Science.gov

Sample records for multi-type network approach

  1. Prediction of Multi-Type Membrane Proteins in Human by an Integrated Approach

    PubMed Central

    Chen, Lei; Zhang, Ning; Huang, Tao; Cai, Yu-Dong

    2014-01-01

    Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request. PMID:24676214

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

    NASA Astrophysics Data System (ADS)

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

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

  3. Wireless Sensor Networks Approach

    NASA Technical Reports Server (NTRS)

    Perotti, Jose M.

    2003-01-01

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

  4. Network Approach to Disease Diagnosis

    NASA Astrophysics Data System (ADS)

    Sharma, Amitabh; Bashan, Amir; Barabasi, Alber-Laszlo

    2014-03-01

    Human diseases could be viewed as perturbations of the underlying biological system. A thorough understanding of the topological and dynamical properties of the biological system is crucial to explain the mechanisms of many complex diseases. Recently network-based approaches have provided a framework for integrating multi-dimensional biological data that results in a better understanding of the pathophysiological state of complex diseases. Here we provide a network-based framework to improve the diagnosis of complex diseases. This framework is based on the integration of transcriptomics and the interactome. We analyze the overlap between the differentially expressed (DE) genes and disease genes (DGs) based on their locations in the molecular interaction network (''interactome''). Disease genes and their protein products tend to be much more highly connected than random, hence defining a disease sub-graph (called disease module) in the interactome. DE genes, even though different from the known set of DGs, may be significantly associated with the disease when considering their closeness to the disease module in the interactome. This new network approach holds the promise to improve the diagnosis of patients who cannot be diagnosed using conventional tools. Support was provided by HL066289 and HL105339 grants from the U.S. National Institutes of Health.

  5. Bayesian Approach to Network Modularity

    PubMed Central

    Hofman, Jake M.; Wiggins, Chris H.

    2009-01-01

    We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem, accurately recovering the true number of modules. Our approach is based on Bayesian methods for model selection which have been used with success for almost a century, implemented using a variational technique developed only in the past decade. We apply the technique to synthetic and real networks and outline how the method naturally allows selection among competing models. PMID:18643711

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

    PubMed Central

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

    2012-01-01

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

  7. Network growth approach to macroevolution

    NASA Astrophysics Data System (ADS)

    Qin, Shao-Meng; Chen, Yong; Zhang, Pan

    2007-07-01

    We propose a novel network growth model coupled with the competition interaction to simulate macroevolution. Our work shows that competition plays an important role in macroevolution and it is more rational to describe the interaction between species by network structures. Our model presents a complete picture of the development of phyla and the splitting process. It is found that periodic mass extinction occurred in our networks without any extraterrestrial factors and the lifetime distribution of species is very close to the fossil record. We also perturb networks with two scenarios of mass extinctions on different hierarchic levels in order to study their recovery.

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

    NASA Astrophysics Data System (ADS)

    Ghiassian, Susan Dina

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

  9. Random matrix approach to shareholding networks

    NASA Astrophysics Data System (ADS)

    Souma, Wataru; Fujiwara, Yoshi; Aoyama, Hideaki

    2004-12-01

    A shareholding network is represented by a symmetrical adjacency matrix. The random matrix theoretical approach to this matrix shows that the spectrum follows a power law distribution, ρ(λ)∼|λ|, in the tail part. It is also shown that the degree distribution of this network follows a power law distribution, p(k)∼k, in the large degree range. The scaling law δ=2γ-1 is found in this network. The reason why this relation holds is attributed to the local tree-like structure of the shareholding network.

  10. Adolescent pregnancy: networking and the interdisciplinary approach.

    PubMed

    Canada, M J

    1986-01-01

    The networking approach to providing needed services to pregnant and parenting teenagers has numerous merits. An historical overview of the formation of the Brooklyn Teen Pregnancy Network highlights service agency need for information and resource sharing, and improved client referral systems as key factors in the genesis of the Network. The borough-wide approach and its spread as an agency model throughout New York City's other boroughs and several other northeastern cities is also attributed to its positive client impact, including: improved family communication and cooperation; early prenatal care with its concomitant improved pregnancy outcomes; financial support for teens; continued teen education; and parenting skills development. Resource information is provided regarding networks operating in the Greater New York metropolitan area. A planned Eastern Regional network initiative is under development. PMID:3745501

  11. Approaching human language with complex networks

    NASA Astrophysics Data System (ADS)

    Cong, Jin; Liu, Haitao

    2014-12-01

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

  12. Common cold outbreaks: A network theory approach

    NASA Astrophysics Data System (ADS)

    Vishkaie, Faranak Rajabi; Bakouie, Fatemeh; Gharibzadeh, Shahriar

    2014-11-01

    In this study, at first we evaluated the network structure in social encounters by which respiratory diseases can spread. We considered common-cold and recorded a sample of human population and actual encounters between them. Our results show that the database structure presents a great value of clustering. In the second step, we evaluated dynamics of disease spread with SIR model by assigning a function to each node of the structural network. The rate of disease spread in networks was observed to be inversely correlated with characteristic path length. Therefore, the shortcuts have a significant role in increasing spread rate. We conclude that the dynamics of social encounters' network stands between the random and the lattice in network spectrum. Although in this study we considered the period of common-cold disease for network dynamics, it seems that similar approaches may be useful for other airborne diseases such as SARS.

  13. A neural network approach to cloud classification

    NASA Technical Reports Server (NTRS)

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

    1990-01-01

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

  14. Queuing network approach for building evacuation planning

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  15. Alternative approach to community detection in networks.

    PubMed

    Medus, A D; Dorso, C O

    2009-06-01

    The problem of community detection is relevant in many disciplines of science and modularity optimization is the widely accepted method for this purpose. It has recently been shown that this approach presents a resolution limit by which it is not possible to detect communities with sizes smaller than a threshold, which depends on the network size. Moreover, it might happen that the communities resulting from such an approach do not satisfy the usual qualitative definition of commune; i.e., nodes in a commune are more connected among themselves than to nodes outside the commune. In this paper we present a different method for community detection in complex networks. We define merit factors based on the weak and strong community definitions formulated by Radicchi [Proc. Natl. Acad. Sci. U.S.A. 101, 2658 (2004)] and we show that these local definitions avoid the resolution limit problem found in the modularity optimization approach. PMID:19658568

  16. A low dimensional approach on network characterization.

    PubMed

    Li, Benjamin Y S; Zhan, Choujun; Yeung, Lam F; Ko, King T; Yang, Genke

    2014-01-01

    In many applications, one may need to characterize a given network among a large set of base networks, and these networks are large in size and diverse in structure over the search space. In addition, the characterization algorithms are required to have low volatility and with a small circle of uncertainty. For large datasets, these algorithms are computationally intensive and inefficient. However, under the context of network mining, a major concern of some applications is speed. Hence, we are motivated to develop a fast characterization algorithm, which can be used to quickly construct a graph space for analysis purpose. Our approach is to transform a network characterization measure, commonly formulated based on similarity matrices, into simple vector form signatures. We shall show that the [Formula: see text] similarity matrix can be represented by a dyadic product of two N-dimensional signature vectors; thus the network alignment process, which is usually solved as an assignment problem, can be reduced into a simple alignment problem based on separate signature vectors. PMID:25329146

  17. A network approach to evaluate ecosystem vulnerability

    NASA Astrophysics Data System (ADS)

    Goodwell, Allison; Kumar, Praveen

    2016-04-01

    Ecohydrologic systems exhibit shifts in behavior due to natural or human induced perturbations or stresses. These shifts result from changes in dependencies between many interacting components. A framework that defines a system based on these shifting interactions is needed to holistically evaluate properties such as resilience, vulnerability, or health that cannot be reached through the isolated study of component behaviors. This study uses a network approach in which ecohydrologic time-series data are nodes, and information theoretic measures that capture various aspects of time dependencies are links. It has been shown that an information decomposition approach can be used to determine the relative redundant (shared by multiple source nodes to a target), synergistic (arising only from the knowledge of multiple source nodes), or unique (only provided by an individual source node) information within a given detected link. We construct networks from flux tower and ecohydrologic model output nodes and evaluate how these evolve in terms of connectivity, dominant time scales of interactions, link uniqueness, and link stability over time windows ranging from several hours to several weeks as ecosystems respond to shifting environmental conditions. We associate these network properties with simulated and observed vegetation responses, and show that a network framework can be used to detect critical interactions that dictate ecosystem vulnerabilities to extremes.

  18. A Network Approach to Rare Disease Modeling

    NASA Astrophysics Data System (ADS)

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

    2011-03-01

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

  19. Learning about knowledge: A complex network approach

    NASA Astrophysics Data System (ADS)

    da Fontoura Costa, Luciano

    2006-08-01

    An approach to modeling knowledge acquisition in terms of walks along complex networks is described. Each subset of knowledge is represented as a node, and relations between such knowledge are expressed as edges. Two types of edges are considered, corresponding to free and conditional transitions. The latter case implies that a node can only be reached after visiting previously a set of nodes (the required conditions). The process of knowledge acquisition can then be simulated by considering the number of nodes visited as a single agent moves along the network, starting from its lowest layer. It is shown that hierarchical networks—i.e., networks composed of successive interconnected layers—are related to compositions of the prerequisite relationships between the nodes. In order to avoid deadlocks—i.e., unreachable nodes—the subnetwork in each layer is assumed to be a connected component. Several configurations of such hierarchical knowledge networks are simulated and the performance of the moving agent quantified in terms of the percentage of visited nodes after each movement. The Barabási-Albert and random models are considered for the layer and interconnecting subnetworks. Although all subnetworks in each realization have the same number of nodes, several interconnectivities, defined by the average node degree of the interconnection networks, have been considered. Two visiting strategies are investigated: random choice among the existing edges and preferential choice to so far untracked edges. A series of interesting results are obtained, including the identification of a series of plateaus of knowledge stagnation in the case of the preferential movement strategy in the presence of conditional edges.

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

    ERIC Educational Resources Information Center

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

    2007-01-01

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

  1. A Complex Network Approach to Stylometry.

    PubMed

    Amancio, Diego Raphael

    2015-01-01

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

  2. A Complex Network Approach to Stylometry

    PubMed Central

    Amancio, Diego Raphael

    2015-01-01

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

  3. A Mixed Methods Approach to Network Data Collection

    PubMed Central

    Rice, Eric; Holloway, Ian W.; Barman-Adhikari, Anamika; Fuentes, Dahlia; Brown, C. Hendricks; Palinkas, Lawrence A.

    2013-01-01

    There is a growing interest in examining network processes with a mix of qualitative and quantitative network data. Research has consistently shown that free recall name generators entail recall bias and result in missing data that affects the quality of social network data. This study describes a mixed methods approach for collecting social network data, combining a free recall name generator in the context of an online survey with network relations data coded from transcripts of semi-structured qualitative interviews. The combined network provides substantially more information about the network space, both quantitatively and qualitatively. While network density was relatively stable across networks generated from different data collection methodologies, there were noticeable differences in centrality and component structure across networks. The approach presented here involved limited participant burden and generated more complete data than either technique alone could provide. We make suggestions for further development of this method. PMID:25285047

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

    NASA Technical Reports Server (NTRS)

    Rounds, Fred

    1991-01-01

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

  5. A Sociospatial Approach to Understanding Terrorist Networks

    SciTech Connect

    Medina, Richard M; Hepner, George F.

    2011-01-01

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

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

    ERIC Educational Resources Information Center

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

    2007-01-01

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

  7. Structural factoring approach for analyzing stochastic networks

    NASA Technical Reports Server (NTRS)

    Hayhurst, Kelly J.; Shier, Douglas R.

    1991-01-01

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

  8. Implementing the Fussy Baby Network[R] Approach

    ERIC Educational Resources Information Center

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Bakshi, K.

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

  10. Fault detection and diagnosis using neural network approaches

    NASA Technical Reports Server (NTRS)

    Kramer, Mark A.

    1992-01-01

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

  11. Networking in the Vancouver Hospitals: An Incremental Management Approach

    PubMed Central

    Cassidy, Paul A.; Morrison, J. Ian; Hardwick, David F.

    1985-01-01

    This paper describes a multi-site network application involving electronic transmission of video, audio and data among five teaching hospitals in Vancouver, B.C. The rationale behind a network approach is explained and the system design is outlined. The network includes both long-haul cable based network technolgies (LHN) and Local Area Network technologies (LAN) that currently integrate 2 mainframes and 11 mini computers operating in the five sites. Philosophical and strategical approaches to implementation of the network are explained. Particular attention is drawn to the notion of the incremental management of change and the role of product champions. It is suggested that the use of network technology enables a wide variety of semi-autonomous departments and computer applications to “touch without intrusion.” It is emphasized the implementation of such change requires sophisticated technical and human engineering skills.

  12. A network dynamics approach to chemical reaction networks

    NASA Astrophysics Data System (ADS)

    van der Schaft, A. J.; Rao, S.; Jayawardhana, B.

    2016-04-01

    A treatment of a chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption, the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a very simple derivation of a number of key results in the chemical reaction network theory, and which directly relates to the thermodynamics and port-Hamiltonian formulation of the system. Central in this formulation is the definition of a balanced Laplacian matrix on the graph of chemical complexes together with a resulting fundamental inequality. This immediately leads to the characterisation of the set of equilibria and their stability. Furthermore, the assumption of complex balancedness is revisited from the point of view of Kirchhoff's matrix tree theorem. Both the form of the dynamics and the deduced behaviour are very similar to consensus dynamics, and provide additional perspectives to the latter. Finally, using the classical idea of extending the graph of chemical complexes by a 'zero' complex, a complete steady-state stability analysis of mass action kinetics reaction networks with constant inflows and mass action kinetics outflows is given, and a unified framework is provided for structure-preserving model reduction of this important class of open reaction networks.

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

    PubMed Central

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

    2014-01-01

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

  14. A Layered Approach To Pacs Network Architecture

    NASA Astrophysics Data System (ADS)

    Hegde, Shankar S.; Prewitt, Judith M.

    1984-08-01

    Although the functions performed by the different nodes on the PACS network are many, it is possible to formulate a minimum set of service primitives such that the application software residing at the nodes can utilize those primitives to perform the functions. These primitives define the framework for the communication interface. The question of how these primitives fit into the concept of a layered network architecture is explored in this paper. The OSI model as applicable to the PACS network is described, the areas that need standardization are briefly mentioned, and the ongoing standardization efforts are addressed from the OSI perspective.

  15. Epidemics in networks: a master equation approach

    NASA Astrophysics Data System (ADS)

    Cotacallapa, M.; Hase, M. O.

    2016-02-01

    A problem closely related to epidemiology, where a subgraph of ‘infected’ links is defined inside a larger network, is investigated. This subgraph is generated from the underlying network by a random variable, which decides whether a link is able to propagate a disease/information. The relaxation timescale of this random variable is examined in both annealed and quenched limits, and the effectiveness of propagation of disease/information is analyzed. The dynamics of the model is governed by a master equation and two types of underlying network are considered: one is scale-free and the other has exponential degree distribution. We have shown that the relaxation timescale of the contagion variable has a major influence on the topology of the subgraph of infected links, which determines the efficiency of spreading of disease/information over the network.

  16. A Sensible Approach to Wireless Networking.

    ERIC Educational Resources Information Center

    Ahmed, S. Faruq

    2002-01-01

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

  17. A new approach for fault identification in computer networks

    NASA Astrophysics Data System (ADS)

    Zhao, Dong; Wang, Tao

    2004-04-01

    Effective management of computer networks has become a more and more difficult job because of the rapid development of the network systems. Fault identification is to find where is the problem of the network and what is it. Data mining generally refers to the process of extracting models from large stores of data. We can use data mining techniques to help us in the fault identification task. Existing approaches of fault identification are introduced and a new approach of fault identification is proposed. This approach improves MSDD algorithm but it need more computation. So some new techniques are used to increase the efficiency.

  18. Sampling of Complex Networks: A Datamining Approach

    NASA Astrophysics Data System (ADS)

    Loecher, Markus; Dohrmann, Jakob; Bauer, Gernot

    2007-03-01

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

  19. A Networks Approach to Modeling Enzymatic Reactions.

    PubMed

    Imhof, P

    2016-01-01

    Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. PMID:27497170

  20. Adding network approaches to a neurobiological framework of resilience.

    PubMed

    Levit-Binnun, Nava; Golland, Yulia

    2015-01-01

    In their paper, Kalisch et al. make an important attempt to create a unifying theoretical framework for the neuroscientific study of general resilience mechanisms. We suggest that such attempts can benefit tremendously by incorporating the recently emerging network approaches that enable the characterization of complex brain network architecture and dynamics, in both health and disease. PMID:26786965

  1. Challenges to a Learning Approach through a Global Network.

    ERIC Educational Resources Information Center

    Lee, In-Sook

    Computer networking is a new educational approach that can well serve the educational needs in a society of dynamic and constant changes. This paper examines effective ways of establishing a computer network-based learning system in the Korean educational system. The Korean Educational Development Institute (KEDI) conducted a one-year research…

  2. A Gaussian graphical model approach to climate networks

    NASA Astrophysics Data System (ADS)

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

    2014-06-01

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

  3. A Gaussian graphical model approach to climate networks

    SciTech Connect

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

    2014-06-15

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

  4. A Gaussian graphical model approach to climate networks.

    PubMed

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

    2014-06-01

    Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately. PMID:24985417

  5. A Gateway Approach to Library System Networking.

    ERIC Educational Resources Information Center

    Anderson, David A.; Duggan, Michael T.

    1987-01-01

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

  6. Infections on Temporal Networks--A Matrix-Based Approach.

    PubMed

    Koher, Andreas; Lentz, Hartmut H K; Hövel, Philipp; Sokolov, Igor M

    2016-01-01

    We extend the concept of accessibility in temporal networks to model infections with a finite infectious period such as the susceptible-infected-recovered (SIR) model. This approach is entirely based on elementary matrix operations and unifies the disease and network dynamics within one algebraic framework. We demonstrate the potential of this formalism for three examples of networks with high temporal resolution: networks of social contacts, sexual contacts, and livestock-trade. Our investigations provide a new methodological framework that can be used, for instance, to estimate the epidemic threshold, a quantity that determines disease parameters, for which a large-scale outbreak can be expected. PMID:27035128

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

    NASA Astrophysics Data System (ADS)

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

    2011-06-01

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

  8. A neural-network approach to robotic control

    NASA Technical Reports Server (NTRS)

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

    1993-01-01

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

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

    PubMed

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

    2015-05-01

    Contemporary definitions of leadership advance a view of the phenomenon as relational, situated in specific social contexts, involving patterned emergent processes, and encompassing both formal and informal influence. Paralleling these views is a growing interest in leveraging social network approaches to study leadership. Social network approaches provide a set of theories and methods with which to articulate and investigate, with greater precision and rigor, the wide variety of relational perspectives implied by contemporary leadership theories. Our goal is to advance this domain through an integrative conceptual review. We begin by answering the question of why-Why adopt a network approach to study leadership? Then, we offer a framework for organizing prior research. Our review reveals 3 areas of research, which we term: (a) leadership in networks, (b) leadership as networks, and (c) leadership in and as networks. By clarifying the conceptual underpinnings, key findings, and themes within each area, this review serves as a foundation for future inquiry that capitalizes on, and programmatically builds upon, the insights of prior work. Our final contribution is to advance an agenda for future research that harnesses the confluent ideas at the intersection of leadership in and as networks. Leadership in and as networks represents a paradigm shift in leadership research-from an emphasis on the static traits and behaviors of formal leaders whose actions are contingent upon situational constraints, toward an emphasis on the complex and patterned relational processes that interact with the embedding social context to jointly constitute leadership emergence and effectiveness. PMID:25798551

  10. Chemical Approaches to Probe Metabolic Networks

    PubMed Central

    Medina-Cleghorn, Daniel; Nomura, Daniel K.

    2013-01-01

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

  11. Complex network approach to fractional time series

    SciTech Connect

    Manshour, Pouya

    2015-10-15

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

  12. Complex network approach to fractional time series

    NASA Astrophysics Data System (ADS)

    Manshour, Pouya

    2015-10-01

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

  13. Neural Flows in Hopfield Network Approach

    NASA Astrophysics Data System (ADS)

    Ionescu, Carmen; Panaitescu, Emilian; Stoicescu, Mihai

    2013-12-01

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

  14. An efficient neural network approach to dynamic robot motion planning.

    PubMed

    Yang, S X; Meng, M

    2000-03-01

    In this paper, a biologically inspired neural network approach to real-time collision-free motion planning of mobile robots or robot manipulators in a nonstationary environment is proposed. Each neuron in the topologically organized neural network has only local connections, whose neural dynamics is characterized by a shunting equation. Thus the computational complexity linearly depends on the neural network size. The real-time robot motion is planned through the dynamic activity landscape of the neural network without any prior knowledge of the dynamic environment, without explicitly searching over the free workspace or the collision paths, and without any learning procedures. Therefore it is computationally efficient. The global stability of the neural network is guaranteed by qualitative analysis and the Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies. PMID:10935758

  15. A Bayesian Networks approach to Operational Risk

    NASA Astrophysics Data System (ADS)

    Aquaro, V.; Bardoscia, M.; Bellotti, R.; Consiglio, A.; De Carlo, F.; Ferri, G.

    2010-04-01

    A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters; since the main aim is to understand the role of the correlations among the losses, the assessments of domain experts are not used. The algorithm has been validated on synthetic time series. It should be stressed that the proposed algorithm has been thought for the practical implementation in a mid or small sized bank, since it has a small impact on the organizational structure of a bank and requires an investment in human resources which is limited to the computational area.

  16. A Constructive Neural-Network Approach to Modeling Psychological Development

    ERIC Educational Resources Information Center

    Shultz, Thomas R.

    2012-01-01

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

  17. Alcohol Expectancy Multiaxial Assessment: A Memory Network-Based Approach

    ERIC Educational Resources Information Center

    Goldman, Mark S.; Darkes, Jack

    2004-01-01

    Despite several decades of activity, alcohol expectancy research has yet to merge measurement approaches with developing memory theory. This article offers an expectancy assessment approach built on a conceptualization of expectancy as an information processing network. The authors began with multidimensional scaling models of expectancy space,…

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

    PubMed Central

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Cetinkaya, Cem P.; Harmancioglu, Nilgun B.

    2014-05-01

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

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

    PubMed Central

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

    2012-01-01

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

  1. Network Reverse Engineering Approach in Synthetic Biology

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Zañudo, Jorge G. T.; Albert, Réka

    2013-06-01

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

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

    NASA Technical Reports Server (NTRS)

    Moe, Karen L. (Editor)

    1991-01-01

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

  4. A network approach to clinical intervention in neurodegenerative diseases.

    PubMed

    Santiago, Jose A; Potashkin, Judith A

    2014-12-01

    Network biology has become a powerful tool to dissect the molecular mechanisms triggering neurodegeneration. Recent developments in network biology have led to the discovery of disease-causing genes, diagnostic biomarkers, and therapeutic targets for several neurodegenerative diseases including Alzheimer's, Parkinson's, and Huntington's diseases. Network-based approaches have provided the molecular rationale for the relationship among cancer, diabetes, and neurodegenerative diseases, and have uncovered unexpected links between apparently unrelated diseases. Here, we summarize the recent advances in network biology to untangle the molecular underpinnings giving rise to the most prevalent neurodegenerative diseases. We propose that network analysis provides a feasible and practical tool for identifying biologically meaningful biomarkers and potential therapeutic targets for clinical intervention in neurodegenerative diseases. PMID:25455073

  5. A neural network approach to job-shop scheduling.

    PubMed

    Zhou, D N; Cherkassky, V; Baldwin, T R; Olson, D E

    1991-01-01

    A novel analog computational network is presented for solving NP-complete constraint satisfaction problems, i.e. job-shop scheduling. In contrast to most neural approaches to combinatorial optimization based on quadratic energy cost function, the authors propose to use linear cost functions. As a result, the network complexity (number of neurons and the number of resistive interconnections) grows only linearly with problem size, and large-scale implementations become possible. The proposed approach is related to the linear programming network described by D.W. Tank and J.J. Hopfield (1985), which also uses a linear cost function for a simple optimization problem. It is shown how to map a difficult constraint-satisfaction problem onto a simple neural net in which the number of neural processors equals the number of subjobs (operations) and the number of interconnections grows linearly with the total number of operations. Simulations show that the authors' approach produces better solutions than existing neural approaches to job-shop scheduling, i.e. the traveling salesman problem-type Hopfield approach and integer linear programming approach of J.P.S. Foo and Y. Takefuji (1988), in terms of the quality of the solution and the network complexity. PMID:18276371

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

    PubMed

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

    2014-07-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    SciTech Connect

    Shi, Cindy

    2015-07-17

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

  9. Intelligent Resource Management for Local Area Networks: Approach and Evolution

    NASA Technical Reports Server (NTRS)

    Meike, Roger

    1988-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

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

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

    PubMed Central

    Gebicke-Haerter, Peter J

    2016-01-01

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

  12. Neural Network Approach To Sensory Fusion

    NASA Astrophysics Data System (ADS)

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

    1988-08-01

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

  13. Groove Sizing Using a Robust Neural Network Approach

    NASA Astrophysics Data System (ADS)

    Le Brusquet, L.; Davoust, M.-E.; Fleury, G.

    2003-03-01

    The remote field eddy current technique is used to inspect conductive pipes from the inside. The problem is to calculate an estimation of groove dimensions from observed data. A first approach was previously developed using a two-step parametric inversion. Results from this first approach are produced using a new model. A second approach using a neural network is presented. This technique is known for the lack of robustness which may occur when precautions are not sufficient. This paper presents these precautions and the results of both approaches.

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

    PubMed Central

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

    2016-01-01

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

  15. Hierarchical polynomial network approach to automated target recognition

    NASA Astrophysics Data System (ADS)

    Kim, Richard Y.; Drake, Keith C.; Kim, Tony Y.

    1994-02-01

    A hierarchical recognition methodology using abductive networks at several levels of object recognition is presented. Abductive networks--an innovative numeric modeling technology using networks of polynomial nodes--results from nearly three decades of application research and development in areas including statistical modeling, uncertainty management, genetic algorithms, and traditional neural networks. The systems uses pixel-registered multisensor target imagery provided by the Tri-Service Laser Radar sensor. Several levels of recognition are performed using detection, classification, and identification, each providing more detailed object information. Advanced feature extraction algorithms are applied at each recognition level for target characterization. Abductive polynomial networks process feature information and situational data at each recognition level, providing input for the next level of processing. An expert system coordinates the activities of individual recognition modules and enables employment of heuristic knowledge to overcome the limitations provided by a purely numeric processing approach. The approach can potentially overcome limitations of current systems such as catastrophic degradation during unanticipated operating conditions while meeting strict processing requirements. These benefits result from implementation of robust feature extraction algorithms that do not take explicit advantage of peculiar characteristics of the sensor imagery, and the compact, real-time processing capability provided by abductive polynomial networks.

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

    PubMed

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

    2016-01-01

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

  17. A modular neural network approach to fault diagnosis.

    PubMed

    Rodriguez, C; Rementeria, S; Martin, J I; Lafuente, A; Muguerza, J; Perez, J

    1996-01-01

    Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation. PMID:18255587

  18. A Novel Modulation Classification Approach Using Gabor Filter Network

    PubMed Central

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

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2003-04-01

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

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

    ERIC Educational Resources Information Center

    Burgoyne, John G.

    2010-01-01

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

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

    ERIC Educational Resources Information Center

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

    2010-01-01

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

  2. Event-driven approach of layered multicast to network adaptation in RED-based IP networks

    NASA Astrophysics Data System (ADS)

    Nahm, Kitae; Li, Qing; Kuo, C.-C. J.

    2003-11-01

    In this work, we investigate the congestion control problem for layered video multicast in IP networks of active queue management (AQM) using a simple random early detection (RED) queue model. AQM support from networks improves the visual quality of video streaming but makes network adaptation more di+/-cult for existing layered video multicast proticols that use the event-driven timer-based approach. We perform a simplified analysis on the response of the RED algorithm to burst traffic. The analysis shows that the primary problem lies in the weak correlation between the network feedback and the actual network congestion status when the RED queue is driven by burst traffic. Finally, a design guideline of the layered multicast protocol is proposed to overcome this problem.

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

    NASA Astrophysics Data System (ADS)

    Mavlyanova, Nadira; Zakirova, Zulfiya

    2013-04-01

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

  4. Approaching the thermodynamic limit in equilibrated scale-free networks.

    PubMed

    Waclaw, B; Bogacz, L; Janke, W

    2008-12-01

    We discuss how various models of scale-free complex networks approach their limiting properties when the size N of the network grows. We focus mainly on equilibrated networks and their finite-size degree distributions. Our results show that the position of the cutoff in the degree distribution, k_{cutoff} , scales with N in a different way than predicted for N-->infinity ; that is, subleading corrections to the scaling k_{cutoff} approximately N;{alpha} are strong even for networks of order N approximately 10;{9} nodes. We observe also a logarithmic correction to the scaling for degenerated graphs with the degree distribution pi(k) approximately k;{-3} . On the other hand, the distribution of the maximal degree k_{max} may have a different scaling than the cutoff and, moreover, it approaches the thermodynamic limit much faster. We argue that k_{max} approximately N;{alpha;{'}} with an exponent alpha;{'}=min[alpha,1(gamma-1)] , where gamma is the exponent in the power law pi(k) approximately k;{-gamma} . We also present some results on the cutoff function and the distribution of the maximal degree in equilibrated networks. PMID:19256820

  5. Multiple neural network approaches to clinical expert systems

    NASA Astrophysics Data System (ADS)

    Stubbs, Derek F.

    1990-08-01

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

  6. Optimal active power dispatch by network flow approach

    SciTech Connect

    Carvalho, M.F. ); Soares, S.; Ohishi, T. )

    1988-11-01

    In this paper the optimal active power dispatch problem is formulated as a nonlinear capacitated network flow problem with additional linear constraints. Transmission flow limits and both Kirchhoff's laws are taken into account. The problem is solved by a Generalized Upper Bounding technique that takes advantage of the network flow structure of the problem. The new approach has potential applications on power systems problems such as economic dispatch, load supplying capability, minimum load shedding, and generation-transmission reliability. The paper also reviews the use of transportation models for power system analysis. A detailed illustrative example is presented.

  7. Detecting network modules in fMRI time series: a weighted network analysis approach.

    PubMed

    Mumford, Jeanette A; Horvath, Steve; Oldham, Michael C; Langfelder, Peter; Geschwind, Daniel H; Poldrack, Russell A

    2010-10-01

    Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results. PMID:20553896

  8. Detecting network modules in fMRI time series: A weighted network analysis approach

    PubMed Central

    Mumford, Jeanette A; Horvath, Steve; Oldham, Michael C.; Langfelder, Peter; Geschwind, Daniel H.; Poldrack, Russell A

    2010-01-01

    Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA) is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are, but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel’s membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results. PMID:20553896

  9. Comprehensive spectral approach for community structure analysis on complex networks

    NASA Astrophysics Data System (ADS)

    Danila, Bogdan

    2016-02-01

    A simple but efficient spectral approach for analyzing the community structure of complex networks is introduced. It works the same way for all types of networks, by spectrally splitting the adjacency matrix into a "unipartite" and a "multipartite" component. These two matrices reveal the structure of the network from different perspectives and can be analyzed at different levels of detail. Their entries, or the entries of their lower-rank approximations, provide measures of the affinity or antagonism between the nodes that highlight the communities and the "gateway" links that connect them together. An algorithm is then proposed to achieve the automatic assignment of the nodes to communities based on the information provided by either matrix. This algorithm naturally generates overlapping communities but can also be tuned to eliminate the overlaps.

  10. Fire detection from hyperspectral data using neural network approach

    NASA Astrophysics Data System (ADS)

    Piscini, Alessandro; Amici, Stefania

    2015-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

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

  12. A Spatial Clustering Approach for Stochastic Fracture Network Modelling

    NASA Astrophysics Data System (ADS)

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

    2014-07-01

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

  13. A quantitative neural network approach to understanding aging phenotypes.

    PubMed

    Ash, Jessica A; Rapp, Peter R

    2014-05-01

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

  14. Toward a Behavioral Approach to Privacy for Online Social Networks

    NASA Astrophysics Data System (ADS)

    Banks, Lerone D.; Wu, S. Felix

    We examine the correlation between user interactions and self reported information revelation preferences for users of the popular Online Social Network (OSN), Facebook. Our primary goal is to explore the use of indicators of tie strength to inform localized, per-user privacy preferences for users and their ties within OSNs. We examine the limitations of such an approach and discuss future plans to incorporate this approach into the development of an automated system for helping users define privacy policy. As part of future work, we discuss how to define/expand policy to the entire social network. We also present additional collected data similar to other studies such as perceived tie strength and information revelation preferences for OSN users.

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

    NASA Technical Reports Server (NTRS)

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

    2000-01-01

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

  16. Autonomous control of production networks using a pheromone approach

    NASA Astrophysics Data System (ADS)

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

    2006-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-06-01

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

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

    ERIC Educational Resources Information Center

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

    2016-01-01

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

  19. A network approach to mixing delegates at meetings

    PubMed Central

    Schiavinotto, Tommaso; Lawson, Jonathan LD; Chessel, Anatole; Dodgson, James; Geymonat, Marco; Sato, Masamitsu

    2014-01-01

    Delegates at scientific meetings can come from diverse backgrounds and use very different methods in their research. Promoting interactions between these ‘distant’ delegates is challenging but such interactions could lead to novel interdisciplinary collaborations and unexpected breakthroughs. We have developed a network-based ‘speed dating’ approach that allows us to initiate such distant interactions by pairing every delegate with another delegate who might be of interest to them, but whom they might never have encountered otherwise. Here we describe our approach and its algorithmic implementation. PMID:24497549

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

    PubMed Central

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

    2013-01-01

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

  1. A Predictive Approach to Network Reverse-Engineering

    NASA Astrophysics Data System (ADS)

    Wiggins, Chris

    2005-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-02-01

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

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2015-07-01

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

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

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  7. What can we learn from the network approach in finance?

    NASA Astrophysics Data System (ADS)

    Janos, Kertesz

    2005-03-01

    Correlations between variations of stock prices reveal information about relationships between companies. Different methods of analysis have been applied to such data in order to uncover the taxonomy of the market. We use Mantegna's miminum spanning tree (MST) method for daily data in a dynamic way: By introducing a moving window we study the temporal changes in the structure of the network defined by this ``asset tree.'' The MST is scale free with a significantly changing exponent of the degree distribution for crash periods, which demonstrates the restructuring of the network due to the enhancement of correlations. This approach is compared to that based on what we call ``asset graphs:'' We start from an empty graph with no edges where the vertices correspond to stocks and then, one by one, we insert edges between the vertices according to the rank of their correlation strength. We study the properties of the creatred (weighted) networks, such as topologically different growth types, number and size of clusters and clustering coefficient. Furthermore, we define new tools like subgraph intensity and coherence to describe the role of the weights. We also investigate the time shifted cross correlation functions for high frequency data and find a characteristic time delay in many cases representing that some stocks lead the price changes while others follow them. These data can be used to construct a directed network of influence.

  8. Chemical reaction network approaches to Biochemical Systems Theory.

    PubMed

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

    2015-11-01

    This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed. PMID:26363083

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

    NASA Astrophysics Data System (ADS)

    Lee, Gan Siew; Djauhari, Maman A.

    2013-09-01

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

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

    NASA Technical Reports Server (NTRS)

    Hendrzak, Gary E.

    1993-01-01

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

  11. Bayesian approach for neural networks--review and case studies.

    PubMed

    Lampinen, J; Vehtari, A

    2001-04-01

    We give a short review on the Bayesian approach for neural network learning and demonstrate the advantages of the approach in three real applications. We discuss the Bayesian approach with emphasis on the role of prior knowledge in Bayesian models and in classical error minimization approaches. The generalization capability of a statistical model, classical or Bayesian, is ultimately based on the prior assumptions. The Bayesian approach permits propagation of uncertainty in quantities which are unknown to other assumptions in the model, which may be more generally valid or easier to guess in the problem. The case problem studied in this paper include a regression, a classification, and an inverse problem. In the most thoroughly analyzed regression problem, the best models were those with less restrictive priors. This emphasizes the major advantage of the Bayesian approach, that we are not forced to guess attributes that are unknown, such as the number of degrees of freedom in the model, non-linearity of the model with respect to each input variable, or the exact form for the distribution of the model residuals. PMID:11341565

  12. Collaborative Distributed Scheduling Approaches for Wireless Sensor Network

    PubMed Central

    Niu, Jianjun; Deng, Zhidong

    2009-01-01

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

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

    PubMed Central

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

    2012-01-01

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

  14. A Rawlsian approach to distribute responsibilities in networks.

    PubMed

    Doorn, Neelke

    2010-06-01

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

  15. A Rawlsian Approach to Distribute Responsibilities in Networks

    PubMed Central

    2009-01-01

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

  16. Prognostic models in coronary artery disease: Cox and network approaches

    PubMed Central

    Mora, Antonio; Sicari, Rosa; Cortigiani, Lauro; Carpeggiani, Clara; Picano, Eugenio; Capobianco, Enrico

    2015-01-01

    Predictive assessment of the risk of developing cardiovascular diseases is usually provided by computational approaches centred on Cox models. The complex interdependence structure underlying clinical data patterns can limit the performance of Cox analysis and complicate the interpretation of results, thus calling for complementary and integrative methods. Prognostic models are proposed for studying the risk associated with patients with known or suspected coronary artery disease (CAD) undergoing vasodilator stress echocardiography, an established technique for CAD detection and prognostication. In order to complement standard Cox models, network inference is considered a possible solution to quantify the complex relationships between heterogeneous data categories. In particular, a mutual information network is designed to explore the paths linking patient-associated variables to endpoint events, to reveal prognostic factors and to identify the best possible predictors of death. Data from a prospective, multicentre, observational study are available from a previous study, based on 4313 patients (2532 men; 64±11 years) with known (n=1547) or suspected (n=2766) CAD, who underwent high-dose dipyridamole (0.84 mg kg−1 over 6 min) stress echocardiography with coronary flow reserve (CFR) evaluation of left anterior descending (LAD) artery by Doppler. The overall mortality was the only endpoint analysed by Cox models. The estimated connectivity between clinical variables assigns a complementary value to the proposed network approach in relation to the established Cox model, for instance revealing connectivity paths. Depending on the use of multiple metrics, the constraints of regression analysis in measuring the association strength among clinical variables can be relaxed, and identification of communities and prognostic paths can be provided. On the basis of evidence from various model comparisons, we show in this CAD study that there may be characteristic

  17. Prognostic models in coronary artery disease: Cox and network approaches.

    PubMed

    Mora, Antonio; Sicari, Rosa; Cortigiani, Lauro; Carpeggiani, Clara; Picano, Eugenio; Capobianco, Enrico

    2015-02-01

    Predictive assessment of the risk of developing cardiovascular diseases is usually provided by computational approaches centred on Cox models. The complex interdependence structure underlying clinical data patterns can limit the performance of Cox analysis and complicate the interpretation of results, thus calling for complementary and integrative methods. Prognostic models are proposed for studying the risk associated with patients with known or suspected coronary artery disease (CAD) undergoing vasodilator stress echocardiography, an established technique for CAD detection and prognostication. In order to complement standard Cox models, network inference is considered a possible solution to quantify the complex relationships between heterogeneous data categories. In particular, a mutual information network is designed to explore the paths linking patient-associated variables to endpoint events, to reveal prognostic factors and to identify the best possible predictors of death. Data from a prospective, multicentre, observational study are available from a previous study, based on 4313 patients (2532 men; 64±11 years) with known (n=1547) or suspected (n=2766) CAD, who underwent high-dose dipyridamole (0.84 mg kg(-1) over 6 min) stress echocardiography with coronary flow reserve (CFR) evaluation of left anterior descending (LAD) artery by Doppler. The overall mortality was the only endpoint analysed by Cox models. The estimated connectivity between clinical variables assigns a complementary value to the proposed network approach in relation to the established Cox model, for instance revealing connectivity paths. Depending on the use of multiple metrics, the constraints of regression analysis in measuring the association strength among clinical variables can be relaxed, and identification of communities and prognostic paths can be provided. On the basis of evidence from various model comparisons, we show in this CAD study that there may be characteristic

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

    NASA Astrophysics Data System (ADS)

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

    2013-09-01

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

  19. Neural network approach to B →Xuℓν

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

  20. CCD Image Identification: An Artificial Neural Networks Approach

    NASA Astrophysics Data System (ADS)

    El-Bassuny Alawy, A.; et al.

    An Artificial Neural Network (ANN) technique in supervised mode has been developed to classify stellar, cosmic and noise identities on CCD frames. It has been implemented and coded in the C language for Personal Computers users. Its learning factors and training (cumulative, rms and decision) errors have been investigated. Two sets comprising a few hundred images of stars, cosmic rays and noise of different levels were adopted to train and test the algorithm developed. The present approach has been applied on a CCD frame of the star cluster M67. The results were discussed in comparison with those obtained from DAOPHOTII code out of the same frame. It has been shown that the present approach is fast, precise, efficient and reliable as well as requiring no prior input data for identification.

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

    NASA Astrophysics Data System (ADS)

    Hryniw, Natalia

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

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

    PubMed Central

    Kim, Yoo-Ah; Przytycka, Teresa M.

    2013-01-01

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

  3. A network approach for identifying and delimiting biogeographical regions.

    PubMed

    Vilhena, Daril A; Antonelli, Alexandre

    2015-01-01

    Biogeographical regions (geographically distinct assemblages of species and communities) constitute a cornerstone for ecology, biogeography, evolution and conservation biology. Species turnover measures are often used to quantify spatial biodiversity patterns, but algorithms based on similarity can be sensitive to common sampling biases in species distribution data. Here we apply a community detection approach from network theory that incorporates complex, higher-order presence-absence patterns. We demonstrate the performance of the method by applying it to all amphibian species in the world (c. 6,100 species), all vascular plant species of the USA (c. 17,600) and a hypothetical data set containing a zone of biotic transition. In comparison with current methods, our approach tackles the challenges posed by transition zones and succeeds in retrieving a larger number of commonly recognized biogeographical regions. This method can be applied to generate objective, data-derived identification and delimitation of the world's biogeographical regions. PMID:25907961

  4. A Systems Approach to Scalable Transportation Network Modeling

    SciTech Connect

    Perumalla, Kalyan S

    2006-01-01

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

  5. Patterns of work attitudes: A neural network approach

    NASA Astrophysics Data System (ADS)

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

    2000-05-01

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

  6. Random walk approach for dispersive transport in pipe networks

    NASA Astrophysics Data System (ADS)

    Sämann, Robert; Graf, Thomas; Neuweiler, Insa

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-02-01

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

  8. A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes

    PubMed Central

    Larremore, Daniel B.; Clauset, Aaron; Buckee, Caroline O.

    2013-01-01

    The var genes of the human malaria parasite Plasmodium falciparum present a challenge to population geneticists due to their extreme diversity, which is generated by high rates of recombination. These genes encode a primary antigen protein called PfEMP1, which is expressed on the surface of infected red blood cells and elicits protective immune responses. Var gene sequences are characterized by pronounced mosaicism, precluding the use of traditional phylogenetic tools that require bifurcating tree-like evolutionary relationships. We present a new method that identifies highly variable regions (HVRs), and then maps each HVR to a complex network in which each sequence is a node and two nodes are linked if they share an exact match of significant length. Here, networks of var genes that recombine freely are expected to have a uniformly random structure, but constraints on recombination will produce network communities that we identify using a stochastic block model. We validate this method on synthetic data, showing that it correctly recovers populations of constrained recombination, before applying it to the Duffy Binding Like-α (DBLα) domain of var genes. We find nine HVRs whose network communities map in distinctive ways to known DBLα classifications and clinical phenotypes. We show that the recombinational constraints of some HVRs are correlated, while others are independent. These findings suggest that this micromodular structuring facilitates independent evolutionary trajectories of neighboring mosaic regions, allowing the parasite to retain protein function while generating enormous sequence diversity. Our approach therefore offers a rigorous method for analyzing evolutionary constraints in var genes, and is also flexible enough to be easily applied more generally to any highly recombinant sequences. PMID:24130474

  9. A fuzzy neural network approach for power system evaluations

    NASA Astrophysics Data System (ADS)

    Moghaddas, Javad

    Every real-world dynamical system is nonlinear. Existing methods for solving a nonlinear problem entail linearizing the nonlinear problem and then using the different tools available for solving the linear system. These tools have been well understood for many decades. This research presents the application of Fuzzy Neural Network in reducing the large computational requirements associated with solving nonlinear systems. This approach utilizes fewer and faster steps for solving nonlinear problems. A practical use of this technique is power flow calculation where a large number of nonlinear equations are involved. The power flow problem is formulated as a nonlinear constrained optimization problem with the bus voltages, bus angles, real power injected into the buses, and reactive power injected into the buses as the problem variables. The equality and inequality constraints are appended to this objective function. Fuzzy rules based control is used to assist in choosing suitable penalty functions to form an augmented cost function. The linearized power flow equations at each iteration are translated to a scalar objective function of quadratic form. A neural network structure is given which implements the steepest descent method for minimizing the objective function. This research also presents the application of Fuzzy Clustering to power systems. The technique of Fuzzy Clustering reduces large system states into a few representative clusters, which are sufficient for reliability analysis. The method is then shown for optimal network decomposition based on Fuzzy Clustering. Fuzzy Clustering presents a powerful, globally oriented optimization method, which exploits the mechanism of natural response to reach optima or near optima. The results for an IEEE 14-Bus test system are given and the Fuzzy Clustering algorithm approach is found to produce significantly better solution.

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

    NASA Astrophysics Data System (ADS)

    Wu, Quanwang; Zhu, Qingsheng; Li, Peng

    2015-10-01

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

  11. Neural network approach to classification of infrasound signals

    NASA Astrophysics Data System (ADS)

    Lee, Dong-Chang

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

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

    NASA Technical Reports Server (NTRS)

    Botros, Nazeih M.

    1989-01-01

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

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

    EPA Science Inventory

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

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

    EPA Science Inventory

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

  15. Bayesian approach for network modeling of brain structural features

    NASA Astrophysics Data System (ADS)

    Joshi, Anand A.; Joshi, Shantanu H.; Leahy, Richard M.; Shattuck, David W.; Dinov, Ivo; Toga, Arthur W.

    2010-03-01

    Brain connectivity patterns are useful in understanding brain function and organization. Anatomical brain connectivity is largely determined using the physical synaptic connections between neurons. In contrast statistical brain connectivity in a given brain population refers to the interaction and interdependencies of statistics of multitudes of brain features including cortical area, volume, thickness etc. Traditionally, this dependence has been studied by statistical correlations of cortical features. In this paper, we propose the use of Bayesian network modeling for inferring statistical brain connectivity patterns that relate to causal (directed) as well as non-causal (undirected) relationships between cortical surface areas. We argue that for multivariate cortical data, the Bayesian model provides for a more accurate representation by removing the effect of confounding correlations that get introduced due to canonical dependence between the data. Results are presented for a population of 466 brains, where a SEM (structural equation modeling) approach is used to generate a Bayesian network model, as well as a dependency graph for the joint distribution of cortical areas.

  16. Spreading dynamics on heterogeneous populations: Multitype network approach

    NASA Astrophysics Data System (ADS)

    Vazquez, Alexei

    2006-12-01

    I study the spreading of infectious diseases in heterogeneous populations. The population structure is described by a contact graph where vertices represent agents and edges represent disease transmission channels among them. The population heterogeneity is taken into account by the agent’s subdivision in types and the mixing matrix among them. I introduce a type-network representation for the mixing matrix, allowing an intuitive understanding of the mixing patterns and the calculations. Using an iterative approach I obtain recursive equations for the probability distribution of the outbreak size as a function of time. I demonstrate that the expected outbreak size and its progression in time are determined by the largest eigenvalue of the reproductive number matrix and the characteristic distance between agents on the contact graph. Finally, I discuss the impact of intervention strategies to halt epidemic outbreaks. This work provides both a qualitative understanding and tools to obtain quantitative predictions for the spreading dynamics of heterogeneous populations.

  17. An efficient approach to imaging underground hydraulic networks

    NASA Astrophysics Data System (ADS)

    Kumar, Mohi

    2012-07-01

    To better locate natural resources, treat pollution, and monitor underground networks associated with geothermal plants, nuclear waste repositories, and carbon dioxide sequestration sites, scientists need to be able to accurately characterize and image fluid seepage pathways below ground. With these images, scientists can gain knowledge of soil moisture content, the porosity of geologic formations, concentrations and locations of dissolved pollutants, and the locations of oil fields or buried liquid contaminants. Creating images of the unknown hydraulic environments underfoot is a difficult task that has typically relied on broad extrapolations from characteristics and tests of rock units penetrated by sparsely positioned boreholes. Such methods, however, cannot identify small-scale features and are very expensive to reproduce over a broad area. Further, the techniques through which information is extrapolated rely on clunky and mathematically complex statistical approaches requiring large amounts of computational power.

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

    PubMed

    Pedroche, Francisco; Romance, Miguel; Criado, Regino

    2016-06-01

    In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the usual PageRank algorithm to multiplex networks. We give some analytical relations between these new approaches and the classic PageRank centrality measure, and we illustrate the new parameters presented by computing them on real underground networks. PMID:27368791

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

    NASA Astrophysics Data System (ADS)

    Pedroche, Francisco; Romance, Miguel; Criado, Regino

    2016-06-01

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

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

    NASA Astrophysics Data System (ADS)

    Cong, Jin; Liu, Haitao

    2014-12-01

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

  1. A new approach to blood flow simulation in vascular networks.

    PubMed

    Tamaddon, Houman; Behnia, Mehrdad; Behnia, Masud; Kritharides, Leonard

    2016-01-01

    A proper analysis of blood flow is contingent upon accurate modelling of the branching pattern and vascular geometry of the network of interest. It is challenging to reconstruct the entire vascular network of any organ experimentally, in particular the pulmonary vasculature, because of its very high number of vessels, complexity of the branching pattern and poor accessibility in vivo. The objective of our research is to develop an innovative approach for the reconstruction of the full pulmonary vascular tree from available morphometric data. Our method consists of the use of morphometric data on those parts of the pulmonary vascular tree that are too small to reconstruct by medical imaging methods. This method is a three-step technique that reconstructs the entire pulmonary arterial tree down to the capillary bed. Vessels greater than 2 mm are reconstructed from direct volume and surface analysis using contrast-enhanced computed tomography. Vessels smaller than 2 mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray's laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray's laws to every vessel bifurcation simultaneously leads to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. In conclusion, the present model provides a morphological foundation for future analysis of blood flow in the pulmonary circulation. PMID:26195135

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

    USGS Publications Warehouse

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

    2005-01-01

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

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

    USGS Publications Warehouse

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

    2005-01-01

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

  4. A Complex Network Approach to Distributional Semantic Models

    PubMed Central

    Utsumi, Akira

    2015-01-01

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

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

    ERIC Educational Resources Information Center

    Walker, Mark H.; Lynn, Freda B.

    2013-01-01

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

  6. Assessing the Government Information Locator Service (GILS): A Multi-Method Approach for Evaluating Networked Services.

    ERIC Educational Resources Information Center

    Moen, William E.; McClure, Charles R.; Koelker, June

    1997-01-01

    Describes a multimethod approach used to evaluate the Government Information Locator Service (GILS). Highlights the limitations and opportunities of available approaches to evaluating complex characteristics of networked information services and digital collections. (Author/AEF)

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

    ERIC Educational Resources Information Center

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

    2011-01-01

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

  8. Evaluation of Annual Performance of Multi-type Air-conditioners for Buildings

    NASA Astrophysics Data System (ADS)

    Watanabe, Choyu; Ohashi, Ei-Ichiro; Hirota, Masafumi; Nagamatsu, Katsuaki; Nakayama, Hiroshi

    The partial thermal load performance tests of electric-motor driven multi-type air-conditioners for buildings, the rated cooling and heating capacities of which were 56 kW and 63 kW, respectively, were carried out using the air-enthalpy method testing apparatus. Based on the results of those tests, the applicability of JIS B 8616: 2006, which was developed for the estimation of the annual electricity consumption of packaged air-conditioners with rated cooling capacities less than 28 kW, to the multi-type air-conditioners with larger capacities were examined. It was found that JIS B 8616: 2006 generally overestimates COP under a relatively low thermal load operation. As a result, the annual electricity consumption is underestimated by JIS. The prediction error changes depending of the building uses, and it amounted to -17 % in the case of office and -6 % in the detached shop.

  9. A Novel Quantitative Approach to Concept Analysis: The Internomological Network

    PubMed Central

    Cook, Paul F.; Larsen, Kai R.; Sakraida, Teresa J.; Pedro, Leli

    2012-01-01

    Background When a construct such as patients’ transition to self-management of chronic illness is studied by researchers across multiple disciplines, the meaning of key terms can become confused. This results from inherent problems in language where a term can have multiple meanings (polysemy) and different words can mean the same thing (synonymy). Objectives To test a novel quantitative method for clarifying the meaning of constructs by examining the similarity of published contexts in which they are used. Method Published terms related to the concept transition to self-management of chronic illness were analyzed using the internomological network (INN), a type of latent semantic analysis to calculate the mathematical relationships between constructs based on the contexts in which researchers use each term. This novel approach was tested by comparing results to those from concept analysis, a best-practice qualitative approach to clarifying meanings of terms. By comparing results of the two methods, the best synonyms of transition to self-management, as well as key antecedent, attribute, and consequence terms, were identified. Results Results from INN analysis were consistent with those from concept analysis. The potential synonyms self-management, transition, and adaptation had the greatest utility. Adaptation was the clearest overall synonym, but had lower cross-disciplinary use. The terms coping and readiness had more circumscribed meanings. The INN analysis confirmed key features of transition to self-management, and suggested related concepts not found by the previous review. Discussion The INN analysis is a promising novel methodology that allows researchers to quantify the semantic relationships between constructs. The method works across disciplinary boundaries, and may help to integrate the diverse literature on self-management of chronic illness. PMID:22592387

  10. Structural link prediction based on ant colony approach in social networks

    NASA Astrophysics Data System (ADS)

    Sherkat, Ehsan; Rahgozar, Maseud; Asadpour, Masoud

    2015-02-01

    As the size and number of online social networks are increasing day by day, social network analysis has become a popular issue in many branches of science. The link prediction is one of the key rolling issues in the analysis of social network's evolution. As the size of social networks is increasing, the necessity for scalable link prediction algorithms is being felt more. The aim of this paper is to introduce a new unsupervised structural link prediction algorithm based on the ant colony approach. Recently, ant colony approach has been used for solving some graph problems. Different kinds of networks are used for testing the proposed approach. In some networks, the proposed scalable algorithm has the best result in comparison to other structural unsupervised link prediction algorithms. In order to evaluate the algorithm results, methods like the top- n precision, area under the Receiver Operating Characteristic (ROC) and Precision-Recall curves are carried out on real-world networks.

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

    PubMed Central

    Sundarrajan, Sudharsana; Arumugam, Mohanapriya

    2016-01-01

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

  12. Network-based approaches to climate knowledge discovery

    NASA Astrophysics Data System (ADS)

    Budich, Reinhard; Nyberg, Per; Weigel, Tobias

    2011-11-01

    Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.

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

    PubMed

    Athanasopoulou, Eleftheria; Hadjicostis, Christoforos N

    2005-09-01

    In this paper, we consider distributed systems that can be modeled as finite state machines with known behavior under fault-free conditions, and we study the detection of a general class of faults that manifest themselves as permanent changes in the next-state transition functionality of the system. This scenario could arise in a variety of situations encountered in communication networks, including faults occurred due to design or implementation errors during the execution of communication protocols. In our approach, fault diagnosis is performed by an external observer/diagnoser that functions as a finite state machine and which has access to the input sequence applied to the system but has only limited access to the system state or output. In particular, we assume that the observer/diagnoser is only able to obtain partial information regarding the state of the given system at intermittent time intervals that are determined by certain synchronizing conditions between the system and the observer/diagnoser. By adopting a probabilistic framework, we analyze ways to optimally choose these synchronizing conditions and develop adaptive strategies that achieve a low probability of aliasing, i.e., a low probability that the external observer/diagnoser incorrectly declares the system as fault-free. An application of these ideas in the context of protocol testing/classification is provided as an example. PMID:16252815

  14. Chemoinformatics Approach for Building Molecular Networks from Marine Organisms.

    PubMed

    Karthikeyan, Muthukumarasamy; Nimje, Deepika; Pahujani, Rakhi; Tyagi, Kushal; Bapat, Sanket; Vyas, Renu; Pillai Padmakumar, Krishna

    2015-01-01

    Natural products obtained from marine sources are considered to be a rich and diverse source of potential drugs. In the present work we demonstrate the use of chemoinformatics approach for the design of new molecules inspired by molecules from marine organisms. Accordingly we have assimilated information from two major scientific domains namely chemoinformatics and biodiversity informatics to develop an interactive marine database named MIMMO (Medicinally Important Molecules from Marine Organisms). The database can be queried for species, molecules, scaffolds, drugs, diseases and associated cumulative biological activity spectrum along with links to the literature resources. Molecular informatics analysis of the molecules obtained from MIMMO was performed to study their chemical space. The distinct skeletal features of the biologically active compounds isolated from marine species were identified. Scaffold molecules and species networks were created to identify common scaffolds from marine source and drug space. An analysis of the entire molecular data revealed a unique list of around 2000 molecules from which ten most frequently occurring distinct scaffolds were obtained. PMID:26138570

  15. Network-theoretic approach to model vortex interactions

    NASA Astrophysics Data System (ADS)

    Nair, Aditya; Taira, Kunihiko

    2014-11-01

    We present a network-theoretic approach to describe a system of point vortices in two-dimensional flow. By considering the point vortices as nodes, a complete graph is constructed with edges connecting each vortex to every other vortex. The interactions between the vortices are captured by the graph edge weights. We employ sparsification techniques on these graph representations based on spectral theory to construct sparsified models of the overall vortical interactions. The edge weights are redistributed through spectral sparsification of the graph such that the sum of the interactions associated with each vortex is maintained constant. In addition, sparse configurations maintain similar spectral properties as the original setup. Through the reduction in the number of interactions, key vortex interactions can be highlighted. Identification of vortex structures based on graph sparsification is demonstrated with an example of clusters of point vortices. We also evaluate the computational performance of sparsification for large collection of point vortices. Work supported by US Army Research Office (W911NF-14-1-0386) and US Air Force Office of Scientific Research (YIP: FA9550-13-1-0183).

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

    NASA Astrophysics Data System (ADS)

    Gong, Tao; Shuai, Lan; Wu, Yicheng

    2014-12-01

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

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

    PubMed

    Vitkin, Edward; Shlomi, Tomer

    2012-01-01

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

  18. Ranking Silent Nodes in Information Networks: A Quantitative Approach and Applications

    NASA Astrophysics Data System (ADS)

    Interdonato, Roberto; Tagarelli, Andrea

    This paper overviews recent research findings concerning a new challenging problem in information networks, namely identifying and ranking silent nodes. We present three case studies which show how silent nodes' behavior maps to different situations in computer networks, online social networks, and online collaboration networks, and we discuss major benefits in identifying and ranking silent nodes in such networks. We also provide an overview of our proposed approach, which relies on a new eigenvector- centrality graph-based ranking method built on a silent-oriented network model.

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

    ERIC Educational Resources Information Center

    Csermely, Peter; Rajnai, Gabor; Sulyok, Katalin

    2013-01-01

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

  20. A Network Approach for Evaluating Coherence in Multivariate Systems: An Application to Psychophysiological Emotion Data

    ERIC Educational Resources Information Center

    Hsieh, Fushing; Ferrer, Emilio; Chen, Shuchun; Mauss, Iris B.; John, Oliver; Gross, James J.

    2011-01-01

    We present an approach for evaluating coherence in multivariate systems that considers all the variables simultaneously. We operationalize the multivariate system as a network and define coherence as the efficiency with which a signal is transmitted throughout the network. We illustrate this approach with time series data from 15…

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

    PubMed

    Li, Jun; Zhao, Patrick X

    2016-01-01

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

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

    PubMed Central

    Li, Jun; Zhao, Patrick X.

    2016-01-01

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

  3. Unfolding Accessibility Provides a Macroscopic Approach to Temporal Networks

    NASA Astrophysics Data System (ADS)

    Lentz, Hartmut H. K.; Selhorst, Thomas; Sokolov, Igor M.

    2013-03-01

    An accessibility graph of a network contains a link wherever there is a path of arbitrary length between two nodes. We generalize the concept of accessibility to temporal networks. Building an accessibility graph by consecutively adding paths of growing length (unfolding), we obtain information about the distribution of shortest path durations and characteristic time scales in temporal networks. Moreover, we define causal fidelity to measure the goodness of their static representation. The practicability of our proposed methods is demonstrated for three examples: networks of social contacts, livestock trade, and sexual contacts.

  4. The Cable and Wireless approach to network synchronization

    NASA Technical Reports Server (NTRS)

    Calvert, Robert D.

    1990-01-01

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

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

    PubMed

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

    2014-12-01

    Molecular markers, viz. microsatellites and single nucleotide polymorphisms, have revolutionized breed identification through the use of small samples of biological tissue or germplasm, such as blood, carcass samples, embryos, ova and semen, that show no evident phenotype. Classical tools of molecular data analysis for breed identification have limitations, such as the unavailability of referral breed data, causing increased cost of collection each time, compromised computational accuracy and complexity of the methodology used. We report here the successful use of an artificial neural network (ANN) in background to decrease the cost of genotyping by locus minimization. The webserver is freely accessible (http://nabg.iasri.res.in/bisgoat) to the research community. We demonstrate that the machine learning (ANN) approach for breed identification is capable of multifold advantages such as locus minimization, leading to a drastic reduction in cost, and web availability of reference breed data, alleviating the need for repeated genotyping each time one investigates the identity of an unknown breed. To develop this model web implementation based on ANN, we used 51,850 samples of allelic data of microsatellite-marker-based DNA fingerprinting on 25 loci covering 22 registered goat breeds of India for training. Minimizing loci to up to nine loci through the use of a multilayer perceptron model, we achieved 96.63% training accuracy. This server can be an indispensable tool for identification of existing breeds and new synthetic commercial breeds, leading to protection of intellectual property in case of sovereignty and bio-piracy disputes. This server can be widely used as a model for cost reduction by locus minimization for various other flora and fauna in terms of variety, breed and/or line identification, especially in conservation and improvement programs. PMID:25183434

  6. On Kirchhoff's equations and variational approaches to electrical network analysis

    NASA Astrophysics Data System (ADS)

    Ercan, Alper

    2016-03-01

    Analysis of electrical networks using variational techniques, while repeatedly mentioned in the literature, is not widely known or utilized. In this short communication, we emphasize the connection between Kirchhoff's network equations, energy conservation, and variational analysis techniques using a brief example.

  7. A Neural Network Approach to the Classification of Autism.

    ERIC Educational Resources Information Center

    Cohen, Ira L.; And Others

    1993-01-01

    Neural network technology was compared with simultaneous and stepwise linear discriminant analysis in terms of their ability to classify and predict persons (n=138) as having autism or mental retardation. The neural network methodology was superior in both classifying groups and in generalizing to new cases that were not part of the training…

  8. An approach to efficient mobility management in intelligent networks

    NASA Technical Reports Server (NTRS)

    Murthy, K. M. S.

    1995-01-01

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

  9. A Graph Oriented Approach for Network Forensic Analysis

    ERIC Educational Resources Information Center

    Wang, Wei

    2010-01-01

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

  10. Telecommunication Networks for Libraries and Information Systems: Approaches to Development.

    ERIC Educational Resources Information Center

    Bystrom, John W.

    The focus of this paper is on the development of telecommunication networking by library and information networks. The word "development" here includes all the processes necessary to broad application and encompasses the necessary social, political, industrial and professional responses to technological advancement as well as technological…

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

    PubMed

    Zhou, Zhuping; Hu, Qizhou; Wang, Wei

    2014-01-01

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

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

    PubMed Central

    Zhou, Zhuping; Hu, Qizhou; Wang, Wei

    2014-01-01

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

  13. Distributed Reinforcement Learning Approach for Vehicular Ad Hoc Networks

    NASA Astrophysics Data System (ADS)

    Wu, Celimuge; Kumekawa, Kazuya; Kato, Toshihiko

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-06-01

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

  15. Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration.

    PubMed

    van den Elzen, Stef; Holten, Danny; Blaas, Jorik; van Wijk, Jarke J

    2016-01-01

    We propose a visual analytics approach for the exploration and analysis of dynamic networks. We consider snapshots of the network as points in high-dimensional space and project these to two dimensions for visualization and interaction using two juxtaposed views: one for showing a snapshot and one for showing the evolution of the network. With this approach users are enabled to detect stable states, recurring states, outlier topologies, and gain knowledge about the transitions between states and the network evolution in general. The components of our approach are discretization, vectorization and normalization, dimensionality reduction, and visualization and interaction, which are discussed in detail. The effectiveness of the approach is shown by applying it to artificial and real-world dynamic networks. PMID:26529683

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2016-05-15

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

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

    NASA Astrophysics Data System (ADS)

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

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

  19. Human Identification with Electrocardiogram Signals: a Neural Network Approach

    NASA Astrophysics Data System (ADS)

    Wan, Yongbo; Yao, Jianchu

    2009-05-01

    This paper presents a neural network developed to identify human subjects using electrocardiogram (ECG) signals collected from an "in-house" wearable electrocardiogram (ECG) sensor. In this project, noises were first removed from the raw signals with wavelet filters. ECG cycles were then extracted from the filtered signals and decomposed into wavelet coefficient structures. These coefficient structures were used as input vectors to a 3-layer feedforward neural network that generates the identification results. In the current study, 61 datasets collected from 23 subjects were utilized to train the neural network, which thereafter was tested with 15 new datasets from 15 different subjects. All the 15 subjects in the experiment were successfully identified. The testing results demonstrate that the neural network is effective.

  20. Inference Approaches to Constructing Covert Social Network Topologies

    NASA Astrophysics Data System (ADS)

    Rhodes, Christopher J.

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

  1. The complex networks approach for authorship attribution of books

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

  2. Formal versus self-organised knowledge systems: A network approach

    NASA Astrophysics Data System (ADS)

    Masucci, A. P.

    2011-11-01

    In this work, we consider the topological analysis of symbolic formal systems in the framework of network theory. In particular, we analyse the network extracted by Principia Mathematica of B. Russell and A.N. Whitehead, where the vertices are the statements and two statements are connected with a directed link if one statement is used to demonstrate the other one. We compare the obtained network with other directed acyclic graphs, such as a scientific citation network and a stochastic model. We also introduce a novel topological ordering for directed acyclic graphs and we discuss its properties with respect to the classical one. The main result is the observation that formal systems of knowledge topologically behave similarly to self-organised systems.

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

    PubMed Central

    Sesar, Kristina; Šimić, Nataša; Barišić, Marijana

    2010-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Cannady, James

    2011-06-01

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

  5. Directional MAC approach for wireless body area networks.

    PubMed

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

    2011-01-01

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

  6. Phylogeny of metabolic networks: a spectral graph theoretical approach.

    PubMed

    Deyasi, Krishanu; Banerjee, Anirban; Deb, Bony

    2015-10-01

    Many methods have been developed for finding the commonalities between different organisms in order to study their phylogeny. The structure of metabolic networks also reveals valuable insights into metabolic capacity of species as well as into the habitats where they have evolved. We constructed metabolic networks of 79 fully sequenced organisms and compared their architectures. We used spectral density of normalized Laplacian matrix for comparing the structure of networks. The eigenvalues of this matrix reflect not only the global architecture of a network but also the local topologies that are produced by different graph evolutionary processes like motif duplication or joining. A divergence measure on spectral densities is used to quantify the distances between various metabolic networks, and a split network is constructed to analyse the phylogeny from these distances. In our analysis, we focused on the species that belong to different classes, but appear more related to each other in the phylogeny. We tried to explore whether they have evolved under similar environmental conditions or have similar life histories. With this focus, we have obtained interesting insights into the phylogenetic commonality between different organisms. PMID:26564980

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

    SciTech Connect

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

    2000-02-01

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

  8. Directional MAC Approach for Wireless Body Area Networks

    PubMed Central

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

    2011-01-01

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

  9. Computational approach in estimating the need of ditch network maintenance

    NASA Astrophysics Data System (ADS)

    Lauren, Ari; Hökkä, Hannu; Launiainen, Samuli; Palviainen, Marjo; Repo, Tapani; Leena, Finer; Piirainen, Sirpa

    2015-04-01

    Ditch network maintenance (DNM), implemented annually in 70 000 ha area in Finland, is the most controversial of all forest management practices. Nationwide, it is estimated to increase the forest growth by 1…3 million m3 per year, but simultaneously to cause 65 000 tons export of suspended solids and 71 tons of phosphorus (P) to water courses. A systematic approach that allows simultaneous quantification of the positive and negative effects of DNM is required. Excess water in the rooting zone slows the gas exchange and decreases biological activity interfering with the forest growth in boreal forested peatlands. DNM is needed when: 1) the excess water in the rooting zone restricts the forest growth before the DNM, and 2) after the DNM the growth restriction ceases or decreases, and 3) the benefits of DNM are greater than the caused adverse effects. Aeration in the rooting zone can be used as a drainage criterion. Aeration is affected by several factors such as meteorological conditions, tree stand properties, hydraulic properties of peat, ditch depth, and ditch spacing. We developed a 2-dimensional DNM simulator that allows the user to adjust these factors and to evaluate their effect on the soil aeration at different distance from the drainage ditch. DNM simulator computes hydrological processes and soil aeration along a water flowpath between two ditches. Applying daily time step it calculates evapotranspiration, snow accumulation and melt, infiltration, soil water storage, ground water level, soil water content, air-filled porosity and runoff. The model performance in hydrology has been tested against independent high frequency field monitoring data. Soil aeration at different distance from the ditch is computed under steady-state assumption using an empirical oxygen consumption model, simulated air-filled porosity, and diffusion coefficient at different depths in soil. Aeration is adequate and forest growth rate is not limited by poor aeration if the

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

    PubMed Central

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

    2013-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2010-01-01

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

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

    PubMed

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

    2006-10-01

    In this paper we report on our experiences with using network analysis to discern and analyse ethical issues in research into, and the development of, a new wastewater treatment technology. Using network analysis, we preliminarily interpreted some of our observations in a Group Decision Room (GDR) session where we invited important stakeholders to think about the risks of this new technology. We show how a network approach is useful for understanding the observations, and suggests some relevant ethical issues. We argue that a network approach is also useful for ethical analysis of issues in other fields of research and development. The abandoning of the overarching rationality assumption, which is central to network approaches, does not have to lead to ethical relativism. PMID:17199143

  14. Efficient Learning Strategy of Chinese Characters Based on Network Approach

    PubMed Central

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

    2013-01-01

    We develop an efficient learning strategy of Chinese characters based on the network of the hierarchical structural relations between Chinese characters. A more efficient strategy is that of learning the same number of useful Chinese characters in less effort or time. We construct a node-weighted network of Chinese characters, where character usage frequencies are used as node weights. Using this hierarchical node-weighted network, we propose a new learning method, the distributed node weight (DNW) strategy, which is based on a new measure of nodes' importance that considers both the weight of the nodes and its location in the network hierarchical structure. Chinese character learning strategies, particularly their learning order, are analyzed as dynamical processes over the network. We compare the efficiency of three theoretical learning methods and two commonly used methods from mainstream Chinese textbooks, one for Chinese elementary school students and the other for students learning Chinese as a second language. We find that the DNW method significantly outperforms the others, implying that the efficiency of current learning methods of major textbooks can be greatly improved. PMID:23990887

  15. Rain Gauges Network Design using Discrete Entropy and Kriging Approach

    NASA Astrophysics Data System (ADS)

    Wei, Chiang; Chiang, Jie-Lun; Wey, Tsong-Huei; Yeh, Hui-Chung; Cheng, Yen-Chang

    2010-05-01

    A well designed rainfall network can accurately provide and reflect the information of rainfall in a catchment. However, the adequate number and optimal location of rain gauge stations have yet to obtain a satisfactory result. At alpine area, in particular, due to the high variation of relief, a more accurate design of raingauge network is required. Hence, a proposed model composed of kriging and discrete entropy is introduced in this study to relocate the rainfall network and to obtain the optimal design with the minimum number of rain gauges. The ordinary kriging is used to generate rainfall data of potential locations where rain gauge stations may be installed. The information entropy based on probability is used to measure the uncertainty of rainfall distribution. By calculating the joint entropy and the transferable information, the relocated rain gauges are prioritized and the minimum number and location of the rain gauges in the catchment can be obtained to construct the optimal rainfall network to replace the existing rainfall network. The alpine area located at Experimental Forest of National Taiwan University in central Taiwan is selected as the target area. Comprising 50 existed rain gauges, 346 blocks covering 1 × 1 km2 size are delineated from the target area as the candidate rain gauges to test the proposed algorithm using rainfall records between 1992 and 2009. The result shows that only 2 and 5 candidate rain gauges can represent 62.93% and 85.21% of variance of rainfall distribution respectively.

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

    PubMed

    Deng, Xutao; Geng, Huimin; Ali, Hesham

    2005-08-01

    Reverse-engineering of gene networks using linear models often results in an underdetermined system because of excessive unknown parameters. In addition, the practical utility of linear models has remained unclear. We address these problems by developing an improved method, EXpression Array MINing Engine (EXAMINE), to infer gene regulatory networks from time-series gene expression data sets. EXAMINE takes advantage of sparse graph theory to overcome the excessive-parameter problem with an adaptive-connectivity model and fitting algorithm. EXAMINE also guarantees that the most parsimonious network structure will be found with its incremental adaptive fitting process. Compared to previous linear models, where a fully connected model is used, EXAMINE reduces the number of parameters by O(N), thereby increasing the chance of recovering the underlying regulatory network. The fitting algorithm increments the connectivity during the fitting process until a satisfactory fit is obtained. We performed a systematic study to explore the data mining ability of linear models. A guideline for using linear models is provided: If the system is small (3-20 elements), more than 90% of the regulation pathways can be determined correctly. For a large-scale system, either clustering is needed or it is necessary to integrate information in addition to expression profile. Coupled with the clustering method, we applied EXAMINE to rat central nervous system development (CNS) data with 112 genes. We were able to efficiently generate regulatory networks with statistically significant pathways that have been predicted previously. PMID:15951103

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

    PubMed Central

    2012-01-01

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

  18. Research into alternative network approaches for space operations

    NASA Technical Reports Server (NTRS)

    Kusmanoff, Antone L.; Barton, Timothy J.

    1990-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Koochakzadeh, Ali; Pal, Piya

    2016-05-01

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

  20. Subgraph Covers: An Information-Theoretic Approach to Motif Analysis in Networks

    NASA Astrophysics Data System (ADS)

    Wegner, Anatol E.

    2014-10-01

    Many real-world networks contain a statistically surprising number of certain subgraphs, called network motifs. In the prevalent approach to motif analysis, network motifs are detected by comparing subgraph frequencies in the original network with a statistical null model. In this paper, we propose an alternative approach to motif analysis where network motifs are defined to be connectivity patterns that occur in a subgraph cover that represents the network using minimal total information. A subgraph cover is defined to be a set of subgraphs such that every edge of the graph is contained in at least one of the subgraphs in the cover. Some recently introduced random graph models that can incorporate significant densities of motifs have natural formulations in terms of subgraph covers, and the presented approach can be used to match networks with such models. To prove the practical value of our approach, we also present a heuristic for the resulting NP hard optimization problem and give results for several real-world networks.

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

    ERIC Educational Resources Information Center

    West, Russell F.

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

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

    PubMed

    Mao, Yuxin; Wei, Guiyi

    2010-01-01

    The unattended nature of wireless sensor networks makes them very vulnerable to malicious attacks. Therefore, how to preserve secure data collection is an important issue to wireless sensor networks. In this paper, we propose a novel approach of secure data collection for wireless sensor networks. We explore secret sharing and multipath routing to achieve secure data collection in wireless sensor network with compromised nodes. We present a novel tracing-feedback mechanism, which makes full use of the routing functionality of wireless sensor networks, to improve the quality of data collection. The major advantage of the approach is that the secure paths are constructed as a by-product of data collection. The process of secure routing causes little overhead to the sensor nodes in the network. Compared with existing works, the algorithms of the proposed approach are easy to implement and execute in resource-constrained wireless sensor networks. According to the result of a simulation experiment, the performance of the approach is better than the recent approaches with a similar purpose. PMID:22163424

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

    PubMed

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

    2015-03-01

    This paper investigates the off-line synthesis approach of model predictive control (MPC) for a class of networked control systems (NCSs) with network-induced delays. A new augmented model which can be readily applied to time-varying control law, is proposed to describe the NCS where bounded deterministic network-induced delays may occur in both sensor to controller (S-A) and controller to actuator (C-A) links. Based on this augmented model, a sufficient condition of the closed-loop stability is derived by applying the Lyapunov method. The off-line synthesis approach of model predictive control is addressed using the stability results of the system, which explicitly considers the satisfaction of input and state constraints. Numerical example is given to illustrate the effectiveness of the proposed method. PMID:25538025

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

    ERIC Educational Resources Information Center

    Martin, Julia W.; Hughes, Brian

    2012-01-01

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

  5. Biology Inspired Approach for Communal Behavior in Sensor Networks

    NASA Technical Reports Server (NTRS)

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

    2006-01-01

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

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

    ERIC Educational Resources Information Center

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

    2002-01-01

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

  7. Multi-loop networked process control: a synchronized approach.

    PubMed

    Das, M; Ghosh, R; Goswami, B; Chandra, A K; Balasubramanian, R; Luksch, P; Gupta, A

    2009-01-01

    Modern day process control uses digital controllers which are based on the principle of distributed rather than centralized control. Distributing controllers, sensors and actuators across a plant entails considerable wiring which can be reduced substantially by integrating the components of a control loop over a network. The other advantages include greater flexibility and higher reliability with lower hardware redundancy. The controllers and sensors are on a network and can take over the function of a failed component automatically, without the need of manual reconfiguration, thus eliminating the need of having a redundant component for each and every component. Though elaborate techniques have been developed for Single Input Single Output (SISO) systems, the major challenge lies in extending these ideas to control a practical process plant where de-centralized control is actually achieved through control of individual SISO control loops derived through de-coupling of the original system. Multiple loops increase network load and hence the sampling times associated with the control loops and makes synchronization difficult. This paper presents a methodology by which network based process control can be applied to practical process plants, with a simple direct synchronization mechanism. PMID:19028386

  8. Approach and landing test network interface processor interface control document

    NASA Technical Reports Server (NTRS)

    1976-01-01

    The design requirements are established for all external or interproject interfaces to the Network Interface Processor located in Building 30 at the Lyndon B. Johnson Space Center, Houston, Texas. In addition to external interfaces, software/hardware and special interfaces are also described.

  9. Neural Network Approach to Locating Cryptography in Object Code

    SciTech Connect

    Jason L. Wright; Milos Manic

    2009-09-01

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

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

    SciTech Connect

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

    1998-01-01

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

  11. Neural Networks Based Approach to Enhance Space Hardware Reliability

    NASA Technical Reports Server (NTRS)

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

    2011-01-01

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

  12. A National Interlibrary Loan Network: The OCLC Approach.

    ERIC Educational Resources Information Center

    Jacob, Mary Ellen

    1979-01-01

    Describes the objectives, testing, procedures, and effects on document delivery systems of the OCLC interlibrary loan (ILL) subsystem, an uncoordinated national ILL network which provides users with immediate access to the OCLC On-Line Union Catalog, the ILL Transaction File, and the ILL Message Waiting File. (CWM)

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

    ERIC Educational Resources Information Center

    Johnson, Nicola F.; Humphry, Nicoli

    2012-01-01

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

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

    ERIC Educational Resources Information Center

    Goodson, Justin; Jang, Wooseung; Rantz, Marilyn

    2008-01-01

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

  15. Autoshaping and Automaintenance: A Neural-Network Approach

    ERIC Educational Resources Information Center

    Burgos, Jose E.

    2007-01-01

    This article presents an interpretation of autoshaping, and positive and negative automaintenance, based on a neural-network model. The model makes no distinction between operant and respondent learning mechanisms, and takes into account knowledge of hippocampal and dopaminergic systems. Four simulations were run, each one using an "A-B-A" design…

  16. Durability of a polymer matrix composite: Neural networks approach

    NASA Astrophysics Data System (ADS)

    Al-Haik, Marwan S.

    In this study, the viscoplastic behavior of a carbon-fiber/thixotropic-epoxy matrix composite was investigated through two deferent modeling efforts. The first model is phenomenological in nature and it utilizes the tensile and stress relaxation experiments to predict the creep strain. In the second model, the composite viscoplastic behavior is no longer represented by closed-form constitutive laws, but it is captured by a neural network formulation. The composite was processed and cured using hand lay-up technique together with autoclave curing system. By performing thermomechanical analysis and differential scanning calorimetry, the glass transition temperature of the composite was noticed to degrade. Experiments were performed to examine the tensile, creep, and load relaxation behavior of the composite under different temperatures. It was found that the composite strength and stiffness decrease significantly at elevated temperatures. A phenomenological model was constructed based on the overstress viscoplastic model. In this model, four material's parameters are extracted from tensile and load relaxation tests. These parameters are used by a system of coupled equations to predict the creep strain. The results of the phenomenological model were satisfactory for predicting creep at low temperature conditions, but for the high stress-high temperature regimes, the model failed to predict the creep strain accurately. The neural network model was built directly from the experimental creep tests performed at various stress-temperature conditions. The optimal structure of the neural network was achieved through the universal approximation theory and the dimensionality of the creep problem (stress, temperature, and time). The neural network model was trained to predict the creep strain based on the stress-temperature-time values. The performance of the neural model is captured by the mean squared error between the neural network prediction and the experimental creep

  17. A new integrated approach to seismic network optimization

    NASA Astrophysics Data System (ADS)

    Tramelli, A.; De Natale, G.; Troise, C.; Orazi, M.

    2012-04-01

    A seismic network is usually deployed to monitor the seismicity, to locate earthquakes and compute source parameters. The network configuration is crucial due to the important implications on the quality of the information that can be obtained, therefore, it requires a detailed study in order to maximize the information-to-cost ratio. Fundamental, for the network optimization, is the clear definition of the goals which must be reached, the experimental constraints and the physical relationship between data and model. In order to maximize the performance of a particular design a quantitative measure of such performance must be defined. Once a quality function has been rigorously defined for each individual goal, an optimization criterion can be defined, which maximizes it. In particular, for the seismic location problem such criterion may be based on the minimization of the statistical location errors. A similar criterion of error minimization can be equivalently used for moment tensor determination, double-couple focal mechanisms estimation, scalar source parameters determination, etc. We present here suitable algorithms developed and tested for network optimization. As optimization parameter, we propose to use the ratio between the larger to the smaller eigenvalue of the information matrix. Such ratio is proportional to the ratio between solution and data errors, i.e. it represents the amplification factor which propagates data errors into the solution. The optimization problem tries to define, among a set of M possible sites, which are the N ones (with Nnetwork or to design

  18. Network-based biomarkers enhance classical approaches to prognostic gene expression signatures

    PubMed Central

    2014-01-01

    Background Classical approaches to predicting patient clinical outcome via gene expression information are primarily based on differential expression of unrelated genes (single-gene approaches) or genes related by, for example, biologic pathway or function (gene-sets). Recently, network-based approaches utilising interaction information between genes have emerged. An open problem is whether such approaches add value to the more traditional methods of signature modelling. We explored this question via comparison of the most widely employed single-gene, gene-set, and network-based methods, using gene expression microarray data from two different cancers: melanoma and ovarian. We considered two kinds of network approaches. The first of these identifies informative genes using gene expression and network connectivity information combined, the latter drawn from prior knowledge of protein-protein interactions. The second approach focuses on identification of informative sub-networks (small networks of interacting proteins, again from prior knowledge networks). For all methods we performed 100 rounds of 5-fold cross-validation under 3 different classifiers. For network-based approaches, we considered two different protein-protein interaction networks. We quantified resulting patterns of misclassification and discussed the relative value of each relative to ongoing development of prognostic biomarkers. Results We found that single-gene, gene-set and network methods yielded similar error rates in melanoma and ovarian cancer data. Crucially, however, our novel and detailed patient-level analyses revealed that the different methods were correctly classifying alternate subsets of patients in each cohort. We also found that the network-based NetRank feature selection method was the most stable. Conclusions Next-generation methods of gene expression signature modelling harness data from external networks and are foreshadowed as a standard mode of analysis. But what do they add

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

    PubMed Central

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

    2014-01-01

    We propose a scalable approach for making inference about latent spaces of large networks. With a succinct representation of networks as a bag of triangular motifs, a parsimonious statistical model, and an efficient stochastic variational inference algorithm, we are able to analyze real networks with over a million vertices and hundreds of latent roles on a single machine in a matter of hours, a setting that is out of reach for many existing methods. When compared to the state-of-the-art probabilistic approaches, our method is several orders of magnitude faster, with competitive or improved accuracy for latent space recovery and link prediction. PMID:25400487

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

    PubMed

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

    2016-02-01

    Social network analysis (SNA) is based on a conceptual network representation of social interactions and is an invaluable tool for conservation professionals to increase collaboration, improve information flow, and increase efficiency. We present two approaches to constructing internet-based social networks, and use an existing traditional (survey-based) case study to illustrate in a familiar context the deviations in methods and results. Internet-based approaches to SNA offer a means to overcome institutional hurdles to conducting survey-based SNA, provide unique insight into an institution's web presences, allow for easy snowballing (iterative process that incorporates new nodes in the network), and afford monitoring of social networks through time. The internet-based approaches differ in link definition: hyperlink is based on links on a website that redirect to a different website and relatedness links are based on a Google's "relatedness" operator that identifies pages "similar" to a URL. All networks were initiated with the same start nodes [members of a conservation alliance for the Calumet region around Chicago (n = 130)], but the resulting networks vary drastically from one another. Interpretation of the resulting networks is highly contingent upon how the links were defined. PMID:26503113

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

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

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

  2. A network approach in analysis of the matching hypothesis

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-11-01

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

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

    PubMed

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

    2016-07-01

    Network analysis provides a powerful framework for the interpretation of data. It uses novel reference network-based metrices for module evolution. These could be used to identify module of highly connected genes showing variation in co-expression network. In this study, a co-expression network-based approach was used for analyzing the genes from microarray data. Our approach consists of a simple but robust rank-based network construction. The publicly available gene expression data of Solanum tuberosum under cold and heat stresses were considered to create and analyze a gene co-expression network. The analysis provide highly co-expressed module of bHLH coding genes based on correlation values. Our approach was to analyze the variation of genes expression, according to the time period of stress through co-expression network approach. As the result, the seed genes were identified showing multiple connections with other genes in the same cluster. Seed genes were found to be vary in different time periods of stress. These analyzed seed genes may be utilized further as marker genes for developing the stress tolerant plant species. PMID:27178572

  5. An alternative approach for neural network evolution with a genetic algorithm: crossover by combinatorial optimization.

    PubMed

    García-Pedrajas, Nicolás; Ortiz-Boyer, Domingo; Hervás-Martínez, César

    2006-05-01

    In this work we present a new approach to crossover operator in the genetic evolution of neural networks. The most widely used evolutionary computation paradigm for neural network evolution is evolutionary programming. This paradigm is usually preferred due to the problems caused by the application of crossover to neural network evolution. However, crossover is the most innovative operator within the field of evolutionary computation. One of the most notorious problems with the application of crossover to neural networks is known as the permutation problem. This problem occurs due to the fact that the same network can be represented in a genetic coding by many different codifications. Our approach modifies the standard crossover operator taking into account the special features of the individuals to be mated. We present a new model for mating individuals that considers the structure of the hidden layer and redefines the crossover operator. As each hidden node represents a non-linear projection of the input variables, we approach the crossover as a problem on combinatorial optimization. We can formulate the problem as the extraction of a subset of near-optimal projections to create the hidden layer of the new network. This new approach is compared to a classical crossover in 25 real-world problems with an excellent performance. Moreover, the networks obtained are much smaller than those obtained with classical crossover operator. PMID:16343847

  6. A statistical mechanics approach to autopoietic immune networks

    NASA Astrophysics Data System (ADS)

    Barra, Adriano; Agliari, Elena

    2010-07-01

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

  7. Tracing Road Network Bottleneck by Data Driven Approach.

    PubMed

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

    2016-01-01

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

  8. Tracing Road Network Bottleneck by Data Driven Approach

    PubMed Central

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

    2016-01-01

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

  9. A network biology approach to denitrification in Pseudomonas aeruginosa

    DOE PAGESBeta

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

    2015-02-23

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

  10. A Network Biology Approach to Denitrification in Pseudomonas aeruginosa

    PubMed Central

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

    2015-01-01

    Pseudomonas aeruginosa is a metabolically flexible member of the Gammaproteobacteria. Under anaerobic conditions and the presence of nitrate, P. aeruginosa can perform (complete) denitrification, a respiratory process of dissimilatory nitrate reduction to nitrogen gas via nitrite (NO2), nitric oxide (NO) and nitrous oxide (N2O). This study focuses on understanding the influence of environmental conditions on bacterial denitrification performance, using a mathematical model of a metabolic network in P. aeruginosa. To our knowledge, this is the first mathematical model of denitrification for this bacterium. Analysis of the long-term behavior of the network under changing concentration levels of oxygen (O2), nitrate (NO3), and phosphate (PO4) suggests that PO4 concentration strongly affects denitrification performance. The model provides three predictions on denitrification activity of P. aeruginosa under various environmental conditions, and these predictions are either experimentally validated or supported by pertinent biological literature. One motivation for this study is to capture the effect of PO4 on a denitrification metabolic network of P. aeruginosa in order to shed light on mechanisms for greenhouse gas N2O accumulation during seasonal oxygen depletion in aquatic environments such as Lake Erie (Laurentian Great Lakes, USA). Simulating the microbial production of greenhouse gases in anaerobic aquatic systems such as Lake Erie allows a deeper understanding of the contributing environmental effects that will inform studies on, and remediation strategies for, other hypoxic sites worldwide. PMID:25706405

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

    NASA Astrophysics Data System (ADS)

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

    2002-09-01

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

  12. A network biology approach to denitrification in Pseudomonas aeruginosa.

    PubMed

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

    2015-01-01

    Pseudomonas aeruginosa is a metabolically flexible member of the Gammaproteobacteria. Under anaerobic conditions and the presence of nitrate, P. aeruginosa can perform (complete) denitrification, a respiratory process of dissimilatory nitrate reduction to nitrogen gas via nitrite (NO2), nitric oxide (NO) and nitrous oxide (N2O). This study focuses on understanding the influence of environmental conditions on bacterial denitrification performance, using a mathematical model of a metabolic network in P. aeruginosa. To our knowledge, this is the first mathematical model of denitrification for this bacterium. Analysis of the long-term behavior of the network under changing concentration levels of oxygen (O2), nitrate (NO3), and phosphate (PO4) suggests that PO4 concentration strongly affects denitrification performance. The model provides three predictions on denitrification activity of P. aeruginosa under various environmental conditions, and these predictions are either experimentally validated or supported by pertinent biological literature. One motivation for this study is to capture the effect of PO4 on a denitrification metabolic network of P. aeruginosa in order to shed light on mechanisms for greenhouse gas N2O accumulation during seasonal oxygen depletion in aquatic environments such as Lake Erie (Laurentian Great Lakes, USA). Simulating the microbial production of greenhouse gases in anaerobic aquatic systems such as Lake Erie allows a deeper understanding of the contributing environmental effects that will inform studies on, and remediation strategies for, other hypoxic sites worldwide. PMID:25706405

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

    PubMed Central

    2009-01-01

    Background Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. Results With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. Conclusion We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations. PMID:19961587

  14. A Robust Approach for Clock Offset Estimation in Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Kim, Jang-Sub; Lee, Jaehan; Serpedin, Erchin; Qaraqe, Khalid

    2010-12-01

    The maximum likelihood estimators (MLEs) for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays. However, the MLE performs poorly in the presence of non-Gaussian or nonexponential network delay distributions. Currently, there is a need to develop clock synchronization algorithms that are robust to the distribution of network delays. This paper proposes a clock offset estimator based on the composite particle filter (CPF) to cope with the possible asymmetries and non-Gaussianity of the network delay distributions. Also, a variant of the CPF approach based on the bootstrap sampling (BS) is shown to exhibit good performance in the presence of reduced number of observations. Computer simulations illustrate that the basic CPF and its BS-based variant present superior performance than MLE under general random network delay distributions such as asymmetric Gaussian, exponential, Gamma, Weibull as well as various mixtures.

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

    PubMed

    Christiano Silva, Thiago; Raphael Amancio, Diego

    2013-03-01

    Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods. PMID:23556976

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

    NASA Astrophysics Data System (ADS)

    Christiano Silva, Thiago; Raphael Amancio, Diego

    2013-03-01

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

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

    NASA Astrophysics Data System (ADS)

    Alilou, Shahab

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

  18. Optimal multi-type sensor placement for response and excitation reconstruction

    NASA Astrophysics Data System (ADS)

    Zhang, C. D.; Xu, Y. L.

    2016-01-01

    The need to perform dynamic response reconstruction always arises as the measurement of structural response is often limited to a few locations, especially for a large civil structure. Besides, it is usually very difficult, if not impossible, to measure external excitations under the operation condition of a structure. This study presents an algorithm for optimal placement of multi-type sensors, including strain gauges, displacement transducers and accelerometers, for the best reconstruction of responses of key structural components where there are no sensors installed and the best estimation of external excitations acting on the structure at the same time. The algorithm is developed in the framework of Kalman filter with unknown excitation, in which minimum-variance unbiased estimates of the generalized state of the structure and the external excitations are obtained by virtue of limited sensor measurements. The structural responses of key locations without sensors can then be reconstructed with the estimated generalized state and excitation. The asymptotic stability feature of the filter is utilized for optimal sensor placement. The number and spatial location of the multi-type sensors are determined by adding the optimal sensor which gains the maximal reduction of the estimation error of reconstructed responses. For the given mode number in response reconstruction and the given locations of external excitations, the optimal multi-sensor placement achieved by the proposed method is independent of the type and time evolution of external excitation. A simply-supported overhanging steel beam under multiple types of excitation is numerically studied to demonstrate the feasibility and superiority of the proposed method, and the experimental work is then carried out to testify the effectiveness of the proposed method.

  19. Disrupting Terrorist Networks — A Dynamic Fitness Landscape Approach

    NASA Astrophysics Data System (ADS)

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

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

  20. Artificial neural network approaches for fluorescence lifetime imaging techniques.

    PubMed

    Wu, Gang; Nowotny, Thomas; Zhang, Yongliang; Yu, Hong-Qi; Li, David Day-Uei

    2016-06-01

    A novel high-speed fluorescence lifetime imaging (FLIM) analysis method based on artificial neural networks (ANN) has been proposed. In terms of image generation, the proposed ANN-FLIM method does not require iterative searching procedures or initial conditions, and it can generate lifetime images at least 180-fold faster than conventional least squares curve-fitting software tools. The advantages of ANN-FLIM were demonstrated on both synthesized and experimental data, showing that it has great potential to fuel current revolutions in rapid FLIM technologies. PMID:27244414

  1. Deep space network resource scheduling approach and application

    NASA Technical Reports Server (NTRS)

    Eggemeyer, William C.; Bowling, Alan

    1987-01-01

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

  2. Link removal for the control of stochastically evolving epidemics over networks: a comparison of approaches.

    PubMed

    Enns, Eva A; Brandeau, Margaret L

    2015-04-21

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

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

    PubMed Central

    Brandeau, Margaret L.

    2015-01-01

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

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

    PubMed

    Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata

    2016-01-01

    This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application. PMID:27187394

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

    PubMed Central

    Venčkauskas, Algimantas; Štuikys, Vytautas; Jusas, Nerijus; Burbaitė, Renata

    2016-01-01

    This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application. PMID:27187394

  6. A neural network approach to lung nodule segmentation

    NASA Astrophysics Data System (ADS)

    Hu, Yaoxiu; Menon, Prahlad G.

    2016-03-01

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

  7. Field-theoretic approach to fluctuation effects in neural networks

    SciTech Connect

    Buice, Michael A.; Cowan, Jack D.

    2007-05-15

    A well-defined stochastic theory for neural activity, which permits the calculation of arbitrary statistical moments and equations governing them, is a potentially valuable tool for theoretical neuroscience. We produce such a theory by analyzing the dynamics of neural activity using field theoretic methods for nonequilibrium statistical processes. Assuming that neural network activity is Markovian, we construct the effective spike model, which describes both neural fluctuations and response. This analysis leads to a systematic expansion of corrections to mean field theory, which for the effective spike model is a simple version of the Wilson-Cowan equation. We argue that neural activity governed by this model exhibits a dynamical phase transition which is in the universality class of directed percolation. More general models (which may incorporate refractoriness) can exhibit other universality classes, such as dynamic isotropic percolation. Because of the extremely high connectivity in typical networks, it is expected that higher-order terms in the systematic expansion are small for experimentally accessible measurements, and thus, consistent with measurements in neocortical slice preparations, we expect mean field exponents for the transition. We provide a quantitative criterion for the relative magnitude of each term in the systematic expansion, analogous to the Ginsburg criterion. Experimental identification of dynamic universality classes in vivo is an outstanding and important question for neuroscience.

  8. A simulation-optimisation approach for designing water distribution networks under multiple objectives

    NASA Astrophysics Data System (ADS)

    Grundmann, Jens; Pham Van, Tinh; Müller, Ruben; Schütze, Niels

    2014-05-01

    Especially in arid and semi-arid regions, water distribution networks are of major importance for an integrated water resources management in order to convey water over long distances from sources to consumers. However, to design a network optimally is still a challenge which requires an appropriate determination of: (1) pipe/pump/tank characteristics - decision variables (2) cost/network reliability - objective functions including (3) a given set of constraints. Thereby, objective functions are contradicting, which means that by minimising costs network reliability is decreasing resulting in a higher risk of network failures. For solving this multi-objective design problem, a simulation-optimisation approach is developed. The approach couples a hydraulic network model (Epanet) with an optimiser, namely the covariance matrix adaptation evolution strategy (CMAES). The simulation-optimisation model is applied on international published benchmark cases for single and multi-objective optimisation and simultaneous optimisation of above mentioned decision variables as well as network layout. Results are encouraging. The proposed model performs with similar or better results, which means smaller costs and higher network reliability. Subsequently, the new model is applied for an optimal design and operation of a water distribution system to supply the coastal arid region of Al-Batinah (North of Oman) with water for agricultural production.

  9. Network topology and parameter estimation: from experimental design methods to gene regulatory network kinetics using a community based approach

    PubMed Central

    2014-01-01

    Background Accurate estimation of parameters of biochemical models is required to characterize the dynamics of molecular processes. This problem is intimately linked to identifying the most informative experiments for accomplishing such tasks. While significant progress has been made, effective experimental strategies for parameter identification and for distinguishing among alternative network topologies remain unclear. We approached these questions in an unbiased manner using a unique community-based approach in the context of the DREAM initiative (Dialogue for Reverse Engineering Assessment of Methods). We created an in silico test framework under which participants could probe a network with hidden parameters by requesting a range of experimental assays; results of these experiments were simulated according to a model of network dynamics only partially revealed to participants. Results We proposed two challenges; in the first, participants were given the topology and underlying biochemical structure of a 9-gene regulatory network and were asked to determine its parameter values. In the second challenge, participants were given an incomplete topology with 11 genes and asked to find three missing links in the model. In both challenges, a budget was provided to buy experimental data generated in silico with the model and mimicking the features of different common experimental techniques, such as microarrays and fluorescence microscopy. Data could be bought at any stage, allowing participants to implement an iterative loop of experiments and computation. Conclusions A total of 19 teams participated in this competition. The results suggest that the combination of state-of-the-art parameter estimation and a varied set of experimental methods using a few datasets, mostly fluorescence imaging data, can accurately determine parameters of biochemical models of gene regulation. However, the task is considerably more difficult if the gene network topology is not completely

  10. Copercolating Networks: An Approach for Realizing High-Performance Transparent Conductors using Multicomponent Nanostructured Networks

    NASA Astrophysics Data System (ADS)

    Das, Suprem R.; Sadeque, Sajia; Jeong, Changwook; Chen, Ruiyi; Alam, Muhammad A.; Janes, David B.

    2016-06-01

    Although transparent conductive oxides such as indium tin oxide (ITO) are widely employed as transparent conducting electrodes (TCEs) for applications such as touch screens and displays, new nanostructured TCEs are of interest for future applications, including emerging transparent and flexible electronics. A number of twodimensional networks of nanostructured elements have been reported, including metallic nanowire networks consisting of silver nanowires, metallic carbon nanotubes (m-CNTs), copper nanowires or gold nanowires, and metallic mesh structures. In these single-component systems, it has generally been difficult to achieve sheet resistances that are comparable to ITO at a given broadband optical transparency. A relatively new third category of TCEs consisting of networks of 1D-1D and 1D-2D nanocomposites (such as silver nanowires and CNTs, silver nanowires and polycrystalline graphene, silver nanowires and reduced graphene oxide) have demonstrated TCE performance comparable to, or better than, ITO. In such hybrid networks, copercolation between the two components can lead to relatively low sheet resistances at nanowire densities corresponding to high optical transmittance. This review provides an overview of reported hybrid networks, including a comparison of the performance regimes achievable with those of ITO and single-component nanostructured networks. The performance is compared to that expected from bulk thin films and analyzed in terms of the copercolation model. In addition, performance characteristics relevant for flexible and transparent applications are discussed. The new TCEs are promising, but significant work must be done to ensure earth abundance, stability, and reliability so that they can eventually replace traditional ITO-based transparent conductors.

  11. Neighborhoods and Adolescent Health-Risk Behavior: An Ecological Network Approach1

    PubMed Central

    Browning, Christopher R.; Soller, Brian; Jackson, Aubrey L.

    2014-01-01

    This study integrates insights from social network analysis, activity space perspectives, and theories of urban and spatial processes to present an innovative approach to neighborhood effects on health-risk behavior among youth. We suggest spatial patterns of neighborhood residents’ non-home routine activities may be conceptualized as ecological, or “eco”-networks, which are two-mode networks that indirectly link residents through socio-spatial overlap in routine activities. We further argue structural configurations of eco-networks are consequential for youth’s behavioral health. In this study we focus on a key structural feature of eco-networks—the neighborhood-level extent to which households share two or more activity locations, or eco-network reinforcement—and its association with two dimensions of health-risk behavior, substance use and delinquency/sexual activity. Using geographic data on non-home routine activity locations among respondents from the Los Angeles Family and Neighborhood Survey (L.A.FANS), we constructed neighborhood-specific eco-networks by connecting sampled households to “activity clusters,” which are sets of spatially-proximate activity locations. We then measured eco-network reinforcement and examined its association with adolescent dimensions of health risk behavior employing a sample of 830 youth ages 12-17 nested in 65 census tracts. We also examined whether neighborhood-level social processes (collective efficacy and intergenerational closure) mediate the association between eco-network reinforcement and the outcomes considered. Results indicated eco-network reinforcement exhibits robust negative associations with both substance use and delinquency/sexual activity scales. Eco-network reinforcement effects were not explained by potential mediating variables. In addition to introducing a novel theoretical and empirical approach to neighborhood effects on youth, our findings highlight the importance of eco-network

  12. Study on uncertainty of geospatial semantic Web services composition based on broker approach and Bayesian networks

    NASA Astrophysics Data System (ADS)

    Yang, Xiaodong; Cui, Weihong; Liu, Zhen; Ouyang, Fucheng

    2008-10-01

    The Semantic Web has a major weakness which is lacking of a principled means to represent and reason about uncertainty. This is also located in the services composition approaches such as BPEL4WS and Semantic Description Model. We analyze the uncertainty of Geospatial Web Service composition through mining the knowledge in historical records of composition based on Broker approach and Bayesian Networks. We proved this approach is effective and efficient through a sample scenario in this paper.

  13. Graphical Approach to Model Reduction for Nonlinear Biochemical Networks

    PubMed Central

    Holland, David O.; Krainak, Nicholas C.; Saucerman, Jeffrey J.

    2011-01-01

    Model reduction is a central challenge to the development and analysis of multiscale physiology models. Advances in model reduction are needed not only for computational feasibility but also for obtaining conceptual insights from complex systems. Here, we introduce an intuitive graphical approach to model reduction based on phase plane analysis. Timescale separation is identified by the degree of hysteresis observed in phase-loops, which guides a “concentration-clamp” procedure for estimating explicit algebraic relationships between species equilibrating on fast timescales. The primary advantages of this approach over Jacobian-based timescale decomposition are that: 1) it incorporates nonlinear system dynamics, and 2) it can be easily visualized, even directly from experimental data. We tested this graphical model reduction approach using a 25-variable model of cardiac β1-adrenergic signaling, obtaining 6- and 4-variable reduced models that retain good predictive capabilities even in response to new perturbations. These 6 signaling species appear to be optimal “kinetic biomarkers” of the overall β1-adrenergic pathway. The 6-variable reduced model is well suited for integration into multiscale models of heart function, and more generally, this graphical model reduction approach is readily applicable to a variety of other complex biological systems. PMID:21901136

  14. Are EU networks anticipatory systems? An empirical and analytical approach

    NASA Astrophysics Data System (ADS)

    Leydesdorff, Loet

    2000-05-01

    A social system can be considered as distributed by its very nature. Social communication among humans can be expected to be reflexive. Thus, this system contains uncertainty and the uncertainty is provided with meaning. This dual-layeredness enables the network to organize itself ("autopoietically") into an anticipatory mode. The extent to which anticipatory functions have been developed can be observed, notably in the case of intentional constructions of reflexive layers of organization. In this study, the "Self-Organization of the European Information Society" is analyzed from this angle. Using empirical data, I argue that the increasing unification in representations at the European level allows for another differentiation in terms of the substantive communications that are represented. Insofar as the reflexive layers are differently codified, the anticipatory functions of the system can be strengthened.

  15. A Game-Theoretical Approach to Multimedia Social Networks Security

    PubMed Central

    Liu, Enqiang; Liu, Zengliang; Shao, Fei; Zhang, Zhiyong

    2014-01-01

    The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party's benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders. PMID:24977226

  16. A scenario planning approach for disasters on Swiss road network

    NASA Astrophysics Data System (ADS)

    Mendes, G. A.; Axhausen, K. W.; Andrade, J. S.; Herrmann, H. J.

    2014-05-01

    We study a vehicular traffic scenario on Swiss roads in an emergency situation, calculating how sequentially roads block due to excessive traffic load until global collapse (gridlock) occurs and in this way displays the fragilities of the system. We used a database from Bundesamt für Raumentwicklung which contains length and maximum allowed speed of all roads in Switzerland. The present work could be interesting for government agencies in planning and managing for emergency logistics for a country or a big city. The model used to generate the flux on the Swiss road network was proposed by Mendes et al. [Physica A 391, 362 (2012)]. It is based on the conservation of the number of vehicles and allows for an easy and fast way to follow the formation of traffic jams in large systems. We also analyze the difference between a nonlinear and a linear model and the distribution of fluxes on the Swiss road.

  17. Genetic variants in Alzheimer disease - molecular and brain network approaches.

    PubMed

    Gaiteri, Chris; Mostafavi, Sara; Honey, Christopher J; De Jager, Philip L; Bennett, David A

    2016-07-01

    Genetic studies in late-onset Alzheimer disease (LOAD) are aimed at identifying core disease mechanisms and providing potential biomarkers and drug candidates to improve clinical care of AD. However, owing to the complexity of LOAD, including pathological heterogeneity and disease polygenicity, extraction of actionable guidance from LOAD genetics has been challenging. Past attempts to summarize the effects of LOAD-associated genetic variants have used pathway analysis and collections of small-scale experiments to hypothesize functional convergence across several variants. In this Review, we discuss how the study of molecular, cellular and brain networks provides additional information on the effects of LOAD-associated genetic variants. We then discuss emerging combinations of these omic data sets into multiscale models, which provide a more comprehensive representation of the effects of LOAD-associated genetic variants at multiple biophysical scales. Furthermore, we highlight the clinical potential of mechanistically coupling genetic variants and disease phenotypes with multiscale brain models. PMID:27282653

  18. A competitive neural network approach for meteorological situation clustering

    NASA Astrophysics Data System (ADS)

    Turias, Ignacio J.; González, Francisco J.; Martín, M. a. Luz; Galindo, Pedro L.

    A complete competitive scheme is proposed in this work in order to perform a classification analysis of meteorological data in the 'Campo de Gibraltar' region (in the South of Spain) from 1999 to 2002. The main objectives of the study presented here have been the characterization of the meteorological conditions in the area, using a competitive neural network based on Kohonen learning rule. Standard Principal Component Analysis (PCA) and VARIMAX rotation have allowed interpreting the physical meaning of the classes obtained from the competitive scheme. Quantitative (using three quality indices) and qualitative (from the analysis of the data projection) criteria based on Fisher Discriminant Analysis were introduced to verify the results of the clustering. A randomized procedure is developed to assure the best performance of the models and to select the best model in the experiments. The different experiments developed extracted five classes, which were related to typical meteorological conditions in the area.

  19. Decentralised ? - filtering of networked control systems: a jump system approach

    NASA Astrophysics Data System (ADS)

    Al-Radhawi, Muhammad Ali; Bettayeb, Maamar

    2014-10-01

    We consider the problem of decentralised estimation of discrete-time interconnected systems with local estimators communicating with their subsystems over lossy communication channels. Assuming that the packet losses follow the Gilbert-Elliot model, the networked estimation problem can be formulated into a Markovian jump linear system framework. Modelling subsystem interactions as sum quadratic constrained uncertainties, we design mode-dependent decentralised ?-estimators that robustly stabilise the estimator system and guarantee a given disturbance attenuation level. The estimation gains are derived with necessary and sufficient rank-constrained linear matrix inequality conditions. Results are also provided for local mode-dependent estimators. Estimator synthesis is done using a cone-complementarity linearisation algorithm for the rank-constraints. The results are illustrated via an example.

  20. Transfer Error and Correction Approach in Mobile Network

    NASA Astrophysics Data System (ADS)

    Xiao-kai, Wu; Yong-jin, Shi; Da-jin, Chen; Bing-he, Ma; Qi-li, Zhou

    With the development of information technology and social progress, human demand for information has become increasingly diverse, wherever and whenever people want to be able to easily, quickly and flexibly via voice, data, images and video and other means to communicate. Visual information to the people direct and vivid image, image / video transmission also been widespread attention. Although the third generation mobile communication systems and the emergence and rapid development of IP networks, making video communications is becoming the main business of the wireless communications, however, the actual wireless and IP channel will lead to error generation, such as: wireless channel multi- fading channels generated error and blocking IP packet loss and so on. Due to channel bandwidth limitations, the video communication compression coding of data is often beyond the data, and compress data after the error is very sensitive to error conditions caused a serious decline in image quality.

  1. Neural network approach to classification of traffic flow states

    SciTech Connect

    Yang, H.; Qiao, F.

    1998-11-01

    The classification of traffic flow states in China has traditionally been based on the Highway Capacity Manual, published in the United States. Because traffic conditions are generally different from country to country, though, it is important to develop a practical and useful classification method applicable to Chinese highway traffic. In view of the difficulty and complexity of a mathematical and physical realization, modern pattern recognition methods are considered practical in fulfilling this goal. This study applies a self-organizing neural network pattern recognition method to classify highway traffic states into some distinctive cluster centers. A small scale test with actual data is conducted, and the method is found to be potentially applicable in practice.

  2. System Review about Function Role of ESCC Driver Gene KDM6A by Network Biology Approach.

    PubMed

    Ran, Jihua; Li, Hui; Li, Huiwu

    2016-01-01

    Background. KDM6A (Lysine (K)-Specific Demethylase 6A) is the driver gene related to esophageal squamous cell carcinoma (ESCC). In order to provide more biological insights into KDM6A, in this paper, we treat PPI (protein-protein interaction) network derived from KDM6A as a conceptual framework and follow it to review its biological function. Method. We constructed a PPI network with Cytoscape software and performed clustering of network with Clust&See. Then, we evaluate the pathways, which are statistically involved in the network derived from KDM6A. Lastly, gene ontology analysis of clusters of genes in the network was conducted. Result. The network includes three clusters that consist of 74 nodes connected via 453 edges. Fifty-five pathways are statistically involved in the network and most of them are functionally related to the processes of cell cycle, gene expression, and carcinogenesis. The biology themes of clusters 1, 2, and 3 are chromatin modification, regulation of gene expression by transcription factor complex, and control of cell cycle, respectively. Conclusion. The PPI network presents a panoramic view which can facilitate for us to understand the function role of KDM6A. It is a helpful way by network approach to perform system review on a certain gene. PMID:27294188

  3. System Review about Function Role of ESCC Driver Gene KDM6A by Network Biology Approach

    PubMed Central

    Ran, Jihua; Li, Hui; Li, Huiwu

    2016-01-01

    Background. KDM6A (Lysine (K)-Specific Demethylase 6A) is the driver gene related to esophageal squamous cell carcinoma (ESCC). In order to provide more biological insights into KDM6A, in this paper, we treat PPI (protein-protein interaction) network derived from KDM6A as a conceptual framework and follow it to review its biological function. Method. We constructed a PPI network with Cytoscape software and performed clustering of network with Clust&See. Then, we evaluate the pathways, which are statistically involved in the network derived from KDM6A. Lastly, gene ontology analysis of clusters of genes in the network was conducted. Result. The network includes three clusters that consist of 74 nodes connected via 453 edges. Fifty-five pathways are statistically involved in the network and most of them are functionally related to the processes of cell cycle, gene expression, and carcinogenesis. The biology themes of clusters 1, 2, and 3 are chromatin modification, regulation of gene expression by transcription factor complex, and control of cell cycle, respectively. Conclusion. The PPI network presents a panoramic view which can facilitate for us to understand the function role of KDM6A. It is a helpful way by network approach to perform system review on a certain gene. PMID:27294188

  4. A systems approach to the biology of mood disorders through network analysis of candidate genes.

    PubMed

    Detera-Wadleigh, S D; Akula, N

    2011-05-01

    Meta analysis of association data of mood disorders has shown evidence for the role of particular genes in genetic risk. Integration of association data from meta analysis with differential expression data in brains of mood disorder patients could heighten the level of support for specific genes. To identify molecular mechanisms that may be disrupted in disease, a systems approach that involves analysis of biological networks created by these selected genes was employed.Interaction networks of hierarchical groupings of selected genes were generated using the Michigan Molecular Interactions (MiMI) software. Large networks were deconvoluted into subclusters of core complexes by using a community clustering program, GLay. Network nodes were functionally annotated in DAVID Bioinformatics Resource to identify enriched pathways and functional clusters. MAPK and beta adrenergic receptor signaling pathways were significantly enriched in the ANK3 and CACNA1C network. The PBRM1 network bolstered the enrichment of chromatin remodeling and transcription regulation functional clusters. Lowering the stringency for inclusion of other genes in network seeds increased network complexity and expanded the recruitment of enriched pathways to include signaling by neurotransmitter and hormone receptors, neurotrophin, ErbB and the cell cycle. We present a strategy to interrogate mechanisms in the cellular system that might be perturbed in disease. Network analysis of meta analysis- generated candidate genes that exhibited differential expression in mood disorder brains identified signaling pathways and functional clusters that may be involved in genetic risk for mood disorders. PMID:21547870

  5. An Iterative Approach for the Optimization of Pavement Maintenance Management at the Network Level

    PubMed Central

    Torres-Machí, Cristina; Chamorro, Alondra; Videla, Carlos; Yepes, Víctor

    2014-01-01

    Pavement maintenance is one of the major issues of public agencies. Insufficient investment or inefficient maintenance strategies lead to high economic expenses in the long term. Under budgetary restrictions, the optimal allocation of resources becomes a crucial aspect. Two traditional approaches (sequential and holistic) and four classes of optimization methods (selection based on ranking, mathematical optimization, near optimization, and other methods) have been applied to solve this problem. They vary in the number of alternatives considered and how the selection process is performed. Therefore, a previous understanding of the problem is mandatory to identify the most suitable approach and method for a particular network. This study aims to assist highway agencies, researchers, and practitioners on when and how to apply available methods based on a comparative analysis of the current state of the practice. Holistic approach tackles the problem considering the overall network condition, while the sequential approach is easier to implement and understand, but may lead to solutions far from optimal. Scenarios defining the suitability of these approaches are defined. Finally, an iterative approach gathering the advantages of traditional approaches is proposed and applied in a case study. The proposed approach considers the overall network condition in a simpler and more intuitive manner than the holistic approach. PMID:24741352

  6. Establishment of a hydrological monitoring network in a tropical African catchment: An integrated participatory approach

    NASA Astrophysics Data System (ADS)

    Gomani, M. C.; Dietrich, O.; Lischeid, G.; Mahoo, H.; Mahay, F.; Mbilinyi, B.; Sarmett, J.

    Sound decision making for water resources management has to be based on good knowledge of the dominant hydrological processes of a catchment. This information can only be obtained through establishing suitable hydrological monitoring networks. Research catchments are typically established without involving the key stakeholders, which results in instruments being installed at inappropriate places as well as at high risk of theft and vandalism. This paper presents an integrated participatory approach for establishing a hydrological monitoring network. We propose a framework with six steps beginning with (i) inception of idea; (ii) stakeholder identification; (iii) defining the scope of the network; (iv) installation; (v) monitoring; and (vi) feedback mechanism integrated within the participatory framework. The approach is illustrated using an example of the Ngerengere catchment in Tanzania. In applying the approach, the concept of establishing the Ngerengere catchment monitoring network was initiated in 2008 within the Resilient Agro-landscapes to Climate Change in Tanzania (ReACCT) research program. The main stakeholders included: local communities; Sokoine University of Agriculture; Wami Ruvu Basin Water Office and the ReACCT Research team. The scope of the network was based on expert experience in similar projects and lessons learnt from literature review of similar projects from elsewhere integrated with local expert knowledge. The installations involved reconnaissance surveys, detailed surveys, and expert consultations to identify best sites. First, a Digital Elevation Model, land use, and soil maps were used to identify potential monitoring sites. Local and expert knowledge was collected on flow regimes, indicators of shallow groundwater plant species, precipitation pattern, vegetation, and soil types. This information was integrated and used to select sites for installation of an automatic weather station, automatic rain gauges, river flow gauging stations

  7. Integrative network modeling approaches to personalized cancer medicine

    PubMed Central

    Kidd, Brian A; Readhead, Ben P; Eden, Caroline; Parekh, Samir; Dudley, Joel T

    2016-01-01

    The ability to collect millions of molecular measurements from patients is a now a reality for clinical medicine. This reality has created the challenge of how to integrate these vast amounts of data into models that accurately predict complex pathophysiology and can translate this complexity into clinically actionable outputs. Integrative informatics and data-driven approaches provide a framework for analyzing large-scale datasets and combining them into multiscale models that can be used to determine the key drivers of disease and identify optimal therapies for treating tumors. In this perspective we discuss how an integrative modeling approach is being used to inform individual treatment decisions, highlighting a recent case report that illustrates the challenges and opportunities for personalized oncology. PMID:27019658

  8. Why network approach can promote a new way of thinking in biology

    PubMed Central

    Giuliani, Alessandro; Filippi, Simonetta; Bertolaso, Marta

    2014-01-01

    This work deals with the particular nature of network-based approach in biology. We will comment about the shift from the consideration of the molecular layer as the definitive place where causative process start to the elucidation of the among elements (at any level of biological organization they are located) interaction network as the main goal of scientific explanation. This shift comes from the intrinsic nature of networks where the properties of a specific node are determined by its position in the entire network (top-down explanation) while the global network characteristics emerge from the nodes wiring pattern (bottom-up explanation). This promotes a “middle-out” paradigm formally identical to the time honored chemical thought holding big promises in the study of biological regulation. PMID:24782892

  9. Network approach to autistic traits: group and subgroup analyses of ADOS item scores.

    PubMed

    Anderson, George M; Montazeri, Farhad; de Bildt, Annelies

    2015-10-01

    A network conceptualization might contribute to understanding the occurrence and interacting nature of behavioral traits in the autism realm. Networks were constructed based on correlations of item scores of the Autism Diagnostic Observation Schedule for Modules 1, 2 and 3 obtained for a group of 477 Dutch individuals with developmental disorders. After combining Modules, networks were obtained and compared for male versus female, high- versus low-functioning, seizure versus non-seizure, autism spectrum disorder versus intellectual disability, and younger versus older subjects. The network visualizations and calculated network parameters provide new perspectives that generate new hypothesis and suggest follow-up studies. The approach should be useful in characterizing individuals and groups, in elucidating mechanisms of trait generation and routes to outcome phenotypes, and in suggesting points of intervention. PMID:26206232

  10. A quantitative approach to measure road network information based on edge diversity

    NASA Astrophysics Data System (ADS)

    Wu, Xun; Zhang, Hong; Lan, Tian; Cao, Weiwei; He, Jing

    2015-12-01

    The measure of map information has been one of the key issues in assessing cartographic quality and map generalization algorithms. It is also important for developing efficient approaches to transfer geospatial information. Road network is the most common linear object in real world. Approximately describe road network information will benefit road map generalization, navigation map production and urban planning. Most of current approaches focused on node diversities and supposed that all the edges are the same, which is inconsistent to real-life condition, and thus show limitations in measuring network information. As real-life traffic flow are directed and of different quantities, the original undirected vector road map was first converted to a directed topographic connectivity map. Then in consideration of preferential attachment in complex network study and rich-club phenomenon in social network, the from and to weights of each edge are assigned. The from weight of a given edge is defined as the connectivity of its end node to the sum of the connectivities of all the neighbors of the from nodes of the edge. After getting the from and to weights of each edge, edge information, node information and the whole network structure information entropies could be obtained based on information theory. The approach has been applied to several 1 square mile road network samples. Results show that information entropies based on edge diversities could successfully describe the structural differences of road networks. This approach is a complementarity to current map information measurements, and can be extended to measure other kinds of geographical objects.

  11. Construction of a gene-gene interaction network with a combined score across multiple approaches.

    PubMed

    Zhang, A M; Song, H; Shen, Y H; Liu, Y

    2015-01-01

    Recent progress in computational methods for inves-tigating physical and functional gene interactions has provided new insights into the complexity of biological processes. An essential part of these methods is presented visually in the form of gene interaction networks that can be valuable in exploring the mechanisms of disease. Here, a combined network based on gene pairs with an extra layer of re-liability was constructed after converting and combining the gene pair scores using a novel algorithm across multiple approaches. Four groups of kidney cancer data sets from ArrayExpress were downloaded and analyzed to identify differentially expressed genes using a rank prod-ucts analysis tool. Gene co-expression network, protein-protein interac-tion, co-occurrence network and a combined network were constructed using empirical Bayesian meta-analysis approach, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, an odds ratio formula of the cBioPortal for Cancer Genomics and a novel rank algorithm with combined score, respectively. The topological features of these networks were then compared to evaluate their performances. The results indicated that the gene pairs and their relationship rank-ings were not uniform. The values of topological parameters, such as clustering coefficient and the fitting coefficient R(2) of interaction net-work constructed using our ranked based combination score, were much greater than the other networks. The combined network had a classic small world property which transferred information quickly and displayed great resilience to the dysfunction of low-degree hubs with high-clustering and short average path length. It also followed distinct-ly a scale-free network with a higher reliability. PMID:26125911

  12. A New Approach for Modeling Procaryotic Biochemical Networks With Differential Equations

    NASA Astrophysics Data System (ADS)

    Gebert, Jutta; Radde, Nicole

    2006-06-01

    One major challenge in Computational Biology is the simulation of the processes in a biological cell, which makes it necessary to understand the interactions between cell components. It is convenient to model the entirety of such interactions as biochemical networks. In this paper we present our novel approach to describe these biochemical networks with piecewise linear differential equations and analyze it theoretically. Then we will discuss methods for the parameter estimation from time series measurements including inference of the network topology. Finally we show an application of our model for the bacterium Corynebacterium glutamicum.

  13. An evolutionary game approach for determination of the structural conflicts in signed networks

    PubMed Central

    Tan, Shaolin; Lü, Jinhu

    2016-01-01

    Social or biochemical networks can often divide into two opposite alliances in response to structural conflicts between positive (friendly, activating) and negative (hostile, inhibiting) interactions. Yet, the underlying dynamics on how the opposite alliances are spontaneously formed to minimize the structural conflicts is still unclear. Here, we demonstrate that evolutionary game dynamics provides a felicitous possible tool to characterize the evolution and formation of alliances in signed networks. Indeed, an evolutionary game dynamics on signed networks is proposed such that each node can adaptively adjust its choice of alliances to maximize its own fitness, which yet leads to a minimization of the structural conflicts in the entire network. Numerical experiments show that the evolutionary game approach is universally efficient in quality and speed to find optimal solutions for all undirected or directed, unweighted or weighted signed networks. Moreover, the evolutionary game approach is inherently distributed. These characteristics thus suggest the evolutionary game dynamic approach as a feasible and effective tool for determining the structural conflicts in large-scale on-line signed networks. PMID:26915581

  14. An evolutionary game approach for determination of the structural conflicts in signed networks

    NASA Astrophysics Data System (ADS)

    Tan, Shaolin; Lü, Jinhu

    2016-02-01

    Social or biochemical networks can often divide into two opposite alliances in response to structural conflicts between positive (friendly, activating) and negative (hostile, inhibiting) interactions. Yet, the underlying dynamics on how the opposite alliances are spontaneously formed to minimize the structural conflicts is still unclear. Here, we demonstrate that evolutionary game dynamics provides a felicitous possible tool to characterize the evolution and formation of alliances in signed networks. Indeed, an evolutionary game dynamics on signed networks is proposed such that each node can adaptively adjust its choice of alliances to maximize its own fitness, which yet leads to a minimization of the structural conflicts in the entire network. Numerical experiments show that the evolutionary game approach is universally efficient in quality and speed to find optimal solutions for all undirected or directed, unweighted or weighted signed networks. Moreover, the evolutionary game approach is inherently distributed. These characteristics thus suggest the evolutionary game dynamic approach as a feasible and effective tool for determining the structural conflicts in large-scale on-line signed networks.

  15. A generative modeling approach to connectivity-Electrical conduction in vascular networks.

    PubMed

    Hald, Bjørn Olav

    2016-06-21

    The physiology of biological structures is inherently dynamic and emerges from the interaction and assembly of large collections of small entities. The extent of coupled entities complicates modeling and increases computational load. Here, microvascular networks are used to present a novel generative approach to connectivity based on the observation that biological organization is hierarchical and composed of a limited set of building blocks, i.e. a vascular network consists of blood vessels which in turn are composed by one or more cell types. Fast electrical communication is crucial to synchronize vessel tone across the vast distances within a network. We hypothesize that electrical conduction capacity is delimited by the size of vascular structures and connectivity of the network. Generation and simulation of series of dynamical models of electrical spread within vascular networks of different size and composition showed that (1) Conduction is enhanced in models harboring long and thin endothelial cells that couple preferentially along the longitudinal axis. (2) Conduction across a branch point depends on endothelial connectivity between branches. (3) Low connectivity sub-networks are more sensitive to electrical perturbations. In summary, the capacity for electrical signaling in microvascular networks is strongly shaped by the morphology and connectivity of vascular (particularly endothelial) cells. While the presented software can be used by itself or as a starting point for more sophisticated models of vascular dynamics, the generative approach can be applied to other biological systems, e.g. nervous tissue, the lymphatics, or the biliary system. PMID:27038666

  16. Designing optimal transportation networks: a knowledge-based computer-aided multicriteria approach

    SciTech Connect

    Tung, S.I.

    1986-01-01

    The dissertation investigates the applicability of using knowledge-based expert systems (KBES) approach to solve the single-mode (automobile), fixed-demand, discrete, multicriteria, equilibrium transportation-network-design problem. Previous works on this problem has found that mathematical programming method perform well on small networks with only one objective. Needed is a solution technique that can be used on large networks having multiple, conflicting criteria with different relative importance weights. The KBES approach developed in this dissertation represents a new way to solve network design problems. The development of an expert system involves three major tasks: knowledge acquisition, knowledge representation, and testing. For knowledge acquisition, a computer aided network design/evaluation model (UFOS) was developed to explore the design space. This study is limited to the problem of designing an optimal transportation network by adding and deleting capacity increments to/from any link in the network. Three weighted criteria were adopted for use in evaluating each design alternative: cost, average V/C ratio, and average travel time.

  17. Neural network approach for modification and fitting of digitized data in reverse engineering.

    PubMed

    Ju, Hua; Wang, Wen; Xie, Jin; Chen, Zi-chen

    2004-01-01

    Reverse engineering in the manufacturing field is a process in which the digitized data are obtained from an existing object model or a part of it, and then the CAD model is reconstructed. This paper presents an RBF neural network approach to modify and fit the digitized data. The centers for the RBF are selected by using the orthogonal least squares learning algorithm. A mathematically known surface is used for generating a number of samples for training the networks. The trained networks then generated a number of new points which were compared with the calculating points from the equations. Moreover, a series of practice digitizing curves are used to test the approach. The results showed that this approach is effective in modifying and fitting digitized data and generating data points to reconstruct the surface model. PMID:14663856

  18. A game theory approach to target tracking in sensor networks.

    PubMed

    Gu, Dongbing

    2011-02-01

    In this paper, we investigate a moving-target tracking problem with sensor networks. Each sensor node has a sensor to observe the target and a processor to estimate the target position. It also has wireless communication capability but with limited range and can only communicate with neighbors. The moving target is assumed to be an intelligent agent, which is "smart" enough to escape from the detection by maximizing the estimation error. This adversary behavior makes the target tracking problem more difficult. We formulate this target estimation problem as a zero-sum game in this paper and use a minimax filter to estimate the target position. The minimax filter is a robust filter that minimizes the estimation error by considering the worst case noise. Furthermore, we develop a distributed version of the minimax filter for multiple sensor nodes. The distributed computation is implemented via modeling the information received from neighbors as measurements in the minimax filter. The simulation results show that the target tracking algorithm proposed in this paper provides a satisfactory result. PMID:20194057

  19. Integrated Approach to Reconstruction of Microbial Regulatory Networks

    SciTech Connect

    Rodionov, Dmitry A; Novichkov, Pavel S

    2013-11-04

    This project had the goal(s) of development of integrated bioinformatics platform for genome-scale inference and visualization of transcriptional regulatory networks (TRNs) in bacterial genomes. The work was done in Sanford-Burnham Medical Research Institute (SBMRI, P.I. D.A. Rodionov) and Lawrence Berkeley National Laboratory (LBNL, co-P.I. P.S. Novichkov). The developed computational resources include: (1) RegPredict web-platform for TRN inference and regulon reconstruction in microbial genomes, and (2) RegPrecise database for collection, visualization and comparative analysis of transcriptional regulons reconstructed by comparative genomics. These analytical resources were selected as key components in the DOE Systems Biology KnowledgeBase (SBKB). The high-quality data accumulated in RegPrecise will provide essential datasets of reference regulons in diverse microbes to enable automatic reconstruction of draft TRNs in newly sequenced genomes. We outline our progress toward the three aims of this grant proposal, which were: Develop integrated platform for genome-scale regulon reconstruction; Infer regulatory annotations in several groups of bacteria and building of reference collections of microbial regulons; and Develop KnowledgeBase on microbial transcriptional regulation.

  20. Algebraic approach to small-world network models

    NASA Astrophysics Data System (ADS)

    Rudolph-Lilith, Michelle; Muller, Lyle E.

    2014-01-01

    We introduce an analytic model for directed Watts-Strogatz small-world graphs and deduce an algebraic expression of its defining adjacency matrix. The latter is then used to calculate the small-world digraph's asymmetry index and clustering coefficient in an analytically exact fashion, valid nonasymptotically for all graph sizes. The proposed approach is general and can be applied to all algebraically well-defined graph-theoretical measures, thus allowing for an analytical investigation of finite-size small-world graphs.

  1. Targeted revision: A learning-based approach for incremental community detection in dynamic networks

    NASA Astrophysics Data System (ADS)

    Shang, Jiaxing; Liu, Lianchen; Li, Xin; Xie, Feng; Wu, Cheng

    2016-02-01

    Community detection is a fundamental task in network analysis. Applications on massive dynamic networks require more efficient solutions and lead to incremental community detection, which revises the community assignments of new or changed vertices during network updates. In this paper, we propose to use machine learning classifiers to predict the vertices that need to be inspected for community assignment revision. This learning-based targeted revision (LBTR) approach aims to improve community detection efficiency by filtering out the unchanged vertices from unnecessary processing. In this paper, we design features that can be used for efficient target classification and analyze the time complexity of our framework. We conduct experiments on two real-world datasets, which show our LBTR approach significantly reduces the computational time while keeping a high community detection quality. Furthermore, as compared with the benchmarks, we find our approach's performance is stable on both growing networks and networks with vertex/edge removals. Experiments suggest that one should increase the target classification precision while keeping recall at a reasonable level when implementing our proposed approach. The study provides a unique perspective in incremental community detection.

  2. Multi-level security for computer networking: SAC digital network approach

    SciTech Connect

    Griess, W.; Poutre, D.L.

    1983-10-01

    For telecommunications systems simultaneously handling data of different security levels, multilevel secure (MLS) operation permits maximum use of resources by automatically providing protection to users with various clearances and needs-to-know. The strategic air command (SAC) is upgrading the primary record data system used to command and control its strategic forces. The upgrade, called the SAC Digital Network (SACDIN), is designed to provide multilevel security to support users and external interfaces, with allowed accesses ranging from unclassified to top secret. SACDIN implements a security kernel based upon the Bell and Lapadula security model. This study presents an overview of the SACDIN security architecture and describes the basic message flow across the MLS network. 7 references.

  3. Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

    PubMed Central

    Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

    2015-01-01

    Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states. PMID:26154191

  4. Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

    NASA Astrophysics Data System (ADS)

    Weng, Rui; Shen, Sensen; Tian, Yonglu; Burton, Casey; Xu, Xinyuan; Liu, Yi; Chang, Cuilan; Bai, Yu; Liu, Huwei

    2015-07-01

    Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer’s disease and Parkinson’s disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.

  5. Battery Performance Modelling ad Simulation: a Neural Network Based Approach

    NASA Astrophysics Data System (ADS)

    Ottavianelli, Giuseppe; Donati, Alessandro

    2002-01-01

    This project has developed on the background of ongoing researches within the Control Technology Unit (TOS-OSC) of the Special Projects Division at the European Space Operations Centre (ESOC) of the European Space Agency. The purpose of this research is to develop and validate an Artificial Neural Network tool (ANN) able to model, simulate and predict the Cluster II battery system's performance degradation. (Cluster II mission is made of four spacecraft flying in tetrahedral formation and aimed to observe and study the interaction between sun and earth by passing in and out of our planet's magnetic field). This prototype tool, named BAPER and developed with a commercial neural network toolbox, could be used to support short and medium term mission planning in order to improve and maximise the batteries lifetime, determining which are the future best charge/discharge cycles for the batteries given their present states, in view of a Cluster II mission extension. This study focuses on the five Silver-Cadmium batteries onboard of Tango, the fourth Cluster II satellite, but time restrains have allowed so far to perform an assessment only on the first battery. In their most basic form, ANNs are hyper-dimensional curve fits for non-linear data. With their remarkable ability to derive meaning from complicated or imprecise history data, ANN can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. ANNs learn by example, and this is why they can be described as an inductive, or data-based models for the simulation of input/target mappings. A trained ANN can be thought of as an "expert" in the category of information it has been given to analyse, and this expert can then be used, as in this project, to provide projections given new situations of interest and answer "what if" questions. The most appropriate algorithm, in terms of training speed and memory storage requirements, is clearly the Levenberg

  6. Modeling and controlling the two-phase dynamics of the p53 network: a Boolean network approach

    NASA Astrophysics Data System (ADS)

    Lin, Guo-Qiang; Ao, Bin; Chen, Jia-Wei; Wang, Wen-Xu; Di, Zeng-Ru

    2014-12-01

    Although much empirical evidence has demonstrated that p53 plays a key role in tumor suppression, the dynamics and function of the regulatory network centered on p53 have not yet been fully understood. Here, we develop a Boolean network model to reproduce the two-phase dynamics of the p53 network in response to DNA damage. In particular, we map the fates of cells into two types of Boolean attractors, and we find that the apoptosis attractor does not exist for minor DNA damage, reflecting that the cell is reparable. As the amount of DNA damage increases, the basin of the repair attractor shrinks, accompanied by the rising of the apoptosis attractor and the expansion of its basin, indicating that the cell becomes more irreparable with more DNA damage. For severe DNA damage, the repair attractor vanishes, and the apoptosis attractor dominates the state space, accounting for the exclusive fate of death. Based on the Boolean network model, we explore the significance of links, in terms of the sensitivity of the two-phase dynamics, to perturbing the weights of links and removing them. We find that the links are either critical or ordinary, rather than redundant. This implies that the p53 network is irreducible, but tolerant of small mutations at some ordinary links, and this can be interpreted with evolutionary theory. We further devised practical control schemes for steering the system into the apoptosis attractor in the presence of DNA damage by pinning the state of a single node or perturbing the weight of a single link. Our approach offers insights into understanding and controlling the p53 network, which is of paramount importance for medical treatment and genetic engineering.

  7. Adaptive Critic Neural Network-Based Terminal Area Energy Management and Approach and Landing Guidance

    NASA Technical Reports Server (NTRS)

    Grantham, Katie

    2003-01-01

    Reusable Launch Vehicles (RLVs) have different mission requirements than the Space Shuttle, which is used for benchmark guidance design. Therefore, alternative Terminal Area Energy Management (TAEM) and Approach and Landing (A/L) Guidance schemes can be examined in the interest of cost reduction. A neural network based solution for a finite horizon trajectory optimization problem is presented in this paper. In this approach the optimal trajectory of the vehicle is produced by adaptive critic based neural networks, which were trained off-line to maintain a gradual glideslope.

  8. The Generating Function Approach for Peptide Identification in Spectral Networks

    PubMed Central

    Guthals, Adrian; Boucher, Christina

    2015-01-01

    Abstract Tandem mass (MS/MS) spectrometry has become the method of choice for protein identification and has launched a quest for the identification of every translated protein and peptide. However, computational developments have lagged behind the pace of modern data acquisition protocols and have become a major bottleneck in proteomics analysis of complex samples. As it stands today, attempts to identify MS/MS spectra against large databases (e.g., the human microbiome or 6-frame translation of the human genome) face a search space that is 10–100 times larger than the human proteome, where it becomes increasingly challenging to separate between true and false peptide matches. As a result, the sensitivity of current state-of-the-art database search methods drops by nearly 38% to such low identification rates that almost 90% of all MS/MS spectra are left as unidentified. We address this problem by extending the generating function approach to rigorously compute the joint spectral probability of multiple spectra being matched to peptides with overlapping sequences, thus enabling the confident assignment of higher significance to overlapping peptide–spectrum matches (PSMs). We find that these joint spectral probabilities can be several orders of magnitude more significant than individual PSMs, even in the ideal case when perfect separation between signal and noise peaks could be achieved per individual MS/MS spectrum. After benchmarking this approach on a typical lysate MS/MS dataset, we show that the proposed intersecting spectral probabilities for spectra from overlapping peptides improve peptide identification by 30–62%. PMID:25423621

  9. The generating function approach for Peptide identification in spectral networks.

    PubMed

    Guthals, Adrian; Boucher, Christina; Bandeira, Nuno

    2015-05-01

    Tandem mass (MS/MS) spectrometry has become the method of choice for protein identification and has launched a quest for the identification of every translated protein and peptide. However, computational developments have lagged behind the pace of modern data acquisition protocols and have become a major bottleneck in proteomics analysis of complex samples. As it stands today, attempts to identify MS/MS spectra against large databases (e.g., the human microbiome or 6-frame translation of the human genome) face a search space that is 10-100 times larger than the human proteome, where it becomes increasingly challenging to separate between true and false peptide matches. As a result, the sensitivity of current state-of-the-art database search methods drops by nearly 38% to such low identification rates that almost 90% of all MS/MS spectra are left as unidentified. We address this problem by extending the generating function approach to rigorously compute the joint spectral probability of multiple spectra being matched to peptides with overlapping sequences, thus enabling the confident assignment of higher significance to overlapping peptide-spectrum matches (PSMs). We find that these joint spectral probabilities can be several orders of magnitude more significant than individual PSMs, even in the ideal case when perfect separation between signal and noise peaks could be achieved per individual MS/MS spectrum. After benchmarking this approach on a typical lysate MS/MS dataset, we show that the proposed intersecting spectral probabilities for spectra from overlapping peptides improve peptide identification by 30-62%. PMID:25423621

  10. A biological approach to assembling tissue modules through endothelial capillary network formation.

    PubMed

    Riesberg, Jeremiah J; Shen, Wei

    2015-09-01

    To create functional tissues having complex structures, bottom-up approaches to assembling small tissue modules into larger constructs have been emerging. Most of these approaches are based on chemical reactions or physical interactions at the interface between tissue modules. Here we report a biological assembly approach to integrate small tissue modules through endothelial capillary network formation. When adjacent tissue modules contain appropriate extracellular matrix materials and cell types that support robust endothelial capillary network formation, capillary tubules form and grow across the interface, resulting in assembly of the modules into a single, larger construct. It was shown that capillary networks formed in modules of dense fibrin gels seeded with human umbilical vein endothelial cells (HUVECs) and mesenchymal stem cells (MSCs); adjacent modules were firmly assembled into an integrated construct having a strain to failure of 117 ± 26%, a tensile strength of 2208 ± 83 Pa and a Young's modulus of 2548 ± 574 Pa. Under the same culture conditions, capillary networks were absent in modules of dense fibrin gels seeded with either HUVECs or MSCs alone; adjacent modules disconnected even when handled gently. This biological assembly approach eliminates the need for chemical reactions or physical interactions and their associated limitations. In addition, the integrated constructs are prevascularized, and therefore this bottom-up assembly approach may also help address the issue of vascularization, another key challenge in tissue engineering. PMID:25694020

  11. A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

    PubMed Central

    Akula, Nirmala; Baranova, Ancha; Seto, Donald; Solka, Jeffrey; Nalls, Michael A.; Singleton, Andrew; Ferrucci, Luigi; Tanaka, Toshiko; Bandinelli, Stefania; Cho, Yoon Shin; Kim, Young Jin; Lee, Jong-Young; Han, Bok-Ghee; McMahon, Francis J.

    2011-01-01

    Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses. PMID:21915301

  12. Bayesian Belief Networks Approach for Modeling Irrigation Behavior

    NASA Astrophysics Data System (ADS)

    Andriyas, S.; McKee, M.

    2012-12-01

    Canal operators need information to manage water deliveries to irrigators. Short-term irrigation demand forecasts can potentially valuable information for a canal operator who must manage an on-demand system. Such forecasts could be generated by using information about the decision-making processes of irrigators. Bayesian models of irrigation behavior can provide insight into the likely criteria which farmers use to make irrigation decisions. This paper develops a Bayesian belief network (BBN) to learn irrigation decision-making behavior of farmers and utilizes the resulting model to make forecasts of future irrigation decisions based on factor interaction and posterior probabilities. Models for studying irrigation behavior have been rarely explored in the past. The model discussed here was built from a combination of data about biotic, climatic, and edaphic conditions under which observed irrigation decisions were made. The paper includes a case study using data collected from the Canal B region of the Sevier River, near Delta, Utah. Alfalfa, barley and corn are the main crops of the location. The model has been tested with a portion of the data to affirm the model predictive capabilities. Irrigation rules were deduced in the process of learning and verified in the testing phase. It was found that most of the farmers used consistent rules throughout all years and across different types of crops. Soil moisture stress, which indicates the level of water available to the plant in the soil profile, was found to be one of the most significant likely driving forces for irrigation. Irrigations appeared to be triggered by a farmer's perception of soil stress, or by a perception of combined factors such as information about a neighbor irrigating or an apparent preference to irrigate on a weekend. Soil stress resulted in irrigation probabilities of 94.4% for alfalfa. With additional factors like weekend and irrigating when a neighbor irrigates, alfalfa irrigation

  13. Communication, Collaboration and Cooperation: An Evaluation of Nova Scotia's Borrow Anywhere, Return Anywhere (BARA) Multi-Type Library Initiative

    ERIC Educational Resources Information Center

    van den Hoogen, Suzanne; Parrott, Denise

    2012-01-01

    Partnerships and collaborations among libraries are proven to enhance collective resources. The collaboration of multi-type libraries offers a unique opportunity to explore the potential of different libraries working together to provide the best possible service to their community members. This article provides a detailed report of a multi-type…

  14. Child Multi-Type Maltreatment and Associated Depression and PTSD Symptoms: The Role of Social Support and Stress

    ERIC Educational Resources Information Center

    Vranceanu, Ana-Maria; Hobfoll, Stevan E.; Johnson, Robert J.

    2007-01-01

    Objective: This retrospective, cross-sectional study explored the hypothesis that multiple forms of child abuse and neglect (child multi-type maltreatment; CMM) would be associated with women's lower social support and higher stress in adulthood, and that this, in turn, would amplify their vulnerability to symptoms of depression and posttraumatic…

  15. Infrared and multi-type images fusion algorithm based on contrast pyramid transform

    NASA Astrophysics Data System (ADS)

    Xu, Hua; Wang, Yan; Wu, Yujing; Qian, Yunsheng

    2016-09-01

    A fusion algorithm for infrared and multi-type images based on contrast pyramid transform (CPT) combined with Otsu method and morphology is proposed in this paper. Firstly, two sharpened images are combined to the first fused image based on information entropy weighted scheme. Afterwards, two enhanced images and the first fused one are decomposed into a series of images with different dimensions and spatial frequencies. To the low-frequency layer, the Otsu method is applied to calculate the optimal segmentation threshold of the first fused image, which is subsequently used to determine the pixel values in top layer fused image. With respect to the high-frequency layers, the top-bottom hats morphological transform is employed to each layer before maximum selection criterion. Finally, the series of decomposed images are reconstructed and then superposed with the enhanced image processed by morphological gradient operation as a second fusion to get the final fusion image. Infrared and visible images fusion, infrared and low-light-level (LLL) images fusion, infrared intensity and infrared polarization images fusion, and multi-focus images fusion are discussed in this paper. Both experimental results and objective metrics demonstrate the effectiveness and superiority of the proposed algorithm over the conventional ones used to compare.

  16. Evaluation of Annual Performance of Multi-type Air-Conditioners for Buildings

    NASA Astrophysics Data System (ADS)

    Watanabe, Choyu; Ohashi, Ei-Ichiro; Nagamatsu, Katsuaki; Nakayama, Hiroshi; Hirota, Masafumi

    In this paper, firstly the results of the partial thermal load performance tests of multi-type air-conditioners for buildings were shown. Tests were conducted by using the air-enthalpy method testing apparatus. Two types of air-conditioners, heat pump driven by electric motors (EHP) and that driven by gas engines (GHP), with a rated cooling capacity of 56 kW were tested. The coefficient of performance (COP) and the annual energy consumption measured by the above mentioned tests were closely compared with those predicted by JIS. In EHP,the measured COP indicates the maximum when the indoor thermal load is about 50% of the rated capacity, while COP in GHP decreases gradually as the thermal load is decreased. Based on these results, we examined the accuracies of COP and the annual energy consumption predicted by JIS. It was found that in both EHP and GHP the current calculating method prescribed in JIS could not duplicate the COP decrease that appeared under the low thermal load conditions. As a result, the annual energy consumption is seriously underestimated by JIS. The prediction errors of the annual energy consumption amounted to about 17% for EHP and 38% for GHP

  17. Evaluation of Annual Performance of Multi-type Air-Conditioners for Buildings

    NASA Astrophysics Data System (ADS)

    Hirota, Masafumi; Watanabe, Choyu; Furukawa, Masahide; Nagamatsu, Katsuaki

    The partial load performance tests of multi-type package air-conditioners for buildings powered by electric motors, the rating cooling performance of which was 56 kW, were carried out by using the air-enthalpy method testing apparatus. The coefficient of performance (COP) and annual energy consumption measured by those tests were closely compared with those estimated from the current calculating method (JIS B 8616:2006). It was found that the performance of the air-conditioner changes depending on the outdoor air temperature and the indoor thermal load. The current calculating method could not reproduce the deteriorations of COP that appeared under the low thermal load condition in both the cooling and heating seasons. As a result it seriously underestimated the annual energy consumption; the error amounted to as large as about 20 % of the measured annual electric power consumption. Based on these results, we have proposed new testing conditions for the performance evaluation and a calculation method of the annual energy consumption that can improve the accuracy of the estimation of the annual energy consumption.

  18. Hybrid Evolutionary Approaches to Maximum Lifetime Routing and Energy Efficiency in Sensor Mesh Networks.

    PubMed

    Rahat, Alma A M; Everson, Richard M; Fieldsend, Jonathan E

    2015-01-01

    Mesh network topologies are becoming increasingly popular in battery-powered wireless sensor networks, primarily because of the extension of network range. However, multihop mesh networks suffer from higher energy costs, and the routing strategy employed directly affects the lifetime of nodes with limited energy resources. Hence when planning routes there are trade-offs to be considered between individual and system-wide battery lifetimes. We present a multiobjective routing optimisation approach using hybrid evolutionary algorithms to approximate the optimal trade-off between the minimum lifetime and the average lifetime of nodes in the network. In order to accomplish this combinatorial optimisation rapidly, our approach prunes the search space using k-shortest path pruning and a graph reduction method that finds candidate routes promoting long minimum lifetimes. When arbitrarily many routes from a node to the base station are permitted, optimal routes may be found as the solution to a well-known linear program. We present an evolutionary algorithm that finds good routes when each node is allowed only a small number of paths to the base station. On a real network deployed in the Victoria & Albert Museum, London, these solutions, using only three paths per node, are able to achieve minimum lifetimes of over 99% of the optimum linear program solution's time to first sensor battery failure. PMID:25950392

  19. Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks

    SciTech Connect

    Jin, R; McCallen, S; Almaas, E

    2007-05-28

    Complex networks have been used successfully in scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. Until recently, studies of complex networks have mainly focused on their network topology. However, in many real world applications, the edges and vertices have associated attributes that are frequently represented as vertex or edge weights. Furthermore, these weights are often not static, instead changing with time and forming a time series. Hence, to fully understand the dynamics of the complex network, we have to consider both network topology and related time series data. In this work, we propose a motif mining approach to identify trend motifs for such purposes. Simply stated, a trend motif describes a recurring subgraph where each of its vertices or edges displays similar dynamics over a userdefined period. Given this, each trend motif occurrence can help reveal significant events in a complex system; frequent trend motifs may aid in uncovering dynamic rules of change for the system, and the distribution of trend motifs may characterize the global dynamics of the system. Here, we have developed efficient mining algorithms to extract trend motifs. Our experimental validation using three disparate empirical datasets, ranging from the stock market, world trade, to a protein interaction network, has demonstrated the efficiency and effectiveness of our approach.

  20. An Approach to Integrated Spectrum Efficient Network Enhanced Telemetry (iSENET))

    NASA Technical Reports Server (NTRS)

    Okino, Clayton; Gao, Jay; Clare, Loren; Darden, Scott; Walsh, William; Loh, Kok-kiong

    2006-01-01

    As the integrated Network Enhanced Telemetry (iNET) program moves forward in resolving systems engineering design and architecture definition, critical technology "gaps" and a migration path to realizing the integration of this technology are needed to insure a smooth transition from the current legacy point to point telemetry links to a network oriented telemetry system. Specifically, identified by the DoD aeronautical telemetry community is the need for a migration to a network solution for command, control, and transfer of test data by optimizing the physical, data link, and network layers. In this paper, we present a network-centric telemetry approach based on variants of 802.11 that leverages the open standards as well as the previous Advanced Range Telemetry (ARTM) work on the physical layer waveform. Specifically, we present a burst modem approach based on the recent AOFDM 802.11a work, a TDMA-like MAC layer approach based on 802.11e, and then add additional MAC layer features to allow for the multi-hop aeronautical environment using a variant of the current working standards of 802.11s. The combined benefits of the variants obtained from 802.11a, 802.11e, and 802.11s address the needs for both spectrum efficiency in the aeronautical environment and the iNET program.

  1. A New Collaborative Knowledge-Based Approach for Wireless Sensor Networks

    PubMed Central

    Canada-Bago, Joaquin; Fernandez-Prieto, Jose Angel; Gadeo-Martos, Manuel Angel; Velasco, Juan Ramón

    2010-01-01

    This work presents a new approach for collaboration among sensors in Wireless Sensor Networks. These networks are composed of a large number of sensor nodes with constrained resources: limited computational capability, memory, power sources, etc. Nowadays, there is a growing interest in the integration of Soft Computing technologies into Wireless Sensor Networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks. The objective of this work is to design a collaborative knowledge-based network, in which each sensor executes an adapted Fuzzy Rule-Based System, which presents significant advantages such as: experts can define interpretable knowledge with uncertainty and imprecision, collaborative knowledge can be separated from control or modeling knowledge and the collaborative approach may support neighbor sensor failures and communication errors. As a real-world application of this approach, we demonstrate a collaborative modeling system for pests, in which an alarm about the development of olive tree fly is inferred. The results show that knowledge-based sensors are suitable for a wide range of applications and that the behavior of a knowledge-based sensor may be modified by inferences and knowledge of neighbor sensors in order to obtain a more accurate and reliable output. PMID:22219701

  2. Suspended Sediment Load Prediction Using Artificial Neural Networks Approach

    NASA Astrophysics Data System (ADS)

    Melesse, A.; Ahmad, S.; McClain, M.; Wang, X.; Lim, H.; Nangia, V.

    2007-05-01

    A multilayer perceptron (MLP) ANN with an error back propagation algorithm using historical daily and weekly hydroclimatological data (precipitation P(t), current discharge Q(t), antecedent discharge Q(t-1), and antecedent sediment load SL(t-1) ) is used to predict the suspended sediment load SL(t) at the selected monitoring stations. Performance of ANN was evaluated using different combinations of datasets (Input 1 = P(t), Q(t), Q(t-1), SL(t-1) , Input 2 = I-1 less P(t) and Input 3 = I-2 less Q(t-1), length of record for training (3 and 2 years) and temporal (daily and weekly) simulations. Comparison of the ANN model output with multiple linear regressions (MLR) was made. Daily simulations using Input 1 and three years of training and two years of testing (3*2) performed better (R2 and E of 0.85 and 0.72, respectively ) than the simulation with two years of training and three years of testing (2*3) (R2 and E of 0.64 and 0.46, respectively ). ANN predicted daily values using Input 1 and 3*2 architecture for Missouri (R2 = 0.97) and Mississippi (R2 = 0.96) were better than Rio Grande (R2 = 0.65). Daily predictions were better compared to weekly predictions for all three rivers. ANN predictions for Missouri and Mississippi were superior and Rio Grande was inferior compared to predictions with MLR. The modeling approach presented in this paper can be an efficient alternative to costly monitoring operations for sediment load monitoring programs where hydrological data is readily available. Key terms: ANN, sediment, sediment prediction, rivers, Mississippi, Missouri, Rio Grande

  3. Novel scheduling approaches in the era of multi-telescope networks

    NASA Astrophysics Data System (ADS)

    Saunders, E. S.; Lampoudi, S.; Lister, T. A.; Norbury, M.; Walker, Z.

    2014-08-01

    Las Cumbres Observatory Global Telescope (LCOGT) is developing a worldwide network of fully robotic optical telescopes dedicated to time-domain astronomy. Observatory automation, longitudinal spacing of the sites, and a centralised network scheduler enable a range of observing modes impossible with traditional manual observing from a single location. We discuss the design goals of the LCOGT network scheduler, and in particular examine the unique network characteristics we seek to exploit for novel observing. We present an analysis of the key design trade-offs informing the scheduling architecture and data model, with special emphasis on both the unusual capabilities we have implemented, and some of the limitations of our approach. Finally, we describe some of the lessons we have learnt as we have moved from the beta test phase into full operational deployment in 2014.

  4. Using a hybrid approach to optimize experimental network design for aquifer parameter identification.

    PubMed

    Chang, Liang-Cheng; Chu, Hone-Jay; Lin, Yu-Pin; Chen, Yu-Wen

    2010-10-01

    This research develops an optimum design model of groundwater network using genetic algorithm (GA) and modified Newton approach, based on the experimental design conception. The goal of experiment design is to minimize parameter uncertainty, represented by the covariance matrix determinant of estimated parameters. The design problem is constrained by a specified cost and solved by GA and a parameter identification model. The latter estimates optimum parameter value and its associated sensitivity matrices. The general problem is simplified into two classes of network design problems: an observation network design problem and a pumping network design problem. Results explore the relationship between the experimental design and the physical processes. The proposed model provides an alternative to solve optimization problems for groundwater experimental design. PMID:19757116

  5. The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach

    PubMed Central

    Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan

    2015-01-01

    This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key factor determining the viral prevalence. Specifically, the value range of the maximum eigenvalue is partitioned into three subintervals: viruses tend to extinction very quickly or approach extinction or persist depending on into which subinterval the maximum eigenvalue of the propagation network falls. Consequently, computer virus can be contained by adjusting the propagation network so that its maximum eigenvalue falls into the desired subinterval. PMID:26222539

  6. The Impact of the Network Topology on the Viral Prevalence: A Node-Based Approach.

    PubMed

    Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan

    2015-01-01

    This paper addresses the impact of the structure of the viral propagation network on the viral prevalence. For that purpose, a new epidemic model of computer virus, known as the node-based SLBS model, is proposed. Our analysis shows that the maximum eigenvalue of the underlying network is a key factor determining the viral prevalence. Specifically, the value range of the maximum eigenvalue is partitioned into three subintervals: viruses tend to extinction very quickly or approach extinction or persist depending on into which subinterval the maximum eigenvalue of the propagation network falls. Consequently, computer virus can be contained by adjusting the propagation network so that its maximum eigenvalue falls into the desired subinterval. PMID:26222539

  7. A nonlinear image reconstruction technique for ECT using a combined neural network approach

    NASA Astrophysics Data System (ADS)

    Marashdeh, Q.; Warsito, W.; Fan, L.-S.; Teixeira, F. L.

    2006-08-01

    A combined multilayer feed-forward neural network (MLFF-NN) and analogue Hopfield network is developed for nonlinear image reconstruction of electrical capacitance tomography (ECT). The (nonlinear) forward problem in ECT is solved using the MLFF-NN trained with a set of capacitance data from measurements based on a back-propagation training algorithm with regularization. The inverse problem is solved using an analogue Hopfield network based on a neural-network multi-criteria optimization image reconstruction technique (HN-MOIRT). The nonlinear image reconstruction based on this combined MLFF-NN + HN-MOIRT approach is tested on measured capacitance data not used in training to reconstruct the permittivity distribution. The performance of the technique is compared against commonly used linear Landweber and semi-linear image reconstruction techniques, showing superiority in terms of both stability and quality of reconstructed images.

  8. Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

    PubMed Central

    Havemann, Frank; Gläser, Jochen; Heinz, Michael; Struck, Alexander

    2012-01-01

    The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields. PMID:22479376

  9. Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: a comparison of three approaches.

    PubMed

    Havemann, Frank; Gläser, Jochen; Heinz, Michael; Struck, Alexander

    2012-01-01

    The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields. PMID:22479376

  10. Multimodal signalling in the North American barn swallow: a phenotype network approach.

    PubMed

    Wilkins, Matthew R; Shizuka, Daizaburo; Joseph, Maxwell B; Hubbard, Joanna K; Safran, Rebecca J

    2015-10-01

    Complex signals, involving multiple components within and across modalities, are common in animal communication. However, decomposing complex signals into traits and their interactions remains a fundamental challenge for studies of phenotype evolution. We apply a novel phenotype network approach for studying complex signal evolution in the North American barn swallow (Hirundo rustica erythrogaster). We integrate model testing with correlation-based phenotype networks to infer the contributions of female mate choice and male-male competition to the evolution of barn swallow communication. Overall, the best predictors of mate choice were distinct from those for competition, while moderate functional overlap suggests males and females use some of the same traits to assess potential mates and rivals. We interpret model results in the context of a network of traits, and suggest this approach allows researchers a more nuanced view of trait clustering patterns that informs new hypotheses about the evolution of communication systems. PMID:26423842

  11. A network theory approach for a better understanding of overland flow connectivity

    NASA Astrophysics Data System (ADS)

    Masselink, Rens; Heckmann, Tobias; Temme, Arnaud; Anders, Niels; Keesstra, Saskia

    2016-04-01

    Hydrological connectivity describes the physical coupling, or linkages of different elements within a landscape regarding (sub)surface flows. A firm understanding of hydrological connectivity is important for catchment management applications, for e.g. habitat and species protection, and for flood resistance and resilience improvement. Thinking about (geomorphological) systems as networks can lead to new insights, which has been recognised within the scientific community as well, seeing the recent increase in the use of network (graph) theory within the geosciences. Network theory supports the analysis and understanding of complex systems by providing data structures for modelling objects and their linkages, and a versatile toolbox to quantitatively appraise network structure and properties. The objective of this study was to characterise overland flow connectivity dynamics on hillslopes in a humid sub-Mediterranean environment by using a combination of high-resolution digital-terrain models, overland flow sensors and a network approach. Results showed that there are significant differences between overland flow on agricultural areas and semi-natural shrubs areas. Positive correlations between connectivity and precipitation characteristics were found, while negative correlations between connectivity and soil moisture were found, probably due to soil water repellency. The combination of a structural network to determine potential connectivity with dynamic networks to determine the actual connectivity proved a powerful tool in analysing overland flow connectivity.

  12. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

    PubMed Central

    Spencer, Matt; Eickholt, Jesse; Cheng, Jianlin

    2014-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80% and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test data set of 198 proteins, achieving a Q3 accuracy of 80.7% and a Sov accuracy of 74.2%. PMID:25750595

  13. A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.

    PubMed

    Spencer, Matt; Eickholt, Jesse; Jianlin Cheng

    2015-01-01

    Ab initio protein secondary structure (SS) predictions are utilized to generate tertiary structure predictions, which are increasingly demanded due to the rapid discovery of proteins. Although recent developments have slightly exceeded previous methods of SS prediction, accuracy has stagnated around 80 percent and many wonder if prediction cannot be advanced beyond this ceiling. Disciplines that have traditionally employed neural networks are experimenting with novel deep learning techniques in attempts to stimulate progress. Since neural networks have historically played an important role in SS prediction, we wanted to determine whether deep learning could contribute to the advancement of this field as well. We developed an SS predictor that makes use of the position-specific scoring matrix generated by PSI-BLAST and deep learning network architectures, which we call DNSS. Graphical processing units and CUDA software optimize the deep network architecture and efficiently train the deep networks. Optimal parameters for the training process were determined, and a workflow comprising three separately trained deep networks was constructed in order to make refined predictions. This deep learning network approach was used to predict SS for a fully independent test dataset of 198 proteins, achieving a Q3 accuracy of 80.7 percent and a Sov accuracy of 74.2 percent. PMID:25750595

  14. Compression approach of street networks considering the structural and functional features of streets

    NASA Astrophysics Data System (ADS)

    Liu, Gang; He, Jing; Zhang, Xiping

    2015-10-01

    The compression of networks is an important aspect of complex networks and spatial generalization. Previous studies show that the dual graph for street-street relationships more accurately reflects the morphological features of street networks than the traditional methods. In this study, a dual graph for street-street relationship is constructed based on complex networks theory. We introduce the concept of m-order neighbors and take into account the factors of the node’s degree, closeness centrality, betweenness centrality, and distance within the dual graph. We also consider the importance contributions of the node itself and its 1- to m-order neighbors and define the evaluation model of node importance. We then propose a street compression process based on the evaluation of node importance for dual graph by considering the structural and functional features of streets. The degree distribution and topological similarity index are introduced to evaluate the level of maintaining the global structure and topological characteristics of the road network and to validate the efficiency of the proposed method. A real urban road network is used for the experiments. Results show that the proposed approach can be used in selecting important streets that can retain the global structural properties and topological connectivity of the street network.

  15. t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

    PubMed Central

    Huang, De-Shuang; Wang, Bing

    2013-01-01

    Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm. PMID:23560036

  16. 3D imaging of soil pore network: two different approaches

    NASA Astrophysics Data System (ADS)

    Matrecano, M.; Di Matteo, B.; Mele, G.; Terribile, F.

    2009-04-01

    system but on less noisy images. SSAT system showed more flexibility in terms of sample size although both techniques allowed investigation on REVs (Representative Elementary Volumes) for most of macroscopic properties describing soil processes. Morover, undoubted advantages of not destructivity and ease sample preparation for the Skysan 1172 are balanced by lower overall costs for the SSAT and its potential of producing 3D representation of soil features different from the simple solid/porous phases. Both approaches allow to use exactly the same image analysis procedures on the reconstructed 3D images although require some specific pre-processing treatments.

  17. Networking for Leadership, Inquiry, and Systemic Thinking: A New Approach to Inquiry-Based Learning.

    ERIC Educational Resources Information Center

    Byers, Al; Fitzgerald, Mary Ann

    2002-01-01

    Points out difficulties with a change from traditional teaching methods to a more inquiry-centered approach. Presents theoretical and empirical foundations for the Networking for Leadership, Inquiry, and Systemic Thinking (NLIST) initiative sponsored by the Council of State Science Supervisors (CSSS) and NASA, describes its progress, and outlines…

  18. A Networking Approach to Groundwater Education in West Oakland County, Michigan.

    ERIC Educational Resources Information Center

    Dean, Lillian F.

    1984-01-01

    A public education project in West Oakland County (Michigan) was started to encourage individual citizen responsibility and action related to groundwater protection. A description of the project is provided, showing the effectiveness of using a networking approach for broad public education. (JN)

  19. Collaboration Levels in Asynchronous Discussion Forums: A Social Network Analysis Approach

    ERIC Educational Resources Information Center

    Luhrs, Cecilia; McAnally-Salas, Lewis

    2016-01-01

    Computer Supported Collaborative Learning literature relates high levels of collaboration to enhanced learning outcomes. However, an agreement on what is considered a high level of collaboration is unclear, especially if a qualitative approach is taken. This study describes how methods of Social Network Analysis were used to design a collaboration…

  20. Identification of tipping elements of the Indian Summer Monsoon using climate network approach

    NASA Astrophysics Data System (ADS)

    Stolbova, Veronika; Surovyatkina, Elena; Kurths, Jurgen

    2015-04-01

    Spatial and temporal variability of the rainfall is a vital question for more than one billion of people inhabiting the Indian subcontinent. Indian Summer Monsoon (ISM) rainfall is crucial for India's economy, social welfare, and environment and large efforts are being put into predicting the Indian Summer Monsoon. For predictability of the ISM, it is crucial to identify tipping elements - regions over the Indian subcontinent which play a key role in the spatial organization of the Indian monsoon system. Here, we use climate network approach for identification of such tipping elements of the ISM. First, we build climate networks of the extreme rainfall, surface air temperature and pressure over the Indian subcontinent for pre-monsoon, monsoon and post-monsoon seasons. We construct network of extreme rainfall event using observational satellite data from 1998 to 2012 from the Tropical Rainfall Measuring Mission (TRMM 3B42V7) and reanalysis gridded daily rainfall data for a time period of 57 years (1951-2007) (Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources, APHRODITE). For the network of surface air temperature and pressure fields, we use re-analysis data provided by the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR). Second, we filter out data by coarse-graining the network through network measures, and identify tipping regions of the ISM. Finally, we compare obtained results of the network analysis with surface wind fields and show that occurrence of the tipping elements is mostly caused by monsoonal wind circulation, migration of the Intertropical Convergence Zone (ITCZ) and Westerlies. We conclude that climate network approach enables to select the most informative regions for the ISM, providing realistic description of the ISM dynamics with fewer data, and also help to identify tipping regions of the ISM. Obtained tipping elements deserve a

  1. Networking of networks: a 1990s approach to information for development.

    PubMed

    1992-06-01

    The ability to access and use information is increasingly becoming a crucial determinant of a country's ability to achieve sustainable socioeconomic development. Countries which are able to manage and utilize data and information have a competitive advantage over other nations. Countries which fail to tap into the growing global knowledge base, develop a complementary local knowledge base, promote the dissemination and use of knowledge, and invest in institutional and technical human capital will, however, simply remain or fall behind the competition. Many developing countries lack appropriate strategy, financial support for information centers and networks, timely adoption and use of new technology, adequate telecommunications infrastructure, and coordination at national and regional levels. Further, telecommunications services are costly, research on user group behavior is inadequate, few technically skilled people are available, and governments fail to recognize the importance of joining international information networks. Policy development, maternal-child health and family planning, and information, education, and communication are 3 of the most significant population issues worldwide. To best address these issues, international development agencies are urged to veer from providing capital and to directly support greater access to information and enhanced knowledge leading to sustainable national development. Thus far the UN has helped create global information systems in certain areas, and regional cooperative information systems are being developed. ESCAP has taken the lead in Asia and the Pacific. Gradually, population libraries and information centers are becoming computerized. Greater effort is recommended to recover costs for services and products. Further, donors and country organizations should stress that information is only useful as far as it is used. PMID:12317835

  2. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection

    NASA Astrophysics Data System (ADS)

    Schlechtingen, Meik; Ferreira Santos, Ilmar

    2011-07-01

    This paper presents the research results of a comparison of three different model based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies. The regression based model as the simplest approach to build a normal behavior model is compared to two artificial neural network based approaches, which are a full signal reconstruction and an autoregressive normal behavior model. Based on a real time series containing two generator bearing damages the capabilities of identifying the incipient fault prior to the actual failure are investigated. The period after the first bearing damage is used to develop the three normal behavior models. The developed or trained models are used to investigate how the second damage manifests in the prediction error. Furthermore the full signal reconstruction and the autoregressive approach are applied to further real time series containing gearbox bearing damages and stator temperature anomalies. The comparison revealed all three models being capable of detecting incipient faults. However, they differ in the effort required for model development and the remaining operational time after first indication of damage. The general nonlinear neural network approaches outperform the regression model. The remaining seasonality in the regression model prediction error makes it difficult to detect abnormality and leads to increased alarm levels and thus a shorter remaining operational period. For the bearing damages and the stator anomalies under investigation the full signal reconstruction neural network gave the best fault visibility and thus led to the highest confidence level.

  3. A two-stage approach for a multi-objective component assignment problem for a stochastic-flow network

    NASA Astrophysics Data System (ADS)

    Lin, Yi-Kuei; Yeh, Cheng-Ta

    2013-03-01

    Many real-life systems, such as computer systems, manufacturing systems and logistics systems, are modelled as stochastic-flow networks (SFNs) to evaluate network reliability. Here, network reliability, defined as the probability that the network successfully transmits d units of data/commodity from an origin to a destination, is a performance indicator of the systems. Network reliability maximization is a particular objective, but is costly for many system supervisors. This article solves the multi-objective problem of reliability maximization and cost minimization by finding the optimal component assignment for SFN, in which a set of multi-state components is ready to be assigned to the network. A two-stage approach integrating Non-dominated Sorting Genetic Algorithm II and simple additive weighting are proposed to solve this problem, where network reliability is evaluated in terms of minimal paths and recursive sum of disjoint products. Several practical examples related to computer networks are utilized to demonstrate the proposed approach.

  4. A Neuronal Network Switch for Approach-Avoidance Toggled by Appetitive State

    PubMed Central

    Hirayama, Keiko; Gillette, Rhanor

    2011-01-01

    Summary Concrete examples of computation and implementation of cost-benefit decisions at the level of neuronal circuits are largely lacking. Such decisions are based on appetitive state, which is the integration of sensation, internal state and memory. Value-based decisions are accessible in neuronal circuitry of simple systems [1]. In one such, the predatory sea-slug Pleurobranchaea, appetite is readily quantified in behavior [2] and related to approach-avoidance decision [3]. Moreover, motor aspects of feeding and turning can be observed as fictive motor output in the isolated CNS [4,5]. Here we found that the excitation state of the feeding motor network both manifested appetitive state and controlled expression of orienting vs. avoidance. In isolated CNS’s spontaneous feeding network activity varied proportionally to donor feeding thresholds. CNS’s from low and high feeding threshold donors expressed fictive orienting or avoidance, respectively, in response to brief stimulation of sensory nerves. Artificially exciting the feeding network converted fictive avoidance to orienting. Thus, the feeding network embodied appetitive state and toggled approach-avoidance decision by configuring response symmetry of the premotor turn network. A resulting model suggests a basic cost-benefit decision module from which to consider evolutionary elaboration of the circuitry to serve more intricate valuation processes in complex animals. PMID:22197246

  5. Statistical methods and neural network approaches for classification of data from multiple sources

    NASA Technical Reports Server (NTRS)

    Benediktsson, Jon Atli; Swain, Philip H.

    1990-01-01

    Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

  6. Fostering Earth Observation Regional Networks - Integrative and iterative approaches to capacity building

    NASA Astrophysics Data System (ADS)

    Habtezion, S.

    2015-12-01

    Fostering Earth Observation Regional Networks - Integrative and iterative approaches to capacity building Fostering Earth Observation Regional Networks - Integrative and iterative approaches to capacity building Senay Habtezion (shabtezion@start.org) / Hassan Virji (hvirji@start.org)Global Change SySTem for Analysis, Training and Research (START) (www.start.org) 2000 Florida Avenue NW, Suite 200 Washington, DC 20009 USA As part of the Global Observation of Forest and Land Cover Dynamics (GOFC-GOLD) project partnership effort to promote use of earth observations in advancing scientific knowledge, START works to bridge capacity needs related to earth observations (EOs) and their applications in the developing world. GOFC-GOLD regional networks, fostered through the support of regional and thematic workshops, have been successful in (1) enabling participation of scientists for developing countries and from the US to collaborate on key GOFC-GOLD and Land Cover and Land Use Change (LCLUC) issues, including NASA Global Data Set validation and (2) training young developing country scientists to gain key skills in EOs data management and analysis. Members of the regional networks are also engaged and reengaged in other EOs programs (e.g. visiting scientists program; data initiative fellowship programs at the USGS EROS Center and Boston University), which has helped strengthen these networks. The presentation draws from these experiences in advocating for integrative and iterative approaches to capacity building through the lens of the GOFC-GOLD partnership effort. Specifically, this presentation describes the role of the GODC-GOLD partnership in nurturing organic networks of scientists and EOs practitioners in Asia, Africa, Eastern Europe and Latin America.

  7. Behavior-based network management: a unique model-based approach to implementing cyber superiority

    NASA Astrophysics Data System (ADS)

    Seng, Jocelyn M.

    2016-05-01

    Behavior-Based Network Management (BBNM) is a technological and strategic approach to mastering the identification and assessment of network behavior, whether human-driven or machine-generated. Recognizing that all five U.S. Air Force (USAF) mission areas rely on the cyber domain to support, enhance and execute their tasks, BBNM is designed to elevate awareness and improve the ability to better understand the degree of reliance placed upon a digital capability and the operational risk.2 Thus, the objective of BBNM is to provide a holistic view of the digital battle space to better assess the effects of security, monitoring, provisioning, utilization management, allocation to support mission sustainment and change control. Leveraging advances in conceptual modeling made possible by a novel advancement in software design and implementation known as Vector Relational Data Modeling (VRDM™), the BBNM approach entails creating a network simulation in which meaning can be inferred and used to manage network behavior according to policy, such as quickly detecting and countering malicious behavior. Initial research configurations have yielded executable BBNM models as combinations of conceptualized behavior within a network management simulation that includes only concepts of threats and definitions of "good" behavior. A proof of concept assessment called "Lab Rat," was designed to demonstrate the simplicity of network modeling and the ability to perform adaptation. The model was tested on real world threat data and demonstrated adaptive and inferential learning behavior. Preliminary results indicate this is a viable approach towards achieving cyber superiority in today's volatile, uncertain, complex and ambiguous (VUCA) environment.

  8. A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases.

    PubMed

    Berenstein, Ariel José; Magariños, María Paula; Chernomoretz, Ariel; Agüero, Fernán

    2016-01-01

    Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent

  9. A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases

    PubMed Central

    Chernomoretz, Ariel; Agüero, Fernán

    2016-01-01

    Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent

  10. A New Approach in Advance Network Reservation and Provisioning for High-Performance Scientific Data Transfers

    SciTech Connect

    Balman, Mehmet; Chaniotakis, Evangelos; Shoshani, Arie; Sim, Alex

    2010-01-28

    Scientific applications already generate many terabytes and even petabytes of data from supercomputer runs and large-scale experiments. The need for transferring data chunks of ever-increasing sizes through the network shows no sign of abating. Hence, we need high-bandwidth high speed networks such as ESnet (Energy Sciences Network). Network reservation systems, i.e. ESnet's OSCARS (On-demand Secure Circuits and Advance Reservation System) establish guaranteed bandwidth of secure virtual circuits at a certain time, for a certain bandwidth and length of time. OSCARS checks network availability and capacity for the specified period of time, and allocates requested bandwidth for that user if it is available. If the requested reservation cannot be granted, no further suggestion is returned back to the user. Further, there is no possibility from the users view-point to make an optimal choice. We report a new algorithm, where the user specifies the total volume that needs to be transferred, a maximum bandwidth that he/she can use, and a desired time period within which the transfer should be done. The algorithm can find alternate allocation possibilities, including earliest time for completion, or shortest transfer duration - leaving the choice to the user. We present a novel approach for path finding in time-dependent networks, and a new polynomial algorithm to find possible reservation options according to given constraints. We have implemented our algorithm for testing and incorporation into a future version of ESnet?s OSCARS. Our approach provides a basis for provisioning end-to-end high performance data transfers over storage and network resources.

  11. An automated approach for constructing road network graph from multispectral images

    NASA Astrophysics Data System (ADS)

    Sun, Weihua; Messinger, David W.

    2012-06-01

    We present an approach for automatically building a road network graph from multispectralWorldView II images in suburban and urban areas. In this graph, the road parts are represented by edges and their connectivity by vertices. This approach consists of an image processing chain utilizing both high-resolution spatial features as well as multiple band spectral signatures from satellite images. Based on an edge-preserving filtered image, a two-pass spatial-spectral flood fill technique is adopted to extract a road class map. This technique requires only one pixel as the initial training set and collects spatially adjacent and spectrally similar pixels to the initial points as a second level training set for a higher accuracy asphalt classification. Based on the road class map, a road network graph is built after going through a curvilinear detector and a knowledge based system. The graph projects a logical representation of the road network in an urban image. Rules can be made to filter salient road parts with different width as well as ruling out parking lots from the asphalt class map. This spatial spectral joint approach we propose here is capable of building up a road network connectivity graph and this graph lays a foundation for further road related tasks.

  12. Propagation of New Innovations: An Approach to Classify Human Behavior and Movement from Available Social Network Data

    NASA Technical Reports Server (NTRS)

    Mahmud, Faisal; Samiul, Hasan

    2010-01-01

    It is interesting to observe new innovations, products, or ideas propagating into the society. One important factor of this propagation is the role of individual's social network; while another factor is individual's activities. In this paper, an approach will be made to analyze the propagation of different ideas in a popular social network. Individuals' responses to different activities in the network will be analyzed. The properties of network will also be investigated for successful propagation of innovations.

  13. A Geovisual Analytic Approach to Understanding Geo-Social Relationships in the International Trade Network

    PubMed Central

    Luo, Wei; Yin, Peifeng; Di, Qian; Hardisty, Frank; MacEachren, Alan M.

    2014-01-01

    The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly ‘balkanized’ (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above. PMID:24558409

  14. A holistic approach to ZigBee performance enhancement for home automation networks.

    PubMed

    Betzler, August; Gomez, Carles; Demirkol, Ilker; Paradells, Josep

    2014-01-01

    Wireless home automation networks are gaining importance for smart homes. In this ambit, ZigBee networks play an important role. The ZigBee specification defines a default set of protocol stack parameters and mechanisms that is further refined by the ZigBee Home Automation application profile. In a holistic approach, we analyze how the network performance is affected with the tuning of parameters and mechanisms across multiple layers of the ZigBee protocol stack and investigate possible performance gains by implementing and testing alternative settings. The evaluations are carried out in a testbed of 57 TelosB motes. The results show that considerable performance improvements can be achieved by using alternative protocol stack configurations. From these results, we derive two improved protocol stack configurations for ZigBee wireless home automation networks that are validated in various network scenarios. In our experiments, these improved configurations yield a relative packet delivery ratio increase of up to 33.6%, a delay decrease of up to 66.6% and an improvement of the energy efficiency for battery powered devices of up to 48.7%, obtainable without incurring any overhead to the network. PMID:25196004

  15. A neural network approach for monitoring of volcanic SO2 and cloud height using hyperspectral measurements

    NASA Astrophysics Data System (ADS)

    Piscini, Alessandro; Carboni, Elisa; Del Frate, Fabio; Grainger, Roy Gordon

    2014-10-01

    In this study two neural networks were implemented in order to emulate a retrieval model and to estimate the sulphur dioxide (SO2) columnar content and cloud height from volcanic eruption. ANNs were trained using all Infrared Atmospheric Sounding Interferometer (IASI) channels in Thermal Infrared (TIR) as inputs, and the corresponding values of SO2 content and height of volcanic cloud obtained using the Oxford SO2 retrievals as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajökull volcano (Iceland) occurred in 2010 and to three IASI images of the Grímsvötn volcanic eruption that occurred in May 2011, in order to evaluate the networks for an unknown eruption. The results of validation, both for Eyjafjallajökull independent data-sets, provided root mean square error (RMSE) values between neural network outputs and targets lower than 20 DU for SO2 total column and 200 mb for cloud height, therefore demonstrating the feasibility to estimate SO2 values using a neural network approach, and its importance in near real time monitoring activities, owing to its fast application. Concerning the validation carried out with neural networks on images from the Grímsvötn eruption, the RMSE of the outputs remained lower than the Standard Deviation (STD) of targets, and the neural network underestimated retrieval only where target outputs showed different statistics than those used during the training phase.

  16. A Holistic Approach to ZigBee Performance Enhancement for Home Automation Networks

    PubMed Central

    Betzler, August; Gomez, Carles; Demirkol, Ilker; Paradells, Josep

    2014-01-01

    Wireless home automation networks are gaining importance for smart homes. In this ambit, ZigBee networks play an important role. The ZigBee specification defines a default set of protocol stack parameters and mechanisms that is further refined by the ZigBee Home Automation application profile. In a holistic approach, we analyze how the network performance is affected with the tuning of parameters and mechanisms across multiple layers of the ZigBee protocol stack and investigate possible performance gains by implementing and testing alternative settings. The evaluations are carried out in a testbed of 57 TelosB motes. The results show that considerable performance improvements can be achieved by using alternative protocol stack configurations. From these results, we derive two improved protocol stack configurations for ZigBee wireless home automation networks that are validated in various network scenarios. In our experiments, these improved configurations yield a relative packet delivery ratio increase of up to 33.6%, a delay decrease of up to 66.6% and an improvement of the energy efficiency for battery powered devices of up to 48.7%, obtainable without incurring any overhead to the network. PMID:25196004

  17. A geovisual analytic approach to understanding geo-social relationships in the international trade network.

    PubMed

    Luo, Wei; Yin, Peifeng; Di, Qian; Hardisty, Frank; MacEachren, Alan M

    2014-01-01

    The world has become a complex set of geo-social systems interconnected by networks, including transportation networks, telecommunications, and the internet. Understanding the interactions between spatial and social relationships within such geo-social systems is a challenge. This research aims to address this challenge through the framework of geovisual analytics. We present the GeoSocialApp which implements traditional network analysis methods in the context of explicitly spatial and social representations. We then apply it to an exploration of international trade networks in terms of the complex interactions between spatial and social relationships. This exploration using the GeoSocialApp helps us develop a two-part hypothesis: international trade network clusters with structural equivalence are strongly 'balkanized' (fragmented) according to the geography of trading partners, and the geographical distance weighted by population within each network cluster has a positive relationship with the development level of countries. In addition to demonstrating the potential of visual analytics to provide insight concerning complex geo-social relationships at a global scale, the research also addresses the challenge of validating insights derived through interactive geovisual analytics. We develop two indicators to quantify the observed patterns, and then use a Monte-Carlo approach to support the hypothesis developed above. PMID:24558409

  18. A Lyapunov-Razumikhin approach for stability analysis of logistics networks with time-delays

    NASA Astrophysics Data System (ADS)

    Dashkovskiy, Sergey; Karimi, Hamid Reza; Kosmykov, Michael

    2012-05-01

    Logistics network represents a complex system where different elements that are logistic locations interact with each other. This interaction contains delays caused by time needed for delivery of the material. Complexity of the system, time-delays and perturbations in a customer demand may cause unstable behaviour of the network. This leads to the loss of the customers and high inventory costs. Thus the investigation of the network on stability is desired during its design. In this article we consider local input-to-state stability of such logistics networks. Their behaviour is described by a functional differential equation with a constant time-delay. We are looking for verifiable conditions that guarantee stability of the network under consideration. Lyapunov-Razumikhin functions and the local small gain condition are utilised to obtain such conditions. Our stability conditions for the logistics network are based on the information about the interconnection properties between logistic locations and their production rates. Finally, numerical results are provided to demonstrate the proposed approach.

  19. First principles and effective theory approaches to dynamics of complex networks

    NASA Astrophysics Data System (ADS)

    Dehmamy, Nima

    This dissertation concerns modeling two aspects of dynamics of complex networks: (1) response dynamics and (2) growth and formation. A particularly challenging class of networks are ones in which both nodes and links are evolving over time -- the most prominent example is a financial network. In the first part of the dissertation we present a model for the response dynamics in networks near a metastable point. We start with a Landau-Ginzburg approach and show that the most general lowest order Lagrangians for dynamical weighted networks can be used to derive conditions for stability under external shocks. Using a closely related model, which is easier to solve numerically, we propose a powerful and intuitive set of equations for response dynamics of financial networks. We find the stability conditions of the model and find two phases: "calm" phase , in which changes are sub-exponential and where the system moves to a new, close-by equilibrium; "frantic" phase, where changes are exponential, with negative blows resulting in crashes and positive ones leading to formation of "bubbles". We empirically verify these claims by analyzing data from Eurozone crisis of 2009-2012 and stock markets. We show that the model correctly identifies the time-line of the Eurozone crisis, and in the stock market data it correctly reproduces the auto-correlations and phases observed in the data. The second half of the dissertation addresses the following question: Do networks that form due to local interactions (local in real space, or in an abstract parameter space) have characteristics different from networks formed of random or non-local interactions? Using interacting fields obeying Fokker-Planck equations we show that many network characteristics such as degree distribution, degree-degree correlation and clustering can either be derived analytically or there are analytical bounds on their behaviour. In particular, we derive recursive equations for all powers of the ensemble average

  20. Space Network Control (SNC) Conference on Resource Allocation Concepts and Approaches. Overview

    NASA Technical Reports Server (NTRS)

    1991-01-01

    In session 1 of the conference, Concepts for space network resource allocation was the main topic. In session 2, Space Network Control and user payload operations and control center human-computer interface, was the topic of discussion. The topic of session 3 was Resource allocation tools, technology, and algorithms. Some of the stated goals for the conference are as follows: to survey existing resource allocation concepts and approaches; to identify solutions applicable to the SN problem; to identify fruitful avenues of study in support of SNC development; and to capture knowledge in proceedings and make available to bidders on the SNC concept definition procurement.

  1. An effective access control approach to support mobility in IPv6 networks

    NASA Astrophysics Data System (ADS)

    Peng, Xue-hai; Lin, Chuang

    2005-11-01

    Access control is an important method to improve network security and prevent protected resources from being used by some nodes without authority. Moreover, mobility is an important trend of internet. In this paper, based on the architecture of hierarchical mobile IPv6, we proposed an effective access control approach to support mobility in IPv6 networks, which can ensure the operation of access control when a mobile node roams in these domains with different polices, with decreased delay of access negotiation and cost of delivering messages.

  2. PROMOTING CANCER SCREENING AMONG RURAL AFRICAN AMERICANS: A SOCIAL NETWORK APPROACH.

    PubMed

    Tang, Lu; Mieskowski, Lisa M; Oliver, JoAnn S; Eichorst, Morgan K; Allen, Rebecca S

    2015-01-01

    Obstacles that prevent rural African Americans (AAs) from regularly engaging in cancer screening were explored, and a theoretical approach was formulated utilizing social networks as a culturally sensitive form of health promotion. Disparities in cancer morbidity and mortality continue to exist between AAs and Caucasians in the United States. Often rural dwellers are further disadvantaged because of a potential lack of medical and financial resources and low health literacy. Social networks provide an existing framework where health concerns are discussed and health interventions in cancer screening can strengthen or encourage relevant health behaviors in rural AAs and other disadvantaged populations. PMID:26647487

  3. Approach jamming effectiveness evaluation for surface-type infrared decoy in network centric warship formation

    NASA Astrophysics Data System (ADS)

    Lv, Mingshan

    2015-10-01

    The passive and photoelectrical jamming to anti-ship missile in the condition of network centric warship formation is an important research issue of fleet EW operation. An approach jamming method of shipborne surface-type infrared decoy countering the infrared image guided anti-ship missile is put forward. By analyzing the countering process the jamming effectiveness evaluation model is constructed. By simulation the method is proved t reasonable and effective. This method breaks through the traditional restrict that the passive and photoelectricity jamming measure can only be used in the end self-defence and provides a new method for network centric worship formation to support each other.

  4. a Hybrid Approach of Neural Network with Particle Swarm Optimization for Tobacco Pests Prediction

    NASA Astrophysics Data System (ADS)

    Lv, Jiake; Wang, Xuan; Xie, Deti; Wei, Chaofu

    Forecasting pests emergence levels plays a significant role in regional crop planting and management. The accuracy, which is derived from the accuracy of the forecasting approach used, will determine the economics of the operation of the pests prediction. Conventional methods including time series, regression analysis or ARMA model entail exogenous input together with a number of assumptions. The use of neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with some drawbacks such as very slow convergence and easy entrapment in a local minimum. This paper presents a hybrid approach of neural network with particle swarm optimization for developing the accuracy of predictions. The approach is applied to forecast Alternaria alternate Keissl emergence level of the WuLong Country, one of the most important tobacco planting areas in Chongqing. Traditional ARMA model and BP neural network are investigated as comparison basis. The experimental results show that the proposed approach can achieve better prediction performance.

  5. Pandora, a PAthway and Network DiscOveRy Approach based on common biological evidence

    PubMed Central

    Zhang, Kelvin Xi; Ouellette, B. F. Francis

    2010-01-01

    Motivation: Many biological phenomena involve extensive interactions between many of the biological pathways present in cells. However, extraction of all the inherent biological pathways remains a major challenge in systems biology. With the advent of high-throughput functional genomic techniques, it is now possible to infer biological pathways and pathway organization in a systematic way by integrating disparate biological information. Results: Here, we propose a novel integrated approach that uses network topology to predict biological pathways. We integrated four types of biological evidence (protein–protein interaction, genetic interaction, domain–domain interaction and semantic similarity of Gene Ontology terms) to generate a functionally associated network. This network was then used to develop a new pathway finding algorithm to predict biological pathways in yeast. Our approach discovered 195 biological pathways and 31 functionally redundant pathway pairs in yeast. By comparing our identified pathways to three public pathway databases (KEGG, BioCyc and Reactome), we observed that our approach achieves a maximum positive predictive value of 12.8% and improves on other predictive approaches. This study allows us to reconstruct biological pathways and delineates cellular machinery in a systematic view. Availability: The method has been implemented in Perl and is available for downloading from http://www.oicr.on.ca/research/ouellette/pandora. It is distributed under the terms of GPL (http://opensource.org/licenses/gpl-2.0.php) Contact: francis@oicr.on.ca Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20031970

  6. Implementing a Trauma-Informed Approach in Pediatric Health Care Networks.

    PubMed

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

    2016-01-01

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

  7. A multilevel approach to network meta-analysis within a frequentist framework.

    PubMed

    Greco, Teresa; Edefonti, Valeria; Biondi-Zoccai, Giuseppe; Decarli, Adriano; Gasparini, Mauro; Zangrillo, Alberto; Landoni, Giovanni

    2015-05-01

    Meta-analysis is a powerful tool to summarize knowledge. Pairwise or network meta-analysis may be carried out with multivariate models that account for the dependence between treatment estimates and quantify the correlation across studies. From a different perspective, meta-analysis may be viewed as a special case of multilevel analysis having a hierarchical data structure. Hence, we introduce an alternative frequentist approach, called multilevel network meta-analysis, which also allows to account for publication bias and the presence of inconsistency. We propose our approach for a three-level data structure set-up: arms within studies at the first level, studies within study designs at the second level and design configuration at the third level. This strategy differs from the traditional frequentist modeling because it works directly on an arm-based data structure. An advantage of using multilevel analysis is its flexibility, since it naturally allows to add further levels to the model and to accommodate for multiple outcome variables. Moreover, multilevel modeling may be carried out with widely available statistical programs. Finally, we compare the results from our approach with those from a Bayesian network meta-analysis on a binary endpoint which examines the effect on mortality of some anesthetics at the longest follow-up available. In addition, we compare results from the Bayesian and multilevel network meta-analysis approaches on a publicly available "Thrombolytic drugs" database. We also provide the reader with a blueprint of SAS codes for fitting the proposed models, although our approach does not rely on any specific software. PMID:25804722

  8. Artificial neural network modeling of fixed bed biosorption using radial basis approach

    NASA Astrophysics Data System (ADS)

    Saha, Dipendu; Bhowal, Avijit; Datta, Siddhartha

    2010-04-01

    In modern day scenario, biosorption is a cost effective separation technology for the removal of various pollutants from wastewater and waste streams from various process industries. The difficulties associated in rigorous mathematical modeling of a fixed bed bio-adsorbing systems due to the complexities of the process often makes the development of pure black-box artificial neural network (ANN) models particularly useful in this field. In this work, radial basis function network has been employed as ANN to model the breakthrough curves in fixed bed biosorption. The prediction has been compared to the experimental breakthrough curves of Cadmium, Lanthanum and a dye available in the literature. Results show that this network gives fairly accurate representation of the actual breakthrough curves. The results obtained from ANN modeling approach shows the better agreement between experimental and predicted breakthrough curves as the error for all these situations are within 6%.

  9. An Integrative Network Approach to Map the Transcriptome to the Phenome

    PubMed Central

    Mehan, Michael R.; Nunez-Iglesias, Juan; Kalakrishnan, Mrinal; Waterman, Michael S.

    2009-01-01

    Abstract Although many studies have been successful in the discovery of cooperating groups of genes, mapping these groups to phenotypes has proved a much more challenging task. In this article, we present the first genome-wide mapping of gene coexpression modules onto the phenome. We annotated coexpression networks from 136 microarray datasets with phenotypes from the Unified Medical Language System (UMLS). We then designed an efficient graph-based simulated annealing approach to identify coexpression modules frequently and specifically occurring in datasets related to individual phenotypes. By requiring phenotype-specific recurrence, we ensure the robustness of our findings. We discovered 118,772 modules specific to 42 phenotypes, and developed validation tests combining Gene Ontology, GeneRIF and UMLS. Our method is generally applicable to any kind of abundant network data with defined phenotype association, and thus paves the way for genome-wide, gene network-phenotype maps. PMID:19630539

  10. Extending an operational meteorological monitoring network through machine learning and classical geo-statistical approaches

    NASA Astrophysics Data System (ADS)

    Appelhans, Tim; Mwangomo, Ephraim; Otte, Insa; Detsch, Florian; Nauss, Thomas; Hemp, Andreas; Ndyamkama, Jimmy

    2015-04-01

    This study introduces the set-up and characteristics of a meteorological station network on the southern slopes of Mt. Kilimanjaro, Tanzania. The set-up follows a hierarchical approach covering an elevational as well as a land-use disturbance gradient. The network consists of 52 basic stations measuring ambient air temperature and above ground air humidity and 11 precipitation measurement sites. We provide in depth descriptions of various machine learning and classical geo-statistical methods used to fill observation gaps and extend the spatial coverage of the network to a total of 60 research sites. Performance statistics for these methods indicate that the presented data sets provide reliable measurements of the meteorological reality at Mt. Kilimanjaro. These data provide an excellent basis for ecological studies and are also of great value for regional atmospheric numerical modelling studies for which such comprehensive in-situ validation observations are rare, especially in tropical regions of complex terrain.

  11. How Structure Shapes Dynamics: Knowledge Development in Wikipedia - A Network Multilevel Modeling Approach

    PubMed Central

    Halatchliyski, Iassen; Cress, Ulrike

    2014-01-01

    Using a longitudinal network analysis approach, we investigate the structural development of the knowledge base of Wikipedia in order to explain the appearance of new knowledge. The data consists of the articles in two adjacent knowledge domains: psychology and education. We analyze the development of networks of knowledge consisting of interlinked articles at seven snapshots from 2006 to 2012 with an interval of one year between them. Longitudinal data on the topological position of each article in the networks is used to model the appearance of new knowledge over time. Thus, the structural dimension of knowledge is related to its dynamics. Using multilevel modeling as well as eigenvector and betweenness measures, we explain the significance of pivotal articles that are either central within one of the knowledge domains or boundary-crossing between the two domains at a given point in time for the future development of new knowledge in the knowledge base. PMID:25365319

  12. Optimal design of sewer networks using cellular automata-based hybrid methods: Discrete and continuous approaches

    NASA Astrophysics Data System (ADS)

    Afshar, M. H.; Rohani, M.

    2012-01-01

    In this article, cellular automata based hybrid methods are proposed for the optimal design of sewer networks and their performance is compared with some of the common heuristic search methods. The problem of optimal design of sewer networks is first decomposed into two sub-optimization problems which are solved iteratively in a two stage manner. In the first stage, the pipe diameters of the network are assumed fixed and the nodal cover depths of the network are determined by solving a nonlinear sub-optimization problem. A cellular automata (CA) method is used for the solution of the optimization problem with the network nodes considered as the cells and their cover depths as the cell states. In the second stage, the nodal cover depths calculated from the first stage are fixed and the pipe diameters are calculated by solving a second nonlinear sub-optimization problem. Once again a CA method is used to solve the optimization problem of the second stage with the pipes considered as the CA cells and their corresponding diameters as the cell states. Two different updating rules are derived and used for the CA of the second stage depending on the treatment of the pipe diameters. In the continuous approach, the pipe diameters are considered as continuous variables and the corresponding updating rule is derived mathematically from the original objective function of the problem. In the discrete approach, however, an adhoc updating rule is derived and used taking into account the discrete nature of the pipe diameters. The proposed methods are used to optimally solve two sewer network problems and the results are presented and compared with those obtained by other methods. The results show that the proposed CA based hybrid methods are more efficient and effective than the most powerful search methods considered in this work.

  13. Two-edge disjoint survivable network design problem with relays: a hybrid genetic algorithm and Lagrangian heuristic approach

    NASA Astrophysics Data System (ADS)

    Konak, Abdullah

    2014-01-01

    This article presents a network design problem with relays considering the two-edge network connectivity. The problem arises in telecommunications and logistic networks where a constraint is imposed on the distance that a commodity can travel on a route without being processed by a relay, and the survivability of the network is critical in case of a component failure. The network design problem involves selecting two-edge disjoint paths between source and destination node pairs and determining the location of relays to minimize the network design cost. The formulated problem is solved by a hybrid approach of a genetic algorithm (GA) and a Lagrangian heuristic such that the GA searches for two-edge disjoint paths for each commodity, and the Lagrangian heuristic is used to determine relays on these paths. The performance of the proposed hybrid approach is compared to the previous approaches from the literature, with promising results.

  14. A Bayesian network approach to the database search problem in criminal proceedings

    PubMed Central

    2012-01-01

    Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions

  15. Integrative network-based approach identifies key genetic elements in breast invasive carcinoma

    PubMed Central

    2015-01-01

    Background Breast cancer is a genetically heterogeneous type of cancer that belongs to the most prevalent types with a high mortality rate. Treatment and prognosis of breast cancer would profit largely from a correct classification and identification of genetic key drivers and major determinants driving the tumorigenesis process. In the light of the availability of tumor genomic and epigenomic data from different sources and experiments, new integrative approaches are needed to boost the probability of identifying such genetic key drivers. We present here an integrative network-based approach that is able to associate regulatory network interactions with the development of breast carcinoma by integrating information from gene expression, DNA methylation, miRNA expression, and somatic mutation datasets. Results Our results showed strong association between regulatory elements from different data sources in terms of the mutual regulatory influence and genomic proximity. By analyzing different types of regulatory interactions, TF-gene, miRNA-mRNA, and proximity analysis of somatic variants, we identified 106 genes, 68 miRNAs, and 9 mutations that are candidate drivers of oncogenic processes in breast cancer. Moreover, we unraveled regulatory interactions among these key drivers and the other elements in the breast cancer network. Intriguingly, about one third of the identified driver genes are targeted by known anti-cancer drugs and the majority of the identified key miRNAs are implicated in cancerogenesis of multiple organs. Also, the identified driver mutations likely cause damaging effects on protein functions. The constructed gene network and the identified key drivers were compared to well-established network-based methods. Conclusion The integrated molecular analysis enabled by the presented network-based approach substantially expands our knowledge base of prospective genomic drivers of genes, miRNAs, and mutations. For a good part of the identified key drivers

  16. EnArchi: A Robust and Manageable Approach for Dynamic Large-scale Sensor Networks

    NASA Astrophysics Data System (ADS)

    Wang, X.; Frolik, J.

    2006-12-01

    Recent advances in wireless, sensing, and embedded systems technologies are poised to enable the use of thousands of low-cost nodes to form a wireless sensor network. These networks promise monitoring of environments that are remote or inaccessible, providing users with critical information with unprecedented spatial and temporal resolution. One major hurdle facing practical, large-scale sensor network deployments is the difficulties of managing the vast number (1000s) of sensor nodes. An emerging and promising approach is to use tiered architectures, where the higher tier (backbone) is formed with a limited number of resource-rich master nodes and a highly-populated, lower tier consisting of energy-constrained, low-cost end nodes. Given that the related networking issues of the backbone are relatively well understood, what is needed, and what is discussed in this presentation, is an approach for the end nodes in the lower tier (an approach dubbed 'EnArchi') that is robust and manageable under the dynamic constraints imposed by the application. In our approach, inspired by Von Neumann, EnArchi end nodes operate as homogeneous, stochastically self- controlled autonomous agents, and like their natural counterparts (e.g., cells) "process information and proceed in their actions based on data received from their environment in light of rules and instructions they hold internally" (quoted from http://www.medaloffreedom.com/JohnvonNeumann.htm). While the individual EnArchi automata are simple in design, as a group they enable emerging complex systems-level behavior. Our work shows EnArchi is amenable to maintaining an overall network-level quality of service in large-scale deployments even under the dynamic scenarios of high sensor fallout rates and/or network replenishment; as far as we are aware of, our approach is the only one appeared in the literature that achieves an energy efficiency close to optimal for this task. In this presentation, we will present our results

  17. Neuro-classification of multi-type Landsat Thematic Mapper data

    NASA Technical Reports Server (NTRS)

    Zhuang, Xin; Engel, Bernard A.; Fernandez, R. N.; Johannsen, Chris J.

    1991-01-01

    Neural networks have been successful in image classification and have shown potential for classifying remotely sensed data. This paper presents classifications of multitype Landsat Thematic Mapper (TM) data using neural networks. The Landsat TM Image for March 23, 1987 with accompanying ground observation data for a study area In Miami County, Indiana, U.S.A. was utilized to assess recognition of crop residues. Principal components and spectral ratio transformations were performed on the TM data. In addition, a layer of the geographic information system (GIS) for the study site was incorporated to generate GIS-enhanced TM data. This paper discusses (1) the performance of neuro-classification on each type of data, (2) how neural networks recognized each type of data as a new image and (3) comparisons of the results for each type of data obtained using neural networks, maximum likelihood, and minimum distance classifiers.

  18. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

    PubMed Central

    Dülger, L. Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

  19. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242).

    PubMed

    Almusawi, Ahmed R J; Dülger, L Canan; Kapucu, Sadettin

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the applicability and the efficiency of the proposed approach in robotic motion control. The inclusion of current configuration of joint angles in ANN significantly increased the accuracy of ANN estimation of the joint angles output. The new controller design has advantages over the existing techniques for minimizing the position error in unconventional tasks and increasing the accuracy of ANN in estimation of robot's joint angles. PMID:27610129

  20. Monitoring Anthropogenic Ocean Sound from Shipping Using an Acoustic Sensor Network and a Compressive Sensing Approach.

    PubMed

    Harris, Peter; Philip, Rachel; Robinson, Stephen; Wang, Lian

    2016-01-01

    Monitoring ocean acoustic noise has been the subject of considerable recent study, motivated by the desire to assess the impact of anthropogenic noise on marine life. A combination of measuring ocean sound using an acoustic sensor network and modelling sources of sound and sound propagation has been proposed as an approach to estimating the acoustic noise map within a region of interest. However, strategies for developing a monitoring network are not well established. In this paper, considerations for designing a network are investigated using a simulated scenario based on the measurement of sound from ships in a shipping lane. Using models for the sources of the sound and for sound propagation, a noise map is calculated and measurements of the noise map by a sensor network within the region of interest are simulated. A compressive sensing algorithm, which exploits the sparsity of the representation of the noise map in terms of the sources, is used to estimate the locations and levels of the sources and thence the entire noise map within the region of interest. It is shown that although the spatial resolution to which the sound sources can be identified is generally limited, estimates of aggregated measures of the noise map can be obtained that are more reliable compared with those provided by other approaches. PMID:27011187

  1. A new generic approach for optoelectronic hardware realizations of neural networks models

    SciTech Connect

    Agranat, A.; Neugebauer, C.F.; Yariv, A.

    1988-09-01

    A new generic approach for realizing neural networks (NN) is presented. The underlying principle of the new approach is to take advantage of the fact that signal processing in silicon is an advanced and mature technology, and to incorporate optics where silicon fails, namely in the interconnectivity problem. The basic idea is described. The system consists of two main subassemblies: a 2D Spatial Light Modulator (SLM) and an integrated circuit to which the authors shall henceforth refer to as the Neural Processor (NP). The synaptic efficacies matrix W is stored in the SLM. Thus by imaging the SLM contents onto an array detector which serves as the input unit of the NP, W is loaded in parallel into the NP. The NP then updates the state of the network in parallel/semiparallel-synchronous/asynchronous manner (depending on the structure of the NP).

  2. A Multiple Mobility Support Approach (MMSA) Based on PEAS for NCW in Wireless Sensor Networks

    PubMed Central

    Koo, Bong-Joo; Kim, Seog-Bong; Park, Jong-Yil; Park, Kang-Min

    2011-01-01

    Wireless Sensor Networks (WSNs) can be implemented as one of sensor systems in Network Centric Warfare (NCW). Mobility support and energy efficiency are key concerns for this application, due to multiple mobile users and stimuli in real combat field. However, mobility support approaches that can be adopted in this circumstance are rare. This paper proposes Multiple Mobility Support Approach (MMSA) based on Probing Environment and Adaptive Sleeping (PEAS) to support the simultaneous mobility of both multiple users and stimuli by sharing the information of stimuli in WSNs. Simulations using Qualnet are conducted, showing that MMSA can support multiple mobile users and stimuli with good energy efficiency. It is expected that the proposed MMSA can be applied to real combat field. PMID:22346606

  3. Analysis of network type exchange in the health care system: a stakeholder approach.

    PubMed

    Yang, Wen-Hui; Hu, Jer-San; Chou, Ya-Yen

    2012-06-01

    The present study aims to offer a different perspective of network systems using the health care system in Taiwan as an example. By establishing a paradigm that conforms to the reality of the health care system, this study expects to develop a correct analysis approach. The study applies the stakeholder analysis approach and performs the sampling according to the principles of the qualitative method. The main findings include (1) The health care system is a regulated network type exchange system, in which a third party affects all exchanges among stakeholders; (2) under mutual intervention of interests, stakeholders pursue common interests in appearance but individual interests in reality; (3) the intervening impacts on stakeholder interests come from a common source, which dominates the operating and dynamics of the entire system. PMID:21046205

  4. Evaluating a Novel Cellular Automata-Based Distributed Power Management Approach for Mobile Wireless Sensor Networks

    NASA Astrophysics Data System (ADS)

    Adabi, Sepideh; Adabi, Sahar; Rezaee, Ali

    According to the traditional definition of Wireless Sensor Networks (WSNs), static sensors have limited the feasibility of WSNs in some kind of approaches, so the mobility was introduced in WSN. Mobile nodes in a WSN come equipped with battery and from the point of deployment, this battery reserve becomes a valuable resource since it cannot be replenished. Hence, maximizing the network lifetime by minimizing the energy is an important challenge in Mobile WSN. Energy conservation can be accomplished by different approaches. In this paper, we presented an energy conservation solution based on Cellular Automata. The main objective of this solution is based on dynamically adjusting the transmission range and switching between operational states of the sensor nodes.

  5. A novel pattern mining approach for identifying cognitive activity in EEG based functional brain networks.

    PubMed

    Thilaga, M; Vijayalakshmi, R; Nadarajan, R; Nandagopal, D

    2016-06-01

    The complex nature of neuronal interactions of the human brain has posed many challenges to the research community. To explore the underlying mechanisms of neuronal activity of cohesive brain regions during different cognitive activities, many innovative mathematical and computational models are required. This paper presents a novel Common Functional Pattern Mining approach to demonstrate the similar patterns of interactions due to common behavior of certain brain regions. The electrode sites of EEG-based functional brain network are modeled as a set of transactions and node-based complex network measures as itemsets. These itemsets are transformed into a graph data structure called Functional Pattern Graph. By mining this Functional Pattern Graph, the common functional patterns due to specific brain functioning can be identified. The empirical analyses show the efficiency of the proposed approach in identifying the extent to which the electrode sites (transactions) are similar during various cognitive load states. PMID:27401999

  6. A Pareto evolutionary artificial neural network approach for remote sensing image classification

    NASA Astrophysics Data System (ADS)

    Liu, Fujiang; Wu, Xincai; Guo, Yan; Sun, Huashan; Zhou, Feng; Mei, Linlu

    2006-10-01

    This paper presents a Pareto evolutionary artificial neural network (Pareto-EANN) approach based on the evolutionary algorithms for multiobjective optimization augmented with local search for the classification of remote sensing image. Its novelty lies in the use of a multiobjective genetic algorithm where single hidden layers Multilayer Perceptrons (MLP) are employed to indicate the accuracy/complexity trade-off. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy of the classifier and number of hidden units. We compared Pareto-EANN classifiers results of the classification of remote sensing image against standard backpropagation neural network classifiers and EANN classifiers; we show experimentally the efficiency of the proposed methodology.

  7. New Approach for Feature Selection of Thermomechanically Processed HSLA Steel using Pruned-Modular Neural Networks

    NASA Astrophysics Data System (ADS)

    Das, Prasun; Ghosh, Avishek; Bhattacharyay, Bidyut Kr.; Datta, Shubhabrata

    2012-10-01

    A new approach has been used in modeling of strength and ductility of high strength low alloy (HSLA) steel, where a comparative study among fully-connected neural network, modular network and pruned-module architecture has been performed. The important features for modeling such a complex steel processing system have been worked out. Performance evaluation and feature selection in the soft computing domain are the two important activities for modeling input-output relationship. The need arises specially when the system is complex in terms of type of network architecture, number of features involved, number of inter-connections, application domain etc. In this paper, an attempt is made to develop a new metric of performance evaluation, using mean squared error and the total number of inter-connections of a network to improve the understanding about a complex system of thermomechanically controlled processed HSLA steels. The methodology for feature selection is developed next based on the functional form of output in terms of input variables where gradient of the function can be computed in the network.

  8. Inhibitory and excitatory networks balance cell coupling in the suprachiasmatic nucleus: A modeling approach.

    PubMed

    Kingsbury, Nathaniel J; Taylor, Stephanie R; Henson, Michael A

    2016-05-21

    Neuronal coupling contributes to circadian rhythms formation in the suprachiasmatic nucleus (SCN). While the neurotransmitter vasoactive intestinal polypeptide (VIP) is considered essential for synchronizing the oscillations of individual neurons, γ-aminobutyric acid (GABA) does not have a clear functional role despite being highly concentrated in the SCN. While most studies have examined the role of either GABA or VIP, our mathematical modeling approach explored their interplay on networks of SCN neurons. Tuning the parameters that control the release of GABA and VIP enabled us to optimize network synchrony, which was achieved at a peak firing rate during the subjective day of about 7Hz. Furthermore, VIP and GABA modulation could adjust network rhythm amplitude and period without sacrificing synchrony. We also performed simulations of SCN networks to phase shifts during 12h:12h light-dark cycles and showed that GABA networks reduced the average time for the SCN model to re-synchronize. We hypothesized that VIP and GABA balance cell coupling in the SCN to promote synchronization of heterogeneous oscillators while allowing flexibility for adjustment to environmental changes. PMID:26972478

  9. Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design

    NASA Astrophysics Data System (ADS)

    Liu, Li; Olszewski, Piotr; Goh, Pong-Chai

    A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.

  10. Spatial component analysis of MRI data for Alzheimer's disease diagnosis: a Bayesian network approach

    PubMed Central

    Illan, Ignacio A.; Górriz, Juan M.; Ramírez, Javier; Meyer-Base, Anke

    2014-01-01

    This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD. The structure of relations between affected regions allows to detect neurodegeneration with an estimated performance of 88% on more than 400 subjects and predict neurodegeneration with 80% accuracy, supporting the conclusion that modeling the dependencies between components increases the recognition of different patterns of brain degeneration in AD. PMID:25505408

  11. Pathway and Network Approaches for Identification of Cancer Signature Markers from Omics Data

    PubMed Central

    Wang, Jinlian; Zuo, Yiming; Man, Yan-gao; Avital, Itzhak; Stojadinovic, Alexander; Liu, Meng; Yang, Xiaowei; Varghese, Rency S.; Tadesse, Mahlet G; Ressom, Habtom W

    2015-01-01

    The advancement of high throughput omic technologies during the past few years has made it possible to perform many complex assays in a much shorter time than the traditional approaches. The rapid accumulation and wide availability of omic data generated by these technologies offer great opportunities to unravel disease mechanisms, but also presents significant challenges to extract knowledge from such massive data and to evaluate the findings. To address these challenges, a number of pathway and network based approaches have been introduced. This review article evaluates these methods and discusses their application in cancer biomarker discovery using hepatocellular carcinoma (HCC) as an example. PMID:25553089

  12. A Wireless Sensor Network-Based Approach with Decision Support for Monitoring Lake Water Quality

    PubMed Central

    Huang, Xiaoci; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin

    2015-01-01

    Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks. PMID:26610496

  13. A Wireless Sensor Network-Based Approach with Decision Support for Monitoring Lake Water Quality.

    PubMed

    Huang, Xiaoci; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin

    2015-01-01

    Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks. PMID:26610496

  14. A Feedback-Based Secure Path Approach for Wireless Sensor Networks Data Collection

    NASA Astrophysics Data System (ADS)

    Mao, Yuxin

    The unattended nature of WSNs makes them very vulnerable to malicious attacks. In this paper, we propose a novel approach of secure data collection for WSN. We explore secret sharing and multipath routing to achieve secure data collection in a WSN with compromised nodes. We propose to use a novel tracing-feedback mechanism, which makes full use of the routing functionality of WSN, to improve the quality of data collection. The algorithms of the approach are easy to be implemented and performed in WSN. We also evaluate the approach with a simulation experiment and analyze the simulation results in detail. We illustrate that the approach is efficient to support secure data collection in wireless sensor network.

  15. Effective degree Markov-chain approach for discrete-time epidemic processes on uncorrelated networks

    NASA Astrophysics Data System (ADS)

    Cai, Chao-Ran; Wu, Zhi-Xi; Guan, Jian-Yue

    2014-11-01

    Recently, Gómez et al. proposed a microscopic Markov-chain approach (MMCA) [S. Gómez, J. Gómez-Gardeñes, Y. Moreno, and A. Arenas, Phys. Rev. E 84, 036105 (2011), 10.1103/PhysRevE.84.036105] to the discrete-time susceptible-infected-susceptible (SIS) epidemic process and found that the epidemic prevalence obtained by this approach agrees well with that by simulations. However, we found that the approach cannot be straightforwardly extended to a susceptible-infected-recovered (SIR) epidemic process (due to its irreversible property), and the epidemic prevalences obtained by MMCA and Monte Carlo simulations do not match well when the infection probability is just slightly above the epidemic threshold. In this contribution we extend the effective degree Markov-chain approach, proposed for analyzing continuous-time epidemic processes [J. Lindquist, J. Ma, P. Driessche, and F. Willeboordse, J. Math. Biol. 62, 143 (2011), 10.1007/s00285-010-0331-2], to address discrete-time binary-state (SIS) or three-state (SIR) epidemic processes on uncorrelated complex networks. It is shown that the final epidemic size as well as the time series of infected individuals obtained from this approach agree very well with those by Monte Carlo simulations. Our results are robust to the change of different parameters, including the total population size, the infection probability, the recovery probability, the average degree, and the degree distribution of the underlying networks.

  16. A recursive network approach can identify constitutive regulatory circuits in gene expression data

    NASA Astrophysics Data System (ADS)

    Blasi, Monica Francesca; Casorelli, Ida; Colosimo, Alfredo; Blasi, Francesco Simone; Bignami, Margherita; Giuliani, Alessandro

    2005-03-01

    The activity of the cell is often coordinated by the organisation of proteins into regulatory circuits that share a common function. Genome-wide expression profiles might contain important information on these circuits. Current approaches for the analysis of gene expression data include clustering the individual expression measurements and relating them to biological functions as well as modelling and simulation of gene regulation processes by additional computer tools. The identification of the regulative programmes from microarray experiments is limited, however, by the intrinsic difficulty of linear methods to detect low-variance signals and by the sensitivity of the different approaches. Here we face the problem of recognising invariant patterns of correlations among gene expression reminiscent of regulation circuits. We demonstrate that a recursive neural network approach can identify genetic regulation circuits from expression data for ribosomal and genome stability genes. The proposed method, by greatly enhancing the sensitivity of microarray studies, allows the identification of important aspects of genetic regulation networks and might be useful for the discrimination of the different players involved in regulation circuits. Our results suggest that the constitutive regulatory networks involved in the generic organisation of the cell display a high degree of clustering depending on a modular architecture.

  17. Hybrid Swarm Intelligence Optimization Approach for Optimal Data Storage Position Identification in Wireless Sensor Networks

    PubMed Central

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

  18. Hybrid swarm intelligence optimization approach for optimal data storage position identification in wireless sensor networks.

    PubMed

    Mohanasundaram, Ranganathan; Periasamy, Pappampalayam Sanmugam

    2015-01-01

    The current high profile debate with regard to data storage and its growth have become strategic task in the world of networking. It mainly depends on the sensor nodes called producers, base stations, and also the consumers (users and sensor nodes) to retrieve and use the data. The main concern dealt here is to find an optimal data storage position in wireless sensor networks. The works that have been carried out earlier did not utilize swarm intelligence based optimization approaches to find the optimal data storage positions. To achieve this goal, an efficient swam intelligence approach is used to choose suitable positions for a storage node. Thus, hybrid particle swarm optimization algorithm has been used to find the suitable positions for storage nodes while the total energy cost of data transmission is minimized. Clustering-based distributed data storage is utilized to solve clustering problem using fuzzy-C-means algorithm. This research work also considers the data rates and locations of multiple producers and consumers to find optimal data storage positions. The algorithm is implemented in a network simulator and the experimental results show that the proposed clustering and swarm intelligence based ODS strategy is more effective than the earlier approaches. PMID:25734182

  19. A variational approach to the growth dynamics of pre-stressed actin filament networks

    NASA Astrophysics Data System (ADS)

    John, Karin; Stöter, Thomas; Misbah, Chaouqi

    2016-09-01

    In order to model the growth dynamics of elastic bodies with residual stresses a thermodynamically consistent approach is needed such that the cross-coupling between growth and mechanics can be correctly described. In the present work we apply a variational principle to the formulation of the interfacial growth dynamics of dendritic actin filament networks growing from biomimetic beads, an experimentally well studied system, where the buildup of residual stresses governs the network growth. We first introduce the material model for the network via a strain energy density for an isotropic weakly nonlinear elastic material and then derive consistently from this model the dynamic equations for the interfaces, i.e. for a polymerizing internal interface in contact with the bead and a depolymerizing external interface directed towards the solvent. We show that (i) this approach automatically preserves thermodynamic symmetry-properties, which is not the case for the often cited ‘rubber-band-model’ (Sekimoto et al 2004 Eur. Phys. J. E 13 247–59, Plastino et al 2004 Eur. Biophys. J. 33 310–20) and (ii) leads to a robust morphological instability of the treadmilling network interfaces. The nature of the instability depends on the interplay of the two dynamic interfaces. Depending on the biochemical conditions the network envelope evolves into a comet-like shape (i.e. the actin envelope thins out at one side and thickens on the opposite side of the bead) via a varicose instability or it breaks the symmetry via higher order zigzag modes. We conclude that morphological instabilities due to mechano-chemical coupling mechanisms and the presences of mechancial pre-stresses can play a major role in locally organizing the cytoskeleton of living cells.

  20. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes.

    PubMed

    Himmelstein, Daniel S; Baranzini, Sergio E

    2015-07-01

    The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks--graphs with multiple node and edge types--for accomplishing both tasks. First we constructed a network with 18 node types--genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections--and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data

  1. A variational approach to the growth dynamics of pre-stressed actin filament networks.

    PubMed

    John, Karin; Stöter, Thomas; Misbah, Chaouqi

    2016-09-21

    In order to model the growth dynamics of elastic bodies with residual stresses a thermodynamically consistent approach is needed such that the cross-coupling between growth and mechanics can be correctly described. In the present work we apply a variational principle to the formulation of the interfacial growth dynamics of dendritic actin filament networks growing from biomimetic beads, an experimentally well studied system, where the buildup of residual stresses governs the network growth. We first introduce the material model for the network via a strain energy density for an isotropic weakly nonlinear elastic material and then derive consistently from this model the dynamic equations for the interfaces, i.e. for a polymerizing internal interface in contact with the bead and a depolymerizing external interface directed towards the solvent. We show that (i) this approach automatically preserves thermodynamic symmetry-properties, which is not the case for the often cited 'rubber-band-model' (Sekimoto et al 2004 Eur. Phys. J. E 13 247-59, Plastino et al 2004 Eur. Biophys. J. 33 310-20) and (ii) leads to a robust morphological instability of the treadmilling network interfaces. The nature of the instability depends on the interplay of the two dynamic interfaces. Depending on the biochemical conditions the network envelope evolves into a comet-like shape (i.e. the actin envelope thins out at one side and thickens on the opposite side of the bead) via a varicose instability or it breaks the symmetry via higher order zigzag modes. We conclude that morphological instabilities due to mechano-chemical coupling mechanisms and the presences of mechancial pre-stresses can play a major role in locally organizing the cytoskeleton of living cells. PMID:27420637

  2. Three lines to view language network. Comment on "Approaching human language with complex networks" by Cong and Liu

    NASA Astrophysics Data System (ADS)

    Zhao, Yiyi

    2014-12-01

    Language is a kind of complex dynamic network [1-3]. The complexity of language system embodies the interaction and evolution of various languages symbols. The neural network is the physiological basis of language generating and understanding. It also provides a basis to researches on language system by adopting complex network technology and social network analysis. The review [4] gives us three lines to view researches of language network in recent years.

  3. Information theoretic approach to complex biological network reconstruction: application to cytokine release in RAW 264.7 macrophages

    PubMed Central

    2014-01-01

    Background High-throughput methods for biological measurements generate vast amounts of quantitative data, which necessitate the development of advanced approaches to data analysis to help understand the underlying mechanisms and networks. Reconstruction of biological networks from measured data of different components is a significant challenge in systems biology. Results We use an information theoretic approach to reconstruct phosphoprotein-cytokine networks in RAW 264.7 macrophage cells. Cytokines are secreted upon activation of a wide range of regulatory signals transduced by the phosphoprotein network. Identifying these components can help identify regulatory modules responsible for the inflammatory phenotype. The information theoretic approach is based on estimation of mutual information of interactions by using kernel density estimators. Mutual information provides a measure of statistical dependencies between interacting components. Using the topology of the network derived, we develop a data-driven parsimonious input–output model of the phosphoprotein-cytokine network. Conclusions We demonstrate the applicability of our information theoretic approach to reconstruction of biological networks. For the phosphoprotein-cytokine network, this approach not only captures most of the known signaling components involved in cytokine release but also predicts new signaling components involved in the release of cytokines. The results of this study are important for gaining a clear understanding of macrophage activation during the inflammation process. PMID:24964861

  4. A social network approach to analyzing water governance: The case of the Mkindo catchment, Tanzania

    NASA Astrophysics Data System (ADS)

    Stein, C.; Ernstson, H.; Barron, J.

    The governance dimension of water resources management is just as complex and interconnected as the hydrological processes it aims to influence. There is an increasing need (i) to understand the multi-stakeholder governance arrangements that emerge from the cross-scale nature and multifunctional role of water; and (ii) to develop appropriate research tools to analyze them. In this study we demonstrate how social network analysis (SNA), a well-established technique from sociology and organizational research, can be used to empirically map collaborative social networks between actors that either directly or indirectly influence water flows in the Mkindo catchment in Tanzania. We assess how these collaborative social networks affect the capacity to govern water in this particular catchment and explore how knowledge about such networks can be used to facilitate more effective or adaptive water resources management. The study is novel as it applies social network analysis not only to organizations influencing blue water (the liquid water in rivers, lakes and aquifers) but also green water (the soil moisture used by plants). Using a questionnaire and semi-structured interviews, we generated social network data of 70 organizations, ranging from local resource users and village leaders, to higher-level governmental agencies, universities and NGOs. Results show that there is no organization that coordinates the various land and water related activities at the catchment scale. Furthermore, an important result is that village leader play a crucial role linking otherwise disconnected actors, but that they are not adequately integrated into the formal water governance system. Water user associations (WUAs) are in the process of establishment and could bring together actors currently not part of the formal governance system. However, the establishment of WUAs seems to follow a top-down approach not considering the existing informal organization of water users that are revealed

  5. Parametric motion control of robotic arms: A biologically based approach using neural networks

    NASA Technical Reports Server (NTRS)

    Bock, O.; D'Eleuterio, G. M. T.; Lipitkas, J.; Grodski, J. J.

    1993-01-01

    A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements.

  6. Cellular neural network-based hybrid approach toward automatic image registration

    NASA Astrophysics Data System (ADS)

    Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

    2013-01-01

    Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

  7. The multiscale importance of road segments in a network disruption scenario: a risk-based approach.

    PubMed

    Freiria, Susana; Tavares, Alexandre O; Pedro Julião, Rui

    2015-03-01

    This article addresses the problem of the multiscale importance of road networks, with the aim of helping to establish a more resilient network in the event of a road disruption scenario. A new model for identifying the most important roads is described and applied on a local and regional scale. The work presented here represents a step forward, since it focuses on the interaction between identifying the most important roads in a network that connect people and health services, the specificity of the natural hazards that threaten the normal functioning of the network, and an assessment of the consequences of three real-world interruptions from a multiscale perspective. The case studies concern three different past events: road interruptions due to a flood, a forest fire, and a mass movement. On the basis of the results obtained, it is possible to establish the roads for which risk management should be a priority. The multiscale perspective shows that in a road interruption the regional system may have the capacity to reorganize itself, although the interruption may have consequences for local dynamics. Coordination between local and regional scales is therefore important. The model proposed here allows for the scaling of emergency response facilities and human and physical resources. It represents an innovative approach to defining priorities, not only in the prevention phase but also in terms of the response to natural disasters, such as awareness of the consequences of road disruption for the rescue services sent out to local communities. PMID:25263956

  8. A novel approach to thermochromic liquid crystal calibration using neural networks

    NASA Astrophysics Data System (ADS)

    Grewal, G. S.; Bharara, M.; Cobb, J. E.; Dubey, V. N.; Claremont, D. J.

    2006-07-01

    Liquid crystal thermography (LCT) is a common surface temperature measurement technique. Typically, the colour response is calibrated against temperature by building an analytical relation between the temperature and the hue of the colour. A suitable polynomial fit is then used to describe this relation after removing the discontinuity in the hue. The variability of hue at each calibration point determines the temperature resolution. However, this technique does not take into consideration the variability in R, G and B intensities used to determine the hue, leading to uncertainty in the measured temperature. This paper describes a novel technique using neural networks to calibrate thermochromic liquid crystal (TLC) material and compensate for high variability in RGB intensities along with other sources of noise in the data. A TLC-based temperature measurement system and calibration results are presented. In our measurements, the lighting intensity (8-bit mean intensity of black surface ± standard deviation) is changed from a minimum of 16.65 ± 2.30 to a maximum of 31.41 ± 3.85. The neural networks were trained on the steady-state TLC calibration system. The results indicate that the neural networks can cope with the variation in lighting by merging the shifted hue curves into a single curve determined by the regression analysis of the test data. Performance characteristics studied on various network configurations relevant to the analysis are described. This approach may be useful in developing liquid crystal thermography for various biomedical applications.

  9. Community-Based Groundwater Monitoring Network Using a Citizen-Science Approach.

    PubMed

    Little, Kathleen E; Hayashi, Masaki; Liang, Steve

    2016-05-01

    Water level monitoring provides essential information about the condition of aquifers and their responses to water extraction, land-use change, and climatic variability. It is important to have a spatially distributed, long-term monitoring well network for sustainable groundwater resource management. Community-based monitoring involving citizen scientists provides an approach to complement existing government-run monitoring programs. This article demonstrates the feasibility of establishing a large-scale water level monitoring network of private water supply wells using an example from Rocky View County (3900 km(2) ) in Alberta, Canada. In this network, community volunteers measure the water level in their wells, and enter these data through a web-based data portal, which allows the public to view and download these data. The close collaboration among the university researchers, county staff members, and community volunteers enabled the successful implementation and operation of the network for a 5-year pilot period, which generated valuable data sets. The monitoring program was accompanied by education and outreach programs, in which the educational materials on groundwater were developed in collaboration with science teachers from local schools. The methodology used in this study can be easily adopted by other municipalities and watershed stewardship groups interested in groundwater monitoring. As governments are starting to rely increasingly on local municipalities and conservation authorities for watershed management and planning, community-based groundwater monitoring provides an effective and affordable tool for sustainable water resources management. PMID:25825253

  10. A new approach to shortest paths on networks based on the quantum bosonic mechanism

    NASA Astrophysics Data System (ADS)

    Jiang, Xin; Wang, Hailong; Tang, Shaoting; Ma, Lili; Zhang, Zhanli; Zheng, Zhiming

    2011-01-01

    This paper presents quantum bosonic shortest path searching (QBSPS), a natural, practical and highly heuristic physical algorithm for reasoning about the recognition of network structure via quantum dynamics. QBSPS is based on an Anderson-like itinerant bosonic system in which a boson's Green function is used as a navigation pointer for one to accurately approach the terminals. QBSPS is demonstrated by rigorous mathematical and physical proofs and plenty of simulations, showing how it can be used as a greedy routing to seek the shortest path between different locations. In methodology, it is an interesting and new algorithm rooted in the quantum mechanism other than combinatorics. In practice, for the all-pairs shortest-path problem in a random scale-free network with N vertices, QBSPS runs in O(μ(N) ln ln N) time. In application, we suggest that the corresponding experimental realizations are feasible by considering path searching in quantum optical communication networks; in this situation, the method performs a pure local search on networks without requiring the global structure that is necessary for current graph algorithms.

  11. A review of sulfide emissions in sewer networks: overall approach and systemic modelling.

    PubMed

    Carrera, Lucie; Springer, Fanny; Lipeme-Kouyi, Gislain; Buffiere, Pierre

    2016-01-01

    The problems related to hydrogen sulfide in terms of deterioration of sewer networks, toxicity and odor nuisance have become very clear to the network stakeholders and the public. The hydraulic and (bio)chemical phenomena and parameters controlling sulfide formation, emission and their incidences in sewer networks are very complex. Recent research studies have been developed in gravity and pressure sewers and some transfer models have been published. Nevertheless, the models do not take into account all the physical phenomena influencing the emission process. After summing up the main scientific knowledge concerning the production, oxidation, transfer and emission processes, the present review includes: (i) a synthetic analysis of sulfide and hydrogen sulfide emission models in sewer networks, (ii) an estimation of their limit, (iii) perspectives to improve the modelling approach. It shows that sulfide formation and uptake models still need refinements especially for some phenomena such as liquid to gas mass transfer. Transfer models that have been published so far are purposely simplified and valid for simple systems. More efforts have to be undertaken in order to better understand the mechanisms and the dynamics of hydrogen sulfide production and emission in real conditions. PMID:27003062

  12. AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES

    PubMed Central

    DARABOS, CHRISTIAN; QIU, JINGYA; MOORE, JASON H.

    2015-01-01

    Complex diseases are the result of intricate interactions between genetic, epigenetic and environmental factors. In previous studies, we used epidemiological and genetic data linking environmental exposure or genetic variants to phenotypic disease to construct Human Phenotype Networks and separately analyze the effects of both environment and genetic factors on disease interactions. To better capture the intricacies of the interactions between environmental exposure and the biological pathways in complex disorders, we integrate both aspects into a single “tripartite” network. Despite extensive research, the mechanisms by which chemical agents disrupt biological pathways are still poorly understood. In this study, we use our integrated network model to identify specific biological pathway candidates possibly disrupted by environmental agents. We conjecture that a higher number of co-occurrences between an environmental substance and biological pathway pair can be associated with a higher likelihood that the substance is involved in disrupting that pathway. We validate our model by demonstrating its ability to detect known arsenic and signal transduction pathway interactions and speculate on candidate cell-cell junction organization pathways disrupted by cadmium. The validation was supported by distinct publications of cell biology and genetic studies that associated environmental exposure to pathway disruption. The integrated network approach is a novel method for detecting the biological effects of environmental exposures. A better understanding of the molecular processes associated with specific environmental exposures will help in developing targeted molecular therapies for patients who have been exposed to the toxicity of environmental chemicals. PMID:26776169

  13. Evolutionary Game Theoretic Approach to Self-Organized Data Aggregation in Delay Tolerant Networks

    NASA Astrophysics Data System (ADS)

    Kabir, K. Habibul; Sasabe, Masahiro; Takine, Tetsuya

    Custody transfer in delay tolerant networks (DTNs) provides reliable end-to-end data delivery by delegating the responsibility of data transfer among special nodes (custodians) in a hop-by-hop manner. However, storage congestion occurs when data increases and/or the network is partitioned into multiple sub-networks for a long time. The storage congestion can be alleviated by message ferries which move around the network and proactively collect data from the custodians. In such a scenario, data should be aggregated to some custodians so that message ferries can collect them effectively. In this paper, we propose a scheme to aggregate data into selected custodians, called aggregators, in a fully distributed and autonomous manner with the help of evolutionary game theoretic approach. Through theoretical analysis and several simulation experiments, taking account of the uncooperative behavior of nodes, we show that aggregators can be selected in a self-organized manner and the number of aggregators can be controlled to a desired value.

  14. A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

    PubMed

    Sivasamy, Aneetha Avalappampatty; Sundan, Bose

    2015-01-01

    The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

  15. A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

    PubMed Central

    Avalappampatty Sivasamy, Aneetha; Sundan, Bose

    2015-01-01

    The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

  16. A majorization-minimization approach to design of power distribution networks

    SciTech Connect

    Johnson, Jason K; Chertkov, Michael

    2010-01-01

    We consider optimization approaches to design cost-effective electrical networks for power distribution. This involves a trade-off between minimizing the power loss due to resistive heating of the lines and minimizing the construction cost (modeled by a linear cost in the number of lines plus a linear cost on the conductance of each line). We begin with a convex optimization method based on the paper 'Minimizing Effective Resistance of a Graph' [Ghosh, Boyd & Saberi]. However, this does not address the Alternating Current (AC) realm and the combinatorial aspect of adding/removing lines of the network. Hence, we consider a non-convex continuation method that imposes a concave cost of the conductance of each line thereby favoring sparser solutions. By varying a parameter of this penalty we extrapolate from the convex problem (with non-sparse solutions) to the combinatorial problem (with sparse solutions). This is used as a heuristic to find good solutions (local minima) of the non-convex problem. To perform the necessary non-convex optimization steps, we use the majorization-minimization algorithm that performs a sequence of convex optimizations obtained by iteratively linearizing the concave part of the objective. A number of examples are presented which suggest that the overall method is a good heuristic for network design. We also consider how to obtain sparse networks that are still robust against failures of lines and/or generators.

  17. Online learning vector quantization: a harmonic competition approach based on conservation network.

    PubMed

    Wang, J H; Sun, W D

    1999-01-01

    This paper presents a self-creating neural network in which a conservation principle is incorporated with the competitive learning algorithm to harmonize equi-probable and equi-distortion criteria. Each node is associated with a measure of vitality which is updated after each input presentation. The total amount of vitality in the network at any time is 1, hence the name conservation. Competitive learning based on a vitality conservation principle is near-optimum, in the sense that problem of trapping in a local minimum is alleviated by adding perturbations to the learning rate during node generation processes. Combined with a procedure that redistributes the learning rate variables after generation and removal of nodes, the competitive conservation strategy provides a novel approach to the problem of harmonizing equi-error and equi-probable criteria. The training process is smooth and incremental, it not only achieves the biologically plausible learning property, but also facilitates systematic derivations for training parameters. Comparison studies on learning vector quantization involving stationary and nonstationary, structured and nonstructured inputs demonstrate that the proposed network outperforms other competitive networks in terms of quantization error, learning speed, and codeword search efficiency. PMID:18252343

  18. Triangular Alignment (TAME). A Tensor-based Approach for Higher-order Network Alignment

    SciTech Connect

    Mohammadi, Shahin; Gleich, David F.; Kolda, Tamara G.; Grama, Ananth

    2015-11-01

    Network alignment is an important tool with extensive applications in comparative interactomics. Traditional approaches aim to simultaneously maximize the number of conserved edges and the underlying similarity of aligned entities. We propose a novel formulation of the network alignment problem that extends topological similarity to higher-order structures and provide a new objective function that maximizes the number of aligned substructures. This objective function corresponds to an integer programming problem, which is NP-hard. Consequently, we approximate this objective function as a surrogate function whose maximization results in a tensor eigenvalue problem. Based on this formulation, we present an algorithm called Triangular AlignMEnt (TAME), which attempts to maximize the number of aligned triangles across networks. We focus on alignment of triangles because of their enrichment in complex networks; however, our formulation and resulting algorithms can be applied to general motifs. Using a case study on the NAPABench dataset, we show that TAME is capable of producing alignments with up to 99% accuracy in terms of aligned nodes. We further evaluate our method by aligning yeast and human interactomes. Our results indicate that TAME outperforms the state-of-art alignment methods both in terms of biological and topological quality of the alignments.

  19. Controlled recovery of phylogenetic communities from an evolutionary model using a network approach

    NASA Astrophysics Data System (ADS)

    Sousa, Arthur M. Y. R.; Vieira, André P.; Prado, Carmen P. C.; Andrade, Roberto F. S.

    2016-04-01

    This works reports the use of a complex network approach to produce a phylogenetic classification tree of a simple evolutionary model. This approach has already been used to treat proteomic data of actual extant organisms, but an investigation of its reliability to retrieve a traceable evolutionary history is missing. The used evolutionary model includes key ingredients for the emergence of groups of related organisms by differentiation through random mutations and population growth, but purposefully omits other realistic ingredients that are not strictly necessary to originate an evolutionary history. This choice causes the model to depend only on a small set of parameters, controlling the mutation probability and the population of different species. Our results indicate that for a set of parameter values, the phylogenetic classification produced by the used framework reproduces the actual evolutionary history with a very high average degree of accuracy. This includes parameter values where the species originated by the evolutionary dynamics have modular structures. In the more general context of community identification in complex networks, our model offers a simple setting for evaluating the effects, on the efficiency of community formation and identification, of the underlying dynamics generating the network itself.

  20. TCP with source traffic shaping (TCP-STS): an approach for network congestion reduction

    NASA Astrophysics Data System (ADS)

    Elaywe, Ali H.; Kamal, Ahmed E.

    2002-07-01

    The Transmission Control Protocol (TCP), provides flow control functions which are based on the window mechanism. Packet losses are detected by various mechanisms, such as timeouts and duplicate acknowledgements, and are then recovered from using different techniques. A problem that arises with the use of window based mechanisms is that the availability of a large number of credits at the source may cause a source to flood the network with back-to-back packets, which may drive the network into congestion, especially if multiple sources become active at the same time. In this paper we propose a new approach for congestion reduction. The approach works by shaping the traffic at the TCP source, such that the basic TCP flow control mechanism is still preserved, but the packet transmissions are spaced in time in order to prevent a sudden surge of traffic from overflowing the routers' buffers. Simulation results show that this technique can result in an improved network performance, in terms of reduced mean delay, delay variance, and packet dropping ratio.

  1. Calibration of the clock-phase biases of GNSS networks: the closure-ambiguity approach

    NASA Astrophysics Data System (ADS)

    Lannes, A.; Prieur, J.-L.

    2013-08-01

    In global navigation satellite systems (GNSS), the problem of retrieving clock-phase biases from network data has a basic rank defect. We analyse the different ways of removing this rank defect, and define a particular strategy for obtaining these phase biases in a standard form. The minimum-constrained problem to be solved in the least-squares (LS) sense depends on some integer vector which can be fixed in an arbitrary manner. We propose to solve the problem via an undifferenced approach based on the notion of closure ambiguity. We present a theoretical justification of this closure-ambiguity approach (CAA), and the main elements for a practical implementation. The links with other methods are also established. We analyse all those methods in a unified interpretative framework, and derive functional relations between the corresponding solutions and our CAA solution. This could be interesting for many GNSS applications like real-time kinematic PPP for instance. To compare the methods providing LS estimates of clock-phase biases, we define a particular solution playing the role of reference solution. For this solution, when a phase bias is estimated for the first time, its fractional part is confined to the one-cycle width interval centred on zero; the integer-ambiguity set is modified accordingly. Our theoretical study is illustrated with some simple and generic examples; it could have applications in data processing of most GNSS networks, and particularly global networks using GPS, Glonass, Galileo, or BeiDou/Compass satellites.

  2. Controlled recovery of phylogenetic communities from an evolutionary model using a network approach.

    PubMed

    Sousa, Arthur M Y R; Vieira, André P; Prado, Carmen P C; Andrade, Roberto F S

    2016-04-01

    This works reports the use of a complex network approach to produce a phylogenetic classification tree of a simple evolutionary model. This approach has already been used to treat proteomic data of actual extant organisms, but an investigation of its reliability to retrieve a traceable evolutionary history is missing. The used evolutionary model includes key ingredients for the emergence of groups of related organisms by differentiation through random mutations and population growth, but purposefully omits other realistic ingredients that are not strictly necessary to originate an evolutionary history. This choice causes the model to depend only on a small set of parameters, controlling the mutation probability and the population of different species. Our results indicate that for a set of parameter values, the phylogenetic classification produced by the used framework reproduces the actual evolutionary history with a very high average degree of accuracy. This includes parameter values where the species originated by the evolutionary dynamics have modular structures. In the more general context of community identification in complex networks, our model offers a simple setting for evaluating the effects, on the efficiency of community formation and identification, of the underlying dynamics generating the network itself. PMID:27176322

  3. A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data.

    PubMed

    Hajmeer, M; Basheer, I

    2002-10-01

    In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to produce confidence levels for their classification decision. As a practical application of the proposed approach, PNNs were investigated for their ability in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most frequently used traditional statistical method based on logistic regression and multilayer feedforward artificial neural network (MFANN) trained by error backpropagation was also carried out. The PNN-based models were found to outperform linear and nonlinear logistic regression and MFANN in both the classification accuracy and ease by which PNN-based models are developed. PMID:12133614

  4. Solving Bilevel Programming Problems Using a Neural Network Approach and Its Application to Power System Environment

    NASA Astrophysics Data System (ADS)

    Yaakob, Shamshul Bahar; Watada, Junzo

    In this paper, a hybrid neural network approach to solve mixed integer quadratic bilevel programming problems is proposed. Bilevel programming problems arise when one optimization problem, the upper problem, is constrained by another optimization, the lower problem. The mixed integer quadratic bilevel programming problem is transformed into a double-layered neural network. The combination of a genetic algorithm (GA) and a meta-controlled Boltzmann machine (BM) enables us to formulate a hybrid neural network approach to solving bilevel programming problems. The GA is used to generate the feasible partial solutions of the upper level and to provide the parameters for the lower level. The meta-controlled BM is employed to cope with the lower level problem. The lower level solution is transmitted to the upper level. This procedure enables us to obtain the whole upper level solution. The iterative processes can converge on the complete solution of this problem to generate an optimal one. The proposed method leads the mixed integer quadratic bilevel programming problem to a global optimal solution. Finally, a numerical example is used to illustrate the application of the method in a power system environment, which shows that the algorithm is feasible and advantageous.

  5. Towards stable kinetics of large metabolic networks: Nonequilibrium potential function approach

    NASA Astrophysics Data System (ADS)

    Chen, Yong-Cong; Yuan, Ruo-Shi; Ao, Ping; Xu, Min-Juan; Zhu, Xiao-Mei

    2016-06-01

    While the biochemistry of metabolism in many organisms is well studied, details of the metabolic dynamics are not fully explored yet. Acquiring adequate in vivo kinetic parameters experimentally has always been an obstacle. Unless the parameters of a vast number of enzyme-catalyzed reactions happened to fall into very special ranges, a kinetic model for a large metabolic network would fail to reach a steady state. In this work we show that a stable metabolic network can be systematically established via a biologically motivated regulatory process. The regulation is constructed in terms of a potential landscape description of stochastic and nongradient systems. The constructed process draws enzymatic parameters towards stable metabolism by reducing the change in the Lyapunov function tied to the stochastic fluctuations. Biologically it can be viewed as interplay between the flux balance and the spread of workloads on the network. Our approach allows further constraints such as thermodynamics and optimal efficiency. We choose the central metabolism of Methylobacterium extorquens AM1 as a case study to demonstrate the effectiveness of the approach. Growth efficiency on carbon conversion rate versus cell viability and futile cycles is investigated in depth.

  6. Automatic tremor detection and waveform component analysis using a neural network approach

    NASA Astrophysics Data System (ADS)

    Horstmann, T.; Harrington, R. M.; Cochran, E. S.; Wang, T.; Potier, C. E.

    2010-12-01

    Recent studies over the last decade have established that non-volcanic tremor is a ubiquitous phenomenon commonly observed in subduction zones. In recent years, it has also been widely observed in strike-slip faulting environments. In particular, observations of tremor along the San Andreas Fault indicate that many of the events occur at depths ranging between 15 and 45 km, suggesting that tremor typically occurs in the zone where fault slip behaviour transitions between stick-slip and stable-sliding frictional regimes. As such, much of the tremor occurs at or below the depths of microseismicity, and therefore has the potential to provide clues about the slip behaviour of faults at depth. Despite several recent advances, the origin and characteristics of tremor along strike-slip faults are not well-resolved. The emergent phase arrivals, low amplitude waveforms, and variable event durations associated with non-volcanic tremor make automatic tremor event detection a non-trivial task. Recent approaches employ a cross-correlation technique which readily isolates individual template tremor bursts within tremor episodes. However the method tends to detect events with nearly identical waveforms and moveout across stations within an array. We employ a new method to identify tremor in large data volumes using an automated technique that does not require the use of a designated template event. Furthermore, the same method can be used to identify distinctive tremor waveform features, such as frequency content, polarity, and amplitude ratios. We use continuous broadband waveforms from 13 STS-2 seismometers deployed in May 2010 along the Cholame segment of the San Andreas Fault. The maximum station spacing within the array is approximately 25km. We first cross-correlate waveform envelopes to reduce the data volume and find isolated seismic events. Next, we use a neural network approach to cluster events in the reduced data set. Because the unsupervised neural network algorithm

  7. A novel approach to synchronization of nonlinearly coupled network systems with delays

    NASA Astrophysics Data System (ADS)

    Tseng, Jui-Pin

    2016-06-01

    In this investigation, a novel approach to establishing the global synchronization of coupled network systems is presented. Under this approach, individual subsystems can be non-autonomous, and the coupling configuration is rather general. The coupling terms can be non-diffusive, nonlinear, time-dependent, asymmetric, and with time delays. With an iteration scheme, the problem of synchronization is transformed into solving a corresponding linear system of algebraic equations. Subsequently, delay-dependent and delay-independent criteria for global synchronization can be established. We implement the present approach to analyze synchronization of the FitzHugh-Nagumo systems under delayed and nonlinear sigmoidal coupling. Two examples are presented to demonstrate new dynamical scenarios, where oscillatory behavior and multistability emerge or are suppressed as the coupled neurons synchronize under the synchronization criterion. In addition, asynchrony induced by the coupling strength or coupling delay occurs while the synchronization criterion is violated.

  8. An analytical approach to photonic reservoir computing - a network of SOA's - for noisy speech recognition

    NASA Astrophysics Data System (ADS)

    Salehi, Mohammad Reza; Abiri, Ebrahim; Dehyadegari, Louiza

    2013-10-01

    This paper seeks to investigate an approach of photonic reservoir computing for optical speech recognition on an examination isolated digit recognition task. An analytical approach in photonic reservoir computing is further drawn on to decrease time consumption, compared to numerical methods; which is very important in processing large signals such as speech recognition. It is also observed that adjusting reservoir parameters along with a good nonlinear mapping of the input signal into the reservoir, analytical approach, would boost recognition accuracy performance. Perfect recognition accuracy (i.e. 100%) can be achieved for noiseless speech signals. For noisy signals with 0-10 db of signal to noise ratios, however, the accuracy ranges observed varied between 92% and 98%. In fact, photonic reservoir application demonstrated 9-18% improvement compared to classical reservoir networks with hyperbolic tangent nodes.

  9. NASA Water-Cycle Solutions Networks and Community of Practice Approaches to enhance Decision-making

    NASA Astrophysics Data System (ADS)

    Pozzi, W.; Ward, J.; Cox, E. L.; Lawford, R. G.; Matthews, D.; Houser, P.; Doherty, M.

    2009-12-01

    The Japanese Aerospace Exploration Agency (JAXA) has created the Asian Water Cycle Initiative regional network for South Asia and NASA has launched two networks to enhance the rapid transitioning of scientific achievements and NASA technology into operational use. All three networks meet a new type of scientific challenge by providing strong linkage among the scientific communities, the space agencies, and decision makers. We focus here on the two NASA-sponsored networks that carry out complementary approaches: WaterNet focused on large-scale national/international collaborations; North Olympic Peninsula Solution Network developed a local proof of concept project first, then began integration and collaboration at progressively larger scales, culminating with a national-level discourse via the National Association of Resource, Conservation and Development councils (NARC&DC). The ultimate goals of both groups were to bring NASA Science and Technology products to organizations/groups to improve decision making and to create collaborations and networks that would extend beyond the parent groups and expand and continue to be sustainable, after the original projects were completed. This paper provides a summary of lessons learned. The primary objective of the NOPSN is to bring NASA science and technology tools to watershed managers to improve the scientific basis of decision making in NASA national application areas of water management, agricultural efficiency, and ecological forecasting. To achieve this objective, the NOPSN team first developed and implemented a local proof-of-concept project for the Dungeness River, Washington, to improve water forecasting. The team then developed local and regional collaborations with water resource managers, stakeholder groups, and local, state, and federal agencies to identify environmental issues, challenges, and needs that could be addressed with NASA technology. Finally,through its partnership with NARC&D, it provided the NOPSN

  10. On the Interplay between the Evolvability and Network Robustness in an Evolutionary Biological Network: A Systems Biology Approach

    PubMed Central

    Chen, Bor-Sen; Lin, Ying-Po

    2011-01-01

    In the evolutionary process, the random transmission and mutation of genes provide biological diversities for natural selection. In order to preserve functional phenotypes between generations, gene networks need to evolve robustly under the influence of random perturbations. Therefore, the robustness of the phenotype, in the evolutionary process, exerts a selection force on gene networks to keep network functions. However, gene networks need to adjust, by variations in genetic content, to generate phenotypes for new challenges in the network’s evolution, ie, the evolvability. Hence, there should be some interplay between the evolvability and network robustness in evolutionary gene networks. In this study, the interplay between the evolvability and network robustness of a gene network and a biochemical network is discussed from a nonlinear stochastic system point of view. It was found that if the genetic robustness plus environmental robustness is less than the network robustness, the phenotype of the biological network is robust in evolution. The tradeoff between the genetic robustness and environmental robustness in evolution is discussed from the stochastic stability robustness and sensitivity of the nonlinear stochastic biological network, which may be relevant to the statistical tradeoff between bias and variance, the so-called bias/variance dilemma. Further, the tradeoff could be considered as an antagonistic pleiotropic action of a gene network and discussed from the systems biology perspective. PMID:22084563

  11. Object-oriented Approach to High-level Network Monitoring and Management

    NASA Technical Reports Server (NTRS)

    Mukkamala, Ravi

    2000-01-01

    An absolute prerequisite for the management of large investigating methods to build high-level monitoring computer networks is the ability to measure their systems that are built on top of existing monitoring performance. Unless we monitor a system, we cannot tools. Due to the heterogeneous nature of the hope to manage and control its performance. In this underlying systems at NASA Langley Research Center, paper, we describe a network monitoring system that we use an object-oriented approach for the design, we are currently designing and implementing. Keeping, first, we use UML (Unified Modeling Language) to in mind the complexity of the task and the required model users' requirements. Second, we identify the flexibility for future changes, we use an object-oriented existing capabilities of the underlying monitoring design methodology. The system is built using the system. Third, we try to map the former with the latter. APIs offered by the HP OpenView system.

  12. A jazz-based approach for optimal setting of pressure reducing valves in water distribution networks

    NASA Astrophysics Data System (ADS)

    De Paola, Francesco; Galdiero, Enzo; Giugni, Maurizio

    2016-05-01

    This study presents a model for valve setting in water distribution networks (WDNs), with the aim of reducing the level of leakage. The approach is based on the harmony search (HS) optimization algorithm. The HS mimics a jazz improvisation process able to find the best solutions, in this case corresponding to valve settings in a WDN. The model also interfaces with the improved version of a popular hydraulic simulator, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the solutions. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to two case studies, and the obtained results in terms of pressure reductions are comparable with those of competitive metaheuristic algorithms (e.g. genetic algorithms). The results demonstrate the suitability of the HS algorithm for water network management and optimization.

  13. "Improved Geometric Network Model" (IGNM): a novel approach for deriving Connectivity Graphs for Indoor Navigation

    NASA Astrophysics Data System (ADS)

    Mortari, F.; Zlatanova, S.; Liu, L.; Clementini, E.

    2014-04-01

    Over the past few years Personal Navigation Systems have become an established tool for route planning, but they are mainly designed for outdoor environments. Indoor navigation is still a challenging research area for several reasons: positioning is not very accurate, users can freely move between the interior boundaries of buildings, path network construction process may not be easy and straightforward due to complexity of indoor space configurations. Therefore the creation of a good network is essential for deriving overall connectivity of a building and for representing position of objects within the environment. This paper reviews current approaches to automatic derivation of route graphs for indoor navigation and discusses some of their limitations. Then, it introduces a novel algorithmic strategy for extracting a 3D connectivity graph for indoor navigation based on 2D floor plans.

  14. What are the Evolutionary Origins of Mitochondria? A Complex Network Approach

    PubMed Central

    Carvalho, Daniel S.; Andrade, Roberto F. S.; Pinho, Suani T. R.; Góes-Neto, Aristóteles; Lobão, Thierry C. P.; Bomfim, Gilberto C.; El-Hani, Charbel N.

    2015-01-01

    Mitochondria originated endosymbiotically from an Alphaproteobacteria-like ancestor. However, it is still uncertain which extant group of Alphaproteobacteria is phylogenetically closer to the mitochondrial ancestor. The proposed groups comprise the order Rickettsiales, the family Rhodospirillaceae, and the genus Rickettsia. In this study, we apply a new complex network approach to investigate the evolutionary origins of mitochondria, analyzing protein sequences modules in a critical network obtained through a critical similarity threshold between the studied sequences. The dataset included three ATP synthase subunits (4, 6, and 9) and its alphaproteobacterial homologs (b, a, and c). In all the subunits, the results gave no support to the hypothesis that Rickettsiales are closely related to the mitochondrial ancestor. Our findings support the hypothesis that mitochondria share a common ancestor with a clade containing all Alphaproteobacteria orders, except Rickettsiales. PMID:26332127

  15. Delay-decomposing approach to robust stability for switched interval networks with state-dependent switching.

    PubMed

    Li, Ning; Cao, Jinde; Hayat, Tasawar

    2014-08-01

    This paper is concerned with a class of nonlinear uncertain switched networks with discrete time-varying delays . Based on the strictly complete property of the matrices system and the delay-decomposing approach, exploiting a new Lyapunov-Krasovskii functional decomposing the delays in integral terms, the switching rule depending on the state of the network is designed. Moreover, by piecewise delay method, discussing the Lyapunov functional in every different subintervals, some new delay-dependent robust stability criteria are derived in terms of linear matrix inequalities, which lead to much less conservative results than those in the existing references and improve previous results. Finally, an illustrative example is given to demonstrate the validity of the theoretical results. PMID:25009673

  16. Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes

    PubMed Central

    Himmelstein, Daniel S.; Baranzini, Sergio E.

    2015-01-01

    The first decade of Genome Wide Association Studies (GWAS) has uncovered a wealth of disease-associated variants. Two important derivations will be the translation of this information into a multiscale understanding of pathogenic variants and leveraging existing data to increase the power of existing and future studies through prioritization. We explore edge prediction on heterogeneous networks—graphs with multiple node and edge types—for accomplishing both tasks. First we constructed a network with 18 node types—genes, diseases, tissues, pathophysiologies, and 14 MSigDB (molecular signatures database) collections—and 19 edge types from high-throughput publicly-available resources. From this network composed of 40,343 nodes and 1,608,168 edges, we extracted features that describe the topology between specific genes and diseases. Next, we trained a model from GWAS associations and predicted the probability of association between each protein-coding gene and each of 29 well-studied complex diseases. The model, which achieved 132-fold enrichment in precision at 10% recall, outperformed any individual domain, highlighting the benefit of integrative approaches. We identified pleiotropy, transcriptional signatures of perturbations, pathways, and protein interactions as influential mechanisms explaining pathogenesis. Our method successfully predicted the results (with AUROC = 0.79) from a withheld multiple sclerosis (MS) GWAS despite starting with only 13 previously associated genes. Finally, we combined our network predictions with statistical evidence of association to propose four novel MS genes, three of which (JAK2, REL, RUNX3) validated on the masked GWAS. Furthermore, our predictions provide biological support highlighting REL as the causal gene within its gene-rich locus. Users can browse all predictions online (http://het.io). Heterogeneous network edge prediction effectively prioritized genetic associations and provides a powerful new approach for data

  17. A spectral approach for the quantitative description of cardiac collagen network from nonlinear optical imaging.

    PubMed

    Masè, Michela; Cristoforetti, Alessandro; Avogaro, Laura; Tessarolo, Francesco; Piccoli, Federico; Caola, Iole; Pederzolli, Carlo; Graffigna, Angelo; Ravelli, Flavia

    2015-08-01

    The assessment of collagen structure in cardiac pathology, such as atrial fibrillation (AF), is essential for a complete understanding of the disease. This paper introduces a novel methodology for the quantitative description of collagen network properties, based on the combination of nonlinear optical microscopy with a spectral approach of image processing and analysis. Second-harmonic generation (SHG) microscopy was applied to atrial tissue samples from cardiac surgery patients, providing label-free, selective visualization of the collagen structure. The spectral analysis framework, based on 2D-FFT, was applied to the SHG images, yielding a multiparametric description of collagen fiber orientation (angle and anisotropy indexes) and texture scale (dominant wavelength and peak dispersion indexes). The proof-of-concept application of the methodology showed the capability of our approach to detect and quantify differences in the structural properties of the collagen network in AF versus sinus rhythm patients. These results suggest the potential of our approach in the assessment of collagen properties in cardiac pathologies related to a fibrotic structural component. PMID:26737722

  18. Unravelling river system impairments in stream networks with an integrated risk approach.

    PubMed

    Van Looy, Kris; Piffady, Jérémy; Tormos, Thierry; Villeneuve, Bertrand; Valette, Laurent; Chandesris, André; Souchon, Yves

    2015-06-01

    Rivers are complex systems for which it is hard to make reliable assessments of causes and responses to impairments. We present a holistic risk-based framework for river ecosystem assessment integrating all potential intervening processes and functions. Risk approaches allow us to deal with uncertainty both in the construction of indicators for magnitude of stressors and in the inference of environmental processes and their impairment. Yet, here we go further than simply replacing uncertainty by a risk factor. We introduce a more accurate and rigorous notion of risk with a transcription of uncertainty in causal relationships in probability distributions for the magnitude of impairment and the weight of different descriptors, with an associated confidence in the diagnostic. We discuss how Bayesian belief networks and Bayesian hierarchical inference allow us to deal with this risk concept to predict impairments and potential recovery of river ecosystems. We developed a comprehensive approach for river ecosystem assessment, which offers an appealing tool to facilitate diagnosis of the likely causes of impairment and predict future conditions. The ability of the risk approaches to integrate multi-scale quantitative and qualitative descriptors in the identification of multiple stressor sources and pathways in the stream network, and their impairment of specific processes and structures is illustrated for the national-level risk analysis for hydromorphology and pesticide pollution. Not only does the risk-based framework provide a more complete picture of environmental impairments, but it also offers a comprehensive, user-friendly tool to instruct the decision process. PMID:25832345

  19. Unravelling River System Impairments in Stream Networks with an Integrated Risk Approach

    NASA Astrophysics Data System (ADS)

    Van Looy, Kris; Piffady, Jérémy; Tormos, Thierry; Villeneuve, Bertrand; Valette, Laurent; Chandesris, André; Souchon, Yves

    2015-06-01

    Rivers are complex systems for which it is hard to make reliable assessments of causes and responses to impairments. We present a holistic risk-based framework for river ecosystem assessment integrating all potential intervening processes and functions. Risk approaches allow us to deal with uncertainty both in the construction of indicators for magnitude of stressors and in the inference of environmental processes and their impairment. Yet, here we go further than simply replacing uncertainty by a risk factor. We introduce a more accurate and rigorous notion of risk with a transcription of uncertainty in causal relationships in probability distributions for the magnitude of impairment and the weight of different descriptors, with an associated confidence in the diagnostic. We discuss how Bayesian belief networks and Bayesian hierarchical inference allow us to deal with this risk concept to predict impairments and potential recovery of river ecosystems. We developed a comprehensive approach for river ecosystem assessment, which offers an appealing tool to facilitate diagnosis of the likely causes of impairment and predict future conditions. The ability of the risk approaches to integrate multi-scale quantitative and qualitative descriptors in the identification of multiple stressor sources and pathways in the stream network, and their impairment of specific processes and structures is illustrated for the national-level risk analysis for hydromorphology and pesticide pollution. Not only does the risk-based framework provide a more complete picture of environmental impairments, but it also offers a comprehensive, user-friendly tool to instruct the decision process.

  20. A structured approach for the engineering of biochemical network models, illustrated for signalling pathways.

    PubMed

    Breitling, Rainer; Gilbert, David; Heiner, Monika; Orton, Richard

    2008-09-01

    Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. Here we provide a general introduction to the field of formal modelling, which emphasizes the intuitive biochemical basis of the modelling process, but is also accessible for an audience with a background in computing science and/or model engineering. We show how signal transduction cascades can be modelled in a modular fashion, using both a qualitative approach--qualitative Petri nets, and quantitative approaches--continuous Petri nets and ordinary differential equations (ODEs). We review the major elementary building blocks of a cellular signalling model, discuss which critical design decisions have to be made during model building, and present a number of novel computational tools that can help to explore alternative modular models in an easy and intuitive manner. These tools, which are based on Petri net theory, offer convenient ways of composing hierarchical ODE models, and permit a qualitative analysis of their behaviour. We illustrate the central concepts using signal transduction as our main example. The ultimate aim is to introduce a general approach that provides the foundations for a structured formal engineering of large-scale models of biochemical networks. PMID:18573813

  1. Identification of the anti-tumor activity and mechanisms of nuciferine through a network pharmacology approach

    PubMed Central

    Qi, Quan; Li, Rui; Li, Hui-ying; Cao, Yu-bing; Bai, Ming; Fan, Xiao-jing; Wang, Shu-yan; Zhang, Bo; Li, Shao

    2016-01-01

    Aim: Nuciferine is an aporphine alkaloid extracted from lotus leaves, which is a raw material in Chinese medicinal herb for weight loss. In this study we used a network pharmacology approach to identify the anti-tumor activity of nuciferine and the underlying mechanisms. Methods: The pharmacological activities and mechanisms of nuciferine were identified through target profile prediction, clustering analysis and functional enrichment analysis using our traditional Chinese medicine (TCM) network pharmacology platform. The anti-tumor activity of nuciferine was validated by in vitro and in vivo experiments. The anti-tumor mechanisms of nuciferine were predicted through network target analysis and verified by in vitro experiments. Results: The nuciferine target profile was enriched with signaling pathways and biological functions, including “regulation of lipase activity”, “response to nicotine” and “regulation of cell proliferation”. Target profile clustering results suggested that nuciferine to exert anti-tumor effect. In experimental validation, nuciferine (0.8 mg/mL) markedly inhibited the viability of human neuroblastoma SY5Y cells and mouse colorectal cancer CT26 cells in vitro, and nuciferine (0.05 mg/mL) significantly suppressed the invasion of 6 cancer cell lines in vitro. Intraperitoneal injection of nuciferine (9.5 mg/mL, ip, 3 times a week for 3 weeks) significantly decreased the weight of SY5Y and CT26 tumor xenografts in nude mice. Network target analysis and experimental validation in SY5Y and CT26 cells showed that the anti-tumor effect of nuciferine was mediated through inhibiting the PI3K-AKT signaling pathway and IL-1 levels in SY5Y and CT26 cells. Conclusion: By using a TCM network pharmacology method, nuciferine is identified as an anti-tumor agent against human neuroblastoma and mouse colorectal cancer in vitro and in vivo, through inhibiting the PI3K-AKT signaling pathways and IL-1 levels. PMID:27180984

  2. A Network Approach to Bipolar Symptomatology in Patients with Different Course Types

    PubMed Central

    Koenders, M. A.; de Kleijn, R.; Giltay, E. J.; Elzinga, B. M.; Spinhoven, P.; Spijker, A. T.

    2015-01-01

    Objective The longitudinal mood course is highly variable among patients with bipolar disorder(BD). One of the strongest predictors of the future disease course is the past disease course, implying that the vulnerability for developing a specific pattern of symptoms is rather consistent over time. We therefore investigated whether BD patients with different longitudinal course types have symptom correlation networks with typical characteristics. To this end we used network analysis, a rather novel approach in the field of psychiatry. Method Based on two-year monthly life charts, 125 patients with complete 2 year data were categorized into three groups: i.e., a minimally impaired (n = 47), a predominantly depressed (n = 42) and a cycling course (n = 36). Associations between symptoms were defined as the groupwise Spearman’s rank correlation coefficient between each pair of items of the Young Mania Rating Scale (YMRS) and the Quick Inventory of Depressive Symptomatology (QIDS). Weighted symptom networks and centrality measures were compared among the three groups. Results The weighted networks significantly differed among the three groups, with manic and depressed symptoms being most strongly interconnected in the cycling group. The symptoms with top centrality that were most interconnected also differed among the course group; central symptoms in the stable group were elevated mood and increased speech, in the depressed group loss of self-esteem and psychomotor slowness, and in the cycling group concentration loss and suicidality. Conclusion Symptom networks based on the timepoints with most severe symptoms of bipolar patients with different longitudinal course types are significantly different. The clinical interpretation of this finding and its implications are discussed. PMID:26505477

  3. Discriminating between rival biochemical network models: three approaches to optimal experiment design

    PubMed Central

    2010-01-01

    Background The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures. Results In this paper we develop methodologies for the optimal design of experiments with the aim of discriminating between different mathematical models of the same biological system. The first approach determines the 'best' initial condition that maximizes the L2 (energy) distance between the outputs of the rival models. In the second approach, we maximize the L2-distance of the outputs by designing the optimal external stimulus (input) profile of unit L2-norm. Our third method uses optimized structural changes (corresponding, for example, to parameter value changes reflecting gene knock-outs) to achieve the same goal. The numerical implementation of each method is considered in an example, signal processing in starving Dictyostelium amœbæ. Conclusions Model-based design of experiments improves both the reliability and the efficiency of biochemical network model discrimination. This opens the way to model invalidation, which can be used to perfect our understanding of biochemical networks. Our general problem formulation together with the three proposed experiment design methods give the practitioner new tools for a systems biology approach to

  4. Inter-organisational communication networks in healthcare: centralised versus decentralised approaches

    PubMed Central

    Pirnejad, Habibollah; Bal, Roland; Stoop, Arjen P.; Berg, Marc

    2007-01-01

    Background To afford efficient and high quality care, healthcare providers increasingly need to exchange patient data. The existence of a communication network amongst care providers will help them to exchange patient data more efficiently. Information and communication technology (ICT) has much potential to facilitate the development of such a communication network. Moreover, in order to offer integrated care interoperability of healthcare organizations based upon the exchanged data is of crucial importance. However, complications around such a development are beyond technical impediments. Objectives To determine the challenges and complexities involved in building an Inter-organisational Communication network (IOCN) in healthcare and the appropriations in the strategies. Case study Interviews, literature review, and document analysis were conducted to analyse the developments that have taken place toward building a countrywide electronic patient record and its challenges in The Netherlands. Due to the interrelated nature of technical and non-technical problems, a socio-technical approach was used to analyse the data and define the challenges. Results Organisational and cultural changes are necessary before technical solutions can be applied. There are organisational, financial, political, and ethicolegal challenges that have to be addressed appropriately. Two different approaches, one “centralised” and the other “decentralised” have been used by Dutch healthcare providers to adopt the necessary changes and cope with these challenges. Conclusion The best solutions in building an IOCN have to be drawn from both the centralised and the decentralised approaches. Local communication initiatives have to be supervised and supported centrally and incentives at the organisations' interest level have to be created to encourage the stakeholder organisations to adopt the necessary changes. PMID:17627296

  5. Ion track based tunable device as humidity sensor: a neural network approach

    NASA Astrophysics Data System (ADS)

    Sharma, Mamta; Sharma, Anuradha; Bhattacherjee, Vandana

    2013-01-01

    Artificial Neural Network (ANN) has been applied in statistical model development, adaptive control system, pattern recognition in data mining, and decision making under uncertainty. The nonlinear dependence of any sensor output on the input physical variable has been the motivation for many researchers to attempt unconventional modeling techniques such as neural networks and other machine learning approaches. Artificial neural network (ANN) is a computational tool inspired by the network of neurons in biological nervous system. It is a network consisting of arrays of artificial neurons linked together with different weights of connection. The states of the neurons as well as the weights of connections among them evolve according to certain learning rules.. In the present work we focus on the category of sensors which respond to electrical property changes such as impedance or capacitance. Recently, sensor materials have been embedded in etched tracks due to their nanometric dimensions and high aspect ratio which give high surface area available for exposure to sensing material. Various materials can be used for this purpose to probe physical (light intensity, temperature etc.), chemical (humidity, ammonia gas, alcohol etc.) or biological (germs, hormones etc.) parameters. The present work involves the application of TEMPOS structures as humidity sensors. The sample to be studied was prepared using the polymer electrolyte (PEO/NH4ClO4) with CdS nano-particles dispersed in the polymer electrolyte. In the present research we have attempted to correlate the combined effects of voltage and frequency on impedance of humidity sensors using a neural network model and results have indicated that the mean absolute error of the ANN Model for the training data was 3.95% while for the validation data it was 4.65%. The corresponding values for the LR model were 8.28% and 8.35% respectively. It was also demonstrated the percentage improvement of the ANN Model with respect to the

  6. Permutation invariant polynomial neural network approach to fitting potential energy surfaces

    NASA Astrophysics Data System (ADS)

    Jiang, Bin; Guo, Hua

    2013-08-01

    A simple, general, and rigorous scheme for adapting permutation symmetry in molecular systems is proposed and tested for fitting global potential energy surfaces using neural networks (NNs). The symmetry adaptation is realized by using low-order permutation invariant polynomials (PIPs) as inputs for the NNs. This so-called PIP-NN approach is applied to the H + H2 and Cl + H2 systems and the analytical potential energy surfaces for these two systems were accurately reproduced by PIP-NN. The accuracy of the NN potential energy surfaces was confirmed by quantum scattering calculations.

  7. Propagation of computer virus both across the Internet and external computers: A complex-network approach

    NASA Astrophysics Data System (ADS)

    Gan, Chenquan; Yang, Xiaofan; Liu, Wanping; Zhu, Qingyi; Jin, Jian; He, Li

    2014-08-01

    Based on the assumption that external computers (particularly, infected external computers) are connected to the Internet, and by considering the influence of the Internet topology on computer virus spreading, this paper establishes a novel computer virus propagation model with a complex-network approach. This model possesses a unique (viral) equilibrium which is globally attractive. Some numerical simulations are also given to illustrate this result. Further study shows that the computers with higher node degrees are more susceptible to infection than those with lower node degrees. In this regard, some appropriate protective measures are suggested.

  8. Queueing Network Models for Parallel Processing of Task Systems: an Operational Approach

    NASA Technical Reports Server (NTRS)

    Mak, Victor W. K.

    1986-01-01

    Computer performance modeling of possibly complex computations running on highly concurrent systems is considered. Earlier works in this area either dealt with a very simple program structure or resulted in methods with exponential complexity. An efficient procedure is developed to compute the performance measures for series-parallel-reducible task systems using queueing network models. The procedure is based on the concept of hierarchical decomposition and a new operational approach. Numerical results for three test cases are presented and compared to those of simulations.

  9. SIGNATURES OF ILLICIT NUCLEAR PROCUREMENT NETWORKS: AN OVERVIEW OF PRELIMINARY APPROACHES AND RESULTS

    SciTech Connect

    Webster, Jennifer B.; Erikson, Luke E.; Gastelum, Zoe N.; Lewis, Valerie A.; Best, Daniel M.; Hogan, Emilie A.; Chikkagoudar, Satish

    2014-05-12

    The illicit trafficking of strategic nuclear commodities (defined here as the goods needed for a covert nuclear program excluding special nuclear materials) poses a significant challenge to the international nuclear nonproliferation community. Export control regulations, both domestically and internationally, seek to inhibit the spread of strategic nuclear commodities by restricting their sale to parties that may use them for nefarious purposes. However, export controls alone are not sufficient for preventing the illicit transfer of strategic nuclear goods. There are two major pitfalls to relying solely on export control regulations for the deterrence of proliferation of strategic goods. First, export control enforcement today relies heavily on the honesty and willingness of participants to adhere to the legal framework already in place. Secondly, current practices focus on the evaluation of single records which allow for the necessary goods to be purchased separately and hidden within the thousands of legitimate commerce transactions that occur each day, disregarding strategic information regarding several purchases. Our research presents two preliminary data-centric approaches for investigating procurement networks of strategic nuclear commodities. Pacific Northwest National Laboratory (PNNL) has been putting significant effort into nonproliferation activities as an institution, both in terms of the classical nuclear material focused approach and in the examination of other strategic goods necessary to implement a nuclear program. In particular, the PNNL Signature Discovery Initiative (SDI) has codified several scientific methodologies for the detection, characterization, and prediction of signatures that are indicative of a phenomenon of interest. The methodologies and tools developed under SDI have already been applied successfully to problems in bio-forensics, cyber security and power grid balancing efforts and they have now made the nonproliferation of

  10. An Advanced Orbiting Systems Approach to Quality of Service in Space-Based Intelligent Communication Networks

    NASA Technical Reports Server (NTRS)

    Riha, Andrew P.

    2005-01-01

    As humans and robotic technologies are deployed in future constellation systems, differing traffic services will arise, e.g., realtime and non-realtime. In order to provide a quality of service framework that would allow humans and robotic technologies to interoperate over a wide and dynamic range of interactions, a method of classifying data as realtime or non-realtime is needed. In our paper, we present an approach that leverages the Consultative Committee for Space Data Systems (CCSDS) Advanced Orbiting Systems (AOS) data link protocol. Specifically, we redefine the AOS Transfer Frame Replay Flag in order to provide an automated store-and-forward approach on a per-service basis for use in the next-generation Interplanetary Network. In addition to addressing the problem of intermittent connectivity and associated services, we propose a follow-on methodology for prioritizing data through further modification of the AOS Transfer Frame.

  11. Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data

    NASA Astrophysics Data System (ADS)

    Li, Qingquan; Zeng, Zhe; Zhang, Tong; Li, Jonathan; Wu, Zhongheng

    2011-02-01

    Optimal paths computed by conventional path-planning algorithms are usually not "optimal" since realistic traffic information and local road network characteristics are not considered. We present a new experiential approach that computes optimal paths based on the experience of taxi drivers by mining a huge number of floating car trajectories. The approach consists of three steps. First, routes are recovered from original taxi trajectories. Second, an experiential road hierarchy is constructed using travel frequency and speed information for road segments. Third, experiential optimal paths are planned based on the experiential road hierarchy. Compared with conventional path-planning methods, the proposed method provides better experiential optimal path identification. Experiments demonstrate that the travel time is less for these experiential paths than for paths planned by conventional methods. Results obtained for a case study in the city of Wuhan, China, demonstrate that experiential optimal paths can be flexibly obtained in different time intervals, particularly during peak hours.

  12. Towards biological plausibility of electronic noses: A spiking neural network based approach for tea odour classification.

    PubMed

    Sarkar, Sankho Turjo; Bhondekar, Amol P; Macaš, Martin; Kumar, Ritesh; Kaur, Rishemjit; Sharma, Anupma; Gulati, Ashu; Kumar, Amod

    2015-11-01

    The paper presents a novel encoding scheme for neuronal code generation for odour recognition using an electronic nose (EN). This scheme is based on channel encoding using multiple Gaussian receptive fields superimposed over the temporal EN responses. The encoded data is further applied to a spiking neural network (SNN) for pattern classification. Two forms of SNN, a back-propagation based SpikeProp and a dynamic evolving SNN are used to learn the encoded responses. The effects of information encoding on the performance of SNNs have been investigated. Statistical tests have been performed to determine the contribution of the SNN and the encoding scheme to overall odour discrimination. The approach has been implemented in odour classification of orthodox black tea (Kangra-Himachal Pradesh Region) thereby demonstrating a biomimetic approach for EN data analysis. PMID:26356597

  13. New approach for earthquake/tsunami monitoring using dense GPS networks

    PubMed Central

    Li, Xingxing; Ge, Maorong; Zhang, Yong; Wang, Rongjiang; Xu, Peiliang; Wickert, Jens; Schuh, Harald

    2013-01-01

    In recent times increasing numbers of high-rate GPS stations have been installed around the world and set-up to provide data in real-time. These networks provide a great opportunity to quickly capture surface displacements, which makes them important as potential constituents of earthquake/tsunami monitoring and warning systems. The appropriate GPS real-time data analysis with sufficient accuracy for this purpose is a main focus of the current GPS research. In this paper we propose an augmented point positioning method for GPS based hazard monitoring, which can achieve fast or even instantaneous precise positioning without relying on data of a specific reference station. The proposed method overcomes the limitations of the currently mostly used GPS processing approaches of relative positioning and global precise point positioning. The advantages of the proposed approach are demonstrated by using GPS data, which was recorded during the 2011 Tohoku-Oki earthquake in Japan. PMID:24045328

  14. Neural Network Aided Adaptive Extended Kalman Filtering Approach for DGPS Positioning

    NASA Astrophysics Data System (ADS)

    Jwo, Dah-Jing; Huang, Hung-Chih

    2004-09-01

    The extended Kalman filter, when employed in the GPS receiver as the navigation state estimator, provides optimal solutions if the noise statistics for the measurement and system are completely known. In practice, the noise varies with time, which results in performance degradation. The covariance matching method is a conventional adaptive approach for estimation of noise covariance matrices. The technique attempts to make the actual filter residuals consistent with their theoretical covariance. However, this innovation-based adaptive estimation shows very noisy results if the window size is small. To resolve the problem, a multilayered neural network is trained to identify the measurement noise covariance matrix, in which the back-propagation algorithm is employed to iteratively adjust the link weights using the steepest descent technique. Numerical simulations show that based on the proposed approach the adaptation performance is substantially enhanced and the positioning accuracy is substantially improved.

  15. Book Review: Water Diplomacy: A Negotiated Approach to Managing Complex Water Networks

    NASA Astrophysics Data System (ADS)

    Hossain, Faisal

    2013-01-01

    All nations have built their economies around water that is naturally available. Almost all sectors of the economy depend on water. Yet there is conflict among various users for the finite amount of water that is available. Managers and practitioners have long held the notion that competition rather than collaboration is the solution when there is conflict. Water Diplomacy: A Negotiated Approach to Managing Complex Water Networks, by Shafiqul Islam and Lawrence Susskind, provides a refreshingly compelling alternative to overcoming water conflicts. The book argues that the dynamic sociopolitical and socioeconomic constraints of water resources are best addressed in a "diplomacy" framework. The book rebuts, using several case studies, the technically rigid competition approach of today's water sharing practice.

  16. A semi-free weighting matrices approach for neutral-type delayed neural networks

    NASA Astrophysics Data System (ADS)

    Mai, Huanhuan; Liao, Xiaofeng; Li, Chuandong

    2009-03-01

    In this paper, a new approach is proposed for stability issues of neutral-type neural networks (DNNs) with constant delay. First, the semi-free weighting matrices are proposed and used instead of the known free weighting matrices to express the relationship between the terms in the Leibniz-Newton formula to simplify the system synthesis and to obtain less computation demand. Second, global exponential stability conditions which are less conservative and restrictive than the known results are derived. At the same time, based on the above approach, fewer variable matrices are introduced in the construction of the Lyapunov functional and augmented Lyapunov functional. Two examples are given to show their effectiveness and advantages over others.

  17. A hybrid calibration-free/artificial neural networks approach to the quantitative analysis of LIBS spectra

    NASA Astrophysics Data System (ADS)

    D'Andrea, Eleonora; Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano; Lorenzetti, Giulia; Palleschi, Vincenzo; Lazzerini, Beatrice

    2015-03-01

    A `hybrid' method is proposed for the quantitative analysis of materials by LIBS, combining the precision of the calibration-free LIBS (CF-LIBS) algorithm with the quickness of artificial neural networks. The method allows the precise determination of the samples' composition even in the presence of relatively large laser fluctuations and matrix effects. To show the strength and robustness of this approach, a number of synthetic LIBS spectra of Cu-Ni binary alloys with different composition were computer-simulated, in correspondence of different plasma temperatures, electron number densities and ablated mass. The CF-LIBS/ANN approach here proposed demonstrated to be capable, after appropriate training, of `learning' the basic physical relations between the experimentally measured line intensities and the plasma parameters. Because of that the composition of the sample can be correctly determined, as in CF-LIBS measurements, but in a much shorter time.

  18. New approach for earthquake/tsunami monitoring using dense GPS networks.

    PubMed

    Li, Xingxing; Ge, Maorong; Zhang, Yong; Wang, Rongjiang; Xu, Peiliang; Wickert, Jens; Schuh, Harald

    2013-01-01

    In recent times increasing numbers of high-rate GPS stations have been installed around the world and set-up to provide data in real-time. These networks provide a great opportunity to quickly capture surface displacements, which makes them important as potential constituents of earthquake/tsunami monitoring and warning systems. The appropriate GPS real-time data analysis with sufficient accuracy for this purpose is a main focus of the current GPS research. In this paper we propose an augmented point positioning method for GPS based hazard monitoring, which can achieve fast or even instantaneous precise positioning without relying on data of a specific reference station. The proposed method overcomes the limitations of the currently mostly used GPS processing approaches of relative positioning and global precise point positioning. The advantages of the proposed approach are demonstrated by using GPS data, which was recorded during the 2011 Tohoku-Oki earthquake in Japan. PMID:24045328

  19. Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks.

    PubMed

    Chon, K H; Holstein-Rathlou, N H; Marsh, D J; Marmarelis, V Z

    1998-01-01

    Volterra models have been increasingly popular in modeling studies of nonlinear physiological systems. In this paper, feedforward artificial neural networks with two types of activation functions (sigmoidal and polynomial) are utilized for modeling the nonlinear dynamic relation between renal blood pressure and flow data, and their performance is compared to Volterra models obtained by use of the leading kernel estimation method based on Laguerre expansions. The results for the two types of artificial neural networks (sigmoidal and polynomial) and the Volterra models are comparable in terms of normalized mean-square error (NMSE) of the respective output prediction for independent testing data. However, the Volterra models obtained via the Laguerre expansion technique achieve this prediction NMSE with approximately half the number of free parameters relative to either neural-network model. Nonetheless, both approaches are deemed effective in modeling nonlinear dynamic systems and their cooperative use is recommended in general, since they may exhibit different strengths and weaknesses depending on the specific characteristics of each application. PMID:18252466

  20. Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems.

    PubMed

    Vrabie, Draguna; Lewis, Frank

    2009-04-01

    In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided. PMID:19362449

  1. Finding the multipath propagation of multivariable crude oil prices using a wavelet-based network approach

    NASA Astrophysics Data System (ADS)

    Jia, Xiaoliang; An, Haizhong; Sun, Xiaoqi; Huang, Xuan; Gao, Xiangyun

    2016-04-01

    The globalization and regionalization of crude oil trade inevitably give rise to the difference of crude oil prices. The understanding of the pattern of the crude oil prices' mutual propagation is essential for analyzing the development of global oil trade. Previous research has focused mainly on the fuzzy long- or short-term one-to-one propagation of bivariate oil prices, generally ignoring various patterns of periodical multivariate propagation. This study presents a wavelet-based network approach to help uncover the multipath propagation of multivariable crude oil prices in a joint time-frequency period. The weekly oil spot prices of the OPEC member states from June 1999 to March 2011 are adopted as the sample data. First, we used wavelet analysis to find different subseries based on an optimal decomposing scale to describe the periodical feature of the original oil price time series. Second, a complex network model was constructed based on an optimal threshold selection to describe the structural feature of multivariable oil prices. Third, Bayesian network analysis (BNA) was conducted to find the probability causal relationship based on periodical structural features to describe the various patterns of periodical multivariable propagation. Finally, the significance of the leading and intermediary oil prices is discussed. These findings are beneficial for the implementation of periodical target-oriented pricing policies and investment strategies.

  2. Network topology, Transport dynamics, and Vulnerability Analysis in River Deltas: A Graph-Theoretic Approach

    NASA Astrophysics Data System (ADS)

    Tejedor, A.; Foufoula-Georgiou, E.; Longjas, A.; Zaliapin, I. V.

    2014-12-01

    River deltas are intricate landscapes with complex channel networks that self-organize to deliver water, sediment, and nutrients from the apex to the delta top and eventually to the coastal zone. The natural balance of material and energy fluxes which maintains a stable hydrologic, geomorphologic, and ecological state of a river delta, is often disrupted by external factors causing topological and dynamical changes in the delta structure and function. A formal quantitative framework for studying river delta topology and transport dynamics and their response to change is lacking. Here we present such a framework based on spectral graph theory and demonstrate its value in quantifying the complexity of the delta network topology, computing its steady state fluxes, and identifying upstream (contributing) and downstream (nourishment) areas from any point in the network. We use this framework to construct vulnerability maps that quantify the relative change of sediment and water delivery to the shoreline outlets in response to possible perturbations in hundreds of upstream links. This enables us to evaluate which links (hotspots) and what management scenarios would most influence flux delivery to the outlets, paving the way of systematically examining how local or spatially distributed delta interventions can be studied within a systems approach for delta sustainability.

  3. Systems Approach to Studying Animal Sociality: Individual Position versus Group Organization in Dynamic Social Network Models

    PubMed Central

    Hock, Karlo; Ng, Kah Loon; Fefferman, Nina H.

    2010-01-01

    Social networks can be used to represent group structure as a network of interacting components, and also to quantify both the position of each individual and the global properties of a group. In a series of simulation experiments based on dynamic social networks, we test the prediction that social behaviors that help individuals reach prominence within their social group may conflict with their potential to benefit from their social environment. In addition to cases where individuals were able to benefit from improving both their personal relative importance and group organization, using only simple rules of social affiliation we were able to obtain results in which individuals would face a trade-off between these factors. While selection would favor (or work against) social behaviors that concordantly increase (or decrease, respectively) fitness at both individual and group level, when these factors conflict with each other the eventual selective pressure would depend on the relative returns individuals get from their social environment and their position within it. The presented results highlight the importance of a systems approach to studying animal sociality, in which the effects of social behaviors should be viewed not only through the benefits that those provide to individuals, but also in terms of how they affect broader social environment and how in turn this is reflected back on an individual's fitness. PMID:21203425

  4. A numerically-enhanced machine learning approach to damage diagnosis using a Lamb wave sensing network

    NASA Astrophysics Data System (ADS)

    Sbarufatti, C.; Manson, G.; Worden, K.

    2014-09-01

    This paper describes a methodology for the design of a model-based diagnostic unit. The objective of the work is to define a suitable procedure for the design and verification of diagnostic performance in a simulated environment, trying to maximise the generalisation capability of pattern recognition algorithms when tested with real experimental signals. The system is designed and experimentally verified to solve the fatigue crack damage localisation and assessment problems in a realistic, though rather idealised, Structural Health Monitoring (SHM) framework. The study is applied to a piezoelectric Lamb wave sensor network and is validated experimentally on a simple aluminium skin. The analytically-derived dispersion curves for Lamb wave propagation in aluminium are used in order to determine the wave velocities and thus their arrival time at given sensors. The Local Interaction Simulation Approach (LISA) is used to simulate the entire waveform propagation. Once the agreement between analytical, numerical and experimental data is verified on a baseline undamaged condition, the parametric LISA model has been iteratively run, varying the position and the length of a crack on an aluminium skin panel, generating the virtual experience necessary to train a supervised learning regressor based on Artificial Neural Networks (ANNs). After the algorithm structure has been statistically optimised, the network sensitivity to input variations has been evaluated on simulated signals through a technique inspired by information gap theory. Real Lamb wave signals are then processed into the algorithm, providing feasible real-time indication of damage characteristics.

  5. Research and collaboration overview of Institut Pasteur International Network: a bibliometric approach toward research funding decisions

    PubMed Central

    Mostafavi, Ehsan; Bazrafshan, Azam

    2014-01-01

    Background: Institut Pasteur International Network (IPIN), which includes 32 research institutes around the world, is a network of research and expertise to fight against infectious diseases. A scientometric approach was applied to describe research and collaboration activities of IPIN. Methods: Publications were identified using a manual search of IPIN member addresses in Science Citation Index Expanded (SCIE) between 2006 and 2011. Total publications were then subcategorized by geographic regions. Several scientometric indicators and the H-index were employed to estimate the scientific production of each IPIN member. Subject and geographical overlay maps were also applied to visualize the network activities of the IPIN members. Results: A total number of 12667 publications originated from IPIN members. Each author produced an average number of 2.18 papers and each publication received an average of 13.40 citations. European Pasteur Institutes had the largest amount of publications, authored papers, and H-index values. Biochemistry and molecular biology, microbiology, immunology and infectious diseases were the most important research topics, respectively. Geographic mapping of IPIN publications showed wide international collaboration among IPIN members around the world. Conclusion: IPIN has strong ties with national and international authorities and organizations to investigate the current and future health issues. It is recommended to use scientometric and collaboration indicators as measures of research performance in IPIN future policies and investment decisions. PMID:24596896

  6. A restricted branch and bound approach for setting the left turn phase sequences in signalized networks

    SciTech Connect

    Pillai, R.S.; Rathi, A.K.; Cohen, S.

    1994-07-01

    The main objective of synchronized signal timing is to keep traffic moving along arterial in platoons throughout the signal system by proper setting of left turn phase sequence at signals along the arterials/networks. The synchronization of traffic signals located along the urban/suburban arterials in metropolitan areas is perhaps one of the most cost-effective method for improving traffic flow along these streets. The popular technique for solving this problem formulates it as a mixed integer linear program and used Land and Powell branch and bound search to arrive at the optimal solution. The computation time tends to be excessive for realistic multiarterial network problems due to the exhaustive nature of the branch and bound search technique. Furthermore, the Land and Powell branch and bound code is known to be numerically unstable, which results in suboptimal solutions for network problems with a range on the cycle time variable. This paper presents the development of a fast and numerically stable heuristic, developed using MINOS linear programming solver. The new heuristic can generate optimal/near-optimal solutions in a fraction of the time needed to compute the optimal solution by Land and Powell code. The solution technique is based on restricted search using branch and bound technique. The efficiency of the heuristic approach is demonstrated by numerical results for a set of test problems.

  7. Generalized Low-Temperature Fabrication of Scalable Multi-Type Two-Dimensional Nanosheets with a Green Soft Template.

    PubMed

    Wang, Lanfang; Song, Chuang; Shi, Yi; Dang, Liyun; Jin, Ying; Jiang, Hong; Lu, Qingyi; Gao, Feng

    2016-04-11

    Two-dimensional nanosheets with high specific surface areas and fascinating physical and chemical properties have attracted tremendous interests because of their promising potentials in both fundamental research and practical applications. However, the problem of developing a universal strategy with a facile and cost-effective synthesis process for multi-type ultrathin 2 D nanostructures remains unresolved. Herein, we report a generalized low-temperature fabrication of scalable multi-type 2 D nanosheets including metal hydroxides (such as Ni(OH)2, Co(OH)2, Cd(OH)2, and Mg(OH)2), metal oxides (such as ZnO and Mn3O4), and layered mixed transition-metal hydroxides (Ni-Co LDH, Ni-Fe LDH, Co-Fe LDH, and Ni-Co-Fe layered ternary hydroxides) through the rational employment of a green soft-template. The synthesized crystalline inorganic nanosheets possess confined thickness, resulting in ultrahigh surface atom ratios and chemically reactive facets. Upon evaluation as electrode materials for pseudocapacitors, the Ni-Co LDH nanosheets exhibit a high specific capacitance of 1087 F g(-1) at a current density of 1 A g(-1), and excellent stability, with 103% retention after 500 cycles. This strategy is facile and scalable for the production of high-quality ultrathin crystalline inorganic nanosheets, with the possibility of extension to the preparation of other complex nanosheets. PMID:26946433

  8. Examining Food Risk in the Large using a Complex, Networked System-of-sytems Approach

    SciTech Connect

    Ambrosiano, John; Newkirk, Ryan; Mc Donald, Mark P

    2010-12-03

    The food production infrastructure is a highly complex system of systems. Characterizing the risks of intentional contamination in multi-ingredient manufactured foods is extremely challenging because the risks depend on the vulnerabilities of food processing facilities and on the intricacies of the supply-distribution networks that link them. A pure engineering approach to modeling the system is impractical because of the overall system complexity and paucity of data. A methodology is needed to assess food contamination risk 'in the large', based on current, high-level information about manufacturing facilities, corrunodities and markets, that will indicate which food categories are most at risk of intentional contamination and warrant deeper analysis. The approach begins by decomposing the system for producing a multi-ingredient food into instances of two subsystem archetypes: (1) the relevant manufacturing and processing facilities, and (2) the networked corrunodity flows that link them to each other and consumers. Ingredient manufacturing subsystems are modeled as generic systems dynamics models with distributions of key parameters that span the configurations of real facilities. Networks representing the distribution systems are synthesized from general information about food corrunodities. This is done in a series of steps. First, probability networks representing the aggregated flows of food from manufacturers to wholesalers, retailers, other manufacturers, and direct consumers are inferred from high-level approximate information. This is followed by disaggregation of the general flows into flows connecting 'large' and 'small' categories of manufacturers, wholesalers, retailers, and consumers. Optimization methods are then used to determine the most likely network flows consistent with given data. Vulnerability can be assessed for a potential contamination point using a modified CARVER + Shock model. Once the facility and corrunodity flow models are

  9. Message-passing approach for recurrent-state epidemic models on networks

    NASA Astrophysics Data System (ADS)

    Shrestha, Munik; Scarpino, Samuel V.; Moore, Cristopher

    2015-08-01

    Epidemic processes are common out-of-equilibrium phenomena of broad interdisciplinary interest. Recently, dynamic message-passing (DMP) has been proposed as an efficient algorithm for simulating epidemic models on networks, and in particular for estimating the probability that a given node will become infectious at a particular time. To date, DMP has been applied exclusively to models with one-way state changes, as opposed to models like SIS and SIRS where nodes can return to previously inhabited states. Because many real-world epidemics can exhibit such recurrent dynamics, we propose a DMP algorithm for complex, recurrent epidemic models on networks. Our approach takes correlations between neighboring nodes into account while preventing causal signals from backtracking to their immediate source, and thus avoids "echo chamber effects" where a pair of adjacent nodes each amplify the probability that the other is infectious. We demonstrate that this approach well approximates results obtained from Monte Carlo simulation and that its accuracy is often superior to the pair approximation (which also takes second-order correlations into account). Moreover, our approach is more computationally efficient than the pair approximation, especially for complex epidemic models: the number of variables in our DMP approach grows as 2 m k where m is the number of edges and k is the number of states, as opposed to m k2 for the pair approximation. We suspect that the resulting reduction in computational effort, as well as the conceptual simplicity of DMP, will make it a useful tool in epidemic modeling, especially for high-dimensional inference tasks.

  10. A Systems Biology Approach Identifies a Regulatory Network in Parotid Acinar Cell Terminal Differentiation

    PubMed Central

    Metzler, Melissa A.; Venkatesh, Srirangapatnam G.; Lakshmanan, Jaganathan; Carenbauer, Anne L.; Perez, Sara M.; Andres, Sarah A.; Appana, Savitri; Brock, Guy N.; Wittliff, James L.; Darling, Douglas S.

    2015-01-01

    Objective The transcription factor networks that drive parotid salivary gland progenitor cells to terminally differentiate, remain largely unknown and are vital to understanding the regeneration process. Methodology A systems biology approach was taken to measure mRNA and microRNA expression in vivo across acinar cell terminal differentiation in the rat parotid salivary gland. Laser capture microdissection (LCM) was used to specifically isolate acinar cell RNA at times spanning the month-long period of parotid differentiation. Results Clustering of microarray measurements suggests that expression occurs in four stages. mRNA expression patterns suggest a novel role for Pparg which is transiently increased during mid postnatal differentiation in concert with several target gene mRNAs. 79 microRNAs are significantly differentially expressed across time. Profiles of statistically significant changes of mRNA expression, combined with reciprocal correlations of microRNAs and their target mRNAs, suggest a putative network involving Klf4, a differentiation inhibiting transcription factor, which decreases as several targeting microRNAs increase late in differentiation. The network suggests a molecular switch (involving Prdm1, Sox11, Pax5, miR-200a, and miR-30a) progressively decreases repression of Xbp1 gene transcription, in concert with decreased translational repression by miR-214. The transcription factor Xbp1 mRNA is initially low, increases progressively, and may be maintained by a positive feedback loop with Atf6. Transfection studies show that Xbp1Mist1 promoter. In addition, Xbp1 and Mist1 each activate the parotid secretory protein (Psp) gene, which encodes an abundant salivary protein, and is a marker of terminal differentiation. Conclusion This study identifies novel expression patterns of Pparg, Klf4, and Sox11 during parotid acinar cell differentiation, as well as numerous differentially expressed microRNAs. Network analysis identifies a novel stemness arm, a

  11. Improved SST estimates for AVHRR from ATSR data through a neural network approach

    SciTech Connect

    Arulmani, C.; Gurney, R.J.

    1997-08-01

    Many applications such as weather forecasting, oceanography and to determine the possible climate change need frequent data such as Sea Surface Temperature (SST) with wider spatial coverage and reliable accurate estimates. Even though the sampling requirements for SST`s can be met from Advanced Very High Resolution Radiometer (AVHRR) data, the accuracy required from these SSTs remains a challenge. The Along Track Scanning Radiometer (ATSR) retrieved SST`s offer better estimates than the AVHRR Multichannel Sea Surface Temperature (MCSST) algorithm, but the swath width of the ATSR is 512 km and the repetition cycle is approximately 3 days. In this study an attempt has been made to generate a new SST retrieval model for AVHRR using the SST`s retrieved from ATSR data. A multilayer neural network approach is employed to generate the model parameters. The new approach for the retrieval of SST for AVHRR data using the neural network yields the residual error within {plus_minus} 0.24{degrees}C when compared with the ATSR derived SSTs.

  12. Altered functional connectivity of the default mode network in Williams syndrome: a multimodal approach.

    PubMed

    Sampaio, Adriana; Moreira, Pedro Silva; Osório, Ana; Magalhães, Ricardo; Vasconcelos, Cristiana; Férnandez, Montse; Carracedo, Angel; Alegria, Joana; Gonçalves, Óscar F; Soares, José Miguel

    2016-07-01

    Resting state brain networks are implicated in a variety of relevant brain functions. Importantly, abnormal patterns of functional connectivity (FC) have been reported in several neurodevelopmental disorders. In particular, the Default Mode Network (DMN) has been found to be associated with social cognition. We hypothesize that the DMN may be altered in Williams syndrome (WS), a neurodevelopmental genetic disorder characterized by an unique cognitive and behavioral phenotype. In this study, we assessed the architecture of the DMN using fMRI in WS patients and typically developing matched controls (sex and age) in terms of FC and volumetry of the DMN. Moreover, we complemented the analysis with a functional connectome approach. After excluding participants due to movement artifacts (n = 3), seven participants with WS and their respective matched controls were included in the analyses. A decreased FC between the DMN regions was observed in the WS group when compared with the typically developing group. Specifically, we found a decreased FC in a posterior hub of the DMN including the precuneus, calcarine and the posterior cingulate of the left hemisphere. The functional connectome approach showed a focalized and global increased FC connectome in the WS group. The reduced FC of the posterior hub of the DMN in the WS group is consistent with immaturity of the brain FC patterns and may be associated with the singularity of their visual spatial phenotype. PMID:27412230

  13. Criticality in neural ensembles: a mean field approach to expand network size from measured data

    NASA Astrophysics Data System (ADS)

    Wasnik, Vaibhav; Caracheo, Barak; Seamans, Jeremy; Emberly, Eldon

    2014-03-01

    At the point of a second order phase transition also termed as a critical point, systems display long range order and their macroscopic behaviours are independent of the microscopic details making up the system. This makes the idea of criticality interesting for studying biological systems which even though are different microscopically still have similar macroscopic behaviours. Recent high-throughput methods in neuroscience are making it possible to explore whether criticality exists in neural networks. Despite being high-throughput, many data sets are still only a minute sample of the neural system and methods towards expanding these data sets have to be considered in order to study the existence of criticality. Using measurements of firing neurons from the pre-frontal cortex (PFC) of rats, we map the data to a system of Ising spins and calculate the specific heat as a function of the measured network size, looking for the existence of critical points. In order to go to the thermodynamic limit, we propose a mean field approach for expanding such data. Our preliminary results show that such an approach can capture the statistical properties of much larger neuronal populations even when only a smaller subset is measured.

  14. Neural-Network Approach to Hyperspectral Data Analysis for Volcanic Ash Clouds Monitoring

    NASA Astrophysics Data System (ADS)

    Piscini, Alessandro; Ventress, Lucy; Carboni, Elisa; Granger, Roy Gordon; Del Frate, Fabio

    2015-06-01

    In this study three artificial neural networks (ANN) were implemented in order to emulate a retrieval model and to estimate the ash Aerosol optical Depth (AOD), particle effective radius (reff) and cloud height from volcanic eruption using hyperspectral remotely sensed data. ANNs were trained using a selection of Infrared Atmospheric Sounding Interferometer (IASI) channels in Thermal Infrared (TIR) as inputs, and the corresponding ash parameters retrieved obtained using the Oxford retrievals as target outputs. The retrieval is demonstrated for the eruption of the Eyjafjallajokull volcano (Iceland) occurred in 2010. The results of validation provided root mean square error (RMSE) values between neural network outputs and targets lower than standard deviation (STD) of corresponding target outputs, therefore demonstrating the feasibility to estimate volcanic ash parameters using an ANN approach, and its importance in near real time monitoring activities, owing to its fast application. A high accuracy has been achieved for reff and cloud height estimation, while a decreasing in accuracy was obtained when applying the NN approach for AOD estimation, in particular for those values not well characterized during NN training phase.

  15. An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

    PubMed Central

    Guan, Xiangmin; Zhang, Xuejun; Zhu, Yanbo; Sun, Dengfeng; 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

  16. Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks

    PubMed Central

    Bourqui, Romain; Benchimol, William; Gaspin, Christine; Sirand-Pugnet, Pascal; Uricaru, Raluca; Dutour, Isabelle

    2015-01-01

    The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA–mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA–mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks. PMID:25477348

  17. Discovering potential cancer driver genes by an integrated network-based approach.

    PubMed

    Shi, Kai; Gao, Lin; Wang, Bingbo

    2016-08-16

    Although a lot of methods have been proposed to identify driver genes, how to separate the driver mutations from the passenger mutations is still a challenging problem in cancer genomics. The detection of driver genes with rare mutation and low accuracy is unsolved better. In this study, we present an integrated network-based approach to locate potential driver genes in a cohort of patients. The approach is composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between the gene mutations and gene expression, the relationship between the mutated genes and the heterogeneous characteristic of the patient mutation. We analyze three cancer datasets including Glioblastoma multiforme, Ovarian cancer and Breast cancer. Our method has not only identified the known driver genes with high-frequency mutations, but also discovered the potential driver genes with a rare mutation. At the same time, validation by literature search and functional enrichment analysis reveal that the predicted genes are obviously related to these three kinds of cancers. PMID:27426053

  18. Microbial metabolic networks in a complex electrogenic biofilm recovered from a stimulus-induced metatranscriptomics approach

    PubMed Central

    Ishii, Shun’ichi; Suzuki, Shino; Tenney, Aaron; Norden-Krichmar, Trina M.; Nealson, Kenneth H.; Bretschger, Orianna

    2015-01-01

    Microorganisms almost always exist as mixed communities in nature. While the significance of microbial community activities is well appreciated, a thorough understanding about how microbial communities respond to environmental perturbations has not yet been achieved. Here we have used a combination of metagenomic, genome binning, and stimulus-induced metatranscriptomic approaches to estimate the metabolic network and stimuli-induced metabolic switches existing in a complex microbial biofilm that was producing electrical current via extracellular electron transfer (EET) to a solid electrode surface. Two stimuli were employed: to increase EET and to stop EET. An analysis of cell activity marker genes after stimuli exposure revealed that only two strains within eleven binned genomes had strong transcriptional responses to increased EET rates, with one responding positively and the other responding negatively. Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms. These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions. This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli. PMID:26443302

  19. Two-dimensional magnetic modeling of ferromagnetic materials by using a neural networks based hybrid approach

    NASA Astrophysics Data System (ADS)

    Cardelli, E.; Faba, A.; Laudani, A.; Lozito, G. M.; Riganti Fulginei, F.; Salvini, A.

    2016-04-01

    This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented.

  20. An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework.

    PubMed

    Guan, Xiangmin; Zhang, Xuejun; Zhu, Yanbo; Sun, Dengfeng; 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

  1. Microbial metabolic networks in a complex electrogenic biofilm recovered from a stimulus-induced metatranscriptomics approach.

    PubMed

    Ishii, Shun'ichi; Suzuki, Shino; Tenney, Aaron; Norden-Krichmar, Trina M; Nealson, Kenneth H; Bretschger, Orianna

    2015-01-01

    Microorganisms almost always exist as mixed communities in nature. While the significance of microbial community activities is well appreciated, a thorough understanding about how microbial communities respond to environmental perturbations has not yet been achieved. Here we have used a combination of metagenomic, genome binning, and stimulus-induced metatranscriptomic approaches to estimate the metabolic network and stimuli-induced metabolic switches existing in a complex microbial biofilm that was producing electrical current via extracellular electron transfer (EET) to a solid electrode surface. Two stimuli were employed: to increase EET and to stop EET. An analysis of cell activity marker genes after stimuli exposure revealed that only two strains within eleven binned genomes had strong transcriptional responses to increased EET rates, with one responding positively and the other responding negatively. Potential metabolic switches between eleven dominant members were mainly observed for acetate, hydrogen, and ethanol metabolisms. These results have enabled the estimation of a multi-species metabolic network and the associated short-term responses to EET stimuli that induce changes to metabolic flow and cooperative or competitive microbial interactions. This systematic meta-omics approach represents a next step towards understanding complex microbial roles within a community and how community members respond to specific environmental stimuli. PMID:26443302

  2. Convolutional neural network approach for buried target recognition in FL-LWIR imagery

    NASA Astrophysics Data System (ADS)

    Stone, K.; Keller, J. M.

    2014-05-01

    A convolutional neural network (CNN) approach to recognition of buried explosive hazards in forward-looking long-wave infrared (FL-LWIR) imagery is presented. The convolutional filters in the first layer of the network are learned in the frequency domain, making enforcement of zero-phase and zero-dc response characteristics much easier. The spatial domain representations of the filters are forced to have unit l2 norm, and penalty terms are added to the online gradient descent update to encourage orthonormality among the convolutional filters, as well smooth first and second order derivatives in the spatial domain. The impact of these modifications on the generalization performance of the CNN model is investigated. The CNN approach is compared to a second recognition algorithm utilizing shearlet and log-gabor decomposition of the image coupled with cell-structured feature extraction and support vector machine classification. Results are presented for multiple FL-LWIR data sets recently collected from US Army test sites. These data sets include vehicle position information allowing accurate transformation between image and world coordinates and realistic evaluation of detection and false alarm rates.

  3. Portraying emotions at their unfolding: a multilayered approach for probing dynamics of neural networks.

    PubMed

    Raz, Gal; Winetraub, Yonatan; Jacob, Yael; Kinreich, Sivan; Maron-Katz, Adi; Shaham, Galit; Podlipsky, Ilana; Gilam, Gadi; Soreq, Eyal; Hendler, Talma

    2012-04-01

    Dynamic functional integration of distinct neural systems plays a pivotal role in emotional experience. We introduce a novel approach for studying emotion-related changes in the interactions within and between networks using fMRI. It is based on continuous computation of a network cohesion index (NCI), which is sensitive to both strength and variability of signal correlations between pre-defined regions. The regions encompass three clusters (namely limbic, medial prefrontal cortex (mPFC) and cognitive), each previously was shown to be involved in emotional processing. Two sadness-inducing film excerpts were viewed passively, and comparisons between viewer's rated sadness, parasympathetic, and inter-NCI and intra-NCI were obtained. Limbic intra-NCI was associated with reported sadness in both movies. However, the correlation between the parasympathetic-index, the rated sadness and the limbic-NCI occurred in only one movie, possibly related to a "deactivated" pattern of sadness. In this film, rated sadness intensity also correlated with the mPFC intra-NCI, possibly reflecting temporal correspondence between sadness and sympathy. Further, only for this movie, we found an association between sadness rating and the mPFC-limbic inter-NCI time courses. To the contrary, in the other film in which sadness was reported to commingle with horror and anger, dramatic events coincided with disintegration of these networks. Together, this may point to a difference between the cinematic experiences with regard to inter-network dynamics related to emotional regulation. These findings demonstrate the advantage of a multi-layered dynamic analysis for elucidating the uniqueness of emotional experiences with regard to an unguided processing of continuous and complex stimulation. PMID:22285693

  4. Optimizing neural networks for river flow forecasting - Evolutionary Computation methods versus the Levenberg-Marquardt approach

    NASA Astrophysics Data System (ADS)

    Piotrowski, Adam P.; Napiorkowski, Jarosław J.

    2011-09-01

    SummaryAlthough neural networks have been widely applied to various hydrological problems, including river flow forecasting, for at least 15 years, they have usually been trained by means of gradient-based algorithms. Recently nature inspired Evolutionary Computation algorithms have rapidly developed as optimization methods able to cope not only with non-differentiable functions but also with a great number of local minima. Some of proposed Evolutionary Computation algorithms have been tested for neural networks training, but publications which compare their performance with gradient-based training methods are rare and present contradictory conclusions. The main goal of the present study is to verify the applicability of a number of recently developed Evolutionary Computation optimization methods, mostly from the Differential Evolution family, to multi-layer perceptron neural networks training for daily rainfall-runoff forecasting. In the present paper eight Evolutionary Computation methods, namely the first version of Differential Evolution (DE), Distributed DE with Explorative-Exploitative Population Families, Self-Adaptive DE, DE with Global and Local Neighbors, Grouping DE, JADE, Comprehensive Learning Particle Swarm Optimization and Efficient Population Utilization Strategy Particle Swarm Optimization are tested against the Levenberg-Marquardt algorithm - probably the most efficient in terms of speed and success rate among gradient-based methods. The Annapolis River catchment was selected as the area of this study due to its specific climatic conditions, characterized by significant seasonal changes in runoff, rapid floods, dry summers, severe winters with snowfall, snow melting, frequent freeze and thaw, and presence of river ice - conditions which make flow forecasting more troublesome. The overall performance of the Levenberg-Marquardt algorithm and the DE with Global and Local Neighbors method for neural networks training turns out to be superior to other

  5. A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression

    PubMed Central

    So, Nina; Franks, Becca; Lim, Sean; Curley, James P.

    2015-01-01

    Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David’s Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg’s Hub Centrality and Bonacich’s Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing and intensive

  6. The National Ecological Observatory Network's Atmospheric and Terrestrial Instrumentation: Quality Control Approaches

    NASA Astrophysics Data System (ADS)

    Taylor, J. R.; Luo, H.; Ayres, E.; Metzger, S. R.; Loescher, H. W.

    2012-12-01

    The National Ecological Observatory Network's Fundamental Instrument Unit (NEON-FIU) is responsible for making automated terrestrial observations at 60 different sites across the continent. FIU will provide data on key local physical, chemical, and climate forcing, as well as associated biotic responses (CO2, H2O, and energy exchanges). The sheer volume of data that will be generated far exceeds that of any other observatory network or agency, (i.e., > 45 Tb/year from 10's of thousands of remotely deployed sensors). We address the question of how to develop and implement a large ecological observatory that can accommodate such a large volume of data while maintaining high quality. Here, we describe our quality assurance and quality control (QA/QC) program to produce quality data while leveraging cyber infrastructure tools and optimizing technician time. Results focus on novel approaches that advance the quality control techniques that have been historically employed in other networks (DOE-ARM, AmeriFlux, USDA ARS, OK Mesonet) to new state-of-the-art functionality. These automated and semi-automated approaches are also used to inform automated problem tracking to efficiently deploy field staff. Ultimately, NEON will define its own standards for QA/QC and maintenance by building upon these existing frameworks. The overarching philosophy relies on attaining the highest levels of accuracy, precision, and operational time, while efficiently optimizing the effort needed to produce quality data products. Our preliminary results address the challenges associated with automated implementation of sensor command/control, plausibility testing, despiking, and data verification of FIU observations.

  7. Current Trends in Wireless Mesh Sensor Networks: A Review of Competing Approaches

    PubMed Central

    Rodenas-Herraiz, David; Garcia-Sanchez, Antonio-Javier; Garcia-Sanchez, Felipe; Garcia-Haro, Joan

    2013-01-01

    Finding a complete mesh-based solution for low-rate wireless personal area networks (LR-WPANs) is still an open issue. To cope with this concern, different competing approaches have emerged in the Wireless Mesh Sensor Networks (WMSNs) field in the last few years. They are usually supported by the IEEE 802.15.4 standard, the most commonly adopted LR-WPAN recommendation for point-to-point topologies. In this work, we review the most relevant and up-to-date WMSN solutions that extend the IEEE 802.15.4 standard to multi-hop mesh networks. To conduct this review, we start by identifying the most significant WMSN requirements (i.e., interoperability, robustness, scalability, mobility or energy-efficiency) that reveal the benefits and shortcomings of each proposal. Then, we re-examine thoroughly the group of proposals following different design guidelines which are usually considered by end-users and developers. Among all of the approaches reviewed, we highlight the IEEE 802.15.5 standard, a recent recommendation that, in its LR-WPAN version, fully satisfies the greatest number of WMSN requirements. As a result, IEEE 802.15.5 can be an appropriate solution for a wide-range of applications, unlike the majority of the remaining solutions reviewed, which are usually designed to solve particular problems, for instance in the home, building and industrial sectors. In this sense, a description of IEEE 802.15.5 is also included, paying special attention to its efficient energy-saving mechanisms. Finally, possible improvements of this recommendation are pointed out in order to offer hints for future research. PMID:23666128

  8. Artificial neural network approach for moiré fringe center determination

    NASA Astrophysics Data System (ADS)

    Woo, Wing Hon; Ratnam, Mani Maran; Yen, Kin Sam

    2015-11-01

    The moiré effect has been used in high-accuracy positioning and alignment systems for decades. Various methods have been proposed to identify and locate moiré fringes in order to relate the pattern information to dimensional and displacement measurement. These methods can be broadly categorized into manual interpretation based on human knowledge and image processing based on computational algorithms. An artificial neural network (ANN) is proposed to locate moiré fringe centers within circular grating moiré patterns. This ANN approach aims to mimic human decision making by eliminating complex mathematical computations or time-consuming image processing algorithms in moiré fringe recognition. A feed-forward backpropagation ANN architecture was adopted in this work. Parametric studies were performed to optimize the ANN architecture. The finalized ANN approach was able to determine the location of the fringe centers with average deviations of 3.167 pixels out of 200 pixels (≈1.6%) and 6.166 pixels out of 200 pixels (≈3.1%) for real moiré patterns that lie within and outside the training intervals, respectively. In addition, a reduction of 43.4% in the computational time was reported using the ANN approach. Finally, the applicability of the ANN approach for moiré fringe center determination was confirmed.

  9. Nonperturbative heterogeneous mean-field approach to epidemic spreading in complex networks

    NASA Astrophysics Data System (ADS)

    Gómez, Sergio; Gómez-Gardeñes, Jesús; Moreno, Yamir; Arenas, Alex

    2011-09-01

    Since roughly a decade ago, network science has focused among others on the problem of how the spreading of diseases depends on structural patterns. Here, we contribute to further advance our understanding of epidemic spreading processes by proposing a nonperturbative formulation of the heterogeneous mean-field approach that has been commonly used in the physics literature to deal with this kind of spreading phenomena. The nonperturbative equations we propose have no assumption about the proximity of the system to the epidemic threshold, nor any linear approximation of the dynamics. In particular, we first develop a probabilistic description at the node level of the epidemic propagation for the so-called susceptible-infected-susceptible family of models, and after we derive the corresponding heterogeneous mean-field approach. We propose to use the full extension of the approach instead of pruning the expansion to first order, which leads to a nonperturbative formulation that can be solved by fixed-point iteration, and used with reliability far away from the epidemic threshold to assess the prevalence of the epidemics. Our results are in close agreement with Monte Carlo simulations, thus enhancing the predictive power of the classical heterogeneous mean-field approach, while providing a more effective framework in terms of computational time.

  10. Hierarchical Bayesian approaches for detecting inconsistency in network meta-analysis.

    PubMed

    Zhao, Hong; Hodges, James S; Ma, Haijun; Jiang, Qi; Carlin, Bradley P

    2016-09-10

    Network meta-analysis (NMA), also known as multiple treatment comparisons, is commonly used to incorporate direct and indirect evidence comparing treatments. With recent advances in methods and software, Bayesian approaches to NMA have become quite popular and allow models of previously unanticipated complexity. However, when direct and indirect evidence differ in an NMA, the model is said to suffer from inconsistency. Current inconsistency detection in NMA is usually based on contrast-based (CB) models; however, this approach has certain limitations. In this work, we propose an arm-based random effects model, where we detect discrepancy of direct and indirect evidence for comparing two treatments using the fixed effects in the model while flagging extreme trials using the random effects. We define discrepancy factors to characterize evidence of inconsistency for particular treatment comparisons, which is novel in NMA research. Our approaches permit users to address issues previously tackled via CB models. We compare sources of inconsistency identified by our approach and existing loop-based CB methods using real and simulated datasets and demonstrate that our methods can offer powerful inconsistency detection. Copyright © 2016 John Wiley & Sons, Ltd. PMID:27037506

  11. Hierarchical organization of functional connectivity in the mouse brain: a complex network approach

    PubMed Central

    Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

    2016-01-01

    This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges. PMID:27534708

  12. Nonperturbative heterogeneous mean-field approach to epidemic spreading in complex networks.

    PubMed

    Gómez, Sergio; Gómez-Gardeñes, Jesús; Moreno, Yamir; Arenas, Alex

    2011-09-01

    Since roughly a decade ago, network science has focused among others on the problem of how the spreading of diseases depends on structural patterns. Here, we contribute to further advance our understanding of epidemic spreading processes by proposing a nonperturbative formulation of the heterogeneous mean-field approach that has been commonly used in the physics literature to deal with this kind of spreading phenomena. The nonperturbative equations we propose have no assumption about the proximity of the system to the epidemic threshold, nor any linear approximation of the dynamics. In particular, we first develop a probabilistic description at the node level of the epidemic propagation for the so-called susceptible-infected-susceptible family of models, and after we derive the corresponding heterogeneous mean-field approach. We propose to use the full extension of the approach instead of pruning the expansion to first order, which leads to a nonperturbative formulation that can be solved by fixed-point iteration, and used with reliability far away from the epidemic threshold to assess the prevalence of the epidemics. Our results are in close agreement with Monte Carlo simulations, thus enhancing the predictive power of the classical heterogeneous mean-field approach, while providing a more effective framework in terms of computational time. PMID:22060454

  13. Structural Design Principles of Complex Bird Songs: A Network-Based Approach

    PubMed Central

    Sasahara, Kazutoshi; Cody, Martin L.; Cohen, David; Taylor, Charles E.

    2012-01-01

    Bird songs are acoustic communication signals primarily used in male-male aggression and in male-female attraction. These are often monotonous patterns composed of a few phrases, yet some birds have extremely complex songs with a large phrase repertoire, organized in non-random fashion with discernible patterns. Since structure is typically associated with function, the structures of complex bird songs provide important clues to the evolution of animal communication systems. Here we propose an efficient network-based approach to explore structural design principles of complex bird songs, in which the song networks–transition relationships among different phrases and the related structural measures–are employed. We demonstrate how this approach works with an example using California Thrasher songs, which are sequences of highly varied phrases delivered in succession over several minutes. These songs display two distinct features: a large phrase repertoire with a ‘small-world’ architecture, in which subsets of phrases are highly grouped and linked with a short average path length; and a balanced transition diversity amongst phrases, in which deterministic and non-deterministic transition patterns are moderately mixed. We explore the robustness of this approach with variations in sample size and the amount of noise. Our approach enables a more quantitative study of global and local structural properties of complex bird songs than has been possible to date. PMID:23028539

  14. Hierarchical organization of functional connectivity in the mouse brain: a complex network approach.

    PubMed

    Bardella, Giampiero; Bifone, Angelo; Gabrielli, Andrea; Gozzi, Alessandro; Squartini, Tiziano

    2016-01-01

    This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges. PMID:27534708

  15. Networks.

    ERIC Educational Resources Information Center

    Maughan, George R.; Petitto, Karen R.; McLaughlin, Don

    2001-01-01

    Describes the connectivity features and options of modern campus communication and information system networks, including signal transmission (wire-based and wireless), signal switching, convergence of networks, and network assessment variables, to enable campus leaders to make sound future-oriented decisions. (EV)

  16. A Graphical Approach to Diagnosing the Validity of the Conditional Independence Assumptions of a Bayesian Network Given Data

    SciTech Connect

    Walsh, Stephen J.; Whitney, Paul D.

    2012-12-14

    Bayesian networks have attained widespread use in data analysis and decision making. Well studied topics include: efficient inference, evidence propagation, parameter learning from data for complete and incomplete data scenarios, expert elicitation for calibrating Bayesian network probabilities, and structure learning. It is not uncommon for the researcher to assume the structure of the Bayesian network or to glean the structure from expert elicitation or domain knowledge. In this scenario, the model may be calibrated through learning the parameters from relevant data. There is a lack of work on model diagnostics for fitted Bayesian networks; this is the contribution of this paper. We key on the definition of (conditional) independence to develop a graphical diagnostic method which indicates if the conditional independence assumptions imposed when one assumes the structure of the Bayesian network are supported by the data. We develop the approach theoretically and describe a Monte Carlo method to generate uncertainty measures for the consistency of the data with conditional independence assumptions under the model structure. We describe how this theoretical information and the data are presented in a graphical diagnostic tool. We demonstrate the approach through data simulated from Bayesian networks under different conditional independence assumptions. We also apply the diagnostic to a real world data set. The results indicate that our approach is a reasonable way of visualizing and inspecting the conditional independence assumption of a Bayesian network given data.

  17. Classification of boreal forest by satellite and inventory data using neural network approach

    NASA Astrophysics Data System (ADS)

    Romanov, A. A.

    2012-12-01

    The main objective of this research was to develop methodology for boreal (Siberian Taiga) land cover classification in a high accuracy level. The study area covers the territories of Central Siberian several parts along the Yenisei River (60-62 degrees North Latitude): the right bank includes mixed forest and dark taiga, the left - pine forests; so were taken as a high heterogeneity and statistically equal surfaces concerning spectral characteristics. Two main types of data were used: time series of middle spatial resolution satellite images (Landsat 5, 7 and SPOT4) and inventory datasets from the nature fieldworks (used for training samples sets preparation). Method of collecting field datasets included a short botany description (type/species of vegetation, density, compactness of the crowns, individual height and max/min diameters representative of each type, surface altitude of the plot), at the same time the geometric characteristic of each training sample unit corresponded to the spatial resolution of satellite images and geo-referenced (prepared datasets both of the preliminary processing and verification). The network of test plots was planned as irregular and determined by the landscape oriented approach. The main focus of the thematic data processing has been allocated for the use of neural networks (fuzzy logic inc.); therefore, the results of field studies have been converting input parameter of type / species of vegetation cover of each unit and the degree of variability. Proposed approach involves the processing of time series separately for each image mainly for the verification: shooting parameters taken into consideration (time, albedo) and thus expected to assess the quality of mapping. So the input variables for the networks were sensor bands, surface altitude, solar angels and land surface temperature (for a few experiments); also given attention to the formation of the formula class on the basis of statistical pre-processing of results of

  18. A mechanism design approach to bandwidth allocation in tactical data networks

    NASA Astrophysics Data System (ADS)

    Mour, Ankur

    The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems'. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today's software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest

  19. A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data

    PubMed Central

    Zhang, Wanhong; Zhou, Tong

    2015-01-01

    Motivation Identifying gene regulatory networks (GRNs) which consist of a large number of interacting units has become a problem of paramount importance in systems biology. Situations exist extensively in which causal interacting relationships among these units are required to be reconstructed from measured expression data and other a priori information. Though numerous classical methods have been developed to unravel the interactions of GRNs, these methods either have higher computing complexities or have lower estimation accuracies. Note that great similarities exist between identification of genes that directly regulate a specific gene and a sparse vector reconstruction, which often relates to the determination of the number, location and magnitude of nonzero entries of an unknown vector by solving an underdetermined system of linear equations y = Φx. Based on these similarities, we propose a novel framework of sparse reconstruction to identify the structure of a GRN, so as to increase accuracy of causal regulation estimations, as well as to reduce their computational complexity. Results In this paper, a sparse reconstruction framework is proposed on basis of steady-state experiment data to identify GRN structure. Different from traditional methods, this approach is adopted which is well suitable for a large-scale underdetermined problem in inferring a sparse vector. We investigate how to combine the noisy steady-state experiment data and a sparse reconstruction algorithm to identify causal relationships. Efficiency of this method is tested by an artificial linear network, a mitogen-activated protein kinase (MAPK) pathway network and the in silico networks of the DREAM challenges. The performance of the suggested approach is compared with two state-of-the-art algorithms, the widely adopted total least-squares (TLS) method and those available results on the DREAM project. Actual results show that, with a lower computational cost, the proposed method can

  20. The Data Transport Network: A Usenet-Based Approach For Data Retrieval From Remote Field Sites

    NASA Astrophysics Data System (ADS)

    Valentic, T. A.

    2005-12-01

    The Data Transport Network coordinates the collection of scientific data, instrument telemetry and post-processing for the delivery of real-time results over the Internet from instruments located at remote field sites with limited or unreliable network connections. The system was originally developed in 1999 for the distribution of large data sets collected by the radar, lidars and imagers at the NSF upper atmosphere research facility in Sondrestrom, Greenland. The system helped to mitigate disruptions in network connectivity and optimized transfers over the site's low-bandwidth satellite link. The core idea behind the system is to transfer data files as attachments in Usenet messages. The messages collected by a local news server are periodically transmitted to other servers on the Internet when link conditions permit. If the network goes down, data files continue to be stored locally and the server will periodically attempt to deliver the files for upwards of two weeks. Using this simple approach, the Data Transport Network is able to handle a large number of independent data streams from multiple instruments. Each data stream is posted into a separate news group. There are no limitations to the types of data files that can be sent and the system uses standard Internet protocols for encoding, accessing and transmitting files. A common framework allows for new data collection or processing programs to be easily integrated. The two-way nature of the communications also allows for data to be delivered to the site as well, a feature used for the remote control of instruments. In recent years, the Data Transport Network has been applied to small, low-power embedded systems. Coupled with satellite-based communications systems such as Iridium, these miniature Data Transport servers have found application in a number of remote instrument deployments in the Arctic. SRI's involvement as a team member in Veco Polar Resources, the NSF Office of Polar Programs Arctic

  1. An integrated approach to reconstructing genome-scale transcriptional regulatory networks

    SciTech Connect

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.; Leslie, Christina

    2015-02-27

    Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating

  2. An integrated approach to reconstructing genome-scale transcriptional regulatory networks

    DOE PAGESBeta

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.; Leslie, Christina

    2015-02-27

    Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making themmore » highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of

  3. Predicting manual arm strength: A direct comparison between artificial neural network and multiple regression approaches.

    PubMed

    La Delfa, Nicholas J; Potvin, Jim R

    2016-02-29

    In ergonomics, strength prediction has typically been accomplished using linked-segment biomechanical models, and independent estimates of strength about each axis of the wrist, elbow and shoulder joints. It has recently been shown that multiple regression approaches, using the simple task-relevant inputs of hand location and force direction, may be a better method for predicting manual arm strength (MAS) capabilities. Artificial neural networks (ANNs) also serve as a powerful data fitting approach, but their application to occupational biomechanics and ergonomics is limited. Therefore, the purpose of this study was to perform a direct comparison between ANN and regression models, by evaluating their ability to predict MAS with identical sets of development and validation MAS data. Multi-directional MAS data were obtained from 95 healthy female participants at 36 hand locations within the reach envelope. ANN and regression models were developed using a random, but identical, sample of 85% of the MAS data (n=456). The remaining 15% of the data (n=80) were used to validate the two approaches. When compared to the development data, the ANN predictions had a much higher explained variance (90.2% vs. 66.5%) and much lower RMSD (9.3N vs. 17.2N), vs. the regression model. The ANN also performed better with the independent validation data (r(2)=78.6%, RMSD=15.1) compared to the regression approach (r(2)=65.3%, RMSD=18.6N). These results suggest that ANNs provide a more accurate and robust alternative to regression approaches, and should be considered more often in biomechanics and ergonomics evaluations. PMID:26876987

  4. An Integrated Approach to Reconstructing Genome-Scale Transcriptional Regulatory Networks

    PubMed Central

    Imam, Saheed; Noguera, Daniel R.; Donohue, Timothy J.

    2015-01-01

    Transcriptional regulatory networks (TRNs) program cells to dynamically alter their gene expression in response to changing internal or environmental conditions. In this study, we develop a novel workflow for generating large-scale TRN models that integrates comparative genomics data, global gene expression analyses, and intrinsic properties of transcription factors (TFs). An assessment of this workflow using benchmark datasets for the well-studied γ-proteobacterium Escherichia coli showed that it outperforms expression-based inference approaches, having a significantly larger area under the precision-recall curve. Further analysis indicated that this integrated workflow captures different aspects of the E. coli TRN than expression-based approaches, potentially making them highly complementary. We leveraged this new workflow and observations to build a large-scale TRN model for the α-Proteobacterium Rhodobacter sphaeroides that comprises 120 gene clusters, 1211 genes (including 93 TFs), 1858 predicted protein-DNA interactions and 76 DNA binding motifs. We found that ~67% of the predicted gene clusters in this TRN are enriched for functions ranging from photosynthesis or central carbon metabolism to environmental stress responses. We also found that members of many of the predicted gene clusters were consistent with prior knowledge in R. sphaeroides and/or other bacteria. Experimental validation of predictions from this R. sphaeroides TRN model showed that high precision and recall was also obtained for TFs involved in photosynthesis (PpsR), carbon metabolism (RSP_0489) and iron homeostasis (RSP_3341). In addition, this integrative approach enabled generation of TRNs with increased information content relative to R. sphaeroides TRN models built via other approaches. We also show how this approach can be used to simultaneously produce TRN models for each related organism used in the comparative genomics analysis. Our results highlight the advantages of integrating

  5. A fuzzy Bayesian network approach to quantify the human behaviour during an evacuation

    NASA Astrophysics Data System (ADS)

    Ramli, Nurulhuda; Ghani, Noraida Abdul; Ahmad, Nazihah

    2016-06-01

    Bayesian Network (BN) has been regarded as a successful representation of inter-relationship of factors affecting human behavior during an emergency. This paper is an extension of earlier work of quantifying the variables involved in the BN model of human behavior during an evacuation using a well-known direct probability elicitation technique. To overcome judgment bias and reduce the expert's burden in providing precise probability values, a new approach for the elicitation technique is required. This study proposes a new fuzzy BN approach for quantifying human behavior during an evacuation. Three major phases of methodology are involved, namely 1) development of qualitative model representing human factors during an evacuation, 2) quantification of BN model using fuzzy probability and 3) inferencing and interpreting the BN result. A case study of three inter-dependencies of human evacuation factors such as danger assessment ability, information about the threat and stressful conditions are used to illustrate the application of the proposed method. This approach will serve as an alternative to the conventional probability elicitation technique in understanding the human behavior during an evacuation.

  6. Analytical approach to the dynamics of facilitated spin models on random networks

    NASA Astrophysics Data System (ADS)

    Fennell, Peter G.; Gleeson, James P.; Cellai, Davide

    2014-09-01

    Facilitated spin models were introduced some decades ago to mimic systems characterized by a glass transition. Recent developments have shown that a class of facilitated spin models is also able to reproduce characteristic signatures of the structural relaxation properties of glass-forming liquids. While the equilibrium phase diagram of these models can be calculated analytically, the dynamics are usually investigated numerically. Here we propose a network-based approach, called approximate master equation (AME), to the dynamics of the Fredrickson-Andersen model. The approach correctly predicts the critical temperature at which the glass transition occurs. We also find excellent agreement between the theory and the numerical simulations for the transient regime, except in close proximity of the liquid-glass transition. Finally, we analytically characterize the critical clusters of the model and show that the departures between our AME approach and the Monte Carlo can be related to the large interface between blocked and unblocked spins at temperatures close to the glass transition.

  7. Identification and interpretation of patterns in rocket engine data: Artificial intelligence and neural network approaches

    NASA Technical Reports Server (NTRS)

    Ali, Moonis; Whitehead, Bruce; Gupta, Uday K.; Ferber, Harry

    1989-01-01

    This paper describes an expert system which is designed to perform automatic data analysis, identify anomalous events, and determine the characteristic features of these events. We have employed both artificial intelligence and neural net approaches in the design of this expert system. The artificial intelligence approach is useful because it provides (1) the use of human experts' knowledge of sensor behavior and faulty engine conditions in interpreting data; (2) the use of engine design knowledge and physical sensor locations in establishing relationships among the events of multiple sensors; (3) the use of stored analysis of past data of faulty engine conditions; and (4) the use of knowledge-based reasoning in distinguishing sensor failure from actual faults. The neural network approach appears promising because neural nets (1) can be trained on extremely noisy data and produce classifications which are more robust under noisy conditions than other classification techniques; (2) avoid the necessity of noise removal by digital filtering and therefore avoid the need to make assumptions about frequency bands or other signal characteristics of anomalous behavior; (3) can, in effect, generate their own feature detectors based on the characteristics of the sensor data used in training; and (4) are inherently parallel and therefore are potentially implementable in special-purpose parallel hardware.

  8. Visualising the invisible: a network approach to reveal the informal social side of student learning.

    PubMed

    Hommes, J; Rienties, B; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

    2012-12-01

    World-wide, universities in health sciences have transformed their curriculum to include collaborative learning and facilitate the students' learning process. Interaction has been acknowledged to be the synergistic element in this learning context. However, students spend the majority of their time outside their classroom and interaction does not stop outside the classroom. Therefore we studied how informal social interaction influences student learning. Moreover, to explore what really matters in the students learning process, a model was tested how the generally known important constructs-prior performance, motivation and social integration-relate to informal social interaction and student learning. 301 undergraduate medical students participated in this cross-sectional quantitative study. Informal social interaction was assessed using self-reported surveys following the network approach. Students' individual motivation, social integration and prior performance were assessed by the Academic Motivation Scale, the College Adaption Questionnaire and students' GPA respectively. A factual knowledge test represented student' learning. All social networks were positively associated with student learning significantly: friendships (β = 0.11), providing information to other students (β = 0.16), receiving information from other students (β = 0.25). Structural equation modelling revealed a model in which social networks increased student learning (r = 0.43), followed by prior performance (r = 0.31). In contrast to prior literature, students' academic motivation and social integration were not associated with students' learning. Students' informal social interaction is strongly associated with students' learning. These findings underline the need to change our focus from the formal context (classroom) to the informal context to optimize student learning and deliver modern medics. PMID:22294429

  9. Analytical approach to cross-layer protocol optimization in wireless sensor networks

    NASA Astrophysics Data System (ADS)

    Hortos, William S.

    2008-04-01

    In the distributed operations of route discovery and maintenance, strong interaction occurs across mobile ad hoc network (MANET) protocol layers. Quality of service (QoS) requirements of multimedia service classes must be satisfied by the cross-layer protocol, along with minimization of the distributed power consumption at nodes and along routes to battery-limited energy constraints. In previous work by the author, cross-layer interactions in the MANET protocol are modeled in terms of a set of concatenated design parameters and associated resource levels by multivariate point processes (MVPPs). Determination of the "best" cross-layer design is carried out using the optimal control of martingale representations of the MVPPs. In contrast to the competitive interaction among nodes in a MANET for multimedia services using limited resources, the interaction among the nodes of a wireless sensor network (WSN) is distributed and collaborative, based on the processing of data from a variety of sensors at nodes to satisfy common mission objectives. Sensor data originates at the nodes at the periphery of the WSN, is successively transported to other nodes for aggregation based on information-theoretic measures of correlation and ultimately sent as information to one or more destination (decision) nodes. The "multimedia services" in the MANET model are replaced by multiple types of sensors, e.g., audio, seismic, imaging, thermal, etc., at the nodes; the QoS metrics associated with MANETs become those associated with the quality of fused information flow, i.e., throughput, delay, packet error rate, data correlation, etc. Significantly, the essential analytical approach to MANET cross-layer optimization, now based on the MVPPs for discrete random events occurring in the WSN, can be applied to develop the stochastic characteristics and optimality conditions for cross-layer designs of sensor network protocols. Functional dependencies of WSN performance metrics are described in

  10. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks

    PubMed Central

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-01-01

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods. PMID:27399696

  11. A neural network approach to the microwave inverse scattering problem with edge-preserving regularization

    NASA Astrophysics Data System (ADS)

    Gong, Xing; Wang, Yuanmei

    2001-09-01

    A neural network approach to the inverse scattering problem for microwave tomographic reconstruction is presented. Although the technique of microwave tomography has been developed for more than two decades, it is still in its infancy in that it is necessary to solve the inverse scattering problem, which is well known as an ill-posed and nonlinear problem and therefore difficult to deal with. To improve the inherent ill-posedness of the problem, good regularization procedures are required. In this paper, an edge-preserving regularization is proposed with a set of line processes to preserve the edge of the reconstructed image. Since the unknown dielectric permittivities are continuous complex variables and the line processes are binary variables, an augmented Hopfield network is applied to the mixed-variable optimization problem. With this method, a priori knowledge can be conveniently incorporated into the optimization process, and inversions of large matrices are avoided. A numerical example of a simple model illuminated by the transverse magnetic incident waves is reported, and the advantages and limitations of the method are discussed.

  12. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

    NASA Astrophysics Data System (ADS)

    Balabin, Roman M.; Lomakina, Ekaterina I.

    2009-08-01

    Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree-Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6±0.2 kcal mol-1. In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.

  13. A network-driven approach for genome-wide association mapping

    PubMed Central

    Lee, Seunghak; Kong, Soonho; Xing, Eric P.

    2016-01-01

    Motivation: It remains a challenge to detect associations between genotypes and phenotypes because of insufficient sample sizes and complex underlying mechanisms involved in associations. Fortunately, it is becoming more feasible to obtain gene expression data in addition to genotypes and phenotypes, giving us new opportunities to detect true genotype–phenotype associations while unveiling their association mechanisms. Results: In this article, we propose a novel method, NETAM, that accurately detects associations between SNPs and phenotypes, as well as gene traits involved in such associations. We take a network-driven approach: NETAM first constructs an association network, where nodes represent SNPs, gene traits or phenotypes, and edges represent the strength of association between two nodes. NETAM assigns a score to each path from an SNP to a phenotype, and then identifies significant paths based on the scores. In our simulation study, we show that NETAM finds significantly more phenotype-associated SNPs than traditional genotype–phenotype association analysis under false positive control, taking advantage of gene expression data. Furthermore, we applied NETAM on late-onset Alzheimer’s disease data and identified 477 significant path associations, among which we analyzed paths related to beta-amyloid, estrogen, and nicotine pathways. We also provide hypothetical biological pathways to explain our findings. Availability and implementation: Software is available at http://www.sailing.cs.cmu.edu/. Contact: epxing@cs.cmu.edu PMID:27307613

  14. Cluster Analysis of Weighted Bipartite Networks: A New Copula-Based Approach

    PubMed Central

    Chessa, Alessandro; Crimaldi, Irene; Riccaboni, Massimo; Trapin, Luca

    2014-01-01

    In this work we are interested in identifying clusters of “positional equivalent” actors, i.e. actors who play a similar role in a system. In particular, we analyze weighted bipartite networks that describes the relationships between actors on one side and features or traits on the other, together with the intensity level to which actors show their features. We develop a methodological approach that takes into account the underlying multivariate dependence among groups of actors. The idea is that positions in a network could be defined on the basis of the similar intensity levels that the actors exhibit in expressing some features, instead of just considering relationships that actors hold with each others. Moreover, we propose a new clustering procedure that exploits the potentiality of copula functions, a mathematical instrument for the modelization of the stochastic dependence structure. Our clustering algorithm can be applied both to binary and real-valued matrices. We validate it with simulations and applications to real-world data. PMID:25303095

  15. Two approaches to glassy dynamics and diffusion on actin filament networks

    NASA Astrophysics Data System (ADS)

    Snider, Joseph

    In spite of mass effort to understand glasses, basic features are still not completely known. Even whether or not glasses, as in windows, bottles, etc., are solids or liquids is not settled, let alone their thermodynamics. To make some headway in understanding glasses, this dissertation will take two distinct approaches. First, a direct simulation of a glassy system will be performed and compared to experiments, and from this the thermodynamics will be found. Second, rather than looking directly at a specific system, a general energy landscape appropriate for glass will be considered, and a new numeric technique to exactly calculate thermodynamic quantities will be presented and applied. The second part of this thesis will study diffusion on actin filament networks. Intracellular molecular motor-driven transport is essential for such diverse processes as mitosis, neuronal function, and mitochondrial transport. In vitro studies clarify these motors' function at the single molecule level but fail to elucidate how effective transport emerges from the collective behavior of multiple motors on a filamentary network. We investigate how the combined system of Myosin-V (MV) motors plus actin filaments is used to transport pigment granules in Xenopus melanophores. By analyzing single particle tracking data, we construct simulations and test a hypothesis that cells regulate transport by controlling how often granules switch from one filament to another, rather than, for example, altering motor activity at the single molecule level.