotype robustness criterion for synthetic gene networks is the following: if intrinsic robustness + genetic robustness + environmental robustness ≦ network robustness, then the phenotype robustness can be maintained in spite of intrinsic parameter fluctuations, genetic variations, and environmental disturbances. Therefore, the trade-offs between intrinsic robustness, genetic robustness, environmental robustness, and network robustness in synthetic biology can also be investigated through corresponding phenotype robustness criteria from the systematic point of view. Finally, a robust synthetic design that involves network evolution algorithms with desired behavior under intrinsic parameter fluctuations, genetic variations, and environmental

  12. POLARIS: Agent-Based Modeling Framework Development and Implementation for Integrated Travel Demand and Network and Operations Simulations

    SciTech Connect

    Auld, Joshua; Hope, Michael; Ley, Hubert; Sokolov, Vadim; Xu, Bo; Zhang, Kuilin

    2016-03-01

    This paper discusses the development of an agent-based modelling software development kit, and the implementation and validation of a model using it that integrates dynamic simulation of travel demand, network supply and network operations. A description is given of the core utilities in the kit: a parallel discrete event engine, interprocess exchange engine, and memory allocator, as well as a number of ancillary utilities: visualization library, database IO library, and scenario manager. The overall framework emphasizes the design goals of: generality, code agility, and high performance. This framework allows the modeling of several aspects of transportation system that are typically done with separate stand-alone software applications, in a high-performance and extensible manner. The issue of integrating such models as dynamic traffic assignment and disaggregate demand models has been a long standing issue for transportation modelers. The integrated approach shows a possible way to resolve this difficulty. The simulation model built from the POLARIS framework is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning models to be extended to include previously separate aspects of the urban system, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center impacts on various network events such as accidents, congestion and weather events, show the potential of the system.

  13. a Bayesian Network Framework for Automatic Detection of Lunar Impact Craters Based on Optical Images and dem Data

    NASA Astrophysics Data System (ADS)

    Yang, J.; Kang, Z.

    2017-07-01

    Impact craters are among the most noticeable geo-morphological features on the planetary surface and yield significant information on terrain evolution and the history of the solar system. Thus, the recognition of lunar impact craters is an important branch of modern planetary studies. To address problems associated with the insufficient and inaccurate detection of lunar impact craters, this paper extends the strategy that integrates multi-source data and proposes a Bayesian Network (BN) framework for the automatic recognition of impact craters that is based on CCD stereo camera images and associated Digital Elevation Model (DEM) data. The method uses the SVM model to fit the probability distribution of the impact craters in the feature space. SVM model, whose output is used as the intermediate posterior probability, is embedded in the Bayesian network as a node, and the final posterior probability is obtained by integration under the Bayesian network. We validated our proposed framework with both CCD stereo camera images acquired by the Chang'e-2 satellite and DEM data acquired by Lunar Reconnaissance Orbiter (LRO). Experimental results demonstrate that the proposed framework can provide a very high level of accuracy in the recognition phase. Moreover, the results showed a significant improvement in the detection rate, particularly for the detection of sub-kilometer craters, compared with previous approaches.

  14. A peri-motor framework reveals functional segmentation in the motoneuronal network controlling locomotion in Caenorhabditis elegans

    PubMed Central

    Haspel, Gal; O'Donovan, Michael J

    2011-01-01

    The neuronal connectivity dataset of the nematode Caenorhabditis elegans attracts wide attention from computational neuroscientists and experimentalists. However, the dataset is incomplete. The ventral and dorsal nerve cords of a single nematode were reconstructed halfway along the body and the posterior data is missing, leaving 21 of 75 motoneurons of the locomotor network with partial or no connectivity data. Using a new framework for network analysis, the peri-motor space, we identified rules of connectivity that allowed us to approximate the missing data by extrapolation. Motoneurons were mapped into peri-motor space in which each motoneuron is located according to the muscle cells it innervates. In this framework, a pattern of iterative connections emerges which includes most (0.90) of the connections. We identified a repeating unit consisting of 12 motoneurons and 12 muscle cells. The cell bodies of the motoneurons of such a unit are not necessarily anatomical neighbors and there is no obvious anatomical segmentation. A connectivity model, comprised of six repeating units, is a description of the network that is both simplified (modular and without non-iterative connections) and more complete (includes the posterior part) than the original dataset. The peri-motor framework of observed connectivity and the segmented connectivity model give insights and advance the study of the neuronal infrastructure underlying locomotion in C. elegans. Furthermore, we suggest that the tools used herein may be useful to interpret, simplify and represent connectivity data of other motor systems. PMID:21994377

  15. A novel marketing mix and choice architecture framework to nudge restaurant customers toward healthy food environments to reduce obesity in the United States.

    PubMed

    Kraak, V I; Englund, T; Misyak, S; Serrano, E L

    2017-08-01

    This review identified and adapted choice architecture frameworks to develop a novel framework that restaurant owners could use to promote healthy food environments for customers who currently overconsume products high in fat, sugar and sodium that increase their risk of obesity and diet-related non-communicable diseases. This review was conducted in three steps and presented as a narrative summary to demonstrate a proof of concept. Step 1 was a systematic review of nudge or choice architecture frameworks used to categorize strategies that cue healthy behaviours in microenvironments. We searched nine electronic databases between January 2000 and December 2016 and identified 1,244 records. Inclusion criteria led to the selection of five choice architecture frameworks, of which three were adapted and combined with marketing mix principles to highlight eight strategies (i.e. place, profile, portion, pricing, promotion, healthy default picks, prompting or priming and proximity). Step 2 involved conducting a comprehensive evidence review between January 2006 and December 2016 to identify U.S. recommendations for the restaurant sector organized by strategy. Step 3 entailed developing 12 performance metrics for the eight strategies. This framework should be tested to determine its value to assist restaurant owners to promote and socially normalize healthy food environments to reduce obesity and non-communicable diseases. © 2017 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.

  16. The application of statistical mechanics on the study of glassy behaviors in transportation networks and dynamics in models of financial markets

    NASA Astrophysics Data System (ADS)

    Yeung, Chi Ho

    In this thesis, we study two interdisciplinary problems in the framework of statistical physics, which show the broad applicability of physics on problems with various origins. The first problem corresponds to an optimization problem in allocating resources on random regular networks. Frustrations arise from competition for resources. When the initial resources are uniform, different regimes with discrete fractions of satisfied nodes are observed, resembling the Devil's staircase. We apply the spin glass theory in analyses and demonstrate how functional recursions are converted to simple recursions of probabilities. Equilibrium properties such as the average energy and the fraction of free nodes are derived. When the initial resources are bimodally distributed, increases in the fraction of rich nodes induce a glassy transition, entering a glassy phase described by the existence of multiple metastable states, in which we employ the replica symmetry breaking ansatz for analysis. The second problem corresponds to the study of multi-agent systems modeling financial markets. Agents in the system trade among themselves, and self-organize to produce macroscopic trading behaviors resembling the real financial markets. These behaviors include the arbitraging activities, the setting up and the following of price trends. A phase diagram of these behaviors is obtained, as a function of the sensitivity of price and the market impact factor. We finally test the applicability of the models with real financial data including the Hang Seng Index, the Nasdaq Composite and the Dow Jones Industrial Average. A substantial fraction of agents gains faster than the inflation rate of the indices, suggesting the possibility of using multi-agent systems as a tool for real trading.

  17. A Web Service-Based Framework Model for People-Centric Sensing Applications Applied to Social Networking

    PubMed Central

    Nunes, David; Tran, Thanh-Dien; Raposo, Duarte; Pinto, André; Gomes, André; Silva, Jorge Sá

    2012-01-01

    As the Internet evolved, social networks (such as Facebook) have bloomed and brought together an astonishing number of users. Mashing up mobile phones and sensors with these social environments enables the creation of people-centric sensing systems which have great potential for expanding our current social networking usage. However, such systems also have many associated technical challenges, such as privacy concerns, activity detection mechanisms or intermittent connectivity, as well as limitations due to the heterogeneity of sensor nodes and networks. Considering the openness of the Web 2.0, good technical solutions for these cases consist of frameworks that expose sensing data and functionalities as common Web-Services. This paper presents our RESTful Web Service-based model for people-centric sensing frameworks, which uses sensors and mobile phones to detect users’ activities and locations, sharing this information amongst the user’s friends within a social networking site. We also present some screenshot results of our experimental prototype. PMID:22438732

  18. A network-based framework for assessing infrastructure resilience: a case study of the London metro system

    PubMed Central

    Dillon, Trent

    2016-01-01

    Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. It is imperative to build resilience of large-scale infrastructures like metro systems for addressing the threat of natural disasters and man-made attacks in urban areas. Analysis is needed to ensure that these systems are capable of withstanding and containing unexpected perturbations, and develop heuristic strategies for guiding the design of more resilient networks in the future. We present a comprehensive, multi-pronged framework that analyses information on network topology, spatial organization and passenger flow to understand the resilience of the London metro system. Topology of the London metro system is not fault tolerant in terms of maintaining connectivity at the periphery of the network since it does not exhibit small-world properties. The passenger strength distribution follows a power law, suggesting that while the London metro system is robust to random failures, it is vulnerable to disruptions on a few critical stations. The analysis further identifies particular sources of structural and functional vulnerabilities that need to be mitigated for improving the resilience of the London metro network. The insights from our framework provide useful strategies to build resilience for both existing and upcoming metro systems. PMID:27146689

  19. A network-based framework for assessing infrastructure resilience: a case study of the London metro system.

    PubMed

    Chopra, Shauhrat S; Dillon, Trent; Bilec, Melissa M; Khanna, Vikas

    2016-05-01

    Modern society is increasingly dependent on the stability of a complex system of interdependent infrastructure sectors. It is imperative to build resilience of large-scale infrastructures like metro systems for addressing the threat of natural disasters and man-made attacks in urban areas. Analysis is needed to ensure that these systems are capable of withstanding and containing unexpected perturbations, and develop heuristic strategies for guiding the design of more resilient networks in the future. We present a comprehensive, multi-pronged framework that analyses information on network topology, spatial organization and passenger flow to understand the resilience of the London metro system. Topology of the London metro system is not fault tolerant in terms of maintaining connectivity at the periphery of the network since it does not exhibit small-world properties. The passenger strength distribution follows a power law, suggesting that while the London metro system is robust to random failures, it is vulnerable to disruptions on a few critical stations. The analysis further identifies particular sources of structural and functional vulnerabilities that need to be mitigated for improving the resilience of the London metro network. The insights from our framework provide useful strategies to build resilience for both existing and upcoming metro systems. © 2016 The Author(s).

  20. User-Centric Secure Cross-Site Interaction Framework for Online Social Networking Services

    ERIC Educational Resources Information Center

    Ko, Moo Nam

    2011-01-01

    Social networking service is one of major technological phenomena on Web 2.0. Hundreds of millions of users are posting message, photos, and videos on their profiles and interacting with other users, but the sharing and interaction are limited within the same social networking site. Although users can share some content on a social networking site…

  1. Executive Leadership in School Improvement Networks: A Conceptual Framework and Agenda for Research

    ERIC Educational Resources Information Center

    Peurach, Donald J.; Gumus, Emine

    2011-01-01

    The purpose of this analysis is to improve understanding of executive leadership in school improvement networks: for example, networks supported by comprehensive school reform providers, charter management organizations, and education management organizations. In this analysis, we review the literature on networks and executive leadership. We draw…

  2. Executive Leadership in School Improvement Networks: A Conceptual Framework and Agenda for Research

    ERIC Educational Resources Information Center

    Peurach, Donald J.; Gumus, Emine

    2011-01-01

    The purpose of this analysis is to improve understanding of executive leadership in school improvement networks: for example, networks supported by comprehensive school reform providers, charter management organizations, and education management organizations. In this analysis, we review the literature on networks and executive leadership. We draw…

  3. A Framework for the Management of Evolving Requirements in Software Systems Supporting Network-Centric Warfare

    DTIC Science & Technology

    2006-06-01

    NCW environment must be supported by new, innovative networked communication technologies. There are many sources of requirements for these software...support network-centric operations (NCO) in the NCW environment must be supported by new, innovative networked communication technologies. There... communication technologies. These technologies may be modifications to fully developed legacy software systems that are programs of record (POR). They

  4. User-Centric Secure Cross-Site Interaction Framework for Online Social Networking Services

    ERIC Educational Resources Information Center

    Ko, Moo Nam

    2011-01-01

    Social networking service is one of major technological phenomena on Web 2.0. Hundreds of millions of users are posting message, photos, and videos on their profiles and interacting with other users, but the sharing and interaction are limited within the same social networking site. Although users can share some content on a social networking site…

  5. Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market

    PubMed Central

    Long, Haiming; Tang, Nengyu

    2017-01-01

    This study considers the effect of an industry’s network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry’s conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry’s systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust. PMID:28683130

  6. Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market.

    PubMed

    Long, Haiming; Zhang, Ji; Tang, Nengyu

    2017-01-01

    This study considers the effect of an industry's network topology on its systemic risk contribution to the stock market using data from the CSI 300 two-tier industry indices from the Chinese stock market. We first measure industry's conditional-value-at-risk (CoVaR) and the systemic risk contribution (ΔCoVaR) using the fitted time-varying t-copula function. The network of the stock industry is established based on dynamic conditional correlations with the minimum spanning tree. Then, we investigate the connection characteristics and topology of the network. Finally, we utilize seemingly unrelated regression estimation (SUR) of panel data to analyze the relationship between network topology of the stock industry and the industry's systemic risk contribution. The results show that the systemic risk contribution of small-scale industries such as real estate, food and beverage, software services, and durable goods and clothing, is higher than that of large-scale industries, such as banking, insurance and energy. Industries with large betweenness centrality, closeness centrality, and clustering coefficient and small node occupancy layer are associated with greater systemic risk contribution. In addition, further analysis using a threshold model confirms that the results are robust.

  7. Neural network modeling and an uncertainty analysis in Bayesian framework: A case study from the KTB borehole site

    NASA Astrophysics Data System (ADS)

    Maiti, Saumen; Tiwari, Ram Krishna

    2010-10-01

    A new probabilistic approach based on the concept of Bayesian neural network (BNN) learning theory is proposed for decoding litho-facies boundaries from well-log data. We show that how a multi-layer-perceptron neural network model can be employed in Bayesian framework to classify changes in litho-log successions. The method is then applied to the German Continental Deep Drilling Program (KTB) well-log data for classification and uncertainty estimation in the litho-facies boundaries. In this framework, a posteriori distribution of network parameter is estimated via the principle of Bayesian probabilistic theory, and an objective function is minimized following the scaled conjugate gradient optimization scheme. For the model development, we inflict a suitable criterion, which provides probabilistic information by emulating different combinations of synthetic data. Uncertainty in the relationship between the data and the model space is appropriately taken care by assuming a Gaussian a priori distribution of networks parameters (e.g., synaptic weights and biases). Prior to applying the new method to the real KTB data, we tested the proposed method on synthetic examples to examine the sensitivity of neural network hyperparameters in prediction. Within this framework, we examine stability and efficiency of this new probabilistic approach using different kinds of synthetic data assorted with different level of correlated noise. Our data analysis suggests that the designed network topology based on the Bayesian paradigm is steady up to nearly 40% correlated noise; however, adding more noise (˜50% or more) degrades the results. We perform uncertainty analyses on training, validation, and test data sets with and devoid of intrinsic noise by making the Gaussian approximation of the a posteriori distribution about the peak model. We present a standard deviation error-map at the network output corresponding to the three types of the litho-facies present over the entire litho

  8. Dynamic spanning trees in stock market networks: The case of Asia-Pacific

    NASA Astrophysics Data System (ADS)

    Sensoy, Ahmet; Tabak, Benjamin M.

    2014-11-01

    This article proposes a new procedure to evaluate Asia Pacific stock market interconnections using a dynamic setting. Dynamic spanning trees (DST) are constructed using an ARMA-FIEGARCH-cDCC process. The main results show that: 1. the DST significantly shrinks over time; 2. Hong Kong is found to be the key financial market; 3. the DST has a significantly increased stability in the last few years; 4. the removal of the key player has two effects: there is no clear key market any longer and the stability of the DST significantly decreases. These results are important for the design of policies that help develop stock markets and for academics and practitioners.

  9. Investigation of arbitrage opportunities in the Eurodollar futures market using neural networks

    NASA Astrophysics Data System (ADS)

    Yung, Victoria N.; Jouny, Ismail I.; Chambers, Donald

    1996-03-01

    Financial analysis is based on two opposing views: market efficiency theory and technical or fundamental analysis. There are three forms of market efficiency: weak form, semi-strong form and strong form. The weak form of market efficiency precludes the trends and patterns that technical analysis attempts to exploit. This work investigates whether it is possible to detect or predict patterns underlying Eurodollar futures trading. Multilayer perceptrons and radial basis functions were chosen to predict three time series based on different financial models. The findings challenge the existing efficient market theory in the case of Eurodollar futures trading.

  10. The sexual erotic market as an analytical framework for understanding erotic-affective exchanges in interracial sexually intimate and affective relationships.

    PubMed

    Vigoya, Mara Viveros

    2015-01-01

    This paper examines the way in which erotic-affective exchanges in interracial relationships have been analysed in Latin America. It considers how race, gender and class operate within a market of values such that erotic, affective and economic status are shaped by racial, gender and class hierarchies. In this paper I analyse historical and social arrangements that embody the region's political economy of race and sex. Such a perspective allows me to address the simultaneous co-existence of socio-racial exclusion and inclusion and the repressive and productive effects of power, attraction and anxiety as aspects of lived experiences in relation to sexuality. From there, I outline an analytical framework that references an erotic or pleasure-based market in which capital and other resources are exchanged from a structural perspective stressing relationship alliances. I conclude by identifying the scope and limits of such an approach.

  11. Determinants of successful clinical networks: the conceptual framework and study protocol.

    PubMed

    Haines, Mary; Brown, Bernadette; Craig, Jonathan; D'Este, Catherine; Elliott, Elizabeth; Klineberg, Emily; McInnes, Elizabeth; Middleton, Sandy; Paul, Christine; Redman, Sally; Yano, Elizabeth M

    2012-03-13

    Clinical networks are increasingly being viewed as an important strategy for increasing evidence-based practice and improving models of care, but success is variable and characteristics of networks with high impact are uncertain. This study takes advantage of the variability in the functioning and outcomes of networks supported by the Australian New South Wales (NSW) Agency for Clinical Innovation's non-mandatory model of clinical networks to investigate the factors that contribute to the success of clinical networks. The objective of this retrospective study is to examine the association between external support, organisational and program factors, and indicators of success among 19 clinical networks over a three-year period (2006-2008). The outcomes (health impact, system impact, programs implemented, engagement, user perception, and financial leverage) and explanatory factors will be collected using a web-based survey, interviews, and record review. An independent expert panel will provide judgements about the impact or extent of each network's initiatives on health and system impacts. The ratings of the expert panel will be the outcome used in multivariable analyses. Following the rating of network success, a qualitative study will be conducted to provide a more in-depth examination of the most successful networks. This is the first study to combine quantitative and qualitative methods to examine the factors that contribute to the success of clinical networks and, more generally, is the largest study of clinical networks undertaken. The adaptation of expert panel methods to rate the impacts of networks is the methodological innovation of this study. The proposed project will identify the conditions that should be established or encouraged by agencies developing clinical networks and will be of immediate use in forming strategies and programs to maximise the effectiveness of such networks.

  12. Determinants of successful clinical networks: the conceptual framework and study protocol

    PubMed Central

    2012-01-01

    Background Clinical networks are increasingly being viewed as an important strategy for increasing evidence-based practice and improving models of care, but success is variable and characteristics of networks with high impact are uncertain. This study takes advantage of the variability in the functioning and outcomes of networks supported by the Australian New South Wales (NSW) Agency for Clinical Innovation's non-mandatory model of clinical networks to investigate the factors that contribute to the success of clinical networks. Methods/Design The objective of this retrospective study is to examine the association between external support, organisational and program factors, and indicators of success among 19 clinical networks over a three-year period (2006-2008). The outcomes (health impact, system impact, programs implemented, engagement, user perception, and financial leverage) and explanatory factors will be collected using a web-based survey, interviews, and record review. An independent expert panel will provide judgements about the impact or extent of each network's initiatives on health and system impacts. The ratings of the expert panel will be the outcome used in multivariable analyses. Following the rating of network success, a qualitative study will be conducted to provide a more in-depth examination of the most successful networks. Discussion This is the first study to combine quantitative and qualitative methods to examine the factors that contribute to the success of clinical networks and, more generally, is the largest study of clinical networks undertaken. The adaptation of expert panel methods to rate the impacts of networks is the methodological innovation of this study. The proposed project will identify the conditions that should be established or encouraged by agencies developing clinical networks and will be of immediate use in forming strategies and programs to maximise the effectiveness of such networks. PMID:22414246

  13. System, environmental, and policy changes: using the social-ecological model as a framework for evaluating nutrition education and social marketing programs with low-income audiences.

    PubMed

    Gregson, J; Foerster, S B; Orr, R; Jones, L; Benedict, J; Clarke, B; Hersey, J; Lewis, J; Zotz, A K

    2001-01-01

    A variety of nutrition education interventions and social marketing initiatives are being used by the Food Stamp Program to improve food resource management, food safety, dietary quality, and food security for low-income households. The Social-Ecological Model is proposed as a theory-based framework to characterize the nature and results of interventions conducted through large public/private partnerships with the Food Stamp Program. In particular, this article suggests indicators and measures that lend themselves to the pooling of data across counties and states, with special emphasis on systems, environment, and public policy change within organizations at the community and state levels.

  14. Quincy Market. [A Product of] the Regional Math Network: A Teacher Invigoration and Curriculum Development Project.

    ERIC Educational Resources Information Center

    Harvard Univ., Cambridge, MA. Graduate School of Education.

    In this middle school mathematics unit two imaginary characters, Horatio and Portia, decide to make their fortune in Quincy Market (Boston, Massachusetts) running a Bull Market cart. In order to solve the problems that they encounter, they need to learn ratio and proportion, map reading, estimation, area and perimeter, population sampling, problem…

  15. Optimising value and quality in general practice within the primary health care sector through relationship marketing: a conceptual framework.

    PubMed

    Bansal, Manjit K

    2004-01-01

    Discusses the rationale of applying relationship marketing and service quality concepts within the primary health care sector. The use of relational strategies in general practice, by modelling the relationships between practitioners and patients from a marketing perspective, could potentially lead to sustained high quality service being provided, and to more efficient use of resources. This essentially conceptually focused paper addresses an area that has not yet been researched in detail, and furthers understanding of the relationships that facilitate exchange within general practice and service delivery in non-profit, resource-constrained conditions. Deeper understanding of the needs and expectations of patients and the way these can be delivered by general practice can only lead to improvements for all parties involved. The relationship marketing paradigm presents itself as a potentially exciting way of addressing issues associated with ensuring that the highest level of quality is delivered in this area of the UK National Health Service.

  16. Increasing revenue through idea generation at University Health Network.

    PubMed

    Alcia, Lisa

    2013-01-01

    To enhance products and services provided to researchers and generate external revenue, research operations at the University Health Network implemented an ideation revenue generation framework for evaluation of product ideas for launch to external market. The framework consists of coordinated cross-functional teamwork in idea development and formal evaluation by research operations senior management based on standard criteria. The framework accelerates launch to market of products and services, facilitates due diligence review, increases staff competencies and engagement, and helps foster innovative thinking.

  17. Towards a cross-platform software framework to support end-to-end hydrometeorological sensor network deployment

    NASA Astrophysics Data System (ADS)

    Celicourt, P.; Sam, R.; Piasecki, M.

    2016-12-01

    Global phenomena such as climate change and large scale environmental degradation require the collection of accurate environmental data at detailed spatial and temporal scales from which knowledge and actionable insights can be derived using data science methods. Despite significant advances in sensor network technologies, sensors and sensor network deployment remains a labor-intensive, time consuming, cumbersome and expensive task. These factors demonstrate why environmental data collection remains a challenge especially in developing countries where technical infrastructure, expertise and pecuniary resources are scarce. In addition, they also demonstrate the reason why dense and long-term environmental data collection has been historically quite difficult. Moreover, hydrometeorological data collection efforts usually overlook the (critically important) inclusion of a standards-based system for storing, managing, organizing, indexing, documenting and sharing sensor data. We are developing a cross-platform software framework using the Python programming language that will allow us to develop a low cost end-to-end (from sensor to publication) system for hydrometeorological conditions monitoring. The software framework contains provision for sensor, sensor platforms, calibration and network protocols description, sensor programming, data storage, data publication and visualization and more importantly data retrieval in a desired unit system. It is being tested on the Raspberry Pi microcomputer as end node and a laptop PC as the base station in a wireless setting.

  18. Agent-based spin model for financial markets on complex networks: Emergence of two-phase phenomena

    NASA Astrophysics Data System (ADS)

    Kim, Yup; Kim, Hong-Joo; Yook, Soon-Hyung

    2008-09-01

    We study a microscopic model for financial markets on complex networks, motivated by the dynamics of agents and their structure of interaction. The model consists of interacting agents (spins) with local ferromagnetic coupling and global antiferromagnetic coupling. In order to incorporate more realistic situations, we also introduce an external field which changes in time. From numerical simulations, we find that the model shows two-phase phenomena. When the local ferromagnetic interaction is balanced with the global antiferromagnetic interaction, the resulting return distribution satisfies a power law having a single peak at zero values of return, which corresponds to the market equilibrium phase. On the other hand, if local ferromagnetic interaction is dominant, then the return distribution becomes double peaked at nonzero values of return, which characterizes the out-of-equilibrium phase. On random networks, the crossover between two phases comes from the competition between two different interactions. However, on scale-free networks, not only the competition between the different interactions but also the heterogeneity of underlying topology causes the two-phase phenomena. Possible relationships between the critical phenomena of spin system and the two-phase phenomena are discussed.

  19. Correlation network analysis for multi-dimensional data in stocks market

    NASA Astrophysics Data System (ADS)

    Kazemilari, Mansooreh; Djauhari, Maman Abdurachman

    2015-07-01

    This paper shows how the concept of vector correlation can appropriately measure the similarity among multivariate time series in stocks network. The motivation of this paper is (i) to apply the RV coefficient to define the network among stocks where each of them is represented by a multivariate time series; (ii) to analyze that network in terms of topological structure of the stocks of all minimum spanning trees, and (iii) to compare the network topology between univariate correlation based on r and multivariate correlation network based on RV coefficient.

  20. Joint marketing as a framework for targeting men who have sex with men in China: a pilot intervention study.

    PubMed

    Tan, Jingguang; Cai, Rui; Lu, Zuxun; Cheng, Jinquan; de Vlas, Sake J; Richardus, Jan Hendrik

    2013-04-01

    To apply the joint marketing principle as a new intervention approach for targeting men who have sex with men (MSM) who are often difficult to reach in societies with discrimination towards homosexuality and HIV/AIDS. A pilot intervention according to the principles of joint marketing was carried out by the CDC in Shenzhen, China, in MSM social venues. A self-designed questionnaire of HIV knowledge, condom use, and access to HIV-related services was used before and after the pilot intervention to evaluate its effectiveness. The CDC supported gatekeepers of MSM social venues in running their business and thereby increasing their respectability and income. In return, the gatekeepers cooperated with the CDC in reaching the MSM at the venues with health promotion messages and materials. Thus a win-win situation was created, bringing together two noncompetitive parties in reaching out to a shared customer, the MSM. The pilot intervention succeeded in demonstrating acceptability and feasibility of the joint marketing approach targeting MSM. HIV knowledge, the rate of condom use, and access to HIV-related services of participants in the pilot intervention increased significantly. The joint marketing intervention is an innovative way to create synergies between the gatekeepers of MSM social venues and public health officials for reaching and potentially changing HIV high-risk behaviors among MSM.

  1. "Technohesion": Engaging Students of Higher Education through Digital Technology and Interactive Marketing--A Research Agenda and Theoretical Framework

    ERIC Educational Resources Information Center

    Thorpe, Anthony; Lim, Lynn L. K.

    2013-01-01

    This article examines how the development of techno-marketing campaigns might facilitate the engagement of university students in voluntary activities on campus which promote active citizenship and community cohesion where there is a concern about a low take up of such opportunities. The increasing influence of technology upon the forms of social…

  2. Mississippi Curriculum Framework for Marketing and Fashion Merchandising (Program CIP: 08.0705--General Retailing Operations). Secondary Programs.

    ERIC Educational Resources Information Center

    Mississippi Research and Curriculum Unit for Vocational and Technical Education, State College.

    This document, which reflects Mississippi's statutory requirement that instructional programs be based on core curricula and performance-based assessment, contains outlines of the instructional units required in local instructional management plans and daily lesson plans for marketing I-II and fashion merchandising. Presented first are a program…

  3. Towards a Theoretical Framework for the Comparative Understanding of Globalisation, Higher Education, the Labour Market and Inequality

    ERIC Educational Resources Information Center

    Kupfer, Antonia

    2011-01-01

    This paper is a theoretical examination of three major empirical trends that affect many people: globalisation, increasingly close relations between higher education (HE) and labour markets, and increasing social inequality. Its aim is to identify key theoretical resources and their contribution to the development of a comparative theoretical…

  4. "Technohesion": Engaging Students of Higher Education through Digital Technology and Interactive Marketing--A Research Agenda and Theoretical Framework

    ERIC Educational Resources Information Center

    Thorpe, Anthony; Lim, Lynn L. K.

    2013-01-01

    This article examines how the development of techno-marketing campaigns might facilitate the engagement of university students in voluntary activities on campus which promote active citizenship and community cohesion where there is a concern about a low take up of such opportunities. The increasing influence of technology upon the forms of social…

  5. Towards a Theoretical Framework for the Comparative Understanding of Globalisation, Higher Education, the Labour Market and Inequality

    ERIC Educational Resources Information Center

    Kupfer, Antonia

    2011-01-01

    This paper is a theoretical examination of three major empirical trends that affect many people: globalisation, increasingly close relations between higher education (HE) and labour markets, and increasing social inequality. Its aim is to identify key theoretical resources and their contribution to the development of a comparative theoretical…

  6. Network-Based Leadership Development: A Guiding Framework and Resources for Management Educators

    ERIC Educational Resources Information Center

    Cullen-Lester, Kristin L.; Woehler, Meredith L.; Willburn, Phil

    2016-01-01

    Management education and leadership development has traditionally focused on improving human capital (i.e., knowledge, skills, and abilities). Social capital, networks, and networking skills have received less attention. When this content has been incorporated into learning and development experiences, it has often been more ad hoc and has…

  7. Network-Based Leadership Development: A Guiding Framework and Resources for Management Educators

    ERIC Educational Resources Information Center

    Cullen-Lester, Kristin L.; Woehler, Meredith L.; Willburn, Phil

    2016-01-01

    Management education and leadership development has traditionally focused on improving human capital (i.e., knowledge, skills, and abilities). Social capital, networks, and networking skills have received less attention. When this content has been incorporated into learning and development experiences, it has often been more ad hoc and has…

  8. Enriching Professional Learning Networks: A Framework for Identification, Reflection, and Intention

    ERIC Educational Resources Information Center

    Krutka, Daniel G.; Carpenter, Jeffrey Paul; Trust, Torrey

    2017-01-01

    Many educators in the 21st century utilize social media platforms to enrich professional learning networks (PLNs). PLNs are uniquely personalized networks that can support participatory and continuous learning. Social media services can mediate professional engagements with a wide variety of people, spaces and tools that might not otherwise be…

  9. Developmental Evaluation Framework for Innovation and Learning Networks: Integration of the Structure, Process and Outcomes

    ERIC Educational Resources Information Center

    Ramstad, Elise

    2009-01-01

    Purpose: During the past decade new types of broader networks that aim to achieve widespread effects in the working life have emerged. These are typically based on an interactive innovation approach, where knowledge is created jointly together with diverse players. At the moment, the challenge is how to evaluate these complex networks and learning…

  10. Developmental Evaluation Framework for Innovation and Learning Networks: Integration of the Structure, Process and Outcomes

    ERIC Educational Resources Information Center

    Ramstad, Elise

    2009-01-01

    Purpose: During the past decade new types of broader networks that aim to achieve widespread effects in the working life have emerged. These are typically based on an interactive innovation approach, where knowledge is created jointly together with diverse players. At the moment, the challenge is how to evaluate these complex networks and learning…

  11. A Markovian event-based framework for stochastic spiking neural networks.

    PubMed

    Touboul, Jonathan D; Faugeras, Olivier D

    2011-11-01

    In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks.

  12. Using Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research.

    PubMed

    Ogallo, William; Kanter, Andrew S

    2016-01-01

    This paper describes a theory derivation process used to develop a conceptual framework for medication therapy management (MTM) research. The MTM service model and chronic care model were selected as parent theories. Review article abstracts targeting medication therapy management in chronic disease care were retrieved from Ovid Medline (2000-2016). Unique concepts in each abstract were extracted using MetaMap and their pairwise cooccurrence determined. The information was used to construct a network graph of concept co-occurrence that was analyzed to identify content for the new conceptual model. 142 abstracts were analyzed. Medication adherence is the most studied drug therapy problem and co-occurred with concepts related to patient-centered interventions targeting self-management. The enhanced model consists of 65 concepts clustered into 14 constructs. The framework requires additional refinement and evaluation to determine its relevance and applicability across a broad audience including underserved settings.

  13. Using Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research

    PubMed Central

    Ogallo, William; Kanter, Andrew S.

    2016-01-01

    This paper describes a theory derivation process used to develop a conceptual framework for medication therapy management (MTM) research. The MTM service model and chronic care model were selected as parent theories. Review article abstracts targeting medication therapy management in chronic disease care were retrieved from Ovid Medline (2000-2016). Unique concepts in each abstract were extracted using MetaMap and their pairwise cooccurrence determined. The information was used to construct a network graph of concept co-occurrence that was analyzed to identify content for the new conceptual model. 142 abstracts were analyzed. Medication adherence is the most studied drug therapy problem and co-occurred with concepts related to patient-centered interventions targeting self-management. The enhanced model consists of 65 concepts clustered into 14 constructs. The framework requires additional refinement and evaluation to determine its relevance and applicability across a broad audience including underserved settings. PMID:28269895

  14. Frameworks for Understanding the Nature of Interactions, Networking, and Community in a Social Networking Site for Academic Practice

    ERIC Educational Resources Information Center

    Conole, Grainne; Galley, Rebecca; Culver, Juliette

    2011-01-01

    This paper describes a new social networking site, Cloudworks, which has been developed to enable discussion and sharing of learning and teaching ideas/designs and to promote reflective academic practice. The site aims to foster new forms of social and participatory practices (peer critiquing, sharing, user-generated content, aggregation, and…

  15. Public Health Network Structure and Collaboration Effectiveness during the 2015 MERS Outbreak in South Korea: An Institutional Collective Action Framework.

    PubMed

    Kim, KyungWoo; Andrew, Simon A; Jung, Kyujin

    2017-09-15

    Following the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea, this research aims to examine the structural effect of public health network explaining collaboration effectiveness, which is defined as joint efforts to improve quality of service provision, cost savings, and coordination. We tested the bonding and bridging effects on collaboration effectiveness during the MERS outbreak response by utilizing an institutional collective action framework. The analysis results of 114 organizations responding during the crisis show a significant association between the bonding effect and the effectiveness of collaboration, as well as a positive association between risk communication in disseminating public health information and the effectiveness of collaboration.

  16. Public Health Network Structure and Collaboration Effectiveness during the 2015 MERS Outbreak in South Korea: An Institutional Collective Action Framework

    PubMed Central

    Andrew, Simon A.

    2017-01-01

    Following the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea, this research aims to examine the structural effect of public health network explaining collaboration effectiveness, which is defined as joint efforts to improve quality of service provision, cost savings, and coordination. We tested the bonding and bridging effects on collaboration effectiveness during the MERS outbreak response by utilizing an institutional collective action framework. The analysis results of 114 organizations responding during the crisis show a significant association between the bonding effect and the effectiveness of collaboration, as well as a positive association between risk communication in disseminating public health information and the effectiveness of collaboration. PMID:28914780

  17. A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

    SciTech Connect

    Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; Ukkusuri, Satish V.

    2015-01-31

    Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plans in terms of average delay, number of stops, and vehicular emissions at the network level.

  18. A new optimization framework using genetic algorithm and artificial neural network to reduce uncertainties in petroleum reservoir models

    NASA Astrophysics Data System (ADS)

    Maschio, Célio; José Schiozer, Denis

    2015-01-01

    In this article, a new optimization framework to reduce uncertainties in petroleum reservoir attributes using artificial intelligence techniques (neural network and genetic algorithm) is proposed. Instead of using the deterministic values of the reservoir properties, as in a conventional process, the parameters of the probability density function of each uncertain attribute are set as design variables in an optimization process using a genetic algorithm. The objective function (OF) is based on the misfit of a set of models, sampled from the probability density function, and a symmetry factor (which represents the distribution of curves around the history) is used as weight in the OF. Artificial neural networks are trained to represent the production curves of each well and the proxy models generated are used to evaluate the OF in the optimization process. The proposed method was applied to a reservoir with 16 uncertain attributes and promising results were obtained.

  19. A junction-tree based learning algorithm to optimize network wide traffic control: A coordinated multi-agent framework

    DOE PAGES

    Zhu, Feng; Aziz, H. M. Abdul; Qian, Xinwu; ...

    2015-01-31

    Our study develops a novel reinforcement learning algorithm for the challenging coordinated signal control problem. Traffic signals are modeled as intelligent agents interacting with the stochastic traffic environment. The model is built on the framework of coordinated reinforcement learning. The Junction Tree Algorithm (JTA) based reinforcement learning is proposed to obtain an exact inference of the best joint actions for all the coordinated intersections. Moreover, the algorithm is implemented and tested with a network containing 18 signalized intersections in VISSIM. Finally, our results show that the JTA based algorithm outperforms independent learning (Q-learning), real-time adaptive learning, and fixed timing plansmore » in terms of average delay, number of stops, and vehicular emissions at the network level.« less

  20. MATIN: A Random Network Coding Based Framework for High Quality Peer-to-Peer Live Video Streaming

    PubMed Central

    Barekatain, Behrang; Khezrimotlagh, Dariush; Aizaini Maarof, Mohd; Ghaeini, Hamid Reza; Salleh, Shaharuddin; Quintana, Alfonso Ariza; Akbari, Behzad; Cabrera, Alicia Triviño

    2013-01-01

    In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay. PMID:23940530