Electron percolation in realistic models of carbon nanotube networks
Simoneau, Louis-Philippe; Villeneuve, Jérémie; Rochefort, Alain
2015-09-01
The influence of penetrable and curved carbon nanotubes (CNT) on the charge percolation in three-dimensional disordered CNT networks have been studied with Monte-Carlo simulations. By considering carbon nanotubes as solid objects but where the overlap between their electron cloud can be controlled, we observed that the structural characteristics of networks containing lower aspect ratio CNT are highly sensitive to the degree of penetration between crossed nanotubes. Following our efficient strategy to displace CNT to different positions to create more realistic statistical models, we conclude that the connectivity between objects increases with the hard-core/soft-shell radii ratio. In contrast, the presence of curved CNT in the random networks leads to an increasing percolation threshold and to a decreasing electrical conductivity at saturation. The waviness of CNT decreases the effective distance between the nanotube extremities, hence reducing their connectivity and degrading their electrical properties. We present the results of our simulation in terms of thickness of the CNT network from which simple structural parameters such as the volume fraction or the carbon nanotube density can be accurately evaluated with our more realistic models.
Kaewprag, Pacharmon; Newton, Cheryl; Vermillion, Brenda; Hyun, Sookyung; Huang, Kun; Machiraju, Raghu
2017-07-05
We develop predictive models enabling clinicians to better understand and explore patient clinical data along with risk factors for pressure ulcers in intensive care unit patients from electronic health record data. Identifying accurate risk factors of pressure ulcers is essential to determining appropriate prevention strategies; in this work we examine medication, diagnosis, and traditional Braden pressure ulcer assessment scale measurements as patient features. In order to predict pressure ulcer incidence and better understand the structure of related risk factors, we construct Bayesian networks from patient features. Bayesian network nodes (features) and edges (conditional dependencies) are simplified with statistical network techniques. Upon reviewing a network visualization of our model, our clinician collaborators were able to identify strong relationships between risk factors widely recognized as associated with pressure ulcers. We present a three-stage framework for predictive analysis of patient clinical data: 1) Developing electronic health record feature extraction functions with assistance of clinicians, 2) simplifying features, and 3) building Bayesian network predictive models. We evaluate all combinations of Bayesian network models from different search algorithms, scoring functions, prior structure initializations, and sets of features. From the EHRs of 7,717 ICU patients, we construct Bayesian network predictive models from 86 medication, diagnosis, and Braden scale features. Our model not only identifies known and suspected high PU risk factors, but also substantially increases sensitivity of the prediction - nearly three times higher comparing to logistical regression models - without sacrificing the overall accuracy. We visualize a representative model with which our clinician collaborators identify strong relationships between risk factors widely recognized as associated with pressure ulcers. Given the strong adverse effect of pressure ulcers
A Bayesian network model for predicting type 2 diabetes risk based on electronic health records
Xie, Jiang; Liu, Yan; Zeng, Xu; Zhang, Wu; Mei, Zhen
2017-07-01
An extensive, in-depth study of diabetes risk factors (DBRF) is of crucial importance to prevent (or reduce) the chance of suffering from type 2 diabetes (T2D). Accumulation of electronic health records (EHRs) makes it possible to build nonlinear relationships between risk factors and diabetes. However, the current DBRF researches mainly focus on qualitative analyses, and the inconformity of physical examination items makes the risk factors likely to be lost, which drives us to study the novel machine learning approach for risk model development. In this paper, we use Bayesian networks (BNs) to analyze the relationship between physical examination information and T2D, and to quantify the link between risk factors and T2D. Furthermore, with the quantitative analyses of DBRF, we adopt EHR and propose a machine learning approach based on BNs to predict the risk of T2D. The experiments demonstrate that our approach can lead to better predictive performance than the classical risk model.
An Agent-Based Model of Message Propagation in the Facebook Electronic Social Network
Nasrinpour, Hamid Reza; Friesen, Marcia R.; D., Robert; McLeod
2016-01-01
A large scale agent-based model of common Facebook users was designed to develop an understanding of the underlying mechanism of information diffusion within online social networks at a micro-level analysis. The agent-based model network structure is based on a sample from Facebook. Using an erased configuration model and the idea of common neighbours, a new correction procedure was investigated to overcome the problem of missing graph edges to construct a representative sample of the Faceboo...
Szaleniec, Maciej
2012-01-01
Artificial Neural Networks (ANNs) are introduced as robust and versatile tools in quantitative structure-activity relationship (QSAR) modeling. Their application to the modeling of enzyme reactivity is discussed, along with methodological issues. Methods of input variable selection, optimization of network internal structure, data set division and model validation are discussed. The application of ANNs in the modeling of enzyme activity over the last 20 years is briefly recounted. The discussed methodology is exemplified by the case of ethylbenzene dehydrogenase (EBDH). Intelligent Problem Solver and genetic algorithms are applied for input vector selection, whereas k-means clustering is used to partition the data into training and test cases. The obtained models exhibit high correlation between the predicted and experimental values (R(2) > 0.9). Sensitivity analyses and study of the response curves are used as tools for the physicochemical interpretation of the models in terms of the EBDH reaction mechanism. Neural networks are shown to be a versatile tool for the construction of robust QSAR models that can be applied to a range of aspects important in drug design and the prediction of biological activity.
Architecture for networked electronic patient record systems.
Takeda, H; Matsumura, Y; Kuwata, S; Nakano, H; Sakamoto, N; Yamamoto, R
2000-11-01
There have been two major approaches to the development of networked electronic patient record (EPR) architecture. One uses object-oriented methodologies for constructing the model, which include the GEHR project, Synapses, HL7 RIM and so on. The second approach uses document-oriented methodologies, as applied in examples of HL7 PRA. It is practically beneficial to take the advantages of both approaches and to add solution technologies for network security such as PKI. In recognition of the similarity with electronic commerce, a certificate authority as a trusted third party will be organised for establishing networked EPR system. This paper describes a Japanese functional model that has been developed, and proposes a document-object-oriented architecture, which is-compared with other existing models.
Electronic device aspects of neural network memories
Lambe, J.; Moopenn, A.; Thakoor, A. P.
1985-01-01
The basic issues related to the electronic implementation of the neural network model (NNM) for content addressable memories are examined. A brief introduction to the principles of the NNM is followed by an analysis of the information storage of the neural network in the form of a binary connection matrix and the recall capability of such matrix memories based on a hardware simulation study. In addition, materials and device architecture issues involved in the future realization of such networks in VLSI-compatible ultrahigh-density memories are considered. A possible space application of such devices would be in the area of large-scale information storage without mechanical devices.
Collaborative networks: Reference modeling
Camarinha-Matos, L.M.; Afsarmanesh, H.
2008-01-01
Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of
Ganguly, Bhaskar; Ambwani, Tanuj Kumar; Rastogi, Sunil Kumar
2017-01-01
Milk fat is one of the most important economic traits in dairy animals. Yet, the biological machinery involved in milk fat synthesis remains poorly understood. In the present study, expression profiling of 45 genes involved in lipid biosynthesis and secretion was performed using a computational approach to identify those genes that are differentially expressed in mammary tissue. Transcript abundance was observed for genes associated with nine bioprocesses, namely, fatty acid import into cells, xenobiotic and cholesterol transport, acetate and fatty acid activation and intracellular transport, fatty acid synthesis and desaturation, triacylglycerol synthesis, sphingolipid synthesis, lipid droplet formation, ketone body utilization, and regulation of transcription in mammary, skin, and muscle tissue. Relative expression coefficient of the genes was derived based on the transcript abundance across the three tissue types to determine the genes that were preferentially expressed during lactation. 13 genes (ACSS1, ACSS2, ADFP, CD36, FABP3, FASN, GPAM, INSIG1, LPL, SCD5, SPTLC1, SREBF1, and XDH) showed higher expression in the mammary tissue of which 6 (ADFP, FASN, GPAM, LPL, SREBF1, and XDH) showed higher expression during adulthood. Further, interaction networks were mapped for these genes to determine the nature of interactions and to identify the major genes in the milk fat biosynthesis and secretion pathways.
Directory of Open Access Journals (Sweden)
Bhaskar Ganguly
2017-01-01
Full Text Available Milk fat is one of the most important economic traits in dairy animals. Yet, the biological machinery involved in milk fat synthesis remains poorly understood. In the present study, expression profiling of 45 genes involved in lipid biosynthesis and secretion was performed using a computational approach to identify those genes that are differentially expressed in mammary tissue. Transcript abundance was observed for genes associated with nine bioprocesses, namely, fatty acid import into cells, xenobiotic and cholesterol transport, acetate and fatty acid activation and intracellular transport, fatty acid synthesis and desaturation, triacylglycerol synthesis, sphingolipid synthesis, lipid droplet formation, ketone body utilization, and regulation of transcription in mammary, skin, and muscle tissue. Relative expression coefficient of the genes was derived based on the transcript abundance across the three tissue types to determine the genes that were preferentially expressed during lactation. 13 genes (ACSS1, ACSS2, ADFP, CD36, FABP3, FASN, GPAM, INSIG1, LPL, SCD5, SPTLC1, SREBF1, and XDH showed higher expression in the mammary tissue of which 6 (ADFP, FASN, GPAM, LPL, SREBF1, and XDH showed higher expression during adulthood. Further, interaction networks were mapped for these genes to determine the nature of interactions and to identify the major genes in the milk fat biosynthesis and secretion pathways.
Electronic Social Networks, Teaching, and Learning
Pidduck, Anne Banks
2010-01-01
This paper explores the relationship between electronic social networks, teaching, and learning. Previous studies have shown a strong positive correlation between student engagement and learning. By extending this work to engage instructors and add an electronic component, our study shows possible teaching improvement as well. In particular,…
Modeling the citation network by network cosmology.
Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing
2015-01-01
Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.
Modeling the citation network by network cosmology.
Directory of Open Access Journals (Sweden)
Zheng Xie
Full Text Available Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.
DEFF Research Database (Denmark)
Andersen, Kasper Winther
Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...
Artificial neural network modelling
Samarasinghe, Sandhya
2016-01-01
This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .
Modeling network technology deployment rates with different network models
Chung, Yoo
2011-01-01
To understand the factors that encourage the deployment of a new networking technology, we must be able to model how such technology gets deployed. We investigate how network structure influences deployment with a simple deployment model and different network models through computer simulations. The results indicate that a realistic model of networking technology deployment should take network structure into account.
Electronic Circuit Analog of Synthetic Genetic Networks: Revisited
Hellen, Edward H
2016-01-01
Electronic circuits are useful tools for studying potential dynamical behaviors of synthetic genetic networks. The circuit models are complementary to numerical simulations of the networks, especially providing a framework for verification of dynamical behaviors in the presence of intrinsic and extrinsic noise of the electrical systems. Here we present an improved version of our previous design of an electronic analog of genetic networks that includes the 3-gene Repressilator and we show conversions between model parameters and real circuit component values to mimic the numerical results in experiments. Important features of the circuit design include the incorporation of chemical kinetics representing Hill function inhibition, quorum sensing coupling, and additive noise. Especially, we make a circuit design for a systematic change of initial conditions in experiment, which is critically important for studies of dynamical systems' behavior, particularly, when it shows multistability. This improved electronic ...
Radio Channel Modeling in Body Area Networks
An, L.; Bentum, Marinus Jan; Meijerink, Arjan; Scanlon, W.G.
2009-01-01
A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to de- tect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation
Radio channel modeling in body area networks
An, L.; Bentum, Marinus Jan; Meijerink, Arjan; Scanlon, W.G.
2010-01-01
A body area network (BAN) is a network of bodyworn or implanted electronic devices, including wireless sensors which can monitor body parameters or to detect movements. One of the big challenges in BANs is the propagation channel modeling. Channel models can be used to understand wave propagation in
Modeling Epidemic Network Failures
DEFF Research Database (Denmark)
Ruepp, Sarah Renée; Fagertun, Anna Manolova
2013-01-01
This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...
Network Models of Mechanical Assemblies
Whitney, Daniel E.
Recent network research has sought to characterize complex systems with a number of statistical metrics, such as power law exponent (if any), clustering coefficient, community behavior, and degree correlation. Use of such metrics represents a choice of level of abstraction, a balance of generality and detailed accuracy. It has been noted that "social networks" consistently display clustering coefficients that are higher than those of random or generalized random networks, that they have small world properties such as short path lengths, and that they have positive degree correlations (assortative mixing). "Technological" or "non-social" networks display many of these characteristics except that they generally have negative degree correlations (disassortative mixing). [Newman 2003i] In this paper we examine network models of mechanical assemblies. Such systems are well understood functionally. We show that there is a cap on their average nodal degree and that they have negative degree correlations (disassortative mixing). We identify specific constraints arising from first principles, their structural patterns, and engineering practice that suggest why they have these properties. In addition, we note that their main "motif" is closed loops (as it is for electric and electronic circuits), a pattern that conventional network analysis does not detect but which is used by software intended to aid in the design of such systems.
Hewett, Thomas T.
There are a number of areas in psychology where an electronic spreadsheet simulator can be used to study and explore functional relationships among a number of parameters. For example, when dealing with sensation, perception, and pattern recognition, it is sometimes desirable for students to understand both the basic neurophysiology and the…
Models of educational institutions' networking
Shilova Olga Nikolaevna
2015-01-01
The importance of educational institutions' networking in modern sociocultural conditions and a definition of networking in education are presented in the article. The results of research levels, methods and models of educational institutions' networking are presented and substantially disclosed.
Networking Carbon Nanotubes for Integrated Electronics.
Romo-Herrera, J. M.; Terrones, M.; Terrones, H.; Meunier, V.
2006-03-01
The unique electronic and mechanical properties of individual Carbon Nanotubes (CNTs) have attracted much interest as candidates for molecular electronic devices and reinforced materials. However, their integration in organized architectures remains a major challenge. Recent breakthroughs reported on the Self-Assembly of 1D Nanostructures[1], and on the coalescence mechanism for interconnecting CNTs[2], point to the possibility of designing and obtaining Ordered Networks based on CNTs (ON- CNTs). We propose a set with different complex architectures of ON- CNTs based on --but not limited to-- armchair and zigzag nanotubes. In addition to the study of the energetics of the structures, we have systematically investigated their electronic transport properties in the framework of the Landauer-Buttiker formalism and equilibrium Green functions. To take curvature into account, we employed a semi-empirical Hamiltonian based on 4 orbitals (s,px,py,pz) per carbon atom. Further insight is obtained analyzing the electron pathways from a scattering point of view, which allows a real-space analysis of the wave function from the transmitted electrons across the structure. [1]Whang D etal. Nanoletters,3 (2003). Tao A etal. Nanoletters,3 (2003). [2]Terrones M etal. PRL,89 (2002). Endo M etal. Nanoletters,5 (2005).
Techniques for Modelling Network Security
Lech Gulbinovič
2012-01-01
The article compares modelling techniques for network security, including the theory of probability, Markov processes, Petri networks and application of stochastic activity networks. The paper introduces the advantages and disadvantages of the above proposed methods and accepts the method of modelling the network of stochastic activity as one of the most relevant. The stochastic activity network allows modelling the behaviour of the dynamic system where the theory of probability is inappropri...
Programmable synaptic chip for electronic neural networks
Moopenn, A.; Langenbacher, H.; Thakoor, A. P.; Khanna, S. K.
1988-01-01
A binary synaptic matrix chip has been developed for electronic neural networks. The matrix chip contains a programmable 32X32 array of 'long channel' NMOSFET binary connection elements implemented in a 3-micron bulk CMOS process. Since the neurons are kept off-chip, the synaptic chip serves as a 'cascadable' building block for a multi-chip synaptic network as large as 512X512 in size. As an alternative to the programmable NMOSFET (long channel) connection elements, tailored thin film resistors are deposited, in series with FET switches, on some CMOS test chips, to obtain the weak synaptic connections. Although deposition and patterning of the resistors require additional processing steps, they promise substantial savings in silicon area. The performance of synaptic chip in a 32-neuron breadboard system in an associative memory test application is discussed.
Electronic Power Transformer for Power Distribution Networks
Directory of Open Access Journals (Sweden)
Ermuraсhi Iu.V.
2017-12-01
Full Text Available Reducing losses in electricity distribution networks is a current technical problem. This issue also has social and environmental aspects. As a promising solution one can examine the direct distribution from the medium voltage power network using new equipment based on the use of power electronics. The aim of the paper is to propose and argue an innovative technical solution for the realization of the Solid State Transformer (SST in order to decrease the number of energy transformation stages compared to the known solutions, simplifying the topology of the functional scheme with the reduction of production costs and the loss of energy in transformers used in electrical distribution networks. It is proposed the solution of simplifying the topology of the AC/AC electronic transformer by reducing the number of passive electronic components (resistors, inductors, capacitors and active (transistors. The inverter of the SST transformer ensures the switching mode of the transistors, using for this purpose the inductance of the magnetic leakage flux of the high frequency transformer. The robustness of the laboratory sample of the SST 10 / 0.22 kV transformer with the power of 20 kW was manufactured and tested. Testing of the laboratory sample confirmed the functionality of the proposed scheme and the possibility of switching of the transistors to at zero current (ZCS mode with the reduction of the energy losses. In the proposed converter a single high-frequency transformer with a simplified construction with two windings is used, which reduces its mass and the cost of making the transformer. The reduction in the manufacturing cost of the converter is also due to the decrease in the number of links between the functional elements.
Modeling Network Interdiction Tasks
2015-09-17
allow professionals and families to stay in touch through voice or video calls. Power grids provide electricity to homes , offices, and recreational...instances using IBMr ILOGr CPLEXr Optimization Studio V12.6. For each instance, two solutions are deter- mined. First, the MNDP-a model is solved with no...three values: 0.25, 0.50, or 0.75. The DMP-a model is solved for the various random network instances using IBMr ILOGr CPLEXr Optimization Studio V12.6
Coevolutionary modeling in network formation
Al-Shyoukh, Ibrahim
2014-12-03
Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.
Do Network Models Just Model Networks? On The Applicability of Network-Oriented Modeling
Treur, J.; Shmueli, Erez
2017-01-01
In this paper for a Network-Oriented Modelling perspective based on temporal-causal networks it is analysed how generic and applicable it is as a general modelling approach and as a computational paradigm. This results in an answer to the question in the title different from: network models just
Organizational Models of Electronic Information
Directory of Open Access Journals (Sweden)
Chao-chen Chen
1997-12-01
Full Text Available Along with the popularity of Internet, electronic information is getting more and more important. To find efficient organization methods of electronic information is the main issue for information retrieval. Many solutions have been proposed.The aim of this article is to discuss the existing organizational models of electronic information. We explain their backgrounds of developments, their environments of applications and some related issues.[Article content in Chinese
Modeling semiflexible polymer networks
Broedersz, Chase P.; MacKintosh, Fred C.
2014-01-01
Here, we provide an overview of theoretical approaches to semiflexible polymers and their networks. Such semiflexible polymers have large bending rigidities that can compete with the entropic tendency of a chain to crumple up into a random coil. Many studies on semiflexible polymers and their assemblies have been motivated by their importance in biology. Indeed, crosslinked networks of semiflexible polymers form a major structural component of tissue and living cells. Reconstituted networks o...
Complex Networks in Psychological Models
Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.
We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.
Developing Personal Network Business Models
DEFF Research Database (Denmark)
Saugstrup, Dan; Henten, Anders
2006-01-01
on the 'state of the art' in the field of business modeling. Furthermore, the paper suggests three generic business models for PNs: a service oriented model, a self-organized model, and a combination model. Finally, examples of relevant services and applications in relation to three different cases......The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...... are presented and analyzed in light of business modeling of PN....
A model of coauthorship networks
Zhou, Guochang; Li, Jianping; Xie, Zonglin
2017-10-01
A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property
Socio-Psychological Factors in Electronic Networking.
Boshier, Roger
1990-01-01
This paper analyzes electronic mail and its role in adult education, identifies research on the educational implications of electronic mail, and discusses theoretical issues from an economic, psychological, and sociological perspective. (SK)
2016-11-09
standpoint remains more of an art than a science . Even when well executed, the ongoing evolution of the network may violate initial, security-critical design...from a security standpoint remains more of an art than a science . Even when well executed, the ongoing evolution of the network may violate initial...is outside the scope of this paper. As such, we focus on event probabilities. The output of the network porosity model is a stream of timestamped
Telecommunications network modelling, planning and design
Evans, Sharon
2003-01-01
Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.
Campus network security model study
Zhang, Yong-ku; Song, Li-ren
2011-12-01
Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.
Weaver, C G
1984-04-01
The University of Nebraska Medical Center (UNMC) uses five different electronic networks for interlibrary loan (ILL) request transmission. The advantages and problems of using electronic networks for ILL request transmission are discussed. Advantages include speed of request transmission, improved capabilities for locating documents, lower labor costs, improved turnaround time, and production of user reports and statistics. Disadvantages include increased work load, additional staff training, coordination of non-standard networks, determining access protocols, and establishing priorities for handling requests.
Directory of Open Access Journals (Sweden)
Sérgio Roberto da Silva
2016-06-01
Full Text Available Colombia has been one of the first countries to introduce electronic billing process on a voluntary basis, from a traditional to a digital version. In this context, the article analyzes the electronic billing process implemented in Colombia and the advantages. Methodological research is applied, qualitative, descriptive and documentary; where the regulatory framework and the conceptualization of the model is identified; the process of adoption of electronic billing is analyzed, and finally the advantages and disadvantages of its implementation is analyzed. The findings indicate that the model applied in Colombia to issue an electronic billing in sending and receiving process, is not complex, but it requires a small adequate infrastructure and trained personnel to reach all sectors, especially the micro and business which is the largest business network in the country.
Neural network modeling of emotion
Levine, Daniel S.
2007-03-01
This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.
Modeling semiflexible polymer networks
Broedersz, C.P.; MacKintosh, F.C.
2014-01-01
This is an overview of theoretical approaches to semiflexible polymers and their networks. Such semiflexible polymers have large bending rigidities that can compete with the entropic tendency of a chain to crumple up into a random coil. Many studies on semiflexible polymers and their assemblies have
Matsuoka, Takeshi; Tanaka, Shigenori; Ebina, Kuniyoshi
2015-09-07
Photosystem II (PS II) is a protein complex which evolves oxygen and drives charge separation for photosynthesis employing electron and excitation-energy transfer processes over a wide timescale range from picoseconds to milliseconds. While the fluorescence emitted by the antenna pigments of this complex is known as an important indicator of the activity of photosynthesis, its interpretation was difficult because of the complexity of PS II. In this study, an extensive kinetic model which describes the complex and multi-timescale characteristics of PS II is analyzed through the use of the hierarchical coarse-graining method proposed in the authors׳ earlier work. In this coarse-grained analysis, the reaction center (RC) is described by two states, open and closed RCs, both of which consist of oxidized and neutral special pairs being in quasi-equilibrium states. Besides, the PS II model at millisecond scale with three-state RC, which was studied previously, could be derived by suitably adjusting the kinetic parameters of electron transfer between tyrosine and RC. Our novel coarse-grained model of PS II can appropriately explain the light-intensity dependent change of the characteristic patterns of fluorescence induction kinetics from O-J-I-P, which shows two inflection points, J and I, between initial point O and peak point P, to O-J-D-I-P, which shows a dip D between J and I inflection points. Copyright © 2015 Elsevier Ltd. All rights reserved.
Yao, Kun
2015-01-01
We demonstrate a convolutional neural network trained to reproduce the Kohn-Sham kinetic energy of hydrocarbons from electron density. The output of the network is used as a non-local correction to the conventional local and semi-local kinetic functionals. We show that this approximation qualitatively reproduces Kohn-Sham potential energy surfaces when used with conventional exchange correlation functionals. Numerical noise inherited from the non-linearity of the neural network is identified as the major challenge for the model. Finally we examine the features in the density learned by the neural network to anticipate the prospects of generalizing these models.
Mobility Model for Tactical Networks
Rollo, Milan; Komenda, Antonín
In this paper a synthetic mobility model which represents behavior and movement pattern of heterogeneous units in disaster relief and battlefield scenarios is proposed. These operations usually take place in environment without preexisting communication infrastructure and units thus have to be connected by wireless communication network. Units cooperate to fulfill common tasks and communication network has to serve high amount of communication requests, especially data, voice and video stream transmissions. To verify features of topology control, routing and interaction protocols software simulations are usually used, because of their scalability, repeatability and speed. Behavior of all these protocols relies on the mobility model of the network nodes, which has to resemble real-life movement pattern. Proposed mobility model is goal-driven and provides support for various types of units, group mobility and realistic environment model with obstacles. Basic characteristics of the mobility model like node spatial distribution and average node degree were analyzed.
5th International Conference on Electronics, Communications and Networks
2016-01-01
This book comprises peer-reviewed contributions presented at the 5th International Conference on Electronics, Communications and Networks (CECNet 2015), held in Shanghai, China, 12-15 December, 2015. It includes new multi-disciplinary topics spanning a unique depth and breadth of cutting-edge research areas in Electronic Engineering, Communications and Networks, and Computer Technology. More generally, it is of interest to academics, students and professionals involved in Consumer Electronics Technology, Communication Engineering and Technology, Wireless Communication Systems and Technology, and Computer Engineering and Technology.
Modelling freeway networks by hybrid stochastic models
Boel, R.; Mihaylova, L.
2004-01-01
Traffic flow on freeways is a nonlinear, many-particle phenomenon, with complex interactions between the vehicles. This paper presents a stochastic hybrid model of freeway traffic at a time scale and at a level of detail suitable for on-line flow estimation, for routing and ramp metering control. The model describes the evolution of continuous and discrete state variables. The freeway is considered as a network of components, each component representing a different section of the network. The...
Electron tomography reveals unbranched networks of actin filaments in lamellipodia.
Urban, Edit; Jacob, Sonja; Nemethova, Maria; Resch, Guenter P; Small, J Victor
2010-05-01
Eukaryotic cells can initiate movement using the forces exerted by polymerizing actin filaments to extend lamellipodial and filopodial protrusions. In the current model, actin filaments in lamellipodia are organized in a branched, dendritic network. We applied electron tomography to vitreously frozen 'live' cells, fixed cells and cytoskeletons, embedded in vitreous ice or in deep-negative stain. In lamellipodia from four cell types, including rapidly migrating fish keratocytes, we found that actin filaments are almost exclusively unbranched. The vast majority of apparent filament junctions proved to be overlapping filaments, rather than branched end-to-side junctions. Analysis of the tomograms revealed that actin filaments terminate at the membrane interface within a zone several hundred nanometres wide at the lamellipodium front, and yielded the first direct measurements of filament densities. Actin filament pairs were also identified as lamellipodium components and bundle precursors. These data provide a new structural basis for understanding actin-driven protrusion during cell migration.
Manganese oxide microswitch for electronic memory based on neural networks
Ramesham, R.; Daud, T.; Moopenn, A.; Thakoor, A. P.; Khanna, S. K.
1989-01-01
A solid-state, resistance tailorable, programmable-once, binary, nonvolatile memory switch based on manganese oxide thin films is reported. MnO(x) exhibits irreversible memory switching from conducting (on) to insulating (off) state, with the off and on resistance ratio of greater than 10,000. The switching mechanism is current-triggered chemical transformation of a conductive MnO(2-Delta) to an insulating Mn2O3 state. The energy required for switching is of the order of 4-20 nJ/sq micron. The low switching energy, stability of the on and off states, and tailorability of the on state resistance make these microswitches well suited as programmable binary synapses in electronic associative memories based on neural network models.
Network model of security system
Directory of Open Access Journals (Sweden)
Adamczyk Piotr
2016-01-01
Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.
Electronic Properties of Quantum Wire Networks
Kuzmenko, Igor
2005-01-01
Quantum wire networks (``quantum crossbars'', QCB) represent a 2D grid formed by superimposed crossing arrays of parallel conducting quantum wires, molecular chains or metallic single-wall carbon nanotubes. QCB coupled only by capacitive interaction in the crosses have similar low-energy, long-wave properties characterized as a crossed sliding Luttinger liquid (CSLL) phase. In this Thesis we develop a theory of interacting Bose excitations (plasmons) in QCB. We analyze spectrum of boson field...
Trusted intermediating agents in electronic trade networks
T.B. Klos (Tomas); F. Alkemade (Floortje)
2005-01-01
htmlabstract Electronic commerce and trading of information goods significantly impact the role of intermediaries: consumers can bypass intermediating agents by forming direct links to producers. One reason that traditional intermediaries can still make a profit, is that they have more knowledge of
A Multilayer Model of Computer Networks
Shchurov, Andrey A.
2015-01-01
The fundamental concept of applying the system methodology to network analysis declares that network architecture should take into account services and applications which this network provides and supports. This work introduces a formal model of computer networks on the basis of the hierarchical multilayer networks. In turn, individual layers are represented as multiplex networks. The concept of layered networks provides conditions of top-down consistency of the model. Next, we determined the...
A last updating evolution model for online social networks
Bu, Zhan; Xia, Zhengyou; Wang, Jiandong; Zhang, Chengcui
2013-05-01
As information technology has advanced, people are turning to electronic media more frequently for communication, and social relationships are increasingly found on online channels. However, there is very limited knowledge about the actual evolution of the online social networks. In this paper, we propose and study a novel evolution network model with the new concept of “last updating time”, which exists in many real-life online social networks. The last updating evolution network model can maintain the robustness of scale-free networks and can improve the network reliance against intentional attacks. What is more, we also found that it has the “small-world effect”, which is the inherent property of most social networks. Simulation experiment based on this model show that the results and the real-life data are consistent, which means that our model is valid.
Data modeling of network dynamics
Jaenisch, Holger M.; Handley, James W.; Faucheux, Jeffery P.; Harris, Brad
2004-01-01
This paper highlights Data Modeling theory and its use for text data mining as a graphical network search engine. Data Modeling is then used to create a real-time filter capable of monitoring network traffic down to the port level for unusual dynamics and changes in business as usual. This is accomplished in an unsupervised fashion without a priori knowledge of abnormal characteristics. Two novel methods for converting streaming binary data into a form amenable to graphics based search and change detection are introduced. These techniques are then successfully applied to 1999 KDD Cup network attack data log-on sessions to demonstrate that Data Modeling can detect attacks without prior training on any form of attack behavior. Finally, two new methods for data encryption using these ideas are proposed.
Thermal Network Modelling Handbook
1972-01-01
Thermal mathematical modelling is discussed in detail. A three-fold purpose was established: (1) to acquaint the new user with the terminology and concepts used in thermal mathematical modelling, (2) to present the more experienced and occasional user with quick formulas and methods for solving everyday problems, coupled with study cases which lend insight into the relationships that exist among the various solution techniques and parameters, and (3) to begin to catalog in an orderly fashion the common formulas which may be applied to automated conversational language techniques.
Automotive mechatronics automotive networking, driving stability systems, electronics
2015-01-01
As the complexity of automotive vehicles increases this book presents operational and practical issues of automotive mechatronics. It is a comprehensive introduction to controlled automotive systems and provides detailed information of sensors for travel, angle, engine speed, vehicle speed, acceleration, pressure, temperature, flow, gas concentration etc. The measurement principles of the different sensor groups are explained and examples to show the measurement principles applied in different types. Contents Basics of mechatronics.- Architecture.- Electronic control unit.- Software development.- Basic principles of networking.- Automotive networking.- Bus systems.- Automotive sensors.- Sensor measuring principles.- Sensor types.- Electric actuators.- Electrohydraulic actuators.- Electronic transmission control.- Electronic transmission control unit.- Modules for transmission control.- Antilock braking system.- Traction control system.- Electronic stability program.- Automatic brake functions.- Hydraulic modu...
Service entity network virtualization architecture and model
Jin, Xue-Guang; Shou, Guo-Chu; Hu, Yi-Hong; Guo, Zhi-Gang
2017-07-01
Communication network can be treated as a complex network carrying a variety of services and service can be treated as a network composed of functional entities. There are growing interests in multiplex service entities where individual entity and link can be used for different services simultaneously. Entities and their relationships constitute a service entity network. In this paper, we introduced a service entity network virtualization architecture including service entity network hierarchical model, service entity network model, service implementation and deployment of service entity networks. Service entity network oriented multiplex planning model were also studied and many of these multiplex models were characterized by a significant multiplex of the links or entities in different service entity network. Service entity networks were mapped onto shared physical resources by dynamic resource allocation controller. The efficiency of the proposed architecture was illustrated in a simulation environment that allows for comparative performance evaluation. The results show that, compared to traditional networking architecture, this architecture has a better performance.
Mechanically Reinforced Skin-Electronics with Networked Nanocomposite Elastomer.
Han, Seungyong; Kim, Min Ku; Wang, Bo; Wie, Dae Seung; Wang, Shuodao; Lee, Chi Hwan
2016-12-01
Mechanically reinforced skin-electronics are presented by exploiting networked nanocomposite elastomers where high quality metal nanowires serve as conducting paths. Theoretical and experimental studies show that the established skin-electronics exhibit superior mechanical enhancements against crack and delamination phenomena. Device applications include a class of biomedical devices that offers the ability of thermotherapeutic stimulation and electrophysiological monitoring, all via the skin. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Polymer networks: Modeling and applications
Masoud, Hassan
Polymer networks are an important class of materials that are ubiquitously found in natural, biological, and man-made systems. The complex mesoscale structure of these soft materials has made it difficult for researchers to fully explore their properties. In this dissertation, we introduce a coarse-grained computational model for permanently cross-linked polymer networks than can properly capture common properties of these materials. We use this model to study several practical problems involving dry and solvated networks. Specifically, we analyze the permeability and diffusivity of polymer networks under mechanical deformations, we examine the release of encapsulated solutes from microgel capsules during volume transitions, and we explore the complex tribological behavior of elastomers. Our simulations reveal that the network transport properties are defined by the network porosity and by the degree of network anisotropy due to mechanical deformations. In particular, the permeability of mechanically deformed networks can be predicted based on the alignment of network filaments that is characterized by a second order orientation tensor. Moreover, our numerical calculations demonstrate that responsive microcapsules can be effectively utilized for steady and pulsatile release of encapsulated solutes. We show that swollen gel capsules allow steady, diffusive release of nanoparticles and polymer chains, whereas gel deswelling causes burst-like discharge of solutes driven by an outward flow of the solvent initially enclosed within a shrinking capsule. We further demonstrate that this hydrodynamic release can be regulated by introducing rigid microscopic rods in the capsule interior. We also probe the effects of velocity, temperature, and normal load on the sliding of elastomers on smooth and corrugated substrates. Our friction simulations predict a bell-shaped curve for the dependence of the friction coefficient on the sliding velocity. Our simulations also illustrate
Target-Centric Network Modeling
DEFF Research Database (Denmark)
Mitchell, Dr. William L.; Clark, Dr. Robert M.
In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues....... Working through these cases, students will learn to manage and evaluate realistic intelligence accounts....
CNEM: Cluster Based Network Evolution Model
Directory of Open Access Journals (Sweden)
Sarwat Nizamani
2015-01-01
Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks
Biological transportation networks: Modeling and simulation
Albi, Giacomo
2015-09-15
We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.
The use of electronic communication (social network) by open and ...
African Journals Online (AJOL)
social network) by open and distance learners, henceforth referred to as ODL in the work. The case of National Open University of Nigeria, Benin study centre. The study is to explore the electronic device mostly used by open and distance ...
Percolation on shopping and cashback electronic commerce networks
Fu, Tao; Chen, Yini; Qin, Zhen; Guo, Liping
2013-06-01
Many realistic networks live in the form of multiple networks, including interacting networks and interdependent networks. Here we study percolation properties of a special kind of interacting networks, namely Shopping and Cashback Electronic Commerce Networks (SCECNs). We investigate two actual SCECNs to extract their structural properties, and develop a mathematical framework based on generating functions for analyzing directed interacting networks. Then we derive the necessary and sufficient condition for the absence of the system-wide giant in- and out- component, and propose arithmetic to calculate the corresponding structural measures in the sub-critical and supercritical regimes. We apply our mathematical framework and arithmetic to those two actual SCECNs to observe its accuracy, and give some explanations on the discrepancies. We show those structural measures based on our mathematical framework and arithmetic are useful to appraise the status of SCECNs. We also find that the supercritical regime of the whole network is maintained mainly by hyperlinks between different kinds of websites, while those hyperlinks between the same kinds of websites can only enlarge the sizes of in-components and out-components.
Mathematical Modelling Plant Signalling Networks
Muraro, D.
2013-01-01
During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.
Electronic collaboration in dermatology resident training through social networking.
Meeks, Natalie M; McGuire, April L; Carroll, Bryan T
2017-04-01
The use of online educational resources and professional social networking sites is increasing. The field of dermatology is currently under-utilizing online social networking as a means of professional collaboration and sharing of training materials. In this study, we sought to assess the current structure of and satisfaction with dermatology resident education and gauge interest for a professional social networking site for educational collaboration. Two surveys-one for residents and one for faculty-were electronically distributed via the American Society for Dermatologic Surgery and Association of Professors of Dermatology (APD) listserves. The surveys confirmed that there is interest among dermatology residents and faculty in a dermatology professional networking site with the goal to enhance educational collaboration.
Energy modelling in sensor networks
Directory of Open Access Journals (Sweden)
D. Schmidt
2007-06-01
Full Text Available Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.
Probabilistic logic modeling of network reliability for hybrid network architectures
Energy Technology Data Exchange (ETDEWEB)
Wyss, G.D.; Schriner, H.K.; Gaylor, T.R.
1996-10-01
Sandia National Laboratories has found that the reliability and failure modes of current-generation network technologies can be effectively modeled using fault tree-based probabilistic logic modeling (PLM) techniques. We have developed fault tree models that include various hierarchical networking technologies and classes of components interconnected in a wide variety of typical and atypical configurations. In this paper we discuss the types of results that can be obtained from PLMs and why these results are of great practical value to network designers and analysts. After providing some mathematical background, we describe the `plug-and-play` fault tree analysis methodology that we have developed for modeling connectivity and the provision of network services in several current- generation network architectures. Finally, we demonstrate the flexibility of the method by modeling the reliability of a hybrid example network that contains several interconnected ethernet, FDDI, and token ring segments. 11 refs., 3 figs., 1 tab.
Generalization performance of regularized neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1994-01-01
Architecture optimization is a fundamental problem of neural network modeling. The optimal architecture is defined as the one which minimizes the generalization error. This paper addresses estimation of the generalization performance of regularized, complete neural network models. Regularization...
Plant Growth Models Using Artificial Neural Networks
Bubenheim, David
1997-01-01
In this paper, we descrive our motivation and approach to devloping models and the neural network architecture. Initial use of the artificial neural network for modeling the single plant process of transpiration is presented.
Introducing Synchronisation in Deterministic Network Models
DEFF Research Database (Denmark)
Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.
2006-01-01
The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...
Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation.
Fakhry, Ahmed; Zeng, Tao; Ji, Shuiwang
2017-02-01
Accurate reconstruction of anatomical connections between neurons in the brain using electron microscopy (EM) images is considered to be the gold standard for circuit mapping. A key step in obtaining the reconstruction is the ability to automatically segment neurons with a precision close to human-level performance. Despite the recent technical advances in EM image segmentation, most of them rely on hand-crafted features to some extent that are specific to the data, limiting their ability to generalize. Here, we propose a simple yet powerful technique for EM image segmentation that is trained end-to-end and does not rely on prior knowledge of the data. Our proposed residual deconvolutional network consists of two information pathways that capture full-resolution features and contextual information, respectively. We showed that the proposed model is very effective in achieving the conflicting goals in dense output prediction; namely preserving full-resolution predictions and including sufficient contextual information. We applied our method to the ongoing open challenge of 3D neurite segmentation in EM images. Our method achieved one of the top results on this open challenge. We demonstrated the generality of our technique by evaluating it on the 2D neurite segmentation challenge dataset where consistently high performance was obtained. We thus expect our method to generalize well to other dense output prediction problems.
Modeling the Dynamics of Compromised Networks
Energy Technology Data Exchange (ETDEWEB)
Soper, B; Merl, D M
2011-09-12
Accurate predictive models of compromised networks would contribute greatly to improving the effectiveness and efficiency of the detection and control of network attacks. Compartmental epidemiological models have been applied to modeling attack vectors such as viruses and worms. We extend the application of these models to capture a wider class of dynamics applicable to cyber security. By making basic assumptions regarding network topology we use multi-group epidemiological models and reaction rate kinetics to model the stochastic evolution of a compromised network. The Gillespie Algorithm is used to run simulations under a worst case scenario in which the intruder follows the basic connection rates of network traffic as a method of obfuscation.
RMBNToolbox: random models for biochemical networks
Directory of Open Access Journals (Sweden)
Niemi Jari
2007-05-01
Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.
Network Bandwidth Utilization Forecast Model on High Bandwidth Network
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wucherl; Sim, Alex
2014-07-07
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Network bandwidth utilization forecast model on high bandwidth networks
Energy Technology Data Exchange (ETDEWEB)
Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2015-03-30
With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.
Electronic periscope for underground networks; Periscopio electronico para redes subterraneas
Energy Technology Data Exchange (ETDEWEB)
Araujo Filho, Edival Demetrio de [Companhia Energetica de Brasilia, DF (Brazil)]. E-mail: demetrio@ceb.com.br
2000-07-01
This paper aims at the perfection of the operational procedures adopted by the technician of Project and Underground Work Management of the Brasilia Energetic Company, Brazil, during the survey of the system duct bank characteristics (Organized formation of electro duct) . The execution of projects, reviews and budgets depend upon the verification of duct availability in the analysed network. The introduction of the device named Electronic Periscope proposed in this work aims at the efficiency of the field surveys.
Traffic Rules in Electronic Financial Transactions (EFT Networks
Directory of Open Access Journals (Sweden)
Vedran Batoš
2002-01-01
Full Text Available This paper presents the traffic rules in the EFT (ElectronicFinancial Transactions networks, based on the implementationof the solution called Gold-Net developed and implementedby Euronet Worldwide Inc. Following the traffic rulesin EFT networks, out of its worldwide experience, Gold-Netevolved a comprehensive and expandable EFT network solutiondesigned to meet an institution's needs today and in the future.It is an ITM (Integrated Transaction Management solution,modular and expandable, and consists of a comprehensiveEFT software modules with ATM and POS driving capabilities.The combination of ATM management and the onlineconnection form the intercept processing control module. Asthe marketplace grows, this solution ensures that an ente1prisemay position itself for future growth and expanded service offerings.
Neural Network for Nanoscience Scanning Electron Microscope Image Recognition.
Modarres, Mohammad Hadi; Aversa, Rossella; Cozzini, Stefano; Ciancio, Regina; Leto, Angelo; Brandino, Giuseppe Piero
2017-10-16
In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.
An acoustical model based monitoring network
Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der
2010-01-01
In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the
An adaptive complex network model for brain functional networks.
Directory of Open Access Journals (Sweden)
Ignacio J Gomez Portillo
Full Text Available Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution.
Modeling gene regulatory networks: A network simplification algorithm
Ferreira, Luiz Henrique O.; de Castro, Maria Clicia S.; da Silva, Fabricio A. B.
2016-12-01
Boolean networks have been used for some time to model Gene Regulatory Networks (GRNs), which describe cell functions. Those models can help biologists to make predictions, prognosis and even specialized treatment when some disturb on the GRN lead to a sick condition. However, the amount of information related to a GRN can be huge, making the task of inferring its boolean network representation quite a challenge. The method shown here takes into account information about the interactome to build a network, where each node represents a protein, and uses the entropy of each node as a key to reduce the size of the network, allowing the further inferring process to focus only on the main protein hubs, the ones with most potential to interfere in overall network behavior.
Behavioral modeling approach for optical communication network design
Vuorinen, Kimmo; Jacquemod, Gilles; Gaffiot, Frederic; Seassal, Christian
1997-12-01
An optical communication networks can be divided in two levels: communication level, which defines the protocols, the control and the management of the networks and physical level formed by photonic and electronic components in order to transmit and receive the data between different nodes of the network. Traditionally, these two levels are considered separately in the optical communication network design process. This can lead to an erroneous or non-ideal networks implementation, due to the fact that the communication and physical levels are not independent. For example, in WDM communication network the maximum achievable data rate is limited not only by the networks protocol, but depends also on the implementation of the physical level: tuning delay of the optical multiplexers. Also the lack of the possibilities for co-verification of the communication and the physical levels together could lead to misinterpretations between the designers of the different levels and thus induce design faults. Since the prototyping is extremely expensive and time consuming, an integrated simulation of both communication and physical levels is necessary, at least in some extend. In this paper, a behavioral modeling approach that allows a co- simulation of the communication and the physical levels is presented. It is based on the use of a VHDL-AMS-like hardware description language, dedicated to electronic system modeling, but also suitable for modeling and simulation of non- electronic and mixed-domain systems. The behavioral models for photonic and electronic components, as well as the software are integrated in a unique simulator in order to co-simulate the communication (control) and the physical level (data path) of a WDM optical communication network.
The model of social crypto-network
Directory of Open Access Journals (Sweden)
Марк Миколайович Орел
2015-06-01
Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks
Entropy Characterization of Random Network Models
Directory of Open Access Journals (Sweden)
Pedro J. Zufiria
2017-06-01
Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.
The model of social crypto-network
Марк Миколайович Орел
2015-01-01
The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks
Modeling Diagnostic Assessments with Bayesian Networks
Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego
2007-01-01
This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…
Boltzmann-Electron Model in Aleph.
Energy Technology Data Exchange (ETDEWEB)
Hughes, Thomas Patrick; Hooper, Russell
2014-11-01
We apply the Boltzmann-electron model in the electrostatic, particle-in-cell, finite- element code Aleph to a plasma sheath. By assuming a Boltzmann energy distribution for the electrons, the model eliminates the need to resolve the electron plasma fre- quency, and avoids the numerical "grid instability" that can cause unphysical heating of electrons. This allows much larger timesteps to be used than with kinetic electrons. Ions are treated with the standard PIC algorithm. The Boltzmann-electron model re- quires solution of a nonlinear Poisson equation, for which we use an iterative Newton solver (NOX) from the Trilinos Project. Results for the spatial variation of density and voltage in the plasma sheath agree well with an analytic model
Bayesian Network Webserver: a comprehensive tool for biological network modeling.
Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan
2013-11-01
The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.
Rotational nuclear models and electron scattering
Energy Technology Data Exchange (ETDEWEB)
Moya de Guerra, E.
1986-05-01
A review is made of the basic formalism involved in the application of nuclear rotational models to the problem of electron scattering from axially symmetric deformed nuclei. Emphasis is made on the use of electron scattering to extract information on the nature of the collective rotational model. In this respect, the interest of using polarized beam and target is discussed with the help of illustrative examples. Concerning the nuclear structure four rotational models are considered: Two microscopic models, namely the Projected Hartree-Fock (PHF) and cranking models; and two collective models, the rigid rotor and the irrotational flow models. The problem of current conservation within the different models is also discussed.
Effective electron-electron and electron-phonon interactions in the Hubbard-Holstein model
Energy Technology Data Exchange (ETDEWEB)
Aprea, G. [INFM-CNR SMC Center, and Dipartimento di Fisica, Universita di Roma ' La Sapienza' , piazzale Aldo Moro 5, I-00185 Rome (Italy); Di Castro, C. [INFM-CNR SMC Center, and Dipartimento di Fisica, Universita di Roma ' La Sapienza' , piazzale Aldo Moro 5, I-00185 Rome (Italy); Grilli, M. [INFM-CNR SMC Center, and Dipartimento di Fisica, Universita di Roma ' La Sapienza' , piazzale Aldo Moro 5, I-00185 Rome (Italy)]. E-mail marco.grilli@roma1.infn.it; Lorenzana, J. [INFM-CNR SMC Center, and Dipartimento di Fisica, Universita di Roma ' La Sapienza' , piazzale Aldo Moro 5, I-00185 Rome (Italy)
2006-06-12
We investigate the interplay between the electron-electron and the electron-phonon interaction in the Hubbard-Holstein model. We implement the flow-equation method to investigate within this model the effect of correlation on the electron-phonon effective coupling and, conversely, the effect of phonons in the effective electron-electron interaction. Using this technique we obtain analytical momentum-dependent expressions for the effective couplings and we study their behavior for different physical regimes. In agreement with other works on this subject, we find that the electron-electron attraction mediated by phonons in the presence of Hubbard repulsion is peaked at low transferred momenta. The role of the characteristic energies involved is also analyzed.
Comprehensive Power Losses Model for Electronic Power Transformer
DEFF Research Database (Denmark)
Yue, Quanyou; Li, Canbing; Cao, Yijia
2018-01-01
The electronic power transformer (EPT) has highe rpower losses than the conventional transformer. However, the EPT can correct the power factor, compensate the unbalanced current and reduce the line power losses in the distribution network.Therefore, the higher losses of the EPT and the consequent......-losses and considering the impact of the non-unity power factor and the three-phase unbalanced current, the overall power losses in the distribution network when using the EPT to replace the conventional transformer is analyzed, and the conditions in which the application of the EPT can cause less power losses...... reduced power losses in the distribution network require a comprehensive consideration when comparing the power losses of theEPT and conventional transformer. In this paper, a comprehensive power losses analysis model for the EPT in distribution networks is proposed. By analyzing the EPT self...
Object Oriented Modeling Of Social Networks
Zeggelink, Evelien P.H.; Oosten, Reinier van; Stokman, Frans N.
1996-01-01
The aim of this paper is to explain principles of object oriented modeling in the scope of modeling dynamic social networks. As such, the approach of object oriented modeling is advocated within the field of organizational research that focuses on networks. We provide a brief introduction into the
Bayesian estimation of the network autocorrelation model
Dittrich, D.; Leenders, R.T.A.J.; Mulder, J.
2017-01-01
The network autocorrelation model has been extensively used by researchers interested modeling social influence effects in social networks. The most common inferential method in the model is classical maximum likelihood estimation. This approach, however, has known problems such as negative bias of
THE USE OF ELECTRONIC SOCIAL NETWORKS IN SOCIAL AND EDUCATIONAL WORK WITH PUPILS
Natalia V. Oleksiuk; Lesya V. Lebedenko
2015-01-01
Electronic social networks are powerful means of communication for million registered users, because some social groups are gaining considerable popularity among pupils of secondary schools. The author believes that the use of electronic social networks is actual not just for training purposes, but also to implement the social and educational work in the school environment. In publication there have been reviewed electronic social networks, including business networks, comparison of their pro...
Agent-based modeling and network dynamics
Namatame, Akira
2016-01-01
The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...
Dynamic Pricing in Electronic Commerce Using Neural Network
Ghose, Tapu Kumar; Tran, Thomas T.
In this paper, we propose an approach where feed-forward neural network is used for dynamically calculating a competitive price of a product in order to maximize sellers’ revenue. In the approach we considered that along with product price other attributes such as product quality, delivery time, after sales service and seller’s reputation contribute in consumers purchase decision. We showed that once the sellers, by using their limited prior knowledge, set an initial price of a product our model adjusts the price automatically with the help of neural network so that sellers’ revenue is maximized.
Modeling data throughput on communication networks
Energy Technology Data Exchange (ETDEWEB)
Eldridge, J.M.
1993-11-01
New challenges in high performance computing and communications are driving the need for fast, geographically distributed networks. Applications such as modeling physical phenomena, interactive visualization, large data set transfers, and distributed supercomputing require high performance networking [St89][Ra92][Ca92]. One measure of a communication network`s performance is the time it takes to complete a task -- such as transferring a data file or displaying a graphics image on a remote monitor. Throughput, defined as the ratio of the number of useful data bits transmitted per the time required to transmit those bits, is a useful gauge of how well a communication system meets this performance measure. This paper develops and describes an analytical model of throughput. The model is a tool network designers can use to predict network throughput. It also provides insight into those parts of the network that act as a performance bottleneck.
Multidisciplinary Modelling Tools for Power Electronic Circuits
DEFF Research Database (Denmark)
Bahman, Amir Sajjad
This thesis presents multidisciplinary modelling techniques in a Design For Reliability (DFR) approach for power electronic circuits. With increasing penetration of renewable energy systems, the demand for reliable power conversion systems is becoming critical. Since a large part of electricity...... for expensive computation facilities in DFR approach. Therefore, in this thesis focus is placed on the generation of accurate, simple and generic models to study and assess thermal and electrical behavior of power electronic circuits (especially power modules). In this thesis, different power electronic...... is processed through power electronics, highly efficient, sustainable, reliable and cost-effective power electronic devices are needed. Reliability of a product is defined as the ability to perform within its predefined functions under given conditions in a specific time. Because power electronic devices...
Solar models and electron screening
Weiss, A.; Flaskamp, M.; Tsytovich, V. N.
2001-01-01
We investigate the sensitivity of the solar model to changes in the nuclear reaction screening factors. We show that the sound speed profile as determined by helioseismology certainly rules out changes in the screening factors exceeding more than 10%. A slightly improved solar model could be obtained by enhancing screening by about 5% over the Salpeter value. We also discuss how envelope properties of the Sun depend on screening, too. We conclude that the solar model can be used to help settl...
Status of Galileo interim radiation electron model
Garrett, H. B.; Jun, I.; Ratliff, J. M.; Evans, R. W.; Clough, G. A.; McEntire, R. W.
2003-01-01
Measurements of the high energy, omni-directional electron environment by the Galileo spacecraft Energetic Particle Detector (EDP) were used to develop a new model of Jupiter's trapped electron radiation in the jovian equatorial plane for the range 8 to 16 Jupiter radii.
Settings in Social Networks : a Measurement Model
Schweinberger, Michael; Snijders, Tom A.B.
2003-01-01
A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive
Settings in social networks : A measurement model
Schweinberger, M; Snijders, TAB
2003-01-01
A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive
Spinal Cord Injury Model System Information Network
... the UAB-SCIMS Contact the UAB-SCIMS UAB Spinal Cord Injury Model System Newly Injured Health Daily Living Consumer ... Information Network The University of Alabama at Birmingham Spinal Cord Injury Model System (UAB-SCIMS) maintains this Information Network ...
Network interconnections: an architectural reference model
Butscher, B.; Lenzini, L.; Morling, R.; Vissers, C.A.; Popescu-Zeletin, R.; van Sinderen, Marten J.; Heger, D.; Krueger, G.; Spaniol, O.; Zorn, W.
1985-01-01
One of the major problems in understanding the different approaches in interconnecting networks of different technologies is the lack of reference to a general model. The paper develops the rationales for a reference model of network interconnection and focuses on the architectural implications for
Performance modeling of network data services
Energy Technology Data Exchange (ETDEWEB)
Haynes, R.A.; Pierson, L.G.
1997-01-01
Networks at major computational organizations are becoming increasingly complex. The introduction of large massively parallel computers and supercomputers with gigabyte memories are requiring greater and greater bandwidth for network data transfers to widely dispersed clients. For networks to provide adequate data transfer services to high performance computers and remote users connected to them, the networking components must be optimized from a combination of internal and external performance criteria. This paper describes research done at Sandia National Laboratories to model network data services and to visualize the flow of data from source to sink when using the data services.
Learning Bayesian Network Model Structure from Data
National Research Council Canada - National Science Library
Margaritis, Dimitris
2003-01-01
In this thesis I address the important problem of the determination of the structure of directed statistical models, with the widely used class of Bayesian network models as a concrete vehicle of my ideas...
NC truck network model development research.
2008-09-01
This research develops a validated prototype truck traffic network model for North Carolina. The model : includes all counties and metropolitan areas of North Carolina and major economic areas throughout the : U.S. Geographic boundaries, population a...
Network models in economics and finance
Pardalos, Panos; Rassias, Themistocles
2014-01-01
Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.
Modelling the structure of complex networks
DEFF Research Database (Denmark)
Herlau, Tue
networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex......A complex network is a systems in which a discrete set of units interact in a quantifiable manner. Representing systems as complex networks have become increasingly popular in a variety of scientific fields including biology, social sciences and economics. Parallel to this development complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...
A Network Formation Model Based on Subgraphs
Chandrasekhar, Arun
2016-01-01
We develop a new class of random-graph models for the statistical estimation of network formation that allow for substantial correlation in links. Various subgraphs (e.g., links, triangles, cliques, stars) are generated and their union results in a network. We provide estimation techniques for recovering the rates at which the underlying subgraphs were formed. We illustrate the models via a series of applications including testing for incentives to form cross-caste relationships in rural India, testing to see whether network structure is used to enforce risk-sharing, testing as to whether networks change in response to a community's exposure to microcredit, and show that these models significantly outperform stochastic block models in matching observed network characteristics. We also establish asymptotic properties of the models and various estimators, which requires proving a new Central Limit Theorem for correlated random variables.
Gossip spread in social network Models
Johansson, Tobias
2017-04-01
Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.
Synergistic effects in threshold models on networks
Juul, Jonas S.; Porter, Mason A.
2018-01-01
Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.
Optimized null model for protein structure networks.
Milenković, Tijana; Filippis, Ioannis; Lappe, Michael; Przulj, Natasa
2009-06-26
Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by
Optimized null model for protein structure networks.
Directory of Open Access Journals (Sweden)
Tijana Milenković
Full Text Available Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model
Towards Reproducible Descriptions of Neuronal Network Models
Nordlie, Eilen; Gewaltig, Marc-Oliver; Plesser, Hans Ekkehard
2009-01-01
Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing—and thinking about—complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain. PMID:19662159
Towards reproducible descriptions of neuronal network models.
Directory of Open Access Journals (Sweden)
Eilen Nordlie
2009-08-01
Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.
Characterization and Modeling of Network Traffic
DEFF Research Database (Denmark)
Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur
2011-01-01
This paper attempts to characterize and model backbone network traffic, using a small number of statistics. In order to reduce cost and processing power associated with traffic analysis. The parameters affecting the behaviour of network traffic are investigated and the choice is that inter......-arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values....... The model investigates the traffic generation mechanisms, and grouping traffic into flows and applications....
Modeling, Optimization & Control of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat
2014-01-01
in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply....... The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used...... to solve nonlinear optimal control problems. In the water supply system model, the hydraulic resistance of the valve is estimated by real data and it is considered to be a disturbance. The disturbance in our system is updated every 24 hours based on the amount of water usage by consumers every day. Model...
A network model of the interbank market
Li, Shouwei; He, Jianmin; Zhuang, Yaming
2010-12-01
This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.
Model for Microcirculation Transportation Network Design
Directory of Open Access Journals (Sweden)
Qun Chen
2012-01-01
Full Text Available The idea of microcirculation transportation was proposed to shunt heavy traffic on arterial roads through branch roads. The optimization model for designing micro-circulation transportation network was developed to pick out branch roads as traffic-shunting channels and determine their required capacity, trying to minimize the total reconstruction expense and land occupancy subject to saturation and reconstruction space constraints, while accounting for the route choice behaviour of network users. Since micro-circulation transportation network design problem includes both discrete and continuous variables, a discretization method was developed to convert two groups of variables (discrete variables and continuous variables into one group of new discrete variables, transforming the mixed network design problem into a new kind of discrete network design problem with multiple values. The genetic algorithm was proposed to solve the new discrete network design problem. Finally a numerical example demonstrated the efficiency of the model and algorithm.
Modelling of virtual production networks
Directory of Open Access Journals (Sweden)
2011-03-01
Full Text Available Nowadays many companies, especially small and medium-sized enterprises (SMEs, specialize in a limited field of production. It requires forming virtual production networks of cooperating enterprises to manufacture better, faster and cheaper. Apart from that, some production orders cannot be realized, because there is not a company of sufficient production potential. In this case the virtual production networks of cooperating companies can realize these production orders. These networks have larger production capacity and many different resources. Therefore it can realize many more production orders together than each of them separately. Such organization allows for executing high quality product. The maintenance costs of production capacity and used resources are not so high. In this paper a methodology of rapid prototyping of virtual production networks is proposed. It allows to execute production orders on time considered existing logistic constraints.
Modeling Epidemics Spreading on Social Contact Networks.
Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua
2015-09-01
Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.
Random graph models for dynamic networks
Zhang, Xiao; Moore, Cristopher; Newman, Mark E. J.
2017-10-01
Recent theoretical work on the modeling of network structure has focused primarily on networks that are static and unchanging, but many real-world networks change their structure over time. There exist natural generalizations to the dynamic case of many static network models, including the classic random graph, the configuration model, and the stochastic block model, where one assumes that the appearance and disappearance of edges are governed by continuous-time Markov processes with rate parameters that can depend on properties of the nodes. Here we give an introduction to this class of models, showing for instance how one can compute their equilibrium properties. We also demonstrate their use in data analysis and statistical inference, giving efficient algorithms for fitting them to observed network data using the method of maximum likelihood. This allows us, for example, to estimate the time constants of network evolution or infer community structure from temporal network data using cues embedded both in the probabilities over time that node pairs are connected by edges and in the characteristic dynamics of edge appearance and disappearance. We illustrate these methods with a selection of applications, both to computer-generated test networks and real-world examples.
Modeling the interdependent network based on two-mode networks
An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian
2017-10-01
Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.
The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future
National Research Council Canada - National Science Library
Gottesman, Omri; Kuivaniemi, Helena; Tromp, Gerard; Faucett, W Andrew; Li, Rongling; Manolio, Teri A; Sanderson, Saskia C; Kannry, Joseph; Zinberg, Randi; Basford, Melissa A; Brilliant, Murray; Carey, David J; Chisholm, Rex L; Chute, Christopher G; Connolly, John J; Crosslin, David; Denny, Joshua C; Gallego, Carlos J; Haines, Jonathan L; Hakonarson, Hakon; Harley, John; Jarvik, Gail P; Kohane, Isaac; Kullo, Iftikhar J; Larson, Eric B; McCarty, Catherine; Ritchie, Marylyn D; Roden, Dan M; Smith, Maureen E; Böttinger, Erwin P; Williams, Marc S
2013-01-01
The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute-funded consortium engaged in the development of methods and best practices for using the electronic...
Electronic Payments Profitability Extent Model
Directory of Open Access Journals (Sweden)
Rudolf Vohnout
2016-12-01
Full Text Available Cashless payments are recent phenomena, which even increased with the introduction of contactless means like NFC, PayPass or payWave. Such new methods speed-up the entire payment process and in comparison to cash transactions are much simpler and faster. But on the other hand the key question for merchant is if it is worth to have such device, which accept these new payment means or not to have the terminal at all. What is the amount of cash flow, which delimits the cash holdings to be still profitable? This paper tries to give answers to such question by presenting general profitability model, which will address defining the cash threshold amount. The aim is to show that cash holdings could be profitable up to certain amount, but after the threshold is met, cashless payment methods are fairly superior despite their additional costs.
Directory of Open Access Journals (Sweden)
Dewu eDing
2016-04-01
Full Text Available Shewanella oneidensis MR-1 is capable of extracellular electron transfer (EET and hence has attracted considerable attention. The EET pathways mainly consist of c-type cytochromes, along with some other proteins involved in electron transfer processes. By whole genome study and protein interactions inquisition, we constructed a large-scale electron transfer network containing 2276 interactions among 454 electron transfer related proteins in S. oneidensis MR-1. Using the k-shell decomposition method, we identified and analyzed distinct parts of the electron transfer network. We found that there was a negative correlation between the ks (k-shell values and the average DR_100 (disordered regions per 100 amino acids in every shell, which suggested that disordered regions of proteins played an important role during the formation and extension of the electron transfer network. Furthermore, proteins in the top three shells of the network are mainly located in the cytoplasm and inner membrane; these proteins can be responsible for transfer of electrons into the quinone pool in a wide variety of environmental conditions. In most of the other shells, proteins are broadly located throughout the five cellular compartments (cytoplasm, inner membrane, periplasm, outer membrane and extracellular, which ensures the important EET ability of S. oneidensis MR-1. Specifically, the fourth shell was responsible for EET and the c-type cytochromes in the remaining shells of the electron transfer network were involved in aiding EET. Taken together, these results show that there are distinct functional parts in the electron transfer network of S. oneidensis MR-1, and the EET processes could achieve high efficiency through cooperation through such an electron transfer network.
An endogenous model of the credit network
He, Jianmin; Sui, Xin; Li, Shouwei
2016-01-01
In this paper, an endogenous credit network model of firm-bank agents is constructed. The model describes the endogenous formation of firm-firm, firm-bank and bank-bank credit relationships. By means of simulations, the model is capable of showing some obvious similarities with empirical evidence found by other scholars: the upper-tail of firm size distribution can be well fitted with a power-law; the bank size distribution can be lognormally distributed with a power-law tail; the bank in-degrees of the interbank credit network as well as the firm-bank credit network fall into two-power-law distributions.
Tensor network models of multiboundary wormholes
Peach, Alex; Ross, Simon F.
2017-05-01
We study the entanglement structure of states dual to multiboundary wormhole geometries using tensor network models. Perfect and random tensor networks tiling the hyperbolic plane have been shown to provide good models of the entanglement structure in holography. We extend this by quotienting the plane by discrete isometries to obtain models of the multiboundary states. We show that there are networks where the entanglement structure is purely bipartite, extending results obtained in the large temperature limit. We analyse the entanglement structure in a range of examples.
Stochastic discrete model of karstic networks
Jaquet, O.; Siegel, P.; Klubertanz, G.; Benabderrhamane, H.
Karst aquifers are characterised by an extreme spatial heterogeneity that strongly influences their hydraulic behaviour and the transport of pollutants. These aquifers are particularly vulnerable to contamination because of their highly permeable networks of conduits. A stochastic model is proposed for the simulation of the geometry of karstic networks at a regional scale. The model integrates the relevant physical processes governing the formation of karstic networks. The discrete simulation of karstic networks is performed with a modified lattice-gas cellular automaton for a representative description of the karstic aquifer geometry. Consequently, more reliable modelling results can be obtained for the management and the protection of karst aquifers. The stochastic model was applied jointly with groundwater modelling techniques to a regional karst aquifer in France for the purpose of resolving surface pollution issues.
Designing Network-based Business Model Ontology
DEFF Research Database (Denmark)
Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz
2015-01-01
Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....
Queueing Models for Mobile Ad Hoc Networks
de Haan, Roland
2009-01-01
This thesis presents models for the performance analysis of a recent communication paradigm: \\emph{mobile ad hoc networking}. The objective of mobile ad hoc networking is to provide wireless connectivity between stations in a highly dynamic environment. These dynamics are driven by the mobility of
Modelling traffic congestion using queuing networks
Indian Academy of Sciences (India)
Traffic Flow-Density diagrams are obtained using simple Jackson queuing network analysis. Such simple analytical models can be used to capture the effect of non- homogenous traffic. Keywords. Flow-density curves; uninterrupted traffic; Jackson networks. 1. Introduction. Traffic management has become very essential in ...
Electron-Ionic Model of Ball Lightening
Fedosin, Sergey G.; Kim, Anatolii S.
2001-01-01
The model of ball lightning is presented where outside electron envelope is kept by inside volume of positive charges. The moving of electron in outside envelope is a reason of strong magnetic field, which controls the state of hot ionized air inside of ball lightning. The conditions of origins of ball lightning are investigated and the values of parameters for ball lightning of maximum power are calculated.
Possible Roles of Neural Electron Spin Networks in Memory and Consciousness
Hu, H P
2004-01-01
Spin is the origin of quantum effects in both Bohm and Hestenes quantum formulism and a fundamental quantum process associated with the structure of space-time. Thus, we have recently theorized that spin is the mind-pixel and developed a qualitative model of consciousness based on nuclear spins inside neural membranes and proteins. In this paper, we explore the possibility of unpaired electron spins being the mind-pixels. Besides free O2 and NO, the main sources of unpaired electron spins in neural membranes and proteins are transition metal ions and O2 and NO bound/absorbed to large molecules, free radicals produced through biochemical reactions and excited molecular triplet states induced by fluctuating internal magnetic fields. We show that unpaired electron spin networks inside neural membranes and proteins are modulated by action potentials through exchange and dipolar coupling tensors and spin-orbital coupling and g-factor tensors and perturbed by microscopically strong and fluctuating internal magnetic...
Mathematical model of highways network optimization
Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.
2017-12-01
The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.
Modeling trust context in networks
Adali, Sibel
2013-01-01
We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout
Model-based control of networked systems
Garcia, Eloy; Montestruque, Luis A
2014-01-01
This monograph introduces a class of networked control systems (NCS) called model-based networked control systems (MB-NCS) and presents various architectures and control strategies designed to improve the performance of NCS. The overall performance of NCS considers the appropriate use of network resources, particularly network bandwidth, in conjunction with the desired response of the system being controlled. The book begins with a detailed description of the basic MB-NCS architecture that provides stability conditions in terms of state feedback updates . It also covers typical problems in NCS such as network delays, network scheduling, and data quantization, as well as more general control problems such as output feedback control, nonlinear systems stabilization, and tracking control. Key features and topics include: Time-triggered and event-triggered feedback updates Stabilization of uncertain systems subject to time delays, quantization, and extended absence of feedback Optimal control analysis and ...
Complex networks repair strategies: Dynamic models
Fu, Chaoqi; Wang, Ying; Gao, Yangjun; Wang, Xiaoyang
2017-09-01
Network repair strategies are tactical methods that restore the efficiency of damaged networks; however, unreasonable repair strategies not only waste resources, they are also ineffective for network recovery. Most extant research on network repair focuses on static networks, but results and findings on static networks cannot be applied to evolutionary dynamic networks because, in dynamic models, complex network repair has completely different characteristics. For instance, repaired nodes face more severe challenges, and require strategic repair methods in order to have a significant effect. In this study, we propose the Shell Repair Strategy (SRS) to minimize the risk of secondary node failures due to the cascading effect. Our proposed method includes the identification of a set of vital nodes that have a significant impact on network repair and defense. Our identification of these vital nodes reduces the number of switching nodes that face the risk of secondary failures during the dynamic repair process. This is positively correlated with the size of the average degree 〈 k 〉 and enhances network invulnerability.
Regional Computation of TEC Using a Neural Network Model
Leandro, R. F.; Santos, M. C.
2004-05-01
One of the main sources of errors of GPS measurements is the ionosphere refraction. As a dispersive medium, the ionosphere allow its influence to be computed by using dual frequency receivers. In the case of single frequency receivers it is necessary to use models that tell us how big the ionospheric refraction is. The GPS broadcast message carries parameters of this model, namely Klobuchar model. Dual frequency receivers allow to estimate the influence of ionosphere in the GPS signal by the computation of TEC (Total Electron Content) values, that have a direct relationship with the magnitude of the delay caused by the ionosphere. One alternative is to create a regional model based on a network of dual frequency receivers. In this case, the regional behaviour of ionosphere is modelled in a way that it is possible to estimate the TEC values into or near this region. This regional model can be based on polynomials, for example. In this work we will present a Neural Network-based model to the regional computation of TEC. The advantage of using a Neural Network is that it is not necessary to have a great knowledge on the behaviour of the modelled surface due to the adaptation capability of neural networks training process, that is an iterative adjust of the synaptic weights in function of residuals, using the training parameters. Therefore, the previous knowledge of the modelled phenomena is important to define what kind of and how many parameters are needed to train the neural network so that reasonable results are obtained from the estimations. We have used data from the GPS tracking network in Brazil, and we have tested the accuracy of the new model to all locations where there is a station, accessing the efficiency of the model everywhere. TEC values were computed for each station of the network. After that the training parameters data set for the test station was formed, with the TEC values of all others (all stations, except the test one). The Neural Network was
Modeling Network Traffic in Wavelet Domain
Directory of Open Access Journals (Sweden)
Sheng Ma
2004-12-01
Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.
Gene Regulation Networks for Modeling Drosophila Development
Mjolsness, E.
1999-01-01
This chapter will very briefly introduce and review some computational experiments in using trainable gene regulation network models to simulate and understand selected episodes in the development of the fruit fly, Drosophila Melanogaster.
Graphical Model Theory for Wireless Sensor Networks
Energy Technology Data Exchange (ETDEWEB)
Davis, William B.
2002-12-08
Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm.
Mitigating risk during strategic supply network modeling
Müssigmann, Nikolaus
2006-01-01
Mitigating risk during strategic supply network modeling. - In: Managing risks in supply chains / ed. by Wolfgang Kersten ... - Berlin : Schmidt, 2006. - S. 213-226. - (Operations and technology management ; 1)
2014-01-01
The significance of electrical and electronic systems has increased considerably in the last few years and this trend is set to continue. The characteristics feature of innovative systems is the fact that they can work together in a network. This requires powerful bus systems that the electronic control units can use to exchange information. Networking and the various bus systems used in motor vehicles are the prominent new topic in the 5th edition of the "Automotive Electric, Automotive Electronics" technical manual. The existing chapters have also been updated, so that this new edition brings the reader up to date on the subjects of electrical and electronic systems in the motor vehicle. Content Electrical and electronical systems – Basic principles of networking - Examples of networked vehicles – Bus systems – Architecture of electronic systems – Mechatronics – Elektronics – Electronic control Units – Software – Sensors – Actuators – Hybrid drives – Vehicle electrical system – Start...
Road maintenance planning using network flow modelling
Yang, Chao; Remenyte-Prescott, Rasa; Andrews, John
2015-01-01
This paper presents a road maintenance planning model that can be used to balance out maintenance cost and road user cost, since performing road maintenance at night can be convenient for road users but costly for highway agency. Based on the platform of the network traffic flow modelling, the traffic through the worksite and its adjacent road links is evaluated. Thus, maintenance arrangements at a worksite can be optimized considering the overall network performance. In addition, genetic alg...
Update on the Electron Source Model
Energy Technology Data Exchange (ETDEWEB)
Cowee, Misa [Los Alamos National Laboratory; Winske, Dan [Los Alamos National Laboratory
2012-07-17
We summarize work done in FY12 on the Los Alamos Electron Source Model (ESM), which predicts the distribution of beta-decay electrons after a high altitude nuclear explosion (HANE) as a function of L, energy, and pitch angle. In the last year we have compared model results with data taken after the Russian 2 HANE test of 1962 and presented results at the HEART conference. We discuss our future plans to continue comparison with HANE data and to develop the code to allow a more complex set of initial conditions.
Radiation Belt Electron Dynamics: Modeling Atmospheric Losses
Selesnick, R. S.
2003-01-01
The first year of work on this project has been completed. This report provides a summary of the progress made and the plan for the coming year. Also included with this report is a preprint of an article that was accepted for publication in Journal of Geophysical Research and describes in detail most of the results from the first year of effort. The goal for the first year was to develop a radiation belt electron model for fitting to data from the SAMPEX and Polar satellites that would provide an empirical description of the electron losses into the upper atmosphere. This was largely accomplished according to the original plan (with one exception being that, for reasons described below, the inclusion of the loss cone electrons in the model was deferred). The main concerns at the start were to accurately represent the balance between pitch angle diffusion and eastward drift that determines the dominant features of the low altitude data, and then to accurately convert the model into simulated data based on the characteristics of the particular electron detectors. Considerable effort was devoted to achieving these ends. Once the model was providing accurate results it was applied to data sets selected from appropriate periods in 1997, 1998, and 1999. For each interval of -30 to 60 days, the model parameters were calculated daily, thus providing good short and long term temporal resolution, and for a range of radial locations from L = 2.7 to 3.9. .
Posterior Predictive Model Checking in Bayesian Networks
Crawford, Aaron
2014-01-01
This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex…
A simple model for studying interacting networks
Liu, Wenjia; Jolad, Shivakumar; Schmittmann, Beate; Zia, R. K. P.
2011-03-01
Many specific physical networks (e.g., internet, power grid, interstates), have been characterized in considerable detail, but in isolation from each other. Yet, each of these networks supports the functions of the others, and so far, little is known about how their interactions affect their structure and functionality. To address this issue, we consider two coupled model networks. Each network is relatively simple, with a fixed set of nodes, but dynamically generated set of links which has a preferred degree, κ . In the stationary state, the degree distribution has exponential tails (far from κ), an attribute which we can explain. Next, we consider two such networks with different κ 's, reminiscent of two social groups, e.g., extroverts and introverts. Finally, we let these networks interact by establishing a controllable fraction of cross links. The resulting distribution of links, both within and across the two model networks, is investigated and discussed, along with some potential consequences for real networks. Supported in part by NSF-DMR-0705152 and 1005417.
Modeling gene regulatory network motifs using Statecharts.
Fioravanti, Fabio; Helmer-Citterich, Manuela; Nardelli, Enrico
2012-03-28
Gene regulatory networks are widely used by biologists to describe the interactions among genes, proteins and other components at the intra-cellular level. Recently, a great effort has been devoted to give gene regulatory networks a formal semantics based on existing computational frameworks.For this purpose, we consider Statecharts, which are a modular, hierarchical and executable formal model widely used to represent software systems. We use Statecharts for modeling small and recurring patterns of interactions in gene regulatory networks, called motifs. We present an improved method for modeling gene regulatory network motifs using Statecharts and we describe the successful modeling of several motifs, including those which could not be modeled or whose models could not be distinguished using the method of a previous proposal.We model motifs in an easy and intuitive way by taking advantage of the visual features of Statecharts. Our modeling approach is able to simulate some interesting temporal properties of gene regulatory network motifs: the delay in the activation and the deactivation of the "output" gene in the coherent type-1 feedforward loop, the pulse in the incoherent type-1 feedforward loop, the bistability nature of double positive and double negative feedback loops, the oscillatory behavior of the negative feedback loop, and the "lock-in" effect of positive autoregulation. We present a Statecharts-based approach for the modeling of gene regulatory network motifs in biological systems. The basic motifs used to build more complex networks (that is, simple regulation, reciprocal regulation, feedback loop, feedforward loop, and autoregulation) can be faithfully described and their temporal dynamics can be analyzed.
Neural network approaches for noisy language modeling.
Li, Jun; Ouazzane, Karim; Kazemian, Hassan B; Afzal, Muhammad Sajid
2013-11-01
Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.
A quantum-implementable neural network model
Chen, Jialin; Wang, Lingli; Charbon, Edoardo
2017-10-01
A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.
Telestroke network business model strategies.
Fanale, Christopher V; Demaerschalk, Bart M
2012-10-01
Our objective is to summarize the evidence that supports the reliability of telemedicine for diagnosis and efficacy in acute stroke treatment, identify strategies for funding the development of a telestroke network, and to present issues with respect to economic sustainability, cost effectiveness, and the status of reimbursement for telestroke. Copyright © 2012 National Stroke Association. Published by Elsevier Inc. All rights reserved.
Complex networks under dynamic repair model
Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao
2018-01-01
Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.
Markov State Models of gene regulatory networks.
Chu, Brian K; Tse, Margaret J; Sato, Royce R; Read, Elizabeth L
2017-02-06
Gene regulatory networks with dynamics characterized by multiple stable states underlie cell fate-decisions. Quantitative models that can link molecular-level knowledge of gene regulation to a global understanding of network dynamics have the potential to guide cell-reprogramming strategies. Networks are often modeled by the stochastic Chemical Master Equation, but methods for systematic identification of key properties of the global dynamics are currently lacking. The method identifies the number, phenotypes, and lifetimes of long-lived states for a set of common gene regulatory network models. Application of transition path theory to the constructed Markov State Model decomposes global dynamics into a set of dominant transition paths and associated relative probabilities for stochastic state-switching. In this proof-of-concept study, we found that the Markov State Model provides a general framework for analyzing and visualizing stochastic multistability and state-transitions in gene networks. Our results suggest that this framework-adopted from the field of atomistic Molecular Dynamics-can be a useful tool for quantitative Systems Biology at the network scale.
Performance modeling, stochastic networks, and statistical multiplexing
Mazumdar, Ravi R
2013-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan
Modeling acquaintance networks based on balance theory
Directory of Open Access Journals (Sweden)
Vukašinović Vida
2014-09-01
Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models
Developing Novel Explanatory Models for Electronics Education
Pule, Sarah; McCardle, John
2010-01-01
This paper explores how representations of technological concepts may be designed to help students with visual learning styles achieve successful comprehension in the field of electronics. The work accepts a wide definition of what is understood by the visualisation of a model in that it can take different external forms, but also include an…
Self-assembled three dimensional network designs for soft electronics
Jang, Kyung-In; Li, Kan; Chung, Ha Uk; Xu, Sheng; Jung, Han Na; Yang, Yiyuan; Kwak, Jean Won; Jung, Han Hee; Song, Juwon; Yang, Ce; Wang, Ao; Liu, Zhuangjian; Lee, Jong Yoon; Kim, Bong Hoon; Kim, Jae-Hwan; Lee, Jungyup; Yu, Yongjoon; Kim, Bum Jun; Jang, Hokyung; Yu, Ki Jun; Kim, Jeonghyun; Lee, Jung Woo; Jeong, Jae-Woong; Song, Young Min; Huang, Yonggang; Zhang, Yihui; Rogers, John A.
2017-06-01
Low modulus, compliant systems of sensors, circuits and radios designed to intimately interface with the soft tissues of the human body are of growing interest, due to their emerging applications in continuous, clinical-quality health monitors and advanced, bioelectronic therapeutics. Although recent research establishes various materials and mechanics concepts for such technologies, all existing approaches involve simple, two-dimensional (2D) layouts in the constituent micro-components and interconnects. Here we introduce concepts in three-dimensional (3D) architectures that bypass important engineering constraints and performance limitations set by traditional, 2D designs. Specifically, open-mesh, 3D interconnect networks of helical microcoils formed by deterministic compressive buckling establish the basis for systems that can offer exceptional low modulus, elastic mechanics, in compact geometries, with active components and sophisticated levels of functionality. Coupled mechanical and electrical design approaches enable layout optimization, assembly processes and encapsulation schemes to yield 3D configurations that satisfy requirements in demanding, complex systems, such as wireless, skin-compatible electronic sensors.
Self-assembled three dimensional network designs for soft electronics
Jang, Kyung-In; Li, Kan; Chung, Ha Uk; Xu, Sheng; Jung, Han Na; Yang, Yiyuan; Kwak, Jean Won; Jung, Han Hee; Song, Juwon; Yang, Ce; Wang, Ao; Liu, Zhuangjian; Lee, Jong Yoon; Kim, Bong Hoon; Kim, Jae-Hwan; Lee, Jungyup; Yu, Yongjoon; Kim, Bum Jun; Jang, Hokyung; Yu, Ki Jun; Kim, Jeonghyun; Lee, Jung Woo; Jeong, Jae-Woong; Song, Young Min; Huang, Yonggang; Zhang, Yihui; Rogers, John A.
2017-01-01
Low modulus, compliant systems of sensors, circuits and radios designed to intimately interface with the soft tissues of the human body are of growing interest, due to their emerging applications in continuous, clinical-quality health monitors and advanced, bioelectronic therapeutics. Although recent research establishes various materials and mechanics concepts for such technologies, all existing approaches involve simple, two-dimensional (2D) layouts in the constituent micro-components and interconnects. Here we introduce concepts in three-dimensional (3D) architectures that bypass important engineering constraints and performance limitations set by traditional, 2D designs. Specifically, open-mesh, 3D interconnect networks of helical microcoils formed by deterministic compressive buckling establish the basis for systems that can offer exceptional low modulus, elastic mechanics, in compact geometries, with active components and sophisticated levels of functionality. Coupled mechanical and electrical design approaches enable layout optimization, assembly processes and encapsulation schemes to yield 3D configurations that satisfy requirements in demanding, complex systems, such as wireless, skin-compatible electronic sensors. PMID:28635956
Self-assembled three dimensional network designs for soft electronics.
Jang, Kyung-In; Li, Kan; Chung, Ha Uk; Xu, Sheng; Jung, Han Na; Yang, Yiyuan; Kwak, Jean Won; Jung, Han Hee; Song, Juwon; Yang, Ce; Wang, Ao; Liu, Zhuangjian; Lee, Jong Yoon; Kim, Bong Hoon; Kim, Jae-Hwan; Lee, Jungyup; Yu, Yongjoon; Kim, Bum Jun; Jang, Hokyung; Yu, Ki Jun; Kim, Jeonghyun; Lee, Jung Woo; Jeong, Jae-Woong; Song, Young Min; Huang, Yonggang; Zhang, Yihui; Rogers, John A
2017-06-21
Low modulus, compliant systems of sensors, circuits and radios designed to intimately interface with the soft tissues of the human body are of growing interest, due to their emerging applications in continuous, clinical-quality health monitors and advanced, bioelectronic therapeutics. Although recent research establishes various materials and mechanics concepts for such technologies, all existing approaches involve simple, two-dimensional (2D) layouts in the constituent micro-components and interconnects. Here we introduce concepts in three-dimensional (3D) architectures that bypass important engineering constraints and performance limitations set by traditional, 2D designs. Specifically, open-mesh, 3D interconnect networks of helical microcoils formed by deterministic compressive buckling establish the basis for systems that can offer exceptional low modulus, elastic mechanics, in compact geometries, with active components and sophisticated levels of functionality. Coupled mechanical and electrical design approaches enable layout optimization, assembly processes and encapsulation schemes to yield 3D configurations that satisfy requirements in demanding, complex systems, such as wireless, skin-compatible electronic sensors.
Flood routing modelling with Artificial Neural Networks
Directory of Open Access Journals (Sweden)
R. Peters
2006-01-01
Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.
Optimal transportation networks models and theory
Bernot, Marc; Morel, Jean-Michel
2009-01-01
The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.
Electron beam lithographic modeling assisted by artificial intelligence technology
Nakayamada, Noriaki; Nishimura, Rieko; Miura, Satoru; Nomura, Haruyuki; Kamikubo, Takashi
2017-07-01
We propose a new concept of tuning a point-spread function (a "kernel" function) in the modeling of electron beam lithography using the machine learning scheme. Normally in the work of artificial intelligence, the researchers focus on the output results from a neural network, such as success ratio in image recognition or improved production yield, etc. In this work, we put more focus on the weights connecting the nodes in a convolutional neural network, which are naturally the fractions of a point-spread function, and take out those weighted fractions after learning to be utilized as a tuned kernel. Proof-of-concept of the kernel tuning has been demonstrated using the examples of proximity effect correction with 2-layer network, and charging effect correction with 3-layer network. This type of new tuning method can be beneficial to give researchers more insights to come up with a better model, yet it might be too early to be deployed to production to give better critical dimension (CD) and positional accuracy almost instantly.
Mathematical model I. Electron and quantum mechanics
Directory of Open Access Journals (Sweden)
Nitin Ramchandra Gadre
2011-03-01
Full Text Available The basic particle electron obeys various theories like electrodynamics, quantum mechanics and special relativity. Particle under different experimental conditions behaves differently, allowing us to observe different characteristics which become basis for these theories. In this paper, we have made an attempt to suggest a classical picture by studying the requirements of these three modern theories. The basic presumption is: There must be certain structural characteristics in a particle like electron which make it obey postulates of modern theories. As it is ‘difficult’ to find structure of electron experimentally, we make a mathematical attempt. For a classical approach, we require well defined systems and we have studied a system with two charged particles, proton and electron in a hydrogen atom. An attempt has been made to give a model to describe electron as seen by the proton. We then discuss how the model can satisfy the requirements of the three modern theories in a classical manner. The paper discusses basic aspects of relativity and electrodynamics. However the focus of the paper is on quantum mechanics.
Mathematical model I. Electron and quantum mechanics
Gadre, Nitin Ramchandra
2011-03-01
The basic particle electron obeys various theories like electrodynamics, quantum mechanics and special relativity. Particle under different experimental conditions behaves differently, allowing us to observe different characteristics which become basis for these theories. In this paper, we have made an attempt to suggest a classical picture by studying the requirements of these three modern theories. The basic presumption is: There must be certain structural characteristics in a particle like electron which make it obey postulates of modern theories. As it is `difficult' to find structure of electron experimentally, we make a mathematical attempt. For a classical approach, we require well defined systems and we have studied a system with two charged particles, proton and electron in a hydrogen atom. An attempt has been made to give a model to describe electron as seen by the proton. We then discuss how the model can satisfy the requirements of the three modern theories in a classical manner. The paper discusses basic aspects of relativity and electrodynamics. However the focus of the paper is on quantum mechanics.
Scanning electron microscopy investigation of fibrin networks after thermal injury
Directory of Open Access Journals (Sweden)
Etheresia Pretorius
2011-02-01
Full Text Available Injury due to burning is known to impact on coagulation and haemostasis by disturbing the coagulation cascade and is also associated with impaired fibrinolysis. Also, venous thrombosis, pulmonary embolism and hypercoagulability are common during thermal injury. Using a Wistar albino rat model, we investigated in this study whether burn injury affects the ultrastructure of the fibrin networks. A typical fibrin network will contain mostly major, thick fibres with minor, thin fibres distributed amongst them. We found that the clot architecture changes after burn injury, showing more prominent minor, thin fibres in a netted appearance. Also, the clot showed areas of matted fibrin. We suggest that the thrombotic events associated with burn injury are due to the thickened and netlike areas formed when thrombin activates the coagulation cascade. This is due to impaired fibrinolysis activities, causing the resulting fibrin clots not to be successfully disseminated. Small fragments of these netted, clumped areas may therefore break loose and lead to thrombotic events after burn injuries. The current study therefore provided morphological evidence for thrombotic events associated with burn injury.
A Transfer Learning Approach for Network Modeling
Huang, Shuai; Li, Jing; Chen, Kewei; Wu, Teresa; Ye, Jieping; Wu, Xia; Yao, Li
2012-01-01
Networks models have been widely used in many domains to characterize the interacting relationship between physical entities. A typical problem faced is to identify the networks of multiple related tasks that share some similarities. In this case, a transfer learning approach that can leverage the knowledge gained during the modeling of one task to help better model another task is highly desirable. In this paper, we propose a transfer learning approach, which adopts a Bayesian hierarchical model framework to characterize task relatedness and additionally uses the L1-regularization to ensure robust learning of the networks with limited sample sizes. A method based on the Expectation-Maximization (EM) algorithm is further developed to learn the networks from data. Simulation studies are performed, which demonstrate the superiority of the proposed transfer learning approach over single task learning that learns the network of each task in isolation. The proposed approach is also applied to identification of brain connectivity networks of Alzheimer’s disease (AD) from functional magnetic resonance image (fMRI) data. The findings are consistent with the AD literature. PMID:24526804
Modelling complex networks by random hierarchical graphs
Directory of Open Access Journals (Sweden)
M.Wróbel
2008-06-01
Full Text Available Numerous complex networks contain special patterns, called network motifs. These are specific subgraphs, which occur oftener than in randomized networks of Erdős-Rényi type. We choose one of them, the triangle, and build a family of random hierarchical graphs, being Sierpiński gasket-based graphs with random "decorations". We calculate the important characteristics of these graphs - average degree, average shortest path length, small-world graph family characteristics. They depend on probability of decorations. We analyze the Ising model on our graphs and describe its critical properties using a renormalization-group technique.
A Network Model of Credit Risk Contagion
Directory of Open Access Journals (Sweden)
Ting-Qiang Chen
2012-01-01
Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.
Deep space network software cost estimation model
Tausworthe, R. C.
1981-01-01
A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics.
Continuum Modeling of Biological Network Formation
Albi, Giacomo
2017-04-10
We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.
Stochastic modeling and analysis of telecoms networks
Decreusefond, Laurent
2012-01-01
This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an
Neural networks as models of psychopathology.
Aakerlund, L; Hemmingsen, R
1998-04-01
Neural network modeling is situated between neurobiology, cognitive science, and neuropsychology. The structural and functional resemblance with biological computation has made artificial neural networks (ANN) useful for exploring the relationship between neurobiology and computational performance, i.e., cognition and behavior. This review provides an introduction to the theory of ANN and how they have linked theories from neurobiology and psychopathology in schizophrenia, affective disorders, and dementia.
Decomposed Implicit Models of Piecewise - Linear Networks
Directory of Open Access Journals (Sweden)
J. Brzobohaty
1992-05-01
Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.
Green Network Planning Model for Optical Backbones
DEFF Research Database (Denmark)
Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Jensen, Michael
2010-01-01
on the environment in general. In network planning there are existing planning models focused on QoS provisioning, investment minimization or combinations of both and other parameters. But there is a lack of a model for designing green optical backbones. This paper presents novel ideas to be able to define...
Empirical generalization assessment of neural network models
DEFF Research Database (Denmark)
Larsen, Jan; Hansen, Lars Kai
1995-01-01
This paper addresses the assessment of generalization performance of neural network models by use of empirical techniques. We suggest to use the cross-validation scheme combined with a resampling technique to obtain an estimate of the generalization performance distribution of a specific model...
Evaluation of EOR Processes Using Network Models
DEFF Research Database (Denmark)
Larsen, Jens Kjell; Krogsbøll, Anette
1998-01-01
The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels...
Phenomenological network models: Lessons for epilepsy surgery.
Hebbink, Jurgen; Meijer, Hil; Huiskamp, Geertjan; van Gils, Stephan; Leijten, Frans
2017-10-01
The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the "driver," is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient. © 2017 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.
Models of network reliability analysis, combinatorics, and Monte Carlo
Gertsbakh, Ilya B
2009-01-01
Unique in its approach, Models of Network Reliability: Analysis, Combinatorics, and Monte Carlo provides a brief introduction to Monte Carlo methods along with a concise exposition of reliability theory ideas. From there, the text investigates a collection of principal network reliability models, such as terminal connectivity for networks with unreliable edges and/or nodes, network lifetime distribution in the process of its destruction, network stationary behavior for renewable components, importance measures of network elements, reliability gradient, and network optimal reliability synthesis
Delay and Disruption Tolerant Networking MACHETE Model
Segui, John S.; Jennings, Esther H.; Gao, Jay L.
2011-01-01
To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity
A comprehensive Network Security Risk Model for process control networks.
Henry, Matthew H; Haimes, Yacov Y
2009-02-01
The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.
Modeling the Nab Experiment Electronics in SPICE
Blose, Alexander; Crawford, Christopher; Sprow, Aaron; Nab Collaboration
2017-09-01
The goal of the Nab experiment is to measure the neutron decay coefficients a, the electron-neutrino correlation, as well as b, the Fierz interference term to precisely test the Standard Model, as well as probe for Beyond the Standard Model physics. In this experiment, protons from the beta decay of the neutron are guided through a magnetic field into a Silicon detector. Event reconstruction will be achieved via time-of-flight measurement for the proton and direct measurement of the coincident electron energy in highly segmented silicon detectors, so the amplification circuitry needs to preserve fast timing, provide good amplitude resolution, and be packaged in a high-density format. We have designed a SPICE simulation to model the full electronics chain for the Nab experiment in order to understand the contributions of each stage and optimize them for performance. Additionally, analytic solutions to each of the components have been determined where available. We will present a comparison of the output from the SPICE model, analytic solution, and empirically determined data.
Personalized Learning Network Teaching Model
Feng, Zhou
Adaptive learning system on the salient features, expounded personalized learning is adaptive learning system adaptive to learners key to learning. From the perspective of design theory, put forward an adaptive learning system to learn design thinking individual model, and using data mining techniques, the initial establishment of personalized adaptive systems model of learning.
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.
2016-04-01
Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
Modelling Users` Trust in Online Social Networks
Directory of Open Access Journals (Sweden)
Iacob Cătoiu
2014-02-01
Full Text Available Previous studies (McKnight, Lankton and Tripp, 2011; Liao, Lui and Chen, 2011 have shown the crucial role of trust when choosing to disclose sensitive information online. This is the case of online social networks users, who must disclose a certain amount of personal data in order to gain access to these online services. Taking into account privacy calculus model and the risk/benefit ratio, we propose a model of users’ trust in online social networks with four variables. We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. We also discuss the implications and further research opportunities of our study.
Model Microvascular Networks Can Have Many Equilibria.
Karst, Nathaniel J; Geddes, John B; Carr, Russell T
2017-03-01
We show that large microvascular networks with realistic topologies, geometries, boundary conditions, and constitutive laws can exhibit many steady-state flow configurations. This is in direct contrast to most previous studies which have assumed, implicitly or explicitly, that a given network can only possess one equilibrium state. While our techniques are general and can be applied to any network, we focus on two distinct network types that model human tissues: perturbed honeycomb networks and random networks generated from Voronoi diagrams. We demonstrate that the disparity between observed and predicted flow directions reported in previous studies might be attributable to the presence of multiple equilibria. We show that the pathway effect, in which hematocrit is steadily increased along a series of diverging junctions, has important implications for equilibrium discovery, and that our estimates of the number of equilibria supported by these networks are conservative. If a more complete description of the plasma skimming effect that captures red blood cell allocation at junctions with high feed hematocrit were to be obtained empirically, then the number of equilibria found by our approach would at worst remain the same and would in all likelihood increase significantly.
PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK
Directory of Open Access Journals (Sweden)
R. Hadapiningradja Kusumodestoni
2015-11-01
Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.
Artificial neural network cardiopulmonary modeling and diagnosis
Kangas, Lars J.; Keller, Paul E.
1997-01-01
The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.
Spiking modular neural networks: A neural network modeling approach for hydrological processes
National Research Council Canada - National Science Library
Kamban Parasuraman; Amin Elshorbagy; Sean K. Carey
2006-01-01
.... In this study, a novel neural network model called the spiking modular neural networks (SMNNs) is proposed. An SMNN consists of an input layer, a spiking layer, and an associator neural network layer...
PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL
Directory of Open Access Journals (Sweden)
S. Munapo
2012-01-01
Full Text Available
ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.
AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.
Algebraic Statistics for Network Models
2014-02-19
use algebra, combinatorics and Markov bases to give a constructing way of answering this question for ERGMs of interest. Question 2: How do we model...for every function. 06/06/13 Petrović. Manuscripts 8, 10. Invited lecture at the Scientific Session on Commutative Algebra and Combinatorics at the
Network Modeling and Simulation (NEMSE)
2013-07-01
Prioritized Packet Fragmentation", IEEE Trans. Multimedia , Oct. 2012. [13 SYSENG] . Defense Acquisition Guidebook, Chapter 4 System Engineering, and...2012 IEEE High Performance Extreme Computing Conference (HPEC) poster session [1 Ross]. Motivation Air Force Research Lab needs o Capability...is virtual. These eight virtualizations were: System-in-the-Loop (SITL) using OPNET Modeler, COPE, Field Programmable Gate Array ( FPGA Physical
Security Modeling on the Supply Chain Networks
Directory of Open Access Journals (Sweden)
Marn-Ling Shing
2007-10-01
Full Text Available In order to keep the price down, a purchaser sends out the request for quotation to a group of suppliers in a supply chain network. The purchaser will then choose a supplier with the best combination of price and quality. A potential supplier will try to collect the related information about other suppliers so he/she can offer the best bid to the purchaser. Therefore, confidentiality becomes an important consideration for the design of a supply chain network. Chen et al. have proposed the application of the Bell-LaPadula model in the design of a secured supply chain network. In the Bell-LaPadula model, a subject can be in one of different security clearances and an object can be in one of various security classifications. All the possible combinations of (Security Clearance, Classification pair in the Bell-LaPadula model can be thought as different states in the Markov Chain model. This paper extends the work done by Chen et al., provides more details on the Markov Chain model and illustrates how to use it to monitor the security state transition in the supply chain network.
An evolving model of online bipartite networks
Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang
2013-12-01
Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.
Lan Liu; Ryan K. L. Ko; Guangming Ren; Xiaoping Xu
2017-01-01
As the adoption of Software Defined Networks (SDNs) grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses) in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the ne...
An autocatalytic network model for stock markets
Caetano, Marco Antonio Leonel; Yoneyama, Takashi
2015-02-01
The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.
Keystone Business Models for Network Security Processors
Directory of Open Access Journals (Sweden)
Arthur Low
2013-07-01
Full Text Available Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor” models nor the silicon intellectual-property licensing (“IP-licensing” models allow small technology companies to successfully compete. This article describes an alternative approach that produces an ongoing stream of novel network security processors for niche markets through continuous innovation by both large and small companies. This approach, referred to here as the "business ecosystem model for network security processors", includes a flexible and reconfigurable technology platform, a “keystone” business model for the company that maintains the platform architecture, and an extended ecosystem of companies that both contribute and share in the value created by innovation. New opportunities for business model innovation by participating companies are made possible by the ecosystem model. This ecosystem model builds on: i the lessons learned from the experience of the first author as a senior integrated circuit architect for providers of public-key cryptography solutions and as the owner of a semiconductor startup, and ii the latest scholarly research on technology entrepreneurship, business models, platforms, and business ecosystems. This article will be of interest to all technology entrepreneurs, but it will be of particular interest to owners of small companies that provide security solutions and to specialized security professionals seeking to launch their own companies.
A Model of Mental State Transition Network
Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo
Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.
UAV Trajectory Modeling Using Neural Networks
Xue, Min
2017-01-01
Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.
Propagation models for computing biochemical reaction networks
Henzinger, Thomas A; Mateescu, Maria
2011-01-01
We introduce propagation models, a formalism designed to support general and efficient data structures for the transient analysis of biochemical reaction networks. We give two use cases for propagation abstract data types: the uniformization method and numerical integration. We also sketch an implementation of a propagation abstract data type, which uses abstraction to approximate states.
Modelling crime linkage with Bayesian networks
de Zoete, J.; Sjerps, M.; Lagnado, D.; Fenton, N.
2015-01-01
When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model
Lagrangian modeling of switching electrical networks
Scherpen, Jacquelien M.A.; Jeltsema, Dimitri; Klaassens, J. Ben
2003-01-01
In this paper, a general and systematic method is presented to model topologically complete electrical networks, with or without multiple or single switches, within the Euler–Lagrange framework. Apart from the physical insight that can be obtained in this way, the framework has proven to be useful
Computational Modeling of Complex Protein Activity Networks
Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude
2017-01-01
Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a
Modeling Network Transition Constraints with Hypergraphs
DEFF Research Database (Denmark)
Harrod, Steven
2011-01-01
values. A directed hypergraph formulation is derived to address railway network sequencing constraints, and an experimental problem sample solved to estimate the magnitude of objective inflation when interaction effects are ignored. The model is used to demonstrate the value of advance scheduling...
A neural network model for texture discrimination.
Xing, J; Gerstein, G L
1993-01-01
A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.
Propagating semantic information in biochemical network models
Directory of Open Access Journals (Sweden)
Schulz Marvin
2012-01-01
Full Text Available Abstract Background To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation. Results A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements. Conclusions Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.
Distributed Bayesian Networks for User Modeling
DEFF Research Database (Denmark)
Tedesco, Roberto; Dolog, Peter; Nejdl, Wolfgang
2006-01-01
The World Wide Web is a popular platform for providing eLearning applications to a wide spectrum of users. However – as users differ in their preferences, background, requirements, and goals – applications should provide personalization mechanisms. In the Web context, user models used...... of Web-based eLearning platforms. The scenario we are tackling assumes learners who use several systems over time, which are able to create partial Bayesian Networks for user models based on the local system context. In particular, we focus on how to merge these partial user models. Our merge mechanism...... efficiently combines distributed learner models without the need to exchange internal structure of local Bayesian networks, nor local evidence between the involved platforms....
Network traffic model using GIPP and GIBP
Lee, Yong Duk; Van de Liefvoort, Appie; Wallace, Victor L.
1998-10-01
In telecommunication networks, the correlated nature of teletraffic patterns can have significant impact on queuing measures such as queue length, blocking and delay. There is, however, not yet a good general analytical description which can easily incorporate the correlation effect of the traffic, while at the same time maintaining the ease of modeling. The authors have shown elsewhere, that the covariance structures of the generalized Interrupted Poisson Process (GIPP) and the generalized Interrupted Bernoulli Process (GIBP) have an invariance property which makes them reasonably general, yet algebraically manageable, models for representing correlated network traffic. The GIPP and GIBP have a surprisingly rich sets of parameters, yet these invariance properties enable us to easily incorporate the covariance function as well as the interarrival time distribution into the model to better matchobservations. In this paper, we show an application of GIPP and GIBP for matching an analytical model to observed or experimental data.
Model Predictive Control of Sewer Networks
DEFF Research Database (Denmark)
Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik
2016-01-01
The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....
Modeling Multistandard Wireless Networks in OPNET
DEFF Research Database (Denmark)
Zakrzewska, Anna; Berger, Michael Stübert; Ruepp, Sarah Renée
2011-01-01
Future wireless communication is emerging towards one heterogeneous platform. In this new environment wireless access will be provided by multiple radio technologies that are cooperating and complementing one another. The paper investigates the possibilities of developing such a multistandard...... system using OPNET Modeler. A network model consisting of LTE interworking with WLAN and WiMAX is considered from the radio resource management perspective. In particular, implementing a joint packet scheduler across multiple systems is discussed more in detail....
Modelling dendritic ecological networks in space: anintegrated network perspective
Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.
2013-01-01
Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within
Spatial Models and Networks of Living Systems
DEFF Research Database (Denmark)
Juul, Jeppe Søgaard
When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number....... Such systems are known to be stabilized by spatial structure. Finally, I analyse data from a large mobile phone network and show that people who are topologically close in the network have similar communication patterns. This main part of the thesis is based on six different articles, which I have co...
On traffic modelling in GPRS networks
DEFF Research Database (Denmark)
Madsen, Tatiana Kozlova; Schwefel, Hans-Peter; Prasad, Ramjee
2005-01-01
Optimal design and dimensioning of wireless data networks, such as GPRS, requires the knowledge of traffic characteristics of different data services. This paper presents an in-detail analysis of an IP-level traffic measurements taken in an operational GPRS network. The data measurements reported...... here are done at the Gi interface. The aim of this paper is to reveal some key statistics of GPRS data applications and to validate if the existing traffic models can adequately describe traffic volume and inter-arrival time distribution for different services. Additionally, we present a method of user...
Neural Network Model of memory retrieval
Directory of Open Access Journals (Sweden)
Stefano eRecanatesi
2015-12-01
Full Text Available Human memory can store large amount of information. Nevertheless, recalling is often achallenging task. In a classical free recall paradigm, where participants are asked to repeat abriefly presented list of words, people make mistakes for lists as short as 5 words. We present amodel for memory retrieval based on a Hopfield neural network where transition between itemsare determined by similarities in their long-term memory representations. Meanfield analysis ofthe model reveals stable states of the network corresponding (1 to single memory representationsand (2 intersection between memory representations. We show that oscillating feedback inhibitionin the presence of noise induces transitions between these states triggering the retrieval ofdifferent memories. The network dynamics qualitatively predicts the distribution of time intervalsrequired to recall new memory items observed in experiments. It shows that items having largernumber of neurons in their representation are statistically easier to recall and reveals possiblebottlenecks in our ability of retrieving memories. Overall, we propose a neural network model ofinformation retrieval broadly compatible with experimental observations and is consistent with ourrecent graphical model (Romani et al., 2013.
Pace, Wilson D; Fox, Chester H; White, Turner; Graham, Deborah; Schilling, Lisa M; West, David R
2014-01-01
Clinical data research networks require large investments in infrastructure support to maintain their abilities to extract, transform, and load data from varied data sources, expand electronic data sources and develop learning communities. This paper outlines a sustainable business model of ongoing infrastructure support for clinical data research activities. The DARTNet Institute is a not-for-profit 501(c)(3) organization that serves as a support entity for multiple practice-based research networks. Several clinical data research networks working closely with a professional society began collaborating to support shared goals in 2008. This loose affiliation called itself the "DARTNet Collaborative." In 2011, the DARTNet Institute incorporated as an independent, not-for-profit entity. The business structure allows DARTNet to advocate for all partners without operating its own practice-based research network, serve as a legal voice for activities that overlap multiple partners, share personnel resources through service contracts between partners, and purchase low-cost (nonprofit rate) software. DARTNet's business model relies upon four diverse sources of revenue: (1) DARTNet licenses and provides access to a propriety software system that extracts, transforms, and loads data from all major electronic health records (EHRs) utilized in the United States, and which also provides clinical decision support for research studies; (2) DARTNet operates a recognized, national professional-society-quality improvement registry that enables organizations to fulfill Meaningful Use 2 criteria; (3) DARTNet provides access to data for research activities that are funded by direct research dollars, provided at prices that generate excess revenue; and (4) DARTNet provides access to large primary care datasets for observational studies and pregrant analyses such as for sample size development. The ability of the system to support pragmatic trials will be described. The DARTNet model
Collins, R. L.
2008-01-01
One of the more profound mysteries of physics is how nature ties together EM fields to form an electron. A way to do this is examined in this study. A bare magnetic dipole containing a flux quantum spins stably, and produces an inverse square E= -vxB electric field similar to what one finds from charge. Gauss' law finds charge in this model, though there be none. For stability, a current loop about the waist of the magnetic dipole is needed and we must go beyond the classical Maxwell's equati...
A semicontinuum model for the hydrated electron
Energy Technology Data Exchange (ETDEWEB)
Pommeret, S.; Gauduel, Y. (Ecole Polytechnique-ENS, Palaiseau (France))
1991-05-16
Kevan's structural model for the solvated electron has been studied in association with the spherical part of a continuum potential via an imaginary time splitting operator method (SOM). The study focuses on the influence of the continuum potential representing the second solvation shell interaction with the electron. The continuum potential is computed in the self-consistent approximation. The temperature dependence of the continuum potential allows the authors to extend their study from 77 to 300 K. The gyration radius of the solvated electron is found to be of the same order as computed by using simulation methods, and for the best value of the cavity radius more than 60% of the charge is inside the cavity. The results of the present computation are then compared to those obtained by using a full simulation approach or the semicontinuum theory. In the present work, it is interesting to note that at 300 K they have found results that are in good agreement with those computed by using a molecular dynamics approach of the medium.
A improved Network Security Situation Awareness Model
Directory of Open Access Journals (Sweden)
Li Fangwei
2015-08-01
Full Text Available In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.
Distributed Bandpass Filtering and Signal Demodulation in Cortical Network Models
McDonnell, Mark D.
Experimental recordings of cortical activity often exhibit narrowband oscillations, at various center frequencies ranging in the order of 1-200 Hz. Many neuronal mechanisms are known to give rise to oscillations, but here we focus on a population effect known as sparsely synchronised oscillations. In this effect, individual neurons in a cortical network fire irregularly at slow average spike rates (1-10 Hz), but the population spike rate oscillates at gamma frequencies (greater than 40 Hz) in response to spike bombardment from the thalamus. These cortical networks form recurrent (feedback) synapses. Here we describe a model of sparsely synchronized population oscillations using the language of feedback control engineering, where we treat spiking as noisy feedback. We show, using a biologically realistic model of synaptic current that includes a delayed response to inputs, that the collective behavior of the neurons in the network is like a distributed bandpass filter acting on the network inputs. Consequently, the population response has the character of narrowband random noise, and therefore has an envelope and instantaneous frequency with lowpass characteristics. Given that there exist biologically plausible neuronal mechanisms for demodulating the envelope and instantaneous frequency, we suggest there is potential for similar effects to be exploited in nanoscale electronics implementations of engineered communications receivers.
Directory of Open Access Journals (Sweden)
Radi Radi
2011-08-01
Full Text Available Constructive Back Propagation Neural Network (CBPNN is a kind of back propagation neural network trained with constructive algorithm. Training of CBPNN is mainly conducted by developing the network’s architecture which commonly done by adding a number of new neuron units on learning process. Training of the network usually implements fixed method to develop its structure gradually by adding new units constantly. Although this method is simple and able to create an adaptive network for data pattern complexity, but it is wasteful and inefficient for computing. New unit addition affects directly to the computational load of training, speed of convergence, and structure of the final neural network. While increases training load significantly, excessive addition of units also tends to generate a large size of final network. Moreover, addition pattern with small unit number tends to drop off the adaptability of the network and extends time of training. Therefore, there is important to design an adaptive structure development pattern for CBPNN in order to minimize computing load of training. This study proposes Fuzzy Logic (FL algorithm to manage and develop structure of CBPNN. FL method was implemented on two models of CBPNN, i.e. designed with one and two hidden layers, used to recognize aroma patterns on an electronic nose system. The results showed that this method is effective to be applied due to its capability to minimize time of training, to reduce load of computational learning, and generate small size of network.
Model based design of electronic throttle control
Cherian, Fenin; Ranjan, Ashish; Bhowmick, Pathikrit; Rammohan, A.
2017-11-01
With the advent of torque based Engine Management Systems, the precise control and robust performance of the throttle body becomes a key factor in the overall performance of the vehicle. Electronic Throttle Control provides benefits such as improved air-fuel ratio for improving the vehicle performance and lower exhausts emissions to meet the stringent emission norms. Modern vehicles facilitate various features such as Cruise Control, Traction Control, Electronic Stability Program and Pre-crash systems. These systems require control over engine power without driver intervention, which is not possible with conventional mechanical throttle system. Thus these systems are integrated to function with the electronic throttle control. However, due to inherent non-linearities in the throttle body, the control becomes a difficult task. In order to eliminate the influence of this hysteresis at the initial operation of the butterfly valve, a control to compensate the shortage must be added to the duty required for starting throttle operation when the initial operation is detected. Therefore, a lot of work is being done in this field to incorporate the various nonlinearities to achieve robust control. In our present work, the ETB was tested to verify the working of the system. Calibration of the TPS sensors was carried out in order to acquire accurate throttle opening angle. The response of the calibrated system was then plotted against a step input signal. A linear model of the ETB was prepared using Simulink and its response was compared with the experimental data to find out the initial deviation of the model from the actual system. To reduce this deviation, non-linearities from existing literature were introduced to the system and a response analysis was performed to check the deviation from the actual system. Based on this investigation, an introduction of a new nonlinearity parameter can be used in future to reduce the deviation further making the control of the ETB more
eIFL (Electronic Information for Libraries): A Global Initiative of the Soros Foundations Network.
Feret, Blazej; Kay, Michael
This paper presents the history, current status, and future development of eIFL (Electronic Information for Libraries Direct)--a large-scale project run by the Soros Foundations Network and the Open Society Institute. The project aims to provide libraries in developing countries with access to a menu of electronic information resources. In 1999,…
Performance modeling, loss networks, and statistical multiplexing
Mazumdar, Ravi
2009-01-01
This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I
Artificial Neural Network Model for Predicting Compressive
Directory of Open Access Journals (Sweden)
Salim T. Yousif
2013-05-01
Full Text Available Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature. The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor affecting the output of the model. The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.
UAV Trajectory Modeling Using Neural Networks
Xue, Min
2017-01-01
Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural
Interpenetrating Polymer Network (IPN) Adhesives for Electron Beam Cure
National Research Council Canada - National Science Library
Sands, James
2000-01-01
Electron beam (e-beam)-processed polymer adhesives have historically performed poorly compared to traditional adhesive technologies due to a lack of toughness engineered into these new types of adhesive materials...
Kinematic Structural Modelling in Bayesian Networks
Schaaf, Alexander; de la Varga, Miguel; Florian Wellmann, J.
2017-04-01
We commonly capture our knowledge about the spatial distribution of distinct geological lithologies in the form of 3-D geological models. Several methods exist to create these models, each with its own strengths and limitations. We present here an approach to combine the functionalities of two modeling approaches - implicit interpolation and kinematic modelling methods - into one framework, while explicitly considering parameter uncertainties and thus model uncertainty. In recent work, we proposed an approach to implement implicit modelling algorithms into Bayesian networks. This was done to address the issues of input data uncertainty and integration of geological information from varying sources in the form of geological likelihood functions. However, one general shortcoming of implicit methods is that they usually do not take any physical constraints into consideration, which can result in unrealistic model outcomes and artifacts. On the other hand, kinematic structural modelling intends to reconstruct the history of a geological system based on physically driven kinematic events. This type of modelling incorporates simplified, physical laws into the model, at the cost of a substantial increment of usable uncertain parameters. In the work presented here, we show an integration of these two different modelling methodologies, taking advantage of the strengths of both of them. First, we treat the two types of models separately, capturing the information contained in the kinematic models and their specific parameters in the form of likelihood functions, in order to use them in the implicit modelling scheme. We then go further and combine the two modelling approaches into one single Bayesian network. This enables the direct flow of information between the parameters of the kinematic modelling step and the implicit modelling step and links the exclusive input data and likelihoods of the two different modelling algorithms into one probabilistic inference framework. In
Electrical conductivity of a silicone network upon electron irradiation: influence of formulation
Roggero, A.; Dantras, E.; Paulmier, T.; Tonon, C.; Lewandowski, S.; Dagras, S.; Payan, D.
2016-12-01
In this study, the electrical conductivity of a silicone elastomer filled with inorganic fillers was investigated upon electron irradiation. Neat samples consisting of the isolated polysiloxane matrix (with no fillers) were studied in parallel to identify the filler contribution to this evolution. It was shown that exposure to 400 keV electron doses induced a decrease in electrical conductivity for both the filled and neat materials. This decrease was much more pronounced with the filled samples than with the neat ones. Moreover, the activation energy of electrical conductivity (Arrhenius behaviour) doubled in the filled case, while it varied only weakly for the neat case. In light of these results, structure-property relationships were proposed on the basis of the radiation-induced crosslink processes to which this material is subject. In the framework of electronic percolation theory, it is suggested that the radiation-induced formation of SiO3 crosslinks in the polysiloxane network and SiO4 crosslinks at filler-matrix interfaces affects the percolation path of the material, which can be simply modelled by a network of resistors in series. On one hand, their densification increases the overall resistance of the percolation path, which results in the observed decrease of effective electrical conductivity. On the other hand, the steep increase in activation energy in the filled material attributes to the SiO4 crosslinks becoming the most restrictive barrier along the percolation path. In spite of the misleading likeness of electrical conductivities in the pristine state, this study presented evidence that silicone formulation can affect the evolution of electrical properties in radiative environments. To illustrate this conclusion, the use of this material in space applications, especially when directly exposed to the radiative space environment, was discussed. The decrease in electrical conductivity was associated with a progressively increasing risk for the
Systems biology of plant molecular networks: from networks to models
Valentim, F.L.
2015-01-01
Developmental processes are controlled by regulatory networks (GRNs), which are tightly coordinated networks of transcription factors (TFs) that activate and repress gene expression within a spatial and temporal context. In Arabidopsis thaliana, the key components and network structures of the GRNs
Advances in dynamic network modeling in complex transportation systems
Ukkusuri, Satish V
2013-01-01
This book focuses on the latest in dynamic network modeling, including route guidance and traffic control in transportation systems and other complex infrastructure networks. Covers dynamic traffic assignment, flow modeling, mobile sensor deployment and more.
A NEURAL OSCILLATOR-NETWORK MODEL OF TEMPORAL PATTERN GENERATION
Schomaker, Lambert
Most contemporary neural network models deal with essentially static, perceptual problems of classification and transformation. Models such as multi-layer feedforward perceptrons generally do not incorporate time as an essential dimension, whereas biological neural networks are inherently temporal
Wintelism and Production Networks in the Electronics Industry
van de Gevel, A.J.W.
1997-01-01
This paper deals with two interrelated elements of globalization: Wintelism and cross national production networks which have been underexposed in discussions about globalization.Wintelism refers to the shift in competition away from final assembly and vertical control of markets by final assemblers
Model of Opinion Spreading in Social Networks
Kanovsky, Igor
2011-01-01
We proposed a new model, which capture the main difference between information and opinion spreading. In information spreading additional exposure to certain information has a small effect. Contrary, when an actor is exposed to 2 opinioned actors the probability to adopt the opinion is significant higher than in the case of contact with one such actor (called by J. Kleinberg "the 0-1-2 effect"). In each time step if an actor does not have an opinion, we randomly choose 2 his network neighbors. If one of them has an opinion, the actor adopts opinion with some low probability, if two - with a higher probability. Opinion spreading was simulated on different real world social networks and similar random scale-free networks. The results show that small world structure has a crucial impact on tipping point time. The "0-1-2" effect causes a significant difference between ability of the actors to start opinion spreading. Actor is an influencer according to his topological position in the network.
Alexandrov, Natalia (Technical Monitor); Kuby, Michael; Tierney, Sean; Roberts, Tyler; Upchurch, Christopher
2005-01-01
This report reviews six classes of models that are used for studying transportation network topologies. The report is motivated by two main questions. First, what can the "new science" of complex networks (scale-free, small-world networks) contribute to our understanding of transport network structure, compared to more traditional methods? Second, how can geographic information systems (GIS) contribute to studying transport networks? The report defines terms that can be used to classify different kinds of models by their function, composition, mechanism, spatial and temporal dimensions, certainty, linearity, and resolution. Six broad classes of models for analyzing transport network topologies are then explored: GIS; static graph theory; complex networks; mathematical programming; simulation; and agent-based modeling. Each class of models is defined and classified according to the attributes introduced earlier. The paper identifies some typical types of research questions about network structure that have been addressed by each class of model in the literature.
Mathematical model for spreading dynamics of social network worms
Sun, Xin; Liu, Yan-Heng; Li, Bin; Li, Jin; Han, Jia-Wei; Liu, Xue-Jie
2012-04-01
In this paper, a mathematical model for social network worm spreading is presented from the viewpoint of social engineering. This model consists of two submodels. Firstly, a human behavior model based on game theory is suggested for modeling and predicting the expected behaviors of a network user encountering malicious messages. The game situation models the actions of a user under the condition that the system may be infected at the time of opening a malicious message. Secondly, a social network accessing model is proposed to characterize the dynamics of network users, by which the number of online susceptible users can be determined at each time step. Several simulation experiments are carried out on artificial social networks. The results show that (1) the proposed mathematical model can well describe the spreading dynamics of social network worms; (2) weighted network topology greatly affects the spread of worms; (3) worms spread even faster on hybrid social networks.
Modeling regulatory networks with weight matrices
DEFF Research Database (Denmark)
Weaver, D.C.; Workman, Christopher; Stormo, Gary D.
1999-01-01
Systematic gene expression analyses provide comprehensive information about the transcriptional responseto different environmental and developmental conditions. With enough gene expression data points,computational biologists may eventually generate predictive computer models of transcription...... regulation.Such models will require computational methodologies consistent with the behavior of known biologicalsystems that remain tractable. We represent regulatory relationships between genes as linear coefficients orweights, with the "net" regulation influence on a gene's expression being...... the mathematical summation of theindependent regulatory inputs. Test regulatory networks generated with this approach display stable andcyclically stable gene expression levels, consistent with known biological systems. We include variables tomodel the effect of environmental conditions on transcription regulation...
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
The application of neural networks to model a laboratory scale inverse fluidized bed reactor has been studied. A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological ...
Modeling social influence through network autocorrelation : constructing the weight matrix
Leenders, Roger Th. A. J.
Many physical and social phenomena are embedded within networks of interdependencies, the so-called 'context' of these phenomena. In network analysis, this type of process is typically modeled as a network autocorrelation model. Parameter estimates and inferences based on autocorrelation models,
Hsieh, Chih-Sheng; Lee, Lung fei
2017-01-01
In this paper, we model network formation and network interactions under a unified framework. The key feature of our model is to allow individuals to respond to incentives stemming from interaction benefits on certain activities when they choose friends (network links), while capturing homophily in terms of unobserved characteristic variables in network formation and activities. There are two advantages of this modeling approach: first, one can evaluate whether incentives from certain interac...
Challenges on Probabilistic Modeling for Evolving Networks
Ding, Jianguo; Bouvry, Pascal
2013-01-01
With the emerging of new networks, such as wireless sensor networks, vehicle networks, P2P networks, cloud computing, mobile Internet, or social networks, the network dynamics and complexity expands from system design, hardware, software, protocols, structures, integration, evolution, application, even to business goals. Thus the dynamics and uncertainty are unavoidable characteristics, which come from the regular network evolution and unexpected hardware defects, unavoidable software errors,...
Model Order Reduction for Electronic Circuits:
DEFF Research Database (Denmark)
Hjorth, Poul G.; Shontz, Suzanne
Electronic circuits are ubiquitous; they are used in numerous industries including: the semiconductor, communication, robotics, auto, and music industries (among many others). As products become more and more complicated, their electronic circuits also grow in size and complexity. This increased...
Aeronautical telecommunications network advances, challenges, and modeling
Musa, Sarhan M
2015-01-01
Addresses the Challenges of Modern-Day Air Traffic Air traffic control (ATC) directs aircraft in the sky and on the ground to safety, while the Aeronautical Telecommunications Network (ATN) comprises all systems and phases that assist in aircraft departure and landing. The Aeronautical Telecommunications Network: Advances, Challenges, and Modeling focuses on the development of ATN and examines the role of the various systems that link aircraft with the ground. The book places special emphasis on ATC-introducing the modern ATC system from the perspective of the user and the developer-and provides a thorough understanding of the operating mechanism of the ATC system. It discusses the evolution of ATC, explaining its structure and how it works; includes design examples; and describes all subsystems of the ATC system. In addition, the book covers relevant tools, techniques, protocols, and architectures in ATN, including MIPv6, air traffic control (ATC), security of air traffic management (ATM), very-high-frequenc...
Neural Network Program Package for Prosody Modeling
Directory of Open Access Journals (Sweden)
J. Santarius
2004-04-01
Full Text Available This contribution describes the programme for one part of theautomatic Text-to-Speech (TTS synthesis. Some experiments (for example[14] documented the considerable improvement of the naturalness ofsynthetic speech, but this approach requires completing the inputfeature values by hand. This completing takes a lot of time for bigfiles. We need to improve the prosody by other approaches which useonly automatically classified features (input parameters. Theartificial neural network (ANN approach is used for the modeling ofprosody parameters. The program package contains all modules necessaryfor the text and speech signal pre-processing, neural network training,sensitivity analysis, result processing and a module for the creationof the input data protocol for Czech speech synthesizer ARTIC [1].
Achillas, Ch; Vlachokostas, Ch; Aidonis, D; Moussiopoulos, N; Iakovou, E; Banias, G
2010-12-01
Due to the rapid growth of Waste Electrical and Electronic Equipment (WEEE) volumes, as well as the hazardousness of obsolete electr(on)ic goods, this type of waste is now recognised as a priority stream in the developed countries. Policy-making related to the development of the necessary infrastructure and the coordination of all relevant stakeholders is crucial for the efficient management and viability of individually collected waste. This paper presents a decision support tool for policy-makers and regulators to optimise electr(on)ic products' reverse logistics network. To that effect, a Mixed Integer Linear Programming mathematical model is formulated taking into account existing infrastructure of collection points and recycling facilities. The applicability of the developed model is demonstrated employing a real-world case study for the Region of Central Macedonia, Greece. The paper concludes with presenting relevant obtained managerial insights. Copyright © 2010 Elsevier Ltd. All rights reserved.
Towards an evolutionary model of transcription networks.
Directory of Open Access Journals (Sweden)
Dan Xie
2011-06-01
Full Text Available DNA evolution models made invaluable contributions to comparative genomics, although it seemed formidable to include non-genomic features into these models. In order to build an evolutionary model of transcription networks (TNs, we had to forfeit the substitution model used in DNA evolution and to start from modeling the evolution of the regulatory relationships. We present a quantitative evolutionary model of TNs, subjecting the phylogenetic distance and the evolutionary changes of cis-regulatory sequence, gene expression and network structure to one probabilistic framework. Using the genome sequences and gene expression data from multiple species, this model can predict regulatory relationships between a transcription factor (TF and its target genes in all species, and thus identify TN re-wiring events. Applying this model to analyze the pre-implantation development of three mammalian species, we identified the conserved and re-wired components of the TNs downstream to a set of TFs including Oct4, Gata3/4/6, cMyc and nMyc. Evolutionary events on the DNA sequence that led to turnover of TF binding sites were identified, including a birth of an Oct4 binding site by a 2nt deletion. In contrast to recent reports of large interspecies differences of TF binding sites and gene expression patterns, the interspecies difference in TF-target relationship is much smaller. The data showed increasing conservation levels from genomic sequences to TF-DNA interaction, gene expression, TN, and finally to morphology, suggesting that evolutionary changes are larger at molecular levels and smaller at functional levels. The data also showed that evolutionarily older TFs are more likely to have conserved target genes, whereas younger TFs tend to have larger re-wiring rates.
Contributions and challenges for network models in cognitive neuroscience.
Sporns, Olaf
2014-05-01
The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.
Modeling of regional warehouse network generation
Directory of Open Access Journals (Sweden)
Popov Pavel Vladimirovich
2016-08-01
Full Text Available One of the factors that has a significant impact on the socio-economic development of the Russian Federation’s regions is the logistics infrastructure. It provides integrated transportation and distribution service of material flows. One of the main elements of logistics infrastructure is a storage infrastructure, which includes distribution center, distribution-and-sortout and sortout warehouses. It is the most expedient to place distribution center in the vicinity of the regional center. One of the tasks of the distribution network creation within the regions of the Russian Federation is to determine the location, capacity and number of stores. When determining regional network location of general purpose warehouses methodological approaches to solving the problems of location of production and non-production can be used which depend on various economic factors. The mathematical models for solving relevant problems are the deployment models. However, the existing models focus on the dimensionless power storage. The purpose of the given work is to develop a model to determine the optimal location of general-purpose warehouses on the Russian Federation area. At the first stage of the work, the authors assess the main economic indicators influencing the choice of the location of general purpose warehouses. An algorithm for solving the first stage, based on ABC, discriminant and cluster analysis were proposed by the authors in earlier papers. At the second stage the specific locations of general purpose warehouses and their power is chosen to provide the cost minimization for the construction and subsequent maintenance of warehouses and transportation heterogeneous products. In order to solve this problem the authors developed a mathematical model that takes into account the possibility of delivery in heterogeneous goods from suppliers and manufacturers in the distribution and storage sorting with specified set of capacities. The model allows
Bayesian Recurrent Neural Network for Language Modeling.
Chien, Jen-Tzung; Ku, Yuan-Chu
2016-02-01
A language model (LM) is calculated as the probability of a word sequence that provides the solution to word prediction for a variety of information systems. A recurrent neural network (RNN) is powerful to learn the large-span dynamics of a word sequence in the continuous space. However, the training of the RNN-LM is an ill-posed problem because of too many parameters from a large dictionary size and a high-dimensional hidden layer. This paper presents a Bayesian approach to regularize the RNN-LM and apply it for continuous speech recognition. We aim to penalize the too complicated RNN-LM by compensating for the uncertainty of the estimated model parameters, which is represented by a Gaussian prior. The objective function in a Bayesian classification network is formed as the regularized cross-entropy error function. The regularized model is constructed not only by calculating the regularized parameters according to the maximum a posteriori criterion but also by estimating the Gaussian hyperparameter by maximizing the marginal likelihood. A rapid approximation to a Hessian matrix is developed to implement the Bayesian RNN-LM (BRNN-LM) by selecting a small set of salient outer-products. The proposed BRNN-LM achieves a sparser model than the RNN-LM. Experiments on different corpora show the robustness of system performance by applying the rapid BRNN-LM under different conditions.
A Model for Teaching Electronic Commerce Students
Directory of Open Access Journals (Sweden)
Howard C. Woodard
2002-10-01
Full Text Available The teaching of information technology in an ever-changing world at universities presents a challenge. Are courses taught as concepts, while ignoring hands-on courses, leaving the hands-on classes to the technical colleges or trade schools? Does this produce the best employees for industry or give students the knowledge and skills necessary to function in a high-tech world? At GeorgiaCollege & StateUniversity (GC&SU a model was developed that combines both concepts and practical hands-on skill to meet this challenge. Using this model, a program was developed that consists of classroom lecture of concepts as well as practical hands-on exercises for mastering the knowledge and developing the skills necessary to succeed in the high-tech world of electronic commerce. The students become productive day one of a new job assignment. This solves the problem of students having the "book knowledge" but not knowing how to apply what has been learned.
On the Computation of Secondary Electron Emission Models
Clerc, Sebastien; Dennison, JR; Hoffmann, Ryan; Abbott, Jonathon
2006-01-01
Secondary electron emission is a critical contributor to the charge particle current balance in spacecraft charging. Spacecraft charging simulation codes use a parameterized expression for the secondary electron (SE) yield delta(Eo) as a function of the incident electron energy Eo. Simple three-step physics models of the electron penetration, transport, and emission from a solid are typically expressed in terms of the incident electron penetration depth at normal incidence R(Eo) and the mean ...
Optimizing neural network models: motivation and case studies
Harp, S A; T. Samad
2012-01-01
Practical successes have been achieved with neural network models in a variety of domains, including energy-related industry. The large, complex design space presented by neural networks is only minimally explored in current practice. The satisfactory results that nevertheless have been obtained testify that neural networks are a robust modeling technology; at the same time, however, the lack of a systematic design approach implies that the best neural network models generally rem...
A Complex Network Approach to Distributional Semantic Models.
Directory of Open Access Journals (Sweden)
Akira Utsumi
Full Text Available A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.
Inferring gene regression networks with model trees
Directory of Open Access Journals (Sweden)
Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Two stage neural network modelling for robust model predictive control.
Patan, Krzysztof
2017-11-02
The paper proposes a novel robust model predictive control scheme realized by means of artificial neural networks. The neural networks are used twofold: to design the so-called fundamental model of a plant and to catch uncertainty associated with the plant model. In order to simplify the optimization process carried out within the framework of predictive control an instantaneous linearization is applied which renders it possible to define the optimization problem in the form of constrained quadratic programming. Stability of the proposed control system is also investigated by showing that a cost function is monotonically decreasing with respect to time. Derived robust model predictive control is tested and validated on the example of a pneumatic servomechanism working at different operating regimes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Flat electronic bands in fractal-kagomé network and the effect of perturbation
Energy Technology Data Exchange (ETDEWEB)
Nandy, Atanu, E-mail: atanunandy1989@gmail.com; Chakrabarti, Arunava, E-mail: arunava-chakrabarti@yahoo.co.in [Department of Physics, University of Kalyani, Kalyani, West Bengal - 741235 (India)
2016-05-06
We demonstrate an analytical prescription of demonstrating the flat band [FB] states in a fractal incorporated kagomé type network that can give rise to a countable infinity of flat non-dispersive eigenstates with a multitude of localization area. The onset of localization can, in principle, be delayed in space by an appropriate choice of energy regime. The length scale, at which the onset of localization for each mode occurs, can be tuned at will following the formalism developed within the framework of real space renormalization group. This scheme leads to an exact determination of energy eigenvalue for which one can have dispersionless flat electronic bands. Furthermore, we have shown the effect ofuniform magnetic field for the same non-translationally invariant network model that has ultimately led to an‘apparent invisibility’ of such staggered localized states and to generate absolutely continuous sub-bands in the energy spectrum and again an interesting re-entrant behavior of those FB states.
Energy Technology Data Exchange (ETDEWEB)
Kristofferson, D.; Mack, D.
1996-10-01
This is the final report for a DOE funded project on BIOSCI Electronic Newsgroup Network for the biological sciences. A usable network for scientific discussion, major announcements, problem solving, etc. has been created.
Marsman, M; Borsboom, D; Kruis, J; Epskamp, S; van Bork, R; Waldorp, L J; Maas, H L J van der; Maris, G
2017-11-07
In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models-the Ising model from physics-and one of the most important latent variable models-the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.
Social network models predict movement and connectivity in ecological landscapes
Fletcher, Robert J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, Wiley M.
2011-01-01
Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.
Neural Networks For Electrohydrodynamic Effect Modelling
Directory of Open Access Journals (Sweden)
Wiesław Wajs
2004-01-01
Full Text Available This paper presents currently achieved results concerning methods of electrohydrodynamiceffect used in geophysics simulated with feedforward networks trained with backpropagation algorithm, radial basis function networks and generalized regression networks.
A network-oriented business modeling environment
Bisconti, Cristian; Storelli, Davide; Totaro, Salvatore; Arigliano, Francesco; Savarino, Vincenzo; Vicari, Claudia
The development of formal models related to the organizational aspects of an enterprise is fundamental when these aspects must be re-engineered and digitalized, especially when the enterprise is involved in the dynamics and value flows of a business network. Business modeling provides an opportunity to synthesize and make business processes, business rules and the structural aspects of an organization explicit, allowing business managers to control their complexity and guide an enterprise through effective decisional and strategic activities. This chapter discusses the main results of the TEKNE project in terms of software components that enable enterprises to configure, store, search and share models of any aspects of their business while leveraging standard and business-oriented technologies and languages to bridge the gap between the world of business people and IT experts and to foster effective business-to-business collaborations.
Directory of Open Access Journals (Sweden)
Silvye Ane Massaini
2015-01-01
Full Text Available Innovation networks have been identified in the literature as a way to complement firms’ innovative capabilities through collaboration with other partners. To provide empirical evidence for this assertion, this paper investigates the contribution of partners established in innovation networks for innovative performance of firms in the Brazilian electricalelectronics industry. For this purpose, we carried out an exploratory and descriptive survey among 185 companies. The data were analyzed using structural equation modeling (SEM. As the main findings, we observed that the establishment of collaborative relationships with customers, competitors and universities/research institutions can contribute to organizational and process innovation. However, despite obtaining some significant results concerning the contribution of different partners in the network, electrical-electronics industry companies also attach great importance to internal activities to develop their innovations.
Compartmentalization analysis using discrete fracture network models
Energy Technology Data Exchange (ETDEWEB)
La Pointe, P.R.; Eiben, T.; Dershowitz, W. [Golder Associates, Redmond, VA (United States); Wadleigh, E. [Marathon Oil Co., Midland, TX (United States)
1997-08-01
This paper illustrates how Discrete Fracture Network (DFN) technology can serve as a basis for the calculation of reservoir engineering parameters for the development of fractured reservoirs. It describes the development of quantitative techniques for defining the geometry and volume of structurally controlled compartments. These techniques are based on a combination of stochastic geometry, computational geometry, and graph the theory. The parameters addressed are compartment size, matrix block size and tributary drainage volume. The concept of DFN models is explained and methodologies to compute these parameters are demonstrated.
Some queuing network models of computer systems
Herndon, E. S.
1980-01-01
Queuing network models of a computer system operating with a single workload type are presented. Program algorithms are adapted for use on the Texas Instruments SR-52 programmable calculator. By slightly altering the algorithm to process the G and H matrices row by row instead of column by column, six devices and an unlimited job/terminal population could be handled on the SR-52. Techniques are also introduced for handling a simple load dependent server and for studying interactive systems with fixed multiprogramming limits.
Networks model of the East Turkistan terrorism
Li, Ben-xian; Zhu, Jun-fang; Wang, Shun-guo
2015-02-01
The presence of the East Turkistan terrorist network in China can be traced back to the rebellions on the BAREN region in Xinjiang in April 1990. This article intends to research the East Turkistan networks in China and offer a panoramic view. The events, terrorists and their relationship are described using matrices. Then social network analysis is adopted to reveal the network type and the network structure characteristics. We also find the crucial terrorist leader. Ultimately, some results show that the East Turkistan network has big hub nodes and small shortest path, and that the network follows a pattern of small world network with hierarchical structure.
Fundamentals of complex networks models, structures and dynamics
Chen, Guanrong; Li, Xiang
2014-01-01
Complex networks such as the Internet, WWW, transportationnetworks, power grids, biological neural networks, and scientificcooperation networks of all kinds provide challenges for futuretechnological development. In particular, advanced societies havebecome dependent on large infrastructural networks to an extentbeyond our capability to plan (modeling) and to operate (control).The recent spate of collapses in power grids and ongoing virusattacks on the Internet illustrate the need for knowledge aboutmodeling, analysis of behaviors, optimized planning and performancecontrol in such networks. F
Stochastic simulation of HIV population dynamics through complex network modelling
Sloot, P. M. A.; Ivanov, S. V.; Boukhanovsky, A. V.; van de Vijver, D. A. M. C.; Boucher, C. A. B.
We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and
A Search Model with a Quasi-Network
DEFF Research Database (Denmark)
Ejarque, Joao Miguel
This paper adds a quasi-network to a search model of the labor market. Fitting the model to an average unemployment rate and to other moments in the data implies the presence of the network is not noticeable in the basic properties of the unemployment and job finding rates. However, the network c...
Stochastic simulation of HIV population dynamics through complex network modelling
Sloot, P.M.A.; Ivanov, S.V.; Boukhanovsky, A.V.; van de Vijver, D.A.M.C.; Boucher, C.A.B.
2008-01-01
We propose a new way to model HIV infection spreading through the use of dynamic complex networks. The heterogeneous population of HIV exposure groups is described through a unique network degree probability distribution. The time evolution of the network nodes is modelled by a Markov process and
Tang, Chen; Lu, Wenjing; Chen, Song; Zhang, Zhen; Li, Botao; Wang, Wenping; Han, Lin
2007-10-20
We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.
Modeling Electronic Properties of Complex Oxides
Krishnaswamy, Karthik
Complex oxides are a class of materials that have recently emerged as potential candidates for electronic applications owing to their interesting electronic properties. The goal of this dissertation is to develop a fundamental understanding of these electronic properties using a combination of first-principles approaches based on density functional theory (DFT), and Schr odinger-Poisson (SP) simulation (Abstract shortened by ProQuest.
Ekofisk chalk: core measurements, stochastic reconstruction, network modeling and simulation
Energy Technology Data Exchange (ETDEWEB)
Talukdar, Saifullah
2002-07-01
This dissertation deals with (1) experimental measurements on petrophysical, reservoir engineering and morphological properties of Ekofisk chalk, (2) numerical simulation of core flood experiments to analyze and improve relative permeability data, (3) stochastic reconstruction of chalk samples from limited morphological information, (4) extraction of pore space parameters from the reconstructed samples, development of network model using pore space information, and computation of petrophysical and reservoir engineering properties from network model, and (5) development of 2D and 3D idealized fractured reservoir models and verification of the applicability of several widely used conventional up scaling techniques in fractured reservoir simulation. Experiments have been conducted on eight Ekofisk chalk samples and porosity, absolute permeability, formation factor, and oil-water relative permeability, capillary pressure and resistivity index are measured at laboratory conditions. Mercury porosimetry data and backscatter scanning electron microscope images have also been acquired for the samples. A numerical simulation technique involving history matching of the production profiles is employed to improve the relative permeability curves and to analyze hysteresis of the Ekofisk chalk samples. The technique was found to be a powerful tool to supplement the uncertainties in experimental measurements. Porosity and correlation statistics obtained from backscatter scanning electron microscope images are used to reconstruct microstructures of chalk and particulate media. The reconstruction technique involves a simulated annealing algorithm, which can be constrained by an arbitrary number of morphological parameters. This flexibility of the algorithm is exploited to successfully reconstruct particulate media and chalk samples using more than one correlation functions. A technique based on conditional simulated annealing has been introduced for exact reproduction of vuggy
High time resolution reconstruction of electron temperature profiles with a neural network in C-2U
Player, Gabriel; Magee, Richard; Trask, Erik; Korepanov, Sergey; Clary, Ryan; Tri Alpha Energy Team
2017-10-01
One of the most important parameters governing fast ion dynamics in a plasma is the electron temperature, as the fast ion-electron collision rate goes as νei Te3 / 2 . Unfortunately, the electron temperature is difficult to directly measure-methods relying on high-powered laser pulses or fragile probes lead to limited time resolution or measurements restricted to the edge. In order to rectify the lack of time resolution on the Thomson scattering data in the core, a type of learning algorithm, specifically a neural network, was implemented. This network uses 3 hidden layers to correlate information from nearly 250 signals, including magnetics, interferometers, and several arrays of bolometers, with Thomson scattering data over the entire C-2U database, totalling nearly 20,000 samples. The network uses the Levenberg-Marquardt algorithm with Bayesian regularization to learn from the large number of samples and inputs how to accurately reconstruct the entire electron temperature time history at a resolution of 500 kHz, a huge improvement over the 2 time points per shot provided by Thomson scattering. These results can be used in many different types of analysis and plasma characterization-in this work, we use the network to quantify electron heating.
QSAR modelling using combined simple competitive learning networks and RBF neural networks.
Sheikhpour, R; Sarram, M A; Rezaeian, M; Sheikhpour, E
2018-04-01
The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.
VEPCO network model reconciliation of LANL and MZA model data
Energy Technology Data Exchange (ETDEWEB)
NONE
1992-12-15
The LANL DC load flow model of the VEPCO transmission network shows 210 more substations than the AC load flow model produced by MZA utility Consultants. MZA was requested to determine the source of the difference. The AC load flow model used for this study utilizes 2 standard network algorithms (Decoupled or Newton). The solution time of each is affected by the number of substations. The more substations included, the longer the model will take to solve. In addition, the ability of the algorithms to converge to a solution is affected by line loadings and characteristics. Convergence is inhibited by numerous lightly loaded and electrically short lines. The MZA model reduces the total substations to 343 by creating equivalent loads and generation. Most of the omitted substations are lightly loaded and rated at 115 kV. The MZA model includes 16 substations not included in the LANL model. These represent new generation including Non-Utility Generator (NUG) sites, additional substations and an intertie (Wake, to CP and L). This report also contains data from the Italian State AC power flow model and the Duke Power Company AC flow model.
A Model of Genetic Variation in Human Social Networks
Fowler, James H; Christakis, Nicholas A
2008-01-01
Social networks influence the evolution of cooperation and they exhibit strikingly systematic patterns across a wide range of human contexts. Both of these facts suggest that variation in the topological attributes of human social networks might have a genetic basis. While genetic variation accounts for a significant portion of the variation in many complex social behaviors, the heritability of egocentric social network attributes is unknown. Here we show that three of these attributes (in-degree, transitivity, and centrality) are heritable. We then develop a "mirror network" method to test extant network models and show that none accounts for observed genetic variation in human social networks. We propose an alternative "attract and introduce" model that generates significant heritability as well as other important network features, and we show that this model with two simple forms of heterogeneity is well suited to the modeling of real social networks in humans. These results suggest that natural selection ...
National Research Council Canada - National Science Library
McCarty, Catherine A; Chisholm, Rex L; Chute, Christopher G; Kullo, Iftikhar J; Jarvik, Gail P; Larson, Eric B; Li, Rongling; Masys, Daniel R; Ritchie, Marylyn D; Roden, Dan M; Struewing, Jeffery P; Wolf, Wendy A
2011-01-01
The eMERGE (electronic MEdical Records and GEnomics) Network is an NHGRI-supported consortium of five institutions to explore the utility of DNA repositories coupled to Electronic Medical Record (EMR...
Frank, Laurence Emmanuelle
2006-01-01
Feature Network Models (FNM) are graphical structures that represent proximity data in a discrete space with the use of features. A statistical inference theory is introduced, based on the additivity properties of networks and the linear regression framework. Considering features as predictor
PageRank model of opinion formation on Ulam networks
Chakhmakhchyan, L.; Shepelyansky, D.
2013-12-01
We consider a PageRank model of opinion formation on Ulam networks, generated by the intermittency map and the typical Chirikov map. The Ulam networks generated by these maps have certain similarities with such scale-free networks as the World Wide Web (WWW), showing an algebraic decay of the PageRank probability. We find that the opinion formation process on Ulam networks has certain similarities but also distinct features comparing to the WWW. We attribute these distinctions to internal differences in network structure of the Ulam and WWW networks. We also analyze the process of opinion formation in the frame of generalized Sznajd model which protects opinion of small communities.
Neural Networks for Modeling and Control of Particle Accelerators
Edelen, A.L.; Chase, B.E.; Edstrom, D.; Milton, S.V.; Stabile, P.
2016-01-01
We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.
Local food in European supply chains: reconnection and electronic networks
Directory of Open Access Journals (Sweden)
Georgina Holt
2007-04-01
Full Text Available Après une présentation du marché des produits locaux/localisés en Grande Bretagne, ainsi qu’une définition du concept en fonction des circuits de distribution courts, de l’agriculture biologique et du commerce équitable, cet article se fonde sur des études de cas, issus de projets de recherche européens, pour identifier des différents types de réseaux concernés par les concept de produit locaux durables. Les habitudes historiques concernant l’achat des produits alimentaires jouent ici un rôle central et l’article observe l’équilibre entre les composants historiques, sociaux et environnementaux des produits locaux/localisés. A partir de ces terrains de recherche et de ces expériences il s’est avéré possible de déterminer différentes compréhensions de « produits locaux » en relation avec le concept de « distance alimentaire/ food miles ». En se référant à six cas donnés, cet article souligne l’importance des systèmes localisés en matière de durabilité alimentaire, et met en valeur le poids des qualités humaines et sociales dans la balance commerciale.After giving an overview of the market for local food in the UK, as well as a definition of the concept in relation to short supply chains, organic agriculture and fair trade, the article draws on cases encountered through EC-funded research and networking to identify different types of network concerned with the concept of sustaining local food. Historical uses of shopping habits play here a central role and the article observes the balance between historical, social and environmental components of local food. From these researches and experiences, it has been possible to demonstrate a range of understandings in relation to the concept of ‘food miles’. With reference to six cases, the article underlines the importance of local food systems within food sustainability, and highlights the weight of human and social qualities in the market balance.
A scale-free neural network for modelling neurogenesis
Perotti, Juan I.; Tamarit, Francisco A.; Cannas, Sergio A.
2006-11-01
In this work we introduce a neural network model for associative memory based on a diluted Hopfield model, which grows through a neurogenesis algorithm that guarantees that the final network is a small-world and scale-free one. We also analyze the storage capacity of the network and prove that its performance is larger than that measured in a randomly dilute network with the same connectivity.
A graph model for opportunistic network coding
Sorour, Sameh
2015-08-12
© 2015 IEEE. Recent advancements in graph-based analysis and solutions of instantly decodable network coding (IDNC) trigger the interest to extend them to more complicated opportunistic network coding (ONC) scenarios, with limited increase in complexity. In this paper, we design a simple IDNC-like graph model for a specific subclass of ONC, by introducing a more generalized definition of its vertices and the notion of vertex aggregation in order to represent the storage of non-instantly-decodable packets in ONC. Based on this representation, we determine the set of pairwise vertex adjacency conditions that can populate this graph with edges so as to guarantee decodability or aggregation for the vertices of each clique in this graph. We then develop the algorithmic procedures that can be applied on the designed graph model to optimize any performance metric for this ONC subclass. A case study on reducing the completion time shows that the proposed framework improves on the performance of IDNC and gets very close to the optimal performance.
Marketing communications model for innovation networks
Directory of Open Access Journals (Sweden)
Tiago João Freitas Correia
2015-10-01
Full Text Available Innovation is an increasingly relevant concept for the success of any organization, but it also represents a set of internal and external considerations, barriers and challenges to overcome. Along the concept of innovation, new paradigms emerge such as open innovation and co-creation that are simultaneously innovation modifiers and intensifiers in organizations, promoting organizational openness and stakeholder integration within the value creation process. Innovation networks composed by a multiplicity of agents in co-creative work perform as innovation mechanisms to face the increasingly complexity of products, services and markets. Technology, especially the Internet, is an enabler of all process among organizations supported by co-creative platforms for innovation. The definition of marketing communication strategies that promote motivation and involvement of all stakeholders in synergic creation and external promotion is the central aspect of this research. The implementation of the projects is performed by participative workshops with stakeholders from Madan Parque through IDEAS(REVOLUTION methodology and the operational model LinkUp parameterized for the project. The project is divided into the first part, the theoretical framework, and the second part where a model is developed for the marketing communication strategies that appeal to the Madan Parque case study. Keywords: Marketing Communication; Open Innovation, Technology; Innovation Networks; Incubator; Co-Creation.
Determining Application Runtimes Using Queueing Network Modeling
Energy Technology Data Exchange (ETDEWEB)
Elliott, Michael L. [Univ. of San Francisco, CA (United States)
2006-12-14
Determination of application times-to-solution for large-scale clustered computers continues to be a difficult problem in high-end computing, which will only become more challenging as multi-core consumer machines become more prevalent in the market. Both researchers and consumers of these multi-core systems desire reasonable estimates of how long their programs will take to run (time-to-solution, or TTS), and how many resources will be consumed in the execution. Currently there are few methods of determining these values, and those that do exist are either overly simplistic in their assumptions or require great amounts of effort to parameterize and understand. One previously untried method is queuing network modeling (QNM), which is easy to parameterize and solve, and produces results that typically fall within 10 to 30% of the actual TTS for our test cases. Using characteristics of the computer network (bandwidth, latency) and communication patterns (number of messages, message length, time spent in communication), the QNM model of the NAS-PB CG application was applied to MCR and ALC, supercomputers at LLNL, and the Keck Cluster at USF, with average errors of 2.41%, 3.61%, and -10.73%, respectively, compared to the actual TTS observed. While additional work is necessary to improve the predictive capabilities of QNM, current results show that QNM has a great deal of promise for determining application TTS for multi-processor computer systems.
Modeling management of research and education networks
Galagan, D.V.
2004-01-01
Computer networks and their services have become an essential part of research and education. Nowadays every modern R&E institution must have a computer network and provide network services to its students and staff. In addition to its internal computer network, every R&E institution must have a
Modeling stochasticity in biochemical reaction networks
Constantino, P. H.; Vlysidis, M.; Smadbeck, P.; Kaznessis, Y. N.
2016-03-01
Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts.
RAMAN LIGHT SCATTERING IN PSEUDOSPIN-ELECTRON MODEL AT STRONG PSEUDOSPIN-ELECTRON INTERACTION
Directory of Open Access Journals (Sweden)
T.S.Mysakovych
2004-01-01
Full Text Available Anharmonic phonon contributions to Raman scattering in locally anharmonic crystal systems in the framework of the pseudospin-electron model with tunneling splitting of levels are investigated. The case of strong pseudospin-electron coupling is considered. Pseudospin and electron contributions to scattering are taken into account. Frequency dependences of Raman scattering intensity for different values of model parameters and for different polarization of scattering and incident light are investigated.
Modelling of A Trust and Reputation Model in Wireless Networks
Directory of Open Access Journals (Sweden)
Saurabh Mishra
2015-09-01
Full Text Available Security is the major challenge for Wireless Sensor Networks (WSNs. The sensor nodes are deployed in non controlled environment, facing the danger of information leakage, adversary attacks and other threats. Trust and Reputation models are solutions for this problem and to identify malicious, selfish and compromised nodes. This paper aims to evaluate varying collusion effect with respect to static (SW, dynamic (DW, static with collusion (SWC, dynamic with collusion (DWC and oscillating wireless sensor networks to derive the joint resultant of Eigen Trust Model. An attempt has been made for the same by comparing aforementioned networks that are purely dedicated to protect the WSNs from adversary attacks and maintain the security issues. The comparison has been made with respect to accuracy and path length and founded that, collusion for wireless sensor networks seems intractable with the static and dynamic WSNs when varied with specified number of fraudulent nodes in the scenario. Additionally, it consumes more energy and resources in oscillating and collusive environments.
The electronic-commerce-oriented virtual merchandise model
Fang, Xiaocui; Lu, Dongming
2004-03-01
Electronic commerce has been the trend of commerce activities. Providing with Virtual Reality interface, electronic commerce has better expressing capacity and interaction means. But most of the applications of virtual reality technology in EC, 3D model is only the appearance description of merchandises. There is almost no information concerned with commerce information and interaction information. This resulted in disjunction of virtual model and commerce information. So we present Electronic Commerce oriented Virtual Merchandise Model (ECVMM), which combined a model with commerce information, interaction information and figure information of virtual merchandise. ECVMM with abundant information provides better support to information obtainment and communication in electronic commerce.
Multiplicative Attribute Graph Model of Real-World Networks
Energy Technology Data Exchange (ETDEWEB)
Kim, Myunghwan [Stanford Univ., CA (United States); Leskovec, Jure [Stanford Univ., CA (United States)
2010-10-20
Large scale real-world network data, such as social networks, Internet andWeb graphs, is ubiquitous in a variety of scientific domains. The study of such social and information networks commonly finds patterns and explain their emergence through tractable models. In most networks, especially in social networks, nodes also have a rich set of attributes (e.g., age, gender) associatedwith them. However, most of the existing network models focus only on modeling the network structure while ignoring the features of nodes in the network. Here we present a class of network models that we refer to as the Multiplicative Attribute Graphs (MAG), which naturally captures the interactions between the network structure and node attributes. We consider a model where each node has a vector of categorical features associated with it. The probability of an edge between a pair of nodes then depends on the product of individual attributeattribute similarities. The model yields itself to mathematical analysis as well as fit to real data. We derive thresholds for the connectivity, the emergence of the giant connected component, and show that the model gives rise to graphs with a constant diameter. Moreover, we analyze the degree distribution to show that the model can produce networks with either lognormal or power-law degree distribution depending on certain conditions.
Impact of stand-by energy losses in electronic devices on smart network performance
Directory of Open Access Journals (Sweden)
Mandić-Lukić Jasmina S.
2012-01-01
Full Text Available Limited energy resources and environmental concerns due to ever increasing energy consumption, more and more emphasis is being put on energy savings. Smart networks are promoted worldwide as a powerful tool used to improve the energy efficiency through consumption management, as well as to enable the distributed power generation, primarily based on renewable energy sources, to be optimally explored. To make it possible for the smart networks to function, a large number of electronic devices is needed to operate or to be in their stand-by mode. The consumption of these devices is added to the consumption of many other electronic devices already in use in households and offices, thus giving rise to the overall power consumption and threatening to counteract the primary function of smart networks. This paper addresses the consumption of particular electronic devices, with an emphasis placed on their thermal losses when in stand-by mode and their total share in the overall power consumption in certain countries. The thermal losses of electronic devices in their stand-by mode are usually neglected, but it seems theoretically possible that a massive increase in their number can impact net performance of the future smart networks considerably so that above an optimum level of energy savings achieved by their penetration, total consumption begins to increase. Based on the current stand-by energy losses from the existing electronic devices, we propose that the future penetration of smart networks be optimized taking also into account losses from their own electronic devices, required to operate in stand-by mode.
Multilevel method for modeling large-scale networks.
Energy Technology Data Exchange (ETDEWEB)
Safro, I. M. (Mathematics and Computer Science)
2012-02-24
Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from
CNMO: Towards the Construction of a Communication Network Modelling Ontology
Rahman, Muhammad Azizur; Pakstas, Algirdas; Wang, Frank Zhigang
Ontologies that explicitly identify objects, properties, and relationships in specific domains are essential for collaboration that involves sharing of data, knowledge or resources. A communications network modelling ontology (CNMO) has been designed to represent a network model as well as aspects related to its development and actual network operation. Network nodes/sites, link, traffic sources, protocols as well as aspects of the modeling/simulation scenario and operational aspects are defined with their formal representation. A CNMO may be beneficial for various network design/simulation/research communities due to the uniform representation of network models. This ontology is designed using terminology and concepts from various network modeling, simulation and topology generation tools.
Topological evolution of virtual social networks by modeling social activities
Sun, Xin; Dong, Junyu; Tang, Ruichun; Xu, Mantao; Qi, Lin; Cai, Yang
2015-09-01
With the development of Internet and wireless communication, virtual social networks are becoming increasingly important in the formation of nowadays' social communities. Topological evolution model is foundational and critical for social network related researches. Up to present most of the related research experiments are carried out on artificial networks, however, a study of incorporating the actual social activities into the network topology model is ignored. This paper first formalizes two mathematical abstract concepts of hobbies search and friend recommendation to model the social actions people exhibit. Then a social activities based topology evolution simulation model is developed to satisfy some well-known properties that have been discovered in real-world social networks. Empirical results show that the proposed topology evolution model has embraced several key network topological properties of concern, which can be envisioned as signatures of real social networks.
An Efficient Multitask Scheduling Model for Wireless Sensor Networks
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Hongsheng Yin
2014-01-01
Full Text Available The sensor nodes of multitask wireless network are constrained in performance-driven computation. Theoretical studies on the data processing model of wireless sensor nodes suggest satisfying the requirements of high qualities of service (QoS of multiple application networks, thus improving the efficiency of network. In this paper, we present the priority based data processing model for multitask sensor nodes in the architecture of multitask wireless sensor network. The proposed model is deduced with the M/M/1 queuing model based on the queuing theory where the average delay of data packets passing by sensor nodes is estimated. The model is validated with the real data from the Huoerxinhe Coal Mine. By applying the proposed priority based data processing model in the multitask wireless sensor network, the average delay of data packets in a sensor nodes is reduced nearly to 50%. The simulation results show that the proposed model can improve the throughput of network efficiently.
Vehicle Scheduling with Network Flow Models
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Gustavo P. Silva
2010-04-01
Full Text Available
Este trabalho retrata a primeira fase de uma pesquisa de doutorado voltada para a utilização de modelos de fluxo em redes para programação de veículos (de ônibus, em particular. A utilização de modelos deste tipo ainda e muito pouco explorada na literatura, principalmente pela dificuldade imposta pelo grande numero de variáveis resultante. Neste trabalho são apresentadas formulações para tratamento do problema de programação de veículos associados a um único depósito (ou garagem como problema de fluxo em redes, incluindo duas técnicas para reduzir o numero de arcos na rede criada e, conseqüentemente, o numero de variáveis a tratar. Uma destas técnicas de redução de arcos foi implementada e o problema de fluxo resultante foi direcionado para ser resolvido, nesta fase da pesquisa, por uma versão disponível do algoritmo Simplex para redes. Problemas teste baseados em dados reais da cidade de Reading, UK, foram resolvidos com a utilização da formulação de fluxo em redes adotada, e os resultados comparados com aqueles obtidos pelo método heurístico BOOST, o qual tem sido largamente testado e comercializado pela School of Computer Studies da Universidade de Leeds, UK. Os resultados alcançados demonstram a possibilidade de tratamento de problemas reais com a técnica de redução de arcos.
ABSTRACT
This paper presents the successful results of a first phase of a doctoral research addressed to solving vehicle (bus, in particular scheduling problems through network flow formulations. Network flow modeling for this kind of problem is a promising, but not a well explored approach, mainly because of the large number of variables related to number of arcs of real case networks. The paper presents and discusses some network flow formulations for the single depot bus vehicle scheduling problem, along with two techniques of arc reduction. One of these arc reduction techniques has been implemented and the underlying
Bicriteria Models of Vehicles Recycling Network Facility Location
Merkisz-Guranowska, Agnieszka
2012-06-01
The paper presents the issues related to modeling of a vehicle recycling network. The functioning of the recycling network is within the realm of interest of a variety of government agendas, companies participating in the network, vehicle manufacturers and vehicle end users. The interests of these groups need to be considered when deciding about the network organization. The paper presents bicriteria models of network entity location that take into account the preferences of the vehicle owners and network participants related to the network construction and reorganization. A mathematical formulation of the optimization tasks has been presented including the objective functions and limitations that the solutions have to comply with. Then, the models were used for the network optimization in Poland.
Models as Tools of Analysis of a Network Organisation
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Wojciech Pająk
2013-06-01
Full Text Available The paper presents models which may be applied as tools of analysis of a network organisation. The starting point of the discussion is defining the following terms: supply chain and network organisation. Further parts of the paper present basic assumptions analysis of a network organisation. Then the study characterises the best known models utilised in analysis of a network organisation. The purpose of the article is to define the notion and the essence of network organizations and to present the models used for their analysis.
Modelling of electron beam absorption in complex geometries
Klassen, Alexander; Bauereiß, Andreas; Körner, Carolin
2014-02-01
Computational modelling of processes that involve highly energetic electrons like electron beam melting, welding, drilling or electron beam lithography, to name but a few, requires information about the attenuation of the electron beam as it passes through the sample. Depth-dose curves as a function of electron energy, target material as well as local surface obliquity have to be provided in situ during the calculation. The most efficient way to address this issue is by employing mathematical expressions. Therefore, we propose an electron beam model based on a set of semi-empirical equations available from different published literature and on theoretical considerations. Particular stress is thereby put on accuracy and the range of validity of the theoretical approach by comparison with experimental data. Finally, we apply our model to powder-bed based additive manufacturing. The numerical results demonstrate that electron beam absorption and depth of penetration have a strong influence on the quality of the fabricated product.
Speculations on the Impact of Global Electronic Networks on Human Cognition and Human Organization.
Nilan, Michael S.
1993-01-01
Examines the relationship between a society's communication technology and Marshall McLuhan's concerns for human cognition, and between the technology and the ways that humans organize their societies. It is suggested that appropriate development of global electronic networks could have a positive effect on individual and organizational abilities…
Sedighi, M.; Lukosch, S.; van Splunter, S.; Brazier, F.M.T.; Hamedi, M.; van Beers, C.
This paper explores benefits and costs of knowledge exchange perceived by individuals in connected electronic networks of practice (ENoP) in a corporate setting. The results of 25 semi-structured interviews show 9 perceived benefits and 5 perceived costs to be of importance for knowledge exchange.
Sedighi, M.; Lukosch, S.G.; van Splunter, S.; Brazier, F.M.; Hamedi, Mohsen; van Beers, C.P.
2017-01-01
This paper explores benefits and costs of knowledge exchange perceived by individuals in connected electronic networks of practice (ENoP) in a corporate setting. The results of 25 semi-structured interviews show 9 perceived benefits and 5 perceived costs to be of importance for knowledge exchange.
Siddike, Md. Abul Kalam; Kiran, K.
2015-01-01
The main objective of this study is to investigate the perceptions of academic librarians towards the marketing of library services through social networking sites (SNSs) and their understanding of using electronic word-of-mouth (eWOM) as a marketing tool in academic libraries. This study follows a qualitative data-gathering approach of structured…
Stigter, E.C.A.; Jong, G.A.H. de; Jongejan, J.A.; Duine, J.A.; Lugt, J.P. van der; Somers, W.A.C.
1996-01-01
A quinohemoprotein alcohol dehydrogenase (QH-EDH) from Comamonas testosteroni was immobilized on an electrode in a redox polymer network consisting of a polyvinylpyridine partially N-complexed with osmiumbis-(bipyridine)chloride. The enzyme effectively transfers electrons to the electrode via the
Orbital Models and Electronic Structure Theory
DEFF Research Database (Denmark)
Linderberg, Jan
2012-01-01
This tribute to the work by Carl Johan Ballhausen focuses on the emergence of quantitative means for the study of the electronic properties of complexes and molecules. Development, refinement and application of the orbital picture elucidated electric and magnetic features of ranges of molecules w...... when used for the interpretation of electronic transitions, electron spin resonance parameters, rotatory dispersion, nuclear quadrupole couplings as well as geometric bonding patterns. Ballhausen's profound impact on the field cannot be overestimated.......This tribute to the work by Carl Johan Ballhausen focuses on the emergence of quantitative means for the study of the electronic properties of complexes and molecules. Development, refinement and application of the orbital picture elucidated electric and magnetic features of ranges of molecules...
VHDL Model of Electronic-Lock System
Directory of Open Access Journals (Sweden)
J. Noga
2000-04-01
Full Text Available The paper describes the design of an electronic-lock system which wascompleted as part of the Basic VHDL course in the Department of Controland Measurement Faculty of Electrical Engineering and Informatics,Technical University of Ostrava, Czech Republic in co-operation withthe Department if Electronic Engineering, University of Hull, GreatBritain in the frame of TEMPUS project no. S_JEP/09468-95.
Norman, Cameron D; Charnaw-Burger, Jill; Yip, Andrea L; Saad, Sam; Lombardo, Charlotte
2010-10-01
Complex problems require strategies to engage diverse perspectives in a focused, flexible manner, yet few options exist that fit with the current health care and public health system constraints. The Complex Network Electronic Knowledge Translation Research model (CoNEKTR) brings together complexity science, design thinking, social learning theories, systems thinking and eHealth technologies together to support a sustained engagement strategy for social innovation support and enhancing knowledge integration. The CoNEKTR model adapts elements of other face-to-face social organizing methods and combines it with social media and electronic networking tools to create a strategy for idea generation, refinement and social action. Drawing on complexity science, a series of networking and dialogue-enhancing activities are employed to bring diverse groups together, facilitate dialogue and create networks of networks. Ten steps and five core processes informed by complexity science have been developed through this model. Concepts such as emergence, attractors and feedback play an important role in facilitating networking among participants in the model. Using a constrained, focused approach informed by complexity science and using information technology, the CoNEKTR model holds promise as a means to enhance system capacity for knowledge generation, learning and action while working within the limitations faced by busy health professionals. © 2010 Blackwell Publishing Ltd.
Natural Models for Evolution on Networks
Mertzios, George B; Raptopoulos, Christoforos; Spirakis, Paul G
2011-01-01
Evolutionary dynamics have been traditionally studied in the context of homogeneous populations, mainly described my the Moran process. Recently, this approach has been generalized in \\cite{LHN} by arranging individuals on the nodes of a network. Undirected networks seem to have a smoother behavior than directed ones, and thus it is more challenging to find suppressors/amplifiers of selection. In this paper we present the first class of undirected graphs which act as suppressors of selection, by achieving a fixation probability that is at most one half of that of the complete graph, as the number of vertices increases. Moreover, we provide some generic upper and lower bounds for the fixation probability of general undirected graphs. As our main contribution, we introduce the natural alternative of the model proposed in \\cite{LHN}, where all individuals interact simultaneously and the result is a compromise between aggressive and non-aggressive individuals. That is, the behavior of the individuals in our new m...
Sreeprasad, T S; Rodriguez, Alfredo Alexander; Colston, Jonathan; Graham, Augustus; Shishkin, Evgeniy; Pallem, Vasanta; Berry, Vikas
2013-04-10
The two-dimensional (2D) electron cloud, flexible carbon-carbon bonds, chemical modifiability, and size-dependent quantum-confinement and capacitance makes graphene nanostructures (GN) a widely tunable material for electronics. Here we report the oxidation-led edge-roughening and cleavage of long graphene nanoribbons (GNRs) (150 nm wide) synthesized via nanotomy (nanoscale cutting) of graphite (with 2 nm edged diamond knife) to produce graphene quantum dots (GQD). These GQDs (~100-200 nm) selectively interfaced with polyelectrolyte microfiber (diameter = 2-20 μm) form an electrically percolating-network exhibiting a characteristic Coulomb blockade signature with a dry tunneling distance of 0.58 nm and conduction activation energy of 3 meV. We implement this construct to demonstrate the functioning of humidity and pressure sensors and outline their governing model. Here, a 0.36 nm decrease in the average tunneling-barrier-width between GQDs (tunneling barrier = 5.11 eV) increases the conductivity of the device by 43-fold. These devices leverage the modulation in electron tunneling distances caused by pressure and humidity induced water transport across the hygroscopic polymer microfiber (Henry's constant = 0.215 Torr(-1)). This is the foremost example of GQD-based electronic sensors. We envision that this polymer-interfaced GQD percolating network will evolve a new class of sensors leveraging the low mass, low capacitance, high conductivity, and high sensitivity of GQD and the interfacial or dielectric properties of the polymer fiber.
Modeling and Simulation of DC Power Electronics Systems Using Harmonic State Space (HSS) Method
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Bak, Claus Leth
2015-01-01
based on the state-space averaging and generalized averaging, these also have limitations to show the same results as with the non-linear time domain simulations. This paper presents a modeling and simulation method for a large dc power electronic system by using Harmonic State Space (HSS) modeling....... Through this method, the required computation time and CPU memory for large dc power electronics systems can be reduced. Besides, the achieved results show the same results as with the non-linear time domain simulation, but with the faster simulation time which is beneficial in a large network....
A kinetic model for runaway electrons in the ionosphere
Directory of Open Access Journals (Sweden)
G. Garcia
2006-09-01
Full Text Available Electrodynamic models and measurements with satellites and incoherent scatter radars predict large field aligned current densities on one side of the auroral arcs. Different authors and different kinds of studies (experimental or modeling agree that the current density can reach up to hundreds of µA/m^{2}. This large current density could be the cause of many phenomena such as tall red rays or triggering of unstable ion acoustic waves. In the present paper, we consider the issue of electrons moving through an ionospheric gas of positive ions and neutrals under the influence of a static electric field. We develop a kinetic model of collisions including electrons/electrons, electrons/ions and electrons/neutrals collisions. We use a Fokker-Planck approach to describe binary collisions between charged particles with a long-range interaction. We present the essential elements of this collision operator: the Langevin equation for electrons/ions and electrons/electrons collisions and the Monte-Carlo and null collision methods for electrons/neutrals collisions. A computational example is given illustrating the approach to equilibrium and the impact of the different terms (electrons/electrons and electrons/ions collisions on the one hand and electrons/neutrals collisions on the other hand. Then, a parallel electric field is applied in a new sample run. In this run, the electrons move in the z direction parallel to the electric field. The first results show that all the electron distribution functions are non-Maxwellian. Furthermore, runaway electrons can carry a significant part of the total current density, up to 20% of the total current density.
Directory of Open Access Journals (Sweden)
van der Sande Marianne AB
2011-07-01
Full Text Available Abstract Background Chlamydia is the most common curable sexually transmitted infection (STI in the Netherlands. The majority of chlamydia diagnoses are made by general practitioners (GPs. Baseline data from primary care will facilitate the future evaluation of the ongoing large population-based screening in the Netherlands. The aim of this study was to assess the usefulness of electronic medical records for monitoring the incidence of chlamydia cases diagnosed in primary care in the Netherlands. Methods In the electronic records of two regional and two national networks, we identified chlamydia diagnoses by means of ICPC codes (International Classification of Primary Care, laboratory results in free text and the prescription of antibiotics. The year of study was 2007 for the two regional networks and one national network, for the other national network the year of study was 2005. We calculated the incidence of diagnosed chlamydia cases per sex, age group and degree of urbanization. Results A large diversity was observed in the way chlamydia episodes were coded in the four different GP networks and how easily information concerning chlamydia diagnoses could be extracted. The overall incidence ranged from 103.2/100,000 to 590.2/100,000. Differences were partly related to differences between patient populations. Nevertheless, we observed similar trends in the incidence of chlamydia diagnoses in all networks and findings were in line with earlier reports. Conclusions Electronic patient records, originally intended for individual patient care in general practice, can be an additional source of data for monitoring chlamydia incidence in primary care and can be of use in assessing the future impact of population-based chlamydia screening programs. To increase the usefulness of data we recommend more efforts to standardize registration by (specific ICPC code and laboratory results across the existing GP networks.
Precise Network Modeling of Systems Genetics Data Using the Bayesian Network Webserver.
Ziebarth, Jesse D; Cui, Yan
2017-01-01
The Bayesian Network Webserver (BNW, http://compbio.uthsc.edu/BNW ) is an integrated platform for Bayesian network modeling of biological datasets. It provides a web-based network modeling environment that seamlessly integrates advanced algorithms for probabilistic causal modeling and reasoning with Bayesian networks. BNW is designed for precise modeling of relatively small networks that contain less than 20 nodes. The structure learning algorithms used by BNW guarantee the discovery of the best (most probable) network structure given the data. To facilitate network modeling across multiple biological levels, BNW provides a very flexible interface that allows users to assign network nodes into different tiers and define the relationships between and within the tiers. This function is particularly useful for modeling systems genetics datasets that often consist of multiscalar heterogeneous genotype-to-phenotype data. BNW enables users to, within seconds or minutes, go from having a simply formatted input file containing a dataset to using a network model to make predictions about the interactions between variables and the potential effects of experimental interventions. In this chapter, we will introduce the functions of BNW and show how to model systems genetics datasets with BNW.
Multiple Social Networks, Data Models and Measures for
DEFF Research Database (Denmark)
Magnani, Matteo; Rossi, Luca
2017-01-01
Multiple Social Network Analysis is a discipline defining models, measures, methodologies, and algorithms to study multiple social networks together as a single social system. It is particularly valuable when the networks are interconnected, e.g., the same actors are present in more than one...
Throughput capacity computation model for hybrid wireless networks
African Journals Online (AJOL)
wireless networks. We present in this paper, a computational model for obtaining throughput capacity for hybrid wireless networks. For a hybrid network with n nodes and m base stations, we observe through simulation that the throughput capacity increases linearly with the base station infrastructure connected by the wired ...
Modelling crime linkage with Bayesian networks.
de Zoete, Jacob; Sjerps, Marjan; Lagnado, David; Fenton, Norman
2015-05-01
When two or more crimes show specific similarities, such as a very distinct modus operandi, the probability that they were committed by the same offender becomes of interest. This probability depends on the degree of similarity and distinctiveness. We show how Bayesian networks can be used to model different evidential structures that can occur when linking crimes, and how they assist in understanding the complex underlying dependencies. That is, how evidence that is obtained in one case can be used in another and vice versa. The flip side of this is that the intuitive decision to "unlink" a case in which exculpatory evidence is obtained leads to serious overestimation of the strength of the remaining cases. Copyright © 2014 Forensic Science Society. Published by Elsevier Ireland Ltd. All rights reserved.
Structural equation models from paths to networks
Westland, J Christopher
2015-01-01
This compact reference surveys the full range of available structural equation modeling (SEM) methodologies. It reviews applications in a broad range of disciplines, particularly in the social sciences where many key concepts are not directly observable. This is the first book to present SEM’s development in its proper historical context–essential to understanding the application, strengths and weaknesses of each particular method. This book also surveys the emerging path and network approaches that complement and enhance SEM, and that will grow in importance in the near future. SEM’s ability to accommodate unobservable theory constructs through latent variables is of significant importance to social scientists. Latent variable theory and application are comprehensively explained, and methods are presented for extending their power, including guidelines for data preparation, sample size calculation, and the special treatment of Likert scale data. Tables of software, methodologies and fit st...
A mathematical model for networks with structures in the mesoscale
Criado, Regino; Flores, Julio; Gacia Del Amo, Alejandro Jose; Gómez, Jesus; Romance, Miguel
2011-01-01
Abstract The new concept of multilevel network is introduced in order to embody some topological properties of complex systems with structures in the mesoscale which are not completely captured by the classical models. This new model, which generalizes the hyper-network and hyper-structure models, fits perfectly with several real-life complex systems, including social and public transportation networks. We present an analysis of the structural properties of the mu...
Agent Based Modeling on Organizational Dynamics of Terrorist Network
Bo Li; Duoyong Sun; Renqi Zhu; Ze Li
2015-01-01
Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model ...
Adaptive Networks Theory, Models and Applications
Gross, Thilo
2009-01-01
With adaptive, complex networks, the evolution of the network topology and the dynamical processes on the network are equally important and often fundamentally entangled. Recent research has shown that such networks can exhibit a plethora of new phenomena which are ultimately required to describe many real-world networks. Some of those phenomena include robust self-organization towards dynamical criticality, formation of complex global topologies based on simple, local rules, and the spontaneous division of "labor" in which an initially homogenous population of network nodes self-organizes into functionally distinct classes. These are just a few. This book is a state-of-the-art survey of those unique networks. In it, leading researchers set out to define the future scope and direction of some of the most advanced developments in the vast field of complex network science and its applications.
A Cascade-Based Emergency Model for Water Distribution Network
Directory of Open Access Journals (Sweden)
Qing Shuang
2015-01-01
Full Text Available Water distribution network is important in the critical physical infrastructure systems. The paper studies the emergency resource strategies on water distribution network with the approach of complex network and cascading failures. The model of cascade-based emergency for water distribution network is built. The cascade-based model considers the network topology analysis and hydraulic analysis to provide a more realistic result. A load redistribution function with emergency recovery mechanisms is established. From the aspects of uniform distribution, node betweenness, and node pressure, six recovery strategies are given to reflect the network topology and the failure information, respectively. The recovery strategies are evaluated with the complex network indicators to describe the failure scale and failure velocity. The proposed method is applied by an illustrative example. The results showed that the recovery strategy considering the node pressure can enhance the network robustness effectively. Besides, this strategy can reduce the failure nodes and generate the least failure nodes per time.
Integrating public transort networks in the axial model
Gil, J.
2012-01-01
This study presents a first step in the development of a model that integrates public transport networks with the space syntax axial model, towards a network model that can describe the multi?modal movement structure of a city and study its patterns and flows. It describes the method for building an
An intercausal cancellation model for Bayesian-network engineering
Woudenberg, Steven P D; Van Der Gaag, Linda C.; Rademaker, Carin M A
2015-01-01
When constructing Bayesian networks with domain experts, network engineers often use the noisy-OR model, and causal interaction models more generally, to alleviate the burden of probability elicitation: the use of such a model serves to reduce the number of probabilities to be elicited on the one
Common quandaries and their practical solutions in Bayesian network modeling
Bruce G. Marcot
2017-01-01
Use and popularity of Bayesian network (BN) modeling has greatly expanded in recent years, but many common problems remain. Here, I summarize key problems in BN model construction and interpretation,along with suggested practical solutions. Problems in BN model construction include parameterizing probability values, variable definition, complex network structures,...
Directory of Open Access Journals (Sweden)
Lan Liu
2017-01-01
Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when q
Modeling the reemergence of information diffusion in social network
Yang, Dingda; Liao, Xiangwen; Shen, Huawei; Cheng, Xueqi; Chen, Guolong
2018-01-01
Information diffusion in networks is an important research topic in various fields. Existing studies either focus on modeling the process of information diffusion, e.g., independent cascade model and linear threshold model, or investigate information diffusion in networks with certain structural characteristics such as scale-free networks and small world networks. However, there are still several phenomena that have not been captured by existing information diffusion models. One of the prominent phenomena is the reemergence of information diffusion, i.e., a piece of information reemerges after the completion of its initial diffusion process. In this paper, we propose an optimized information diffusion model by introducing a new informed state into traditional susceptible-infected-removed model. We verify the proposed model via simulations in real-world social networks, and the results indicate that the model can reproduce the reemergence of information during the diffusion process.
Spectral Modelling for Spatial Network Analysis
Nourian, P.; Rezvani, S.; Sariyildiz, I.S.; van der Hoeven, F.D.; Attar, Ramtin; Chronis, Angelos; Hanna, Sean; Turrin, Michela
2016-01-01
Spatial Networks represent the connectivity structure between units of space as a weighted graph whose links are weighted as to the strength of connections. In case of urban spatial networks, the units of space correspond closely to streets and in architectural spatial networks the units correspond
Infinite Multiple Membership Relational Modeling for Complex Networks
DEFF Research Database (Denmark)
Mørup, Morten; Schmidt, Mikkel Nørgaard; Hansen, Lars Kai
Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiplemembership latent feature model for networks. Contrary to existing...... multiplemembership models that scale quadratically in the number of vertices the proposedmodel scales linearly in the number of links admittingmultiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show...
ANN Modeling of Electronic Nose Based on Co-doped SnO2 Nanofiber Sensor
Directory of Open Access Journals (Sweden)
S. KHALDI
2016-05-01
Full Text Available We present in this paper a novel neural network based technique to create a model incorporates intelligence for electronic nose. The idea is to create intelligent models; the first one, called selector, can select exactly the nature of gas detected. The second intelligent model is a corrector, which can automatically compensate the electronic nose’s response characteristics and discriminating exactly the detected gas (nature and concentration, and make the response increases all time when the temperature increases. The electronic nose is based on Co-doped SnO2 nanofiber sensor. The MATLAB environment is used during the design phase and optimization. The method discriminates qualitatively and quantitatively between six gases. The advantage of the method is that it uses a small representative database so we can easily implement the model in an electrical simulator.
Performance Modeling for Heterogeneous Wireless Networks with Multiservice Overflow Traffic
DEFF Research Database (Denmark)
Huang, Qian; Ko, King-Tim; Iversen, Villy Bæk
2009-01-01
Performance modeling is important for the purpose of developing efficient dimensioning tools for large complicated networks. But it is difficult to achieve in heterogeneous wireless networks, where different networks have different statistical characteristics in service and traffic models....... Multiservice loss analysis based on multi-dimensional Markov chain becomes intractable in these networks due to intensive computations required. This paper focuses on performance modeling for heterogeneous wireless networks based on a hierarchical overlay infrastructure. A method based on decomposition...... of the correlated traffic is used to achieve an approximate performance modeling for multiservice in hierarchical heterogeneous wireless networks with overflow traffic. The accuracy of the approximate performance obtained by our proposed modeling is verified by simulations....
Piecewise linear and Boolean models of chemical reaction networks.
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-12-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions ([Formula: see text]). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent [Formula: see text] is large. However, while the case of small constant [Formula: see text] appears in practice, it is not well understood. We provide a mathematical analysis of this limit and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed-form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator.
Directory of Open Access Journals (Sweden)
Mohammad Taghi Ameli
2012-01-01
Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.
Molecular modeling and multiscaling issues for electronic material applications
Iwamoto, Nancy; Yuen, Matthew; Fan, Haibo
Volume 1 : Molecular Modeling and Multiscaling Issues for Electronic Material Applications provides a snapshot on the progression of molecular modeling in the electronics industry and how molecular modeling is currently being used to understand material performance to solve relevant issues in this field. This book is intended to introduce the reader to the evolving role of molecular modeling, especially seen through the eyes of the IEEE community involved in material modeling for electronic applications. Part I presents the role that quantum mechanics can play in performance prediction, such as properties dependent upon electronic structure, but also shows examples how molecular models may be used in performance diagnostics, especially when chemistry is part of the performance issue. Part II gives examples of large-scale atomistic methods in material failure and shows several examples of transitioning between grain boundary simulations (on the atomistic level)and large-scale models including an example ...
Heterogeneous information network model for equipment-standard system
Yin, Liang; Shi, Li-Chen; Zhao, Jun-Yan; Du, Song-Yang; Xie, Wen-Bo; Yuan, Fei; Chen, Duan-Bing
2018-01-01
Entity information network is used to describe structural relationships between entities. Taking advantage of its extension and heterogeneity, entity information network is more and more widely applied to relationship modeling. Recent years, lots of researches about entity information network modeling have been proposed, while seldom of them concentrate on equipment-standard system with properties of multi-layer, multi-dimension and multi-scale. In order to efficiently deal with some complex issues in equipment-standard system such as standard revising, standard controlling, and production designing, a heterogeneous information network model for equipment-standard system is proposed in this paper. Three types of entities and six types of relationships are considered in the proposed model. Correspondingly, several different similarity-measuring methods are used in the modeling process. The experiments show that the heterogeneous information network model established in this paper can reflect relationships between entities accurately. Meanwhile, the modeling process has a good performance on time consumption.
Modified Penna bit-string network evolution model for scale-free networks with assortative mixing
Kim, Yup; Choi, Woosik; Yook, Soon-Hyung
2012-02-01
Motivated by biological aging dynamics, we introduce a network evolution model for social interaction networks. In order to study the effect of social interactions originating from biological and sociological reasons on the topological properties of networks, we introduce the activitydependent rewiring process. From the numerical simulations, we show that the degree distribution of the obtained networks follows a power-law distribution with an exponentially decaying tail, P( k) ˜ ( k + c)- γ exp(- k/k 0). The obtained value of γ is in the range 2 networks. Moreover, we also show that the degree-degree correlation of the network is positive, which is a characteristic of social interaction networks. The possible applications of our model to real systems are also discussed.
Ripple-Spreading Network Model Optimization by Genetic Algorithm
Directory of Open Access Journals (Sweden)
Xiao-Bing Hu
2013-01-01
Full Text Available Small-world and scale-free properties are widely acknowledged in many real-world complex network systems, and many network models have been developed to capture these network properties. The ripple-spreading network model (RSNM is a newly reported complex network model, which is inspired by the natural ripple-spreading phenomenon on clam water surface. The RSNM exhibits good potential for describing both spatial and temporal features in the development of many real-world networks where the influence of a few local events spreads out through nodes and then largely determines the final network topology. However, the relationships between ripple-spreading related parameters (RSRPs of RSNM and small-world and scale-free topologies are not as obvious or straightforward as in many other network models. This paper attempts to apply genetic algorithm (GA to tune the values of RSRPs, so that the RSNM may generate these two most important network topologies. The study demonstrates that, once RSRPs are properly tuned by GA, the RSNM is capable of generating both network topologies and therefore has a great flexibility to study many real-world complex network systems.
The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future.
Gottesman, Omri; Kuivaniemi, Helena; Tromp, Gerard; Faucett, W Andrew; Li, Rongling; Manolio, Teri A; Sanderson, Saskia C; Kannry, Joseph; Zinberg, Randi; Basford, Melissa A; Brilliant, Murray; Carey, David J; Chisholm, Rex L; Chute, Christopher G; Connolly, John J; Crosslin, David; Denny, Joshua C; Gallego, Carlos J; Haines, Jonathan L; Hakonarson, Hakon; Harley, John; Jarvik, Gail P; Kohane, Isaac; Kullo, Iftikhar J; Larson, Eric B; McCarty, Catherine; Ritchie, Marylyn D; Roden, Dan M; Smith, Maureen E; Böttinger, Erwin P; Williams, Marc S
2013-10-01
The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute-funded consortium engaged in the development of methods and best practices for using the electronic medical record as a tool for genomic research. Now in its sixth year and second funding cycle, and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from electronic medical records can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and health-care informatics, particularly for electronic phenotyping, genome-wide association studies, genomic medicine implementation, and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here, we describe the evolution, accomplishments, opportunities, and challenges of the network from its inception as a five-group consortium focused on genotype-phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting toward the implementation of genomic medicine.
Network topology for the formation of solvated electrons in binary CaO–Al2O3 composition glasses
Akola, Jaakko; Kohara, Shinji; Ohara, Koji; Fujiwara, Akihiko; Watanabe, Yasuhiro; Masuno, Atsunobu; Usuki, Takeshi; Kubo, Takashi; Nakahira, Atsushi; Nitta, Kiyofumi; Uruga, Tomoya; Weber, J. K. Richard; Benmore, Chris J.
2013-01-01
Glass formation in the CaO–Al2O3 system represents an important phenomenon because it does not contain typical network-forming cations. We have produced structural models of CaO–Al2O3 glasses using combined density functional theory–reverse Monte Carlo simulations and obtained structures that reproduce experiments (X-ray and neutron diffraction, extended X-ray absorption fine structure) and result in cohesive energies close to the crystalline ground states. The O–Ca and O–Al coordination numbers are similar in the eutectic 64 mol % CaO (64CaO) glass [comparable to 12CaO·7Al2O3 (C12A7)], and the glass structure comprises a topologically disordered cage network with large-sized rings. This topologically disordered network is the signature of the high glass-forming ability of 64CaO glass and high viscosity in the melt. Analysis of the electronic structure reveals that the atomic charges for Al are comparable to those for Ca, and the bond strength of Al–O is stronger than that of Ca–O, indicating that oxygen is more weakly bound by cations in CaO-rich glass. The analysis shows that the lowest unoccupied molecular orbitals occurs in cavity sites, suggesting that the C12A7 electride glass [Kim SW, Shimoyama T, Hosono H (2011) Science 333(6038):71–74] synthesized from a strongly reduced high-temperature melt can host solvated electrons and bipolarons. Calculations of 64CaO glass structures with few subtracted oxygen atoms (additional electrons) confirm this observation. The comparable atomic charges and coordination of the cations promote more efficient elemental mixing, and this is the origin of the extended cage structure and hosted solvated (trapped) electrons in the C12A7 glass. PMID:23723350
Network topology for the formation of solvated electrons in binary CaO-Al2O3 composition glasses.
Akola, Jaakko; Kohara, Shinji; Ohara, Koji; Fujiwara, Akihiko; Watanabe, Yasuhiro; Masuno, Atsunobu; Usuki, Takeshi; Kubo, Takashi; Nakahira, Atsushi; Nitta, Kiyofumi; Uruga, Tomoya; Weber, J K Richard; Benmore, Chris J
2013-06-18
Glass formation in the CaO-Al2O3 system represents an important phenomenon because it does not contain typical network-forming cations. We have produced structural models of CaO-Al2O3 glasses using combined density functional theory-reverse Monte Carlo simulations and obtained structures that reproduce experiments (X-ray and neutron diffraction, extended X-ray absorption fine structure) and result in cohesive energies close to the crystalline ground states. The O-Ca and O-Al coordination numbers are similar in the eutectic 64 mol % CaO (64CaO) glass [comparable to 12CaO·7Al2O3 (C12A7)], and the glass structure comprises a topologically disordered cage network with large-sized rings. This topologically disordered network is the signature of the high glass-forming ability of 64CaO glass and high viscosity in the melt. Analysis of the electronic structure reveals that the atomic charges for Al are comparable to those for Ca, and the bond strength of Al-O is stronger than that of Ca-O, indicating that oxygen is more weakly bound by cations in CaO-rich glass. The analysis shows that the lowest unoccupied molecular orbitals occurs in cavity sites, suggesting that the C12A7 electride glass [Kim SW, Shimoyama T, Hosono H (2011) Science 333(6038):71-74] synthesized from a strongly reduced high-temperature melt can host solvated electrons and bipolarons. Calculations of 64CaO glass structures with few subtracted oxygen atoms (additional electrons) confirm this observation. The comparable atomic charges and coordination of the cations promote more efficient elemental mixing, and this is the origin of the extended cage structure and hosted solvated (trapped) electrons in the C12A7 glass.
Network models of frugivory and seed dispersal: Challenges and opportunities
Carlo, Tomás A.; Yang, Suann
2011-11-01
Network analyses have emerged as a new tool to study frugivory and seed dispersal (FSD) mutualisms because networks can model and simplify the complexity of multiple community-wide species interactions. Moreover, network theory suggests that structural properties, such as the presence of highly generalist species, are linked to the stability of mutualistic communities. However, we still lack empirical validation of network model predictions. Here we outline new research avenues to connect network models to FSD processes, and illustrate the challenges and opportunities of this tool with a field study. We hypothesized that generalist frugivores would be important for forest stability by dispersing seeds into deforested areas and initiating reforestation. We then constructed a network of plant-frugivore interactions using published data and identified the most generalist frugivores. To test the importance of generalists we measured: 1) the frequency with which frugivores moved between pasture and forest, 2) the bird-generated seed rain under perches in the pasture, and 3) the perching frequency of birds above seed traps. The generalist frugivores in the forest network were not important for seed dispersal into pastures, and thus for forest recovery, because the forest network excluded habitat heterogeneities, frugivore behavior, and movements. More research is needed to develop ways to incorporate relevant FSD processes into network models in order for these models to be more useful to community ecology and conservation. The network framework can serve to spark and renew interest in FSD and further our understanding of plant-animal communities.
Directory of Open Access Journals (Sweden)
Elio Manuel Castañeda-González
2016-07-01
Full Text Available Because of the large impact that today computer networks, their study in related fields such as Telecommunications Engineering and Electronics is presented to the student with great appeal. However, by digging in content, lacking a strong practical component, you can make this interest decreases considerably. This paper proposes the use of teaching strategies and analogies, media and interactive applications that enhance the teaching of discipline networks and encourage their study. It is part of an analysis of how the teaching of the discipline process is performed and then a description of each of these strategies is done with their respective contribution to student learning.
Power Electronic Systems for Switched Reluctance Generator based Wind Farms and DC Networks
DEFF Research Database (Denmark)
Park, Kiwoo
. Under these circumstances, research on dc network connection with a novel wind power generator system is presented in this thesis, which mainly consists of two major parts: control of a Switched Reluctance Generator (SRG) system and development of dc-dc converters for a dc network system in a wind farm...... components into the torque reference to compensate the ripples in the actual output torque. The effectiveness and resulting improvement in the performance of both the proposed speed controller and torque minimization technique are demonstrated by simulation results. The modern power electronic interfaces...
Hybrid neural network bushing model for vehicle dynamics simulation
Energy Technology Data Exchange (ETDEWEB)
Sohn, Jeong Hyun [Pukyong National University, Busan (Korea, Republic of); Lee, Seung Kyu [Hyosung Corporation, Changwon (Korea, Republic of); Yoo, Wan Suk [Pusan National University, Busan (Korea, Republic of)
2008-12-15
Although the linear model was widely used for the bushing model in vehicle suspension systems, it could not express the nonlinear characteristics of bushing in terms of the amplitude and the frequency. An artificial neural network model was suggested to consider the hysteretic responses of bushings. This model, however, often diverges due to the uncertainties of the neural network under the unexpected excitation inputs. In this paper, a hybrid neural network bushing model combining linear and neural network is suggested. A linear model was employed to represent linear stiffness and damping effects, and the artificial neural network algorithm was adopted to take into account the hysteretic responses. A rubber test was performed to capture bushing characteristics, where sine excitation with different frequencies and amplitudes is applied. Random test results were used to update the weighting factors of the neural network model. It is proven that the proposed model has more robust characteristics than a simple neural network model under step excitation input. A full car simulation was carried out to verify the proposed bushing models. It was shown that the hybrid model results are almost identical to the linear model under several maneuvers
Optical Network Models and Their Application to Software-Defined Network Management
Directory of Open Access Journals (Sweden)
Thomas Szyrkowiec
2017-01-01
Full Text Available Software-defined networking is finding its way into optical networks. Here, it promises a simplification and unification of network management for optical networks allowing automation of operational tasks despite the highly diverse and vendor-specific commercial systems and the complexity and analog nature of optical transmission. Common abstractions and interfaces are a fundamental component for software-defined optical networking. Currently, a number of models for optical networks are available. They all claim to provide open and vendor agnostic management of optical equipment. In this work, we survey and compare the most important models and propose an intent interface for creating virtual topologies which is integrated in the existing model ecosystem.
An image segmentation method based on network clustering model
Jiao, Yang; Wu, Jianshe; Jiao, Licheng
2018-01-01
Network clustering phenomena are ubiquitous in nature and human society. In this paper, a method involving a network clustering model is proposed for mass segmentation in mammograms. First, the watershed transform is used to divide an image into regions, and features of the image are computed. Then a graph is constructed from the obtained regions and features. The network clustering model is applied to realize clustering of nodes in the graph. Compared with two classic methods, the algorithm based on the network clustering model performs more effectively in experiments.
Small is beautiful: models of small neuronal networks.
Lamb, Damon G; Calabrese, Ronald L
2012-08-01
Modeling has contributed a great deal to our understanding of how individual neurons and neuronal networks function. In this review, we focus on models of the small neuronal networks of invertebrates, especially rhythmically active CPG networks. Models have elucidated many aspects of these networks, from identifying key interacting membrane properties to pointing out gaps in our understanding, for example missing neurons. Even the complex CPGs of vertebrates, such as those that underlie respiration, have been reduced to small network models to great effect. Modeling of these networks spans from simplified models, which are amenable to mathematical analyses, to very complicated biophysical models. Some researchers have now adopted a population approach, where they generate and analyze many related models that differ in a few to several judiciously chosen free parameters; often these parameters show variability across animals and thus justify the approach. Models of small neuronal networks will continue to expand and refine our understanding of how neuronal networks in all animals program motor output, process sensory information and learn. Copyright © 2012 Elsevier Ltd. All rights reserved.
[Robustness analysis of adaptive neural network model based on spike timing-dependent plasticity].
Chen, Yunzhi; Xu, Guizhi; Zhou, Qian; Guo, Miaomiao; Guo, Lei; Wan, Xiaowei
2015-02-01
To explore the self-organization robustness of the biological neural network, and thus to provide new ideas and methods for the electromagnetic bionic protection, we studied both the information transmission mechanism of neural network and spike timing-dependent plasticity (STDP) mechanism, and then investigated the relationship between synaptic plastic and adaptive characteristic of biology. Then a feedforward neural network with the Izhikevich model and the STDP mechanism was constructed, and the adaptive robust capacity of the network was analyzed. Simulation results showed that the neural network based on STDP mechanism had good rubustness capacity, and this characteristics is closely related to the STDP mechanisms. Based on this simulation work, the cell circuit with neurons and synaptic circuit which can simulate the information processing mechanisms of biological nervous system will be further built, then the electronic circuits with adaptive robustness will be designed based on the cell circuit.
Mathematical model I. Electron and quantum mechanics
Nitin Ramchandra Gadre
2011-01-01
The basic particle electron obeys various theories like electrodynamics, quantum mechanics and special relativity. Particle under different experimental conditions behaves differently, allowing us to observe different characteristics which become basis for these theories. In this paper, we have made an attempt to suggest a classical picture by studying the requirements of these three modern theories. The basic presumption is: There must be certain structural characteristics in a particle like...
Stochastic Online Learning in Dynamic Networks under Unknown Models
2016-08-02
Stochastic Online Learning in Dynamic Networks under Unknown Models This research aims to develop fundamental theories and practical algorithms for...12211 Research Triangle Park, NC 27709-2211 Online learning , multi-armed bandit, dynamic networks REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S... Online Learning in Dynamic Networks under Unknown Models Report Title This research aims to develop fundamental theories and practical algorithms for
A Cellular Automata Models of Evolution of Transportation Networks
Directory of Open Access Journals (Sweden)
Mariusz Paszkowski
2002-01-01
Full Text Available We present a new approach to modelling of transportation networks. Supply of resources and their influence on the evolution of the consuming environment is a princqral problem considered. ne present two concepts, which are based on cellular automata paradigm. In the first model SCAM4N (Simple Cellular Automata Model of Anastomosing Network, the system is represented by a 2D mesh of elementary cells. The rules of interaction between them are introduced for modelling ofthe water flow and other phenomena connected with anastomosing river: Due to limitations of SCAMAN model, we introduce a supplementary model. The MANGraCA (Model of Anastomosing Network with Graph of Cellular Automata model beside the classical mesh of automata, introduces an additional structure: the graph of cellular automata, which represents the network pattern. Finally we discuss the prospective applications ofthe models. The concepts of juture implementation are also presented.
Runoff Modelling in Urban Storm Drainage by Neural Networks
DEFF Research Database (Denmark)
Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld
1995-01-01
A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....
Electronic learning and constructivism: a model for nursing education.
Kala, Sasikarn; Isaramalai, Sang-Arun; Pohthong, Amnart
2010-01-01
Nurse educators are challenged to teach nursing students to become competent professionals, who have both in-depth knowledge and decision-making skills. The use of electronic learning methods has been found to facilitate the teaching-learning process in nursing education. Although learning theories are acknowledged as useful guides to design strategies and activities of learning, integration of these theories into technology-based courses appears limited. Constructivism is a theoretical paradigm that could prove to be effective in guiding the design of electronic learning experiences for the purpose of providing positive outcomes, such as the acquisition of knowledge and decision-making skills. Therefore, the purposes of this paper are to: describe electronic learning, present a brief overview of what is known about the outcomes of electronic learning, discuss constructivism theory, present a model for electronic learning using constructivism, and describe educators' roles emphasizing the utilization of the model in developing electronic learning experiences in nursing education.
Power Electronic Packaging Design, Assembly Process, Reliability and Modeling
Liu, Yong
2012-01-01
Power Electronic Packaging presents an in-depth overview of power electronic packaging design, assembly,reliability and modeling. Since there is a drastic difference between IC fabrication and power electronic packaging, the book systematically introduces typical power electronic packaging design, assembly, reliability and failure analysis and material selection so readers can clearly understand each task's unique characteristics. Power electronic packaging is one of the fastest growing segments in the power electronic industry, due to the rapid growth of power integrated circuit (IC) fabrication, especially for applications like portable, consumer, home, computing and automotive electronics. This book also covers how advances in both semiconductor content and power advanced package design have helped cause advances in power device capability in recent years. The author extrapolates the most recent trends in the book's areas of focus to highlight where further improvement in materials and techniques can d...
Castet, Jean-Francois; Saleh, Joseph H
2013-01-01
This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the
Directory of Open Access Journals (Sweden)
Jean-Francois Castet
Full Text Available This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also
Artificial Neural Network Modeling of an Inverse Fluidized Bed ...
African Journals Online (AJOL)
MICHAEL
modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data obtained from an inverse fluidized bed reactor treating the starch industry wastewater.
A control model for district heating networks with storage
Scholten, Tjeert; De Persis, Claudio; Tesi, Pietro
2014-01-01
In [1] pressure control of hydraulic networks is investigated. We extend this work to district heating systems with storage capabilities and derive a model taking the topology of the network into account. The goal for the derived model is that it should allow for control of the storage level and
A small-world network model of facial emotion recognition.
Takehara, Takuma; Ochiai, Fumio; Suzuki, Naoto
2016-01-01
Various models have been proposed to increase understanding of the cognitive basis of facial emotions. Despite those efforts, interactions between facial emotions have received minimal attention. If collective behaviours relating to each facial emotion in the comprehensive cognitive system could be assumed, specific facial emotion relationship patterns might emerge. In this study, we demonstrate that the frameworks of complex networks can effectively capture those patterns. We generate 81 facial emotion images (6 prototypes and 75 morphs) and then ask participants to rate degrees of similarity in 3240 facial emotion pairs in a paired comparison task. A facial emotion network constructed on the basis of similarity clearly forms a small-world network, which features an extremely short average network distance and close connectivity. Further, even if two facial emotions have opposing valences, they are connected within only two steps. In addition, we show that intermediary morphs are crucial for maintaining full network integration, whereas prototypes are not at all important. These results suggest the existence of collective behaviours in the cognitive systems of facial emotions and also describe why people can efficiently recognize facial emotions in terms of information transmission and propagation. For comparison, we construct three simulated networks--one based on the categorical model, one based on the dimensional model, and one random network. The results reveal that small-world connectivity in facial emotion networks is apparently different from those networks, suggesting that a small-world network is the most suitable model for capturing the cognitive basis of facial emotions.
Systems and methods for modeling and analyzing networks
Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W
2013-10-29
The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.
Ocean wave prediction using numerical and neural network models
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Prabaharan, N.
This paper presents an overview of the development of the numerical wave prediction models and recently used neural networks for ocean wave hindcasting and forecasting. The numerical wave models express the physical concepts of the phenomena...
PROBLEM OF INTERNET COMMUNICATION OF UPPER SECONDARY SCHOOL PUPILS IN ELECTRONIC SOCIAL NETWORKS
Directory of Open Access Journals (Sweden)
Olga E. Konevshchynska
2017-09-01
Full Text Available The article deals with the problem of Internet communication of upper secondary school pupils during interpersonal communication in electronic social networks. The actuality is proved, the system analysis of the psychological and pedagogical, educational-methodical literature of the researched problem is carried out. External written Internet speaking is considered as an important factor in the communication culture of the individual. It has been determined that high level of Internet communication of both students and teachers in the process of interpersonal communication in electronic social networks are important aspects of raising the level of media culture, media information competence of the individual. Also, these ones are necessary requirements of the information society to fulfill project-oriented, educational-cognitive and effective innovative activity in educational practice.
Directory of Open Access Journals (Sweden)
Sungchul Choi
2016-12-01
Full Text Available The aim of this paper is to propose a method to investigate a firm’s strategic changes. Technologies or technological capabilities are a major resource for achieving competitive advantages, so a firm’s R&D effort to improve capabilities on specific technologies is aligned with strategic direction. Therefore, this research analyzes changes in R&D efforts by identifying key R&D personnel using patent co-inventor network and social network analysis. Based on characteristics of application and granted patents, the method analyzes current and future R&D efforts and so identifies strategic changes of a firm. We conducted an empirical analysis using the patents of Samsung Electronics. Our method analyzed the current and future strategies of Samsung Electronics and the result shows clear strategic changes in their focal technologies and business.
An information spreading model based on online social networks
Wang, Tao; He, Juanjuan; Wang, Xiaoxia
2018-01-01
Online social platforms are very popular in recent years. In addition to spreading information, users could review or collect information on online social platforms. According to the information spreading rules of online social network, a new information spreading model, namely IRCSS model, is proposed in this paper. It includes sharing mechanism, reviewing mechanism, collecting mechanism and stifling mechanism. Mean-field equations are derived to describe the dynamics of the IRCSS model. Moreover, the steady states of reviewers, collectors and stiflers and the effects of parameters on the peak values of reviewers, collectors and sharers are analyzed. Finally, numerical simulations are performed on different networks. Results show that collecting mechanism and reviewing mechanism, as well as the connectivity of the network, make information travel wider and faster, and compared to WS network and ER network, the speed of reviewing, sharing and collecting information is fastest on BA network.
Lattice Boltzmann Model for Electronic Structure Simulations
Mendoza, M; Succi, S
2015-01-01
Recently, a new connection between density functional theory and kinetic theory has been proposed. In particular, it was shown that the Kohn-Sham (KS) equations can be reformulated as a macroscopic limit of the steady-state solution of a suitable single-particle kinetic equation. By using a discrete version of this new formalism, the exchange and correlation energies of simple atoms and the geometrical configuration of the methane molecule were calculated accurately. Here, we discuss the main ideas behind the lattice kinetic approach to electronic structure computations, offer some considerations for prospective extensions, and also show additional numerical results, namely the geometrical configuration of the water molecule.
A Mathematical Model to Improve the Performance of Logistics Network
Directory of Open Access Journals (Sweden)
Muhammad Izman Herdiansyah
2012-01-01
Full Text Available The role of logistics nowadays is expanding from just providing transportation and warehousing to offering total integrated logistics. To remain competitive in the global market environment, business enterprises need to improve their logistics operations performance. The improvement will be achieved when we can provide a comprehensive analysis and optimize its network performances. In this paper, a mixed integer linier model for optimizing logistics network performance is developed. It provides a single-product multi-period multi-facilities model, as well as the multi-product concept. The problem is modeled in form of a network flow problem with the main objective to minimize total logistics cost. The problem can be solved using commercial linear programming package like CPLEX or LINDO. Even in small case, the solver in Excel may also be used to solve such model.Keywords: logistics network, integrated model, mathematical programming, network optimization
Completely random measures for modelling block-structured sparse networks
DEFF Research Database (Denmark)
Herlau, Tue; Schmidt, Mikkel Nørgaard; Mørup, Morten
2016-01-01
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the sense of the Aldous-Hoover representation theorem. Empirical studies of networks indicate that many real-world networks...... [2014] proposed the use of a different notion of exchangeability due to Kallenberg [2006] and obtained a network model which admits power-law behaviour while retaining desirable statistical properties, however this model does not capture latent vertex traits such as block-structure. In this work we re......-introduce the use of block-structure for network models obeying allenberg’s notion of exchangeability and thereby obtain a model which admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model...
A Network Contention Model for the Extreme-scale Simulator
Energy Technology Data Exchange (ETDEWEB)
Engelmann, Christian [ORNL; Naughton III, Thomas J [ORNL
2015-01-01
The Extreme-scale Simulator (xSim) is a performance investigation toolkit for high-performance computing (HPC) hardware/software co-design. It permits running a HPC application with millions of concurrent execution threads, while observing its performance in a simulated extreme-scale system. This paper details a newly developed network modeling feature for xSim, eliminating the shortcomings of the existing network modeling capabilities. The approach takes a different path for implementing network contention and bandwidth capacity modeling using a less synchronous and accurate enough model design. With the new network modeling feature, xSim is able to simulate on-chip and on-node networks with reasonable accuracy and overheads.
Application Interaction Model for Opportunistic Networking
de Souza Schwartz, Ramon; van Dijk, H.W.; Scholten, Johan
In Opportunistic Networks, autonomous nodes discover, assess and potentially seize opportunities for communication and distributed processing whenever these emerge. In this paper, we consider prerequisites for a successful implementation of such a way of processing in networks that consist mainly of
A Model for Telestrok Network Evaluation
DEFF Research Database (Denmark)
Storm, Anna; Günzel, Franziska; Theiss, Stephan
2011-01-01
Different telestroke network concepts have been implemented worldwide to enable fast and efficient treatment of stroke patients in underserved rural areas. Networks could demonstrate the improvement in clinical outcome, but have so far excluded a cost-effectiveness analysis. With health economic ...
Concentration dependent model of protein-protein interaction networks
Zhang, Jingshan
2007-01-01
The scale free structure p(k)~k^{-gamma} of protein-protein interaction networks can be produced by a static physical model. We find the earlier study of deterministic threshold models with exponential fitness distributions can be generalized to explain the apparent scale free degree distribution of the physical model, and this explanation provides a generic mechanism of "scale free" networks. We predict the dependence of gamma on experimental protein concentrations. The clustering coefficient distribution of the model is also studied.
Teaching Behavioral Modeling and Simulation Techniques for Power Electronics Courses
Abramovitz, A.
2011-01-01
This paper suggests a pedagogical approach to teaching the subject of behavioral modeling of switch-mode power electronics systems through simulation by general-purpose electronic circuit simulators. The methodology is oriented toward electrical engineering (EE) students at the undergraduate level, enrolled in courses such as "Power…
Photoinduced electron transfer in model systems of photosynthesis
Hofstra, U.
1988-01-01
This Thesis describes Investigations on photoinduced electron transfer (ET) for several compounds, serving as model systems of the natural photosynthesis. In addition, the properties of the systems, e.g. the conformation in solution and the electronic properties of the photoexcited states
Model and simulation of Krause model in dynamic open network
Zhu, Meixia; Xie, Guangqiang
2017-08-01
The construction of the concept of evolution is an effective way to reveal the formation of group consensus. This study is based on the modeling paradigm of the HK model (Hegsekmann-Krause). This paper analyzes the evolution of multi - agent opinion in dynamic open networks with member mobility. The results of the simulation show that when the number of agents is constant, the interval distribution of the initial distribution will affect the number of the final view, The greater the distribution of opinions, the more the number of views formed eventually; The trust threshold has a decisive effect on the number of views, and there is a negative correlation between the trust threshold and the number of opinions clusters. The higher the connectivity of the initial activity group, the more easily the subjective opinion in the evolution of opinion to achieve rapid convergence. The more open the network is more conducive to the unity of view, increase and reduce the number of agents will not affect the consistency of the group effect, but not conducive to stability.
Competitive Remix Practice and Networks of Support in Electronic Dance Music Economies
Directory of Open Access Journals (Sweden)
Jonathan Karpetz
2014-12-01
Full Text Available The rapid development and dissemination of social media fueled competition platforms has led to immediate shifts in the practices of creative workers in fields related to contest offerings. In Electronic Dance Music (EDM, crowdsourced competitive remix platforms have reoriented certain practices into networks of competition, where winning distinction is often only part of a larger process of further enmeshing DJs, music producers and their supporters in the social media fueled ecosystems that these websites have created. In order to begin to analyze the impact of remix competitions on producers and their personal and professional networks, I intend to trace a line of recent history and debate to reveal the connections linking EDM websites, labels, marketers, DJs, producers and supporters and their engagement with remix competition platforms. I will examine the call for support that is involved in so much of socially networked EDM communication between contestants and their peers surrounding these remix contests.
Training a Network of Electronic Neurons for Control of a Mobile Robot
Vromen, T. G. M.; Steur, E.; Nijmeijer, H.
An adaptive training procedure is developed for a network of electronic neurons, which controls a mobile robot driving around in an unknown environment while avoiding obstacles. The neuronal network controls the angular velocity of the wheels of the robot based on the sensor readings. The nodes in the neuronal network controller are clusters of neurons rather than single neurons. The adaptive training procedure ensures that the input-output behavior of the clusters is identical, even though the constituting neurons are nonidentical and have, in isolation, nonidentical responses to the same input. In particular, we let the neurons interact via a diffusive coupling, and the proposed training procedure modifies the diffusion interaction weights such that the neurons behave synchronously with a predefined response. The working principle of the training procedure is experimentally validated and results of an experiment with a mobile robot that is completely autonomously driving in an unknown environment with obstacles are presented.
Rumor spreading model with noise interference in complex social networks
Zhu, Liang; Wang, Youguo
2017-03-01
In this paper, a modified susceptible-infected-removed (SIR) model has been proposed to explore rumor diffusion on complex social networks. We take variation of connectivity into consideration and assume the variation as noise. On the basis of related literature on virus networks, the noise is described as standard Brownian motion while stochastic differential equations (SDE) have been derived to characterize dynamics of rumor diffusion both on homogeneous networks and heterogeneous networks. Then, theoretical analysis on homogeneous networks has been demonstrated to investigate the solution of SDE model and the steady state of rumor diffusion. Simulations both on Barabási-Albert (BA) network and Watts-Strogatz (WS) network display that the addition of noise accelerates rumor diffusion and expands diffusion size, meanwhile, the spreading speed on BA network is much faster than on WS network under the same noise intensity. In addition, there exists a rumor diffusion threshold in statistical average meaning on homogeneous network which is absent on heterogeneous network. Finally, we find a positive correlation between peak value of infected individuals and noise intensity while a negative correlation between rumor lifecycle and noise intensity overall.
Yan, Hong; Zhong, Mengjuan; Lv, Ze; Wan, Pengbo
2017-11-01
A stretchable, transparent, and body-attachable chemical sensor is assembled from the stretchable nanocomposite network film for ultrasensitive chemical vapor sensing. The stretchable nanocomposite network film is fabricated by in situ preparation of polyaniline/MoS2 (PANI/MoS2 ) nanocomposite in MoS2 suspension and simultaneously nanocomposite deposition onto prestrain elastomeric polydimethylsiloxane substrate. The assembled stretchable electronic sensor demonstrates ultrasensitive sensing performance as low as 50 ppb, robust sensing stability, and reliable stretchability for high-performance chemical vapor sensing. The ultrasensitive sensing performance of the stretchable electronic sensors could be ascribed to the synergistic sensing advantages of MoS2 and PANI, higher specific surface area, the reliable sensing channels of interconnected network, and the effectively exposed sensing materials. It is expected to hold great promise for assembling various flexible stretchable chemical vapor sensors with ultrasensitive sensing performance, superior sensing stability, reliable stretchability, and robust portability to be potentially integrated into wearable electronics for real-time monitoring of environment safety and human healthcare. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Hydrometeorological network for flood monitoring and modeling
Efstratiadis, Andreas; Koussis, Antonis D.; Lykoudis, Spyros; Koukouvinos, Antonis; Christofides, Antonis; Karavokiros, George; Kappos, Nikos; Mamassis, Nikos; Koutsoyiannis, Demetris
2013-08-01
Due to its highly fragmented geomorphology, Greece comprises hundreds of small- to medium-size hydrological basins, in which often the terrain is fairly steep and the streamflow regime ephemeral. These are typically affected by flash floods, occasionally causing severe damages. Yet, the vast majority of them lack flow-gauging infrastructure providing systematic hydrometric data at fine time scales. This has obvious impacts on the quality and reliability of flood studies, which typically use simplistic approaches for ungauged basins that do not consider local peculiarities in sufficient detail. In order to provide a consistent framework for flood design and to ensure realistic predictions of the flood risk -a key issue of the 2007/60/EC Directive- it is essential to improve the monitoring infrastructures by taking advantage of modern technologies for remote control and data management. In this context and in the research project DEUCALION, we have recently installed and are operating, in four pilot river basins, a telemetry-based hydro-meteorological network that comprises automatic stations and is linked to and supported by relevant software. The hydrometric stations measure stage, using 50-kHz ultrasonic pulses or piezometric sensors, or both stage (piezometric) and velocity via acoustic Doppler radar; all measurements are being temperature-corrected. The meteorological stations record air temperature, pressure, relative humidity, wind speed and direction, and precipitation. Data transfer is made via GPRS or mobile telephony modems. The monitoring network is supported by a web-based application for storage, visualization and management of geographical and hydro-meteorological data (ENHYDRIS), a software tool for data analysis and processing (HYDROGNOMON), as well as an advanced model for flood simulation (HYDROGEIOS). The recorded hydro-meteorological observations are accessible over the Internet through the www-application. The system is operational and its
A computational model of hemodynamic parameters in cortical capillary networks.
Safaeian, Navid; Sellier, Mathieu; David, Tim
2011-02-21
The analysis of hemodynamic parameters and functional reactivity of cerebral capillaries is still controversial. To assess the hemodynamic parameters in the cortical capillary network, a generic model was created using 2D voronoi tessellation in which each edge represents a capillary segment. This method is capable of creating an appropriate generic model of cerebral capillary network relating to each part of the brain cortex because the geometric model is able to vary the capillary density. The modeling presented here is based on morphometric parameters extracted from physiological data of the human cortex. The pertinent hemodynamic parameters were obtained by numerical simulation based on effective blood viscosity as a function of hematocrit and microvessel diameter, phase separation and plasma skimming effects. The hemodynamic parameters of capillary networks with two different densities (consistent with the variation of the morphometric data in the human cortical capillary network) were analyzed. The results show pertinent hemodynamic parameters for each model. The heterogeneity (coefficient variation) and the mean value of hematocrits, flow rates and velocities of the both network models were specified. The distributions of blood flow throughout the both models seem to confirm the hypothesis in which all capillaries in a cortical network are recruited at rest (normal condition). The results also demonstrate a discrepancy of the network resistance between two models, which are derived from the difference in the number density of capillary segments between the models. Copyright Â© 2010 Elsevier Ltd. All rights reserved.
A modelling and reasoning framework for social networks policies
Governatori, Guido; Iannella, Renato
2011-02-01
Policy languages (such as privacy and rights) have had little impact on the wider community. Now that social networks have taken off, the need to revisit policy languages and realign them towards social networks requirements has become more apparent. One such language is explored as to its applicability to the social networks masses. We also argue that policy languages alone are not sufficient and thus they should be paired with reasoning mechanisms to provide precise and unambiguous execution models of the policies. To this end, we propose a computationally oriented model to represent, reason with and execute policies for social networks.
Modelling and implementing electronic health records in Denmark
DEFF Research Database (Denmark)
Bernstein, Knut; Rasmussen, Morten Bruun; Vingtoft, Søren
2003-01-01
The Danish Health IT strategy points out that integration between electronic health records (EHR) systems has a high priority. This paper reporst reports new tendencies in modelling and integration platforms globally and how this is reflected in the natinal development....
Electronic Modeling and Design for Extreme Temperatures Project
National Aeronautics and Space Administration — We are developing CAD tools, models and methodologies for electronics design for circuit operation in extreme environments with focus on very low temperatures...
Electronic field emission models beyond the Fowler-Nordheim one
Lepetit, Bruno
2017-12-01
We propose several quantum mechanical models to describe electronic field emission from first principles. These models allow us to correlate quantitatively the electronic emission current with the electrode surface details at the atomic scale. They all rely on electronic potential energy surfaces obtained from three dimensional density functional theory calculations. They differ by the various quantum mechanical methods (exact or perturbative, time dependent or time independent), which are used to describe tunneling through the electronic potential energy barrier. Comparison of these models between them and with the standard Fowler-Nordheim one in the context of one dimensional tunneling allows us to assess the impact on the accuracy of the computed current of the approximations made in each model. Among these methods, the time dependent perturbative one provides a well-balanced trade-off between accuracy and computational cost.
Constitutive Modelling of INCONEL 718 using Artificial Neural Network
Abiriand Bhekisipho Twala, Olufunminiyi
2017-08-01
Artificial neural network is used to model INCONEL 718 in this paper. The model accounts for precipitate hardening in the alloy. The input variables for the neural network model are strain, strain rate, temperature and microstructure state. The output variable is the flow stress. The early stopping technique is combined with Bayesian regularization process in training the network. Sample and non-sample measurement data were taken from the literature. The model predictions of flow stress of the alloy are in good agreement with experimental measurements.
Modeling Temporal Evolution and Multiscale Structure in Networks
DEFF Research Database (Denmark)
Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard
2013-01-01
-point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights......Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change...
Gallagher, H. Colin; Robins, Garry
2015-01-01
As part of the shift within second language acquisition (SLA) research toward complex systems thinking, researchers have called for investigations of social network structure. One strand of social network analysis yet to receive attention in SLA is network statistical models, whereby networks are explained in terms of smaller substructures of…
Hu, Scott B; Wong, Deborah J L; Correa, Aditi; Li, Ning; Deng, Jane C
2016-01-01
Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR) along with a machine learning algorithm called a neural network to develop a prediction model that would increase the predictive accuracy and decrease false alarm rates. Retrospective cohort study. The hematologic malignancy unit in an academic medical center in the United States. Adult patients admitted to the hematologic malignancy unit from 2009 to 2010. None. Vital signs and laboratory values were obtained from the electronic medical record system and then used as predictors (features). A neural network was used to build a model to predict clinical deterioration events (ICU transfer and cardiac arrest). The performance of the neural network model was compared to the VitalPac Early Warning Score (ViEWS). Five hundred sixty five consecutive total admissions were available with 43 admissions resulting in clinical deterioration. Using simulation, the neural network outperformed the ViEWS model with a positive predictive value of 82% compared to 24%, respectively. We developed and tested a neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit. Our neural network model outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised. This system can be readily implemented in a real-time fashion in existing EMR systems.
Modeling the propagation of mobile malware on complex networks
Liu, Wanping; Liu, Chao; Yang, Zheng; Liu, Xiaoyang; Zhang, Yihao; Wei, Zuxue
2016-08-01
In this paper, the spreading behavior of malware across mobile devices is addressed. By introducing complex networks to model mobile networks, which follows the power-law degree distribution, a novel epidemic model for mobile malware propagation is proposed. The spreading threshold that guarantees the dynamics of the model is calculated. Theoretically, the asymptotic stability of the malware-free equilibrium is confirmed when the threshold is below the unity, and the global stability is further proved under some sufficient conditions. The influences of different model parameters as well as the network topology on malware propagation are also analyzed. Our theoretical studies and numerical simulations show that networks with higher heterogeneity conduce to the diffusion of malware, and complex networks with lower power-law exponents benefit malware spreading.
Network Inoculation: Heteroclinics and phase transitions in an epidemic model
Yang, Hui; Gross, Thilo
2016-01-01
In epidemiological modelling, dynamics on networks, and in particular adaptive and heterogeneous networks have recently received much interest. Here we present a detailed analysis of a previously proposed model that combines heterogeneity in the individuals with adaptive rewiring of the network structure in response to a disease. We show that in this model qualitative changes in the dynamics occur in two phase transitions. In a macroscopic description one of these corresponds to a local bifurcation whereas the other one corresponds to a non-local heteroclinic bifurcation. This model thus provides a rare example of a system where a phase transition is caused by a non-local bifurcation, while both micro- and macro-level dynamics are accessible to mathematical analysis. The bifurcation points mark the onset of a behaviour that we call network inoculation. In the respective parameter region exposure of the system to a pathogen will lead to an outbreak that collapses, but leaves the network in a configuration wher...
Business Mododelling of Electronic Brokerage
DEFF Research Database (Denmark)
Skouby, Knud Erik; Øst, Alexander Gorm
1998-01-01
The central task of Business Modelling is to develop a theoretical techno-economic reference model for analyses of the value chain in the production of brokerage services in electronic networks......The central task of Business Modelling is to develop a theoretical techno-economic reference model for analyses of the value chain in the production of brokerage services in electronic networks...
Random field Ising model and community structure in complex networks
Son, S.-W.; Jeong, H.; Noh, J. D.
2006-04-01
We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)
Development of models for exchange of electronic documents
Energy Technology Data Exchange (ETDEWEB)
Glavev, Victor, E-mail: viktor.glavev@gmail.com [Technical University of Sofia, Faculty of Applied Mathematics and Informatics, 8, Kl.Ohridski Blvd., 1000 Sofia (Bulgaria)
2014-11-18
The report presents a model for exchange of electronic documents between different government administrations. It defines electronic messages that are transmitted between them and the way that messages should be processed by software systems. The proposed approach is sufficiently general and allows use of the best applicable information technologies such as data presentation structures and communication protocols. Within the study, a simple implementation of the model is implemented and deployed in various government administrations in Republic of Bulgaria.
Modeling Electronic Circular Dichroism within the Polarizable Embedding Approach
DEFF Research Database (Denmark)
Nørby, Morten S; Olsen, Jógvan Magnus Haugaard; Steinmann, Casper
2017-01-01
We present a systematic investigation of the key components needed to model single chromophore electronic circular dichroism (ECD) within the polarizable embedding (PE) approach. By relying on accurate forms of the embedding potential, where especially the inclusion of local field effects...... sampling. We show that a significant number of snapshots are needed to avoid artifacts in the calculated electronic circular dichroism parameters due to insufficient configurational sampling, thus highlighting the efficiency of the PE model....
DEFF Research Database (Denmark)
Kwon, Jun Bum; Wang, Xiongfei; Blaabjerg, Frede
2017-01-01
. Through this method, the required computation time and CPU memory can be reduced, where this faster simulation can be an advantage of a large network simulation. Besides, the achieved results show the same results as the non-linear time-domain simulation. Furthermore, the HSS modeling can describe how...... with different switching frequency or harmonics from ac-dc converters makes that harmonics and frequency coupling are both problems of ac system and challenges of dc system. This paper presents a modeling and simulation method for a large dc power electronic system by using Harmonic State Space (HSS) modeling...
Dynamical complexity in the perception-based network formation model
Jo, Hang-Hyun; Moon, Eunyoung
2016-12-01
Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider both homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erdős-Rényi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is also discussed.
Superconductor Microwave Kinetic Inductance Detectors: System Model of the Readout Electronics
Directory of Open Access Journals (Sweden)
F. Alimenti
2009-06-01
Full Text Available This paper deals with the readout electronics needed by superconductor Microwave Kinetic Inductance Detectors (MKIDs. MKIDs are typically implemented in the form of cryogenic-cooled high quality factor microwave resonator. The natural frequency of these resonators changes as a millimeter or sub-millimeter wave radiation impinges on the resonator itself. A quantitative system model of the readout electronics (very similar to that of a vector network analyzer has been implemented under ADS environment and tested by several simulation experiments. The developed model is a tool to further optimize the readout electronic and to design the frequency allocation of parallel-connected MKIDs resonators. The applications of MKIDs will be in microwave and millimeter-wave radiometric imaging as well as in radio-astronomy focal plane arrays.
Nonlinear model for thermal effects in free-electron lasers
Peter, Eduardo Alcides; Endler, Antônio; Rizzato, Felipe Barbedo
2014-01-01
In the present work, we extend results of a previous paper [Peter et al., Phys. Plasmas 20, 12 3104 (2013)] and develop a semi-analytical model to account for thermal effects on the nonlinear dynamics of the electron beam in free-electron lasers. We relax the condition of a cold electron beam but still use the concept of compressibility, now associated with a warm beam model, to evaluate the time scale for saturation and the peak laser intensity in high-gain regimes. Although vanishing compre...
Huisman, Tijs; Boucherie, Richardus J.; van Dijk, N.M.
2002-01-01
The performance of new railway networks cannot be measured or simulated, as no detailed train schedules are available. Railway infrastructure and capacities are to be determined long before the actual traffic is known. This paper therefore proposes a solvable queueing network model to compute
McGranaghan, Ryan M.; Mannucci, Anthony J.; Verkhoglyadova, Olga; Malik, Nishant
2017-07-01
We present the first complex network theory-based analysis of high-latitude total electron content (TEC) data, including dependencies on interplanetary magnetic field (IMF) clock angle and hemisphere. We examine several network measures to quantify the spatiotemporal correlation patterns in the TEC data for winter and summer months in 2016. We find that significant structure exists in the correlation patterns, distinguishing the dayside and nightside ionosphere, and specific features in the high latitudes such as the polar cap and auroral oval, including the cusp and ionospheric foot points of magnetospheric boundary layers. These features vary with the IMF, exhibiting a strong dependence on the north-south direction and generally larger variations during the winter months in both hemispheres. Our exploratory results suggest that network analysis of TEC data can be used to study characteristic ionospheric spatial scales at high latitudes, thereby extending the utility of these data. We explore mesoscale and large scale (greater than tens of kilometers and greater than hundreds of kilometers, respectively) as a function of winter/summer season, hemisphere, and IMF direction and conclude that the relative importance of different ionospheric scales is not a constant relationship. Together with an identification of important areas of future work, our findings provide a foundation for the application of network analysis techniques to ionospheric TEC. Our results suggest that network analysis can reveal new physical connections in the ionospheric system.
Stochastic modelling and control of communication networks
Zuraniewski, P.W.
2011-01-01
The unprecedented growth of the Information Technologies sector observed within the past years creates an excellent opportunity to conduct new, exciting and interdisciplinary research. Increasing complexity of the communication networks calls for incorporating rigorously developed and reliable
Analysis and Comparison of Typical Models within Distribution Network Design
DEFF Research Database (Denmark)
Jørgensen, Hans Jacob; Larsen, Allan; Madsen, Oli B.G.
This paper investigates the characteristics of typical optimisation models within Distribution Network Design. During the paper fourteen models known from the literature will be thoroughly analysed. Through this analysis a schematic approach to categorisation of distribution network design models...... for educational purposes. Furthermore, the paper can be seen as a practical introduction to network design modelling as well as a being an art manual or recipe when constructing such a model....... are covered in the categorisation include fixed vs. general networks, specialised vs. general nodes, linear vs. nonlinear costs, single vs. multi commodity, uncapacitated vs. capacitated activities, single vs. multi modal and static vs. dynamic. The models examined address both strategic and tactical planning...
Baig, Christine Keyes; Duhamel, Jean
2010-11-11
The present study reports on the applicability of the fluorescence blob model (FBM) to analyze the complex fluorescence decays obtained with DNA-intercalated ethidium bromide (EB) as it transfers an electron to copper cations bound to the DNA helix. Traditionally, the information retrieved about the electron transfer process taking place between an electron donor intercalated in DNA and an electron acceptor physically and randomly bound to DNA has been limited due to the distribution of distances that quenching can occur over, which leads to a distribution of rate constants resulting in complex fluorescence decays. These complications can be overcome by analyzing the fluorescence data with a fluorescence blob model (FBM) that allows for the study of fluorescence quenching between fluorophores and quenchers randomly spaced along a polymeric backbone. The fluorescence decays obtained for EB intercalated between two DNA base pairs (bp) as it transfers an electron to copper randomly bound to the DNA were well fit with the FBM. In the FBM analysis, electron transfer is characterized by the size of a blob in term of base pairs, N(blob), over which electron transfer occurs, as well as the rate constant of electron transfer inside a blob, k(blob). The present work demonstrates that electron transfer between intercalated EB and randomly bound copper occurs over an average distance that increases with increasing duplex length up to a duplex length of 12 bp, beyond which the distance over which electron transfer occurs remains constant with duplex length and equals 10.8 ± 0.4 bp.
Information Dynamics in Networks: Models and Algorithms
2016-09-13
ICDCS). 29-JUN-15, Columbus, OH, USA. : , . Value-Based Network Externalities and Optimal Auction Design, Conference on Web and Internet Economics...NAME Total Number: NAME Total Number: PERCENT_SUPPORTEDNAME FTE Equivalent: Total Number: ...... ...... Inventions (DD882) Scientific Progress In...Value-based network externalities and optimal auction design. In Web and Internet Economics - 10th International Conference, WINE 2014, Beijing, China, December 14-17, pages 147–160, 2014. 6
Home-Network Security Model in Ubiquitous Environment
Dong-Young Yoo; Jong-Whoi Shin; Jin-Young Choi
2007-01-01
Social interest and demand on Home-Network has been increasing greatly. Although various services are being introduced to respond to such demands, they can cause serious security problems when linked to the open network such as Internet. This paper reviews the security requirements to protect the service users with assumption that the Home-Network environment is connected to Internet and then proposes the security model based on the requirement. The proposed security mode...
A Three-Dimensional Computational Model of Collagen Network Mechanics
Lee, Byoungkoo; Zhou, Xin; Riching, Kristin; Eliceiri, Kevin W.; Keely, Patricia J.; Guelcher, Scott A.; Weaver, Alissa M.; Jiang, Yi
2014-01-01
Extracellular matrix (ECM) strongly influences cellular behaviors, including cell proliferation, adhesion, and particularly migration. In cancer, the rigidity of the stromal collagen environment is thought to control tumor aggressiveness, and collagen alignment has been linked to tumor cell invasion. While the mechanical properties of collagen at both the single fiber scale and the bulk gel scale are quite well studied, how the fiber network responds to local stress or deformation, both structurally and mechanically, is poorly understood. This intermediate scale knowledge is important to understanding cell-ECM interactions and is the focus of this study. We have developed a three-dimensional elastic collagen fiber network model (bead-and-spring model) and studied fiber network behaviors for various biophysical conditions: collagen density, crosslinker strength, crosslinker density, and fiber orientation (random vs. prealigned). We found the best-fit crosslinker parameter values using shear simulation tests in a small strain region. Using this calibrated collagen model, we simulated both shear and tensile tests in a large linear strain region for different network geometry conditions. The results suggest that network geometry is a key determinant of the mechanical properties of the fiber network. We further demonstrated how the fiber network structure and mechanics evolves with a local formation, mimicking the effect of pulling by a pseudopod during cell migration. Our computational fiber network model is a step toward a full biomechanical model of cellular behaviors in various ECM conditions. PMID:25386649
Hybrid network defense model based on fuzzy evaluation.
Cho, Ying-Chiang; Pan, Jen-Yi
2014-01-01
With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.
Neurocognitive networks: findings, models, and theory.
Meehan, Timothy P; Bressler, Steven L
2012-11-01
Through its early history, cognitive neuroscience largely followed a modular paradigm wherein high-level cognitive functions were mapped onto locally segregated brain regions. However, recent evidence drives a continuing shift away from modular theories of cognitive brain function, and toward theories which hold that cognition arises from the integrated activity of large-scale, distributed networks of brain regions. A growing consensus favors the fundamental concept of this new paradigm: the large-scale cognitive brain network, or neurocognitive network. This consensus was the motivation for Neurocognitive Networks 2010 (NCN 2010), a conference sponsored by the Cognitive Neuroscience Program of the National Science Foundation, organized by Drs. Steven Bressler and Craig Richter of Florida Atlantic University (FAU), and held at FAU in Boca Raton, FL on January 29-30, 2010. NCN 2010 gathered together some of today's leading investigators of neurocognitive networks. This paper serves to review their presentations as they relate to the paradigm of neurocognitive networks, as well as to compile the emergent themes, questions, and possible future research directions that arose from the conference. Copyright © 2012 Elsevier Ltd. All rights reserved.
Computer modeling of electron and proton transport in chloroplasts.
Tikhonov, Alexander N; Vershubskii, Alexey V
2014-07-01
Photosynthesis is one of the most important biological processes in biosphere, which provides production of organic substances from atmospheric CO2 and water at expense of solar energy. In this review, we contemplate computer models of oxygenic photosynthesis in the context of feedback regulation of photosynthetic electron transport in chloroplasts, the energy-transducing organelles of the plant cell. We start with a brief overview of electron and proton transport processes in chloroplasts coupled to ATP synthesis and consider basic regulatory mechanisms of oxygenic photosynthesis. General approaches to computer simulation of photosynthetic processes are considered, including the random walk models of plastoquinone diffusion in thylakoid membranes and deterministic approach to modeling electron transport in chloroplasts based on the mass action law. Then we focus on a kinetic model of oxygenic photosynthesis that includes key stages of the linear electron transport, alternative pathways of electron transfer around photosystem I (PSI), transmembrane proton transport and ATP synthesis in chloroplasts. This model includes different regulatory processes: pH-dependent control of the intersystem electron transport, down-regulation of photosystem II (PSII) activity (non-photochemical quenching), the light-induced activation of the Bassham-Benson-Calvin (BBC) cycle. The model correctly describes pH-dependent feedback control of electron transport in chloroplasts and adequately reproduces a variety of experimental data on induction events observed under different experimental conditions in intact chloroplasts (variations of CO2 and O2 concentrations in atmosphere), including a complex kinetics of P700 (primary electron donor in PSI) photooxidation, CO2 consumption in the BBC cycle, and photorespiration. Finally, we describe diffusion-controlled photosynthetic processes in chloroplasts within the framework of the model that takes into account complex architecture of
Modeling of Bandwidth Aggregation over Heterogeneous Wireless Access Networks
DEFF Research Database (Denmark)
Popovska Avramova, Andrijana; Dittmann, Lars
2012-01-01
Motivated by the multihomming capability of the mobile devices and the fact that the heterogeneous wireless access networks overlap in coverage, mobile operators are looking for solutions that will benefit by simultaneous use of the available multiple access interfaces. Multipath or multilink...... applications. The analysis is performed on a multipath model developed with OPNET Modeler, which is an advanced research tool that supports modeling and integration of various kinds of built-in networks....
When individual behaviour matters: homogeneous and network models in epidemiology
Bansal, Shweta; Grenfell, Bryan T.; Meyers, Lauren Ancel
2007-01-01
Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to qua...
Template and Model Driven Development of Standardized Electronic Health Records.
Kropf, Stefan; Chalopin, Claire; Denecke, Kerstin
2015-01-01
Digital patient modeling targets the integration of distributed patient data into one overarching model. For this integration process, both a theoretical standard-based model and information structures combined with concrete instructions in form of a lightweight development process of single standardized Electronic Health Records (EHRs) are needed. In this paper, we introduce such a process along side a standard-based architecture. It allows the modeling and implementation of EHRs in a lightweight Electronic Health Record System (EHRS) core. The approach is demonstrated and tested by a prototype implementation. The results show that the suggested approach is useful and facilitates the development of standardized EHRSs.
Developing a model for application of electronic banking based on electronic trust
Directory of Open Access Journals (Sweden)
Amir Hooshang Nazarpoori
2014-05-01
Full Text Available This study develops a model for application of electronic banking based on electronic trust among costumers of Day bank in KhoramAbad city. A sample of 150 people was selected based on stratified random sampling. Questionnaires were used for the investigation. Results indicate that technology-based factors, user-based factors, and trust had negative relationships with perceived risk types including financial, functional, personal, and private. Moreover, trust including trust in system and trust in bank had a positive relationship with tendency to use and real application of electronic banking.
Iannelli, Luigi
2012-01-01
The increased efficiency and quality constraints imposed on electrical energy systems have inspired a renewed research interest in the study of formal approaches to the analysis and control of power electronics converters. Switched systems represent a useful framework for modeling these converters and the peculiarities of their operating conditions and control goals justify the specific classification of “switched electronic systems”. Indeed, idealized switched models of power converters introduce problems not commonly encountered when analyzing generic switched models or non-switched electrical networks. In that sense the analysis of switched electronic systems represents a source for new ideas and benchmarks for switched and hybrid systems generally. Dynamics and Control of Switched Electronic Systems draws on the expertise of an international group of expert contributors to give an overview of recent advances in the modeling, simulation and control of switched electronic systems. The reader is provided...
Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen
2017-09-05
In several years, deep learning is a modern machine learning technique using in a variety of fields with state-of-the-art performance. Therefore, utilization of deep learning to enhance performance is also an important solution for current bioinformatics field. In this study, we try to use deep learning via convolutional neural networks and position specific scoring matrices to identify electron transport proteins, which is an important molecular function in transmembrane proteins. Our deep learning method can approach a precise model for identifying of electron transport proteins with achieved sensitivity of 80.3%, specificity of 94.4%, and accuracy of 92.3%, with MCC of 0.71 for independent dataset. The proposed technique can serve as a powerful tool for identifying electron transport proteins and can help biologists understand the function of the electron transport proteins. Moreover, this study provides a basis for further research that can enrich a field of applying deep learning in bioinformatics. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Mathematics of epidemics on networks from exact to approximate models
Kiss, István Z; Simon, Péter L
2017-01-01
This textbook provides an exciting new addition to the area of network science featuring a stronger and more methodical link of models to their mathematical origin and explains how these relate to each other with special focus on epidemic spread on networks. The content of the book is at the interface of graph theory, stochastic processes and dynamical systems. The authors set out to make a significant contribution to closing the gap between model development and the supporting mathematics. This is done by: Summarising and presenting the state-of-the-art in modeling epidemics on networks with results and readily usable models signposted throughout the book; Presenting different mathematical approaches to formulate exact and solvable models; Identifying the concrete links between approximate models and their rigorous mathematical representation; Presenting a model hierarchy and clearly highlighting the links between model assumptions and model complexity; Providing a reference source for advanced undergraduate...
Networking automation of ECI’s G-204 electronic engineering laboratory
Directory of Open Access Journals (Sweden)
Hernán Paz Penagos
2010-04-01
Full Text Available Increased use (by students and teachers of the “Escuela Colombiana de Ingeniería Julio Garavito” Electronic Engineering laboratories during the last year has congested access to these laboratories; the School’s Electronic Engineering (Ecitronica programme’s applied Electronic studies’ centre research group thus proposed, designed and developed a research prolect taking advantage of G building’s electrical distribution to offer access facilities, laboratory equipment control, energy saving and improved service quality. The G-204 laboratory’s network sys- tem will have an access control subsystem with client–main computer architecture. The latter consists of a user, schedule, group and work-bank database; the user is connected from any computer (client to the main computer through Internet to reserve his/her turn at laboratory practice by selecting the schedule, group, work-bank, network type required (1Ф or 3Ф and registering co-workers. Access to the G-204 laboratory on the day and time of practice is made by means of an intelligent card reader. Information of public interest produced and controlled by the main computer is displayed on three LCD screens located on one of G building’s second floor walls, as is an electronic clock. The G-204 laboratory temperature and time are continually updated. Work-banks are enabled or disabled by the main computer; the work banks are provided with power (beginning of practice or disconnected (end of practice or due to eventualities to protect the equipment, save energy, facilitate monitors and supervise the logistics of the state of the equipment at the end of each practice. The research group was organised into Transmission Line and Applications sub-groups. Power Line Communications PLC technology was used for to exploring digital modulation alternatives, coding and detecting errors, coupling, data transmission protocols and new applications, all based on channel estimation (networking
Multi-mode clustering model for hierarchical wireless sensor networks
Hu, Xiangdong; Li, Yongfu; Xu, Huifen
2017-03-01
The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.
An extended differentiated optical services model for WDM optical networks
Ouyang, Yong; Zeng, Qingji; Wei, Wei
2004-04-01
The need to provide QoS-guaranteed services in the WDM optical networks is becoming increasingly important because of a variety of candidate client networks (e.g., IP, ATM, SONET/SDH) and the requirement for QoS-delivery within the transport layers. This article addresses the QoS problem and presents a framework of QoS provisioning in the WDM optical network. We first characterize the QoS problem in the WDM optical network by comparing with that in the traditional networks. Then we propose a QoS service model in the optical domain called extended differentiated optical services (E-DoS) model based on a set of optical parameters that captures the quality, the reliability and the priority of an optical connection. Each component of the E-DoS model has been analyzed in detail in this article.
Exponential random graph models for networks with community structure.
Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian
2013-09-01
Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.
Numerical model of the plasma formation at electron beam welding
Trushnikov, D. N.; Mladenov, G. M.
2015-01-01
The model of plasma formation in the keyhole in liquid metal as well as above the electron beam welding zone is described. The model is based on solution of two equations for the density of electrons and the mean electron energy. The mass transfer of heavy plasma particles (neutral atoms, excited atoms, and ions) is taken into account in the analysis by the diffusion equation for a multicomponent mixture. The electrostatic field is calculated using the Poisson equation. Thermionic electron emission is calculated for the keyhole wall. The ionization intensity of the vapors due to beam electrons and high-energy secondary and backscattered electrons is calibrated using the plasma parameters when there is no polarized collector electrode above the welding zone. The calculated data are in good agreement with experimental data. Results for the plasma parameters for excitation of a non-independent discharge are given. It is shown that there is a need to take into account the effect of a strong electric field near the keyhole walls on electron emission (the Schottky effect) in the calculation of the current for a non-independent discharge (hot cathode gas discharge). The calculated electron drift velocities are much bigger than the velocity at which current instabilities arise. This confirms the hypothesis for ion-acoustic instabilities, observed experimentally in previous research.
Task Flow Modeling in Electronic Business Environments
Directory of Open Access Journals (Sweden)
2007-01-01
Full Text Available In recent years, internet based commerce has developed as a new paradigm. Many factors such as "at home delivery", easy ordering, and usually lower prices contributed to the success of the e-commerce. However, more recently, companies realized that one of the major factors in having a successful internet based business is the design of a user interface that is in concordance with the users' expectations, which includes both functionality and user friendly features. The func-tionality feature of an e-business interface is one of the most important elements when discussing about a specific internet based business. In our paper, we present methods to model task flows for e-business interfaces. We strengthen our study with the design modeling of a practical scenario that may appear in an on-line commercial environment.
A Model for an Electronic Information Marketplace
Directory of Open Access Journals (Sweden)
Wei Ge
2005-11-01
Full Text Available As the information content on the Internet increases, the task of locating desired information and assessing its quality becomes increasingly difficult. This development causes users to be more willing to pay for information that is focused on specific issues, verifiable, and available upon request. Thus, the nature of the Internet opens up the opportunity for information trading. In this context, the Internet cannot only be used to close the transaction, but also to deliver the product - desired information - to the user. Early attempts to implement such business models have fallen short of expectations. In this paper, we discuss the limitations of such practices and present a modified business model for information trading, which uses a reverse auction approach together with a multiple-buyer price discovery process
Constraints and entropy in a model of network evolution
Tee, Philip; Wakeman, Ian; Parisis, George; Dawes, Jonathan; Kiss, István Z.
2017-11-01
Barabási-Albert's "Scale Free" model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the "Scale Free" model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the "Scale Free" and "constraints" model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics.
Implicit methods for qualitative modeling of gene regulatory networks.
Garg, Abhishek; Mohanram, Kartik; De Micheli, Giovanni; Xenarios, Ioannis
2012-01-01
Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.
Stochastic S-system modeling of gene regulatory network.
Chowdhury, Ahsan Raja; Chetty, Madhu; Evans, Rob
2015-10-01
Microarray gene expression data can provide insights into biological processes at a system-wide level and is commonly used for reverse engineering gene regulatory networks (GRN). Due to the amalgamation of noise from different sources, microarray expression profiles become inherently noisy leading to significant impact on the GRN reconstruction process. Microarray replicates (both biological and technical), generated to increase the reliability of data obtained under noisy conditions, have limited influence in enhancing the accuracy of reconstruction . Therefore, instead of the conventional GRN modeling approaches which are deterministic, stochastic techniques are becoming increasingly necessary for inferring GRN from noisy microarray data. In this paper, we propose a new stochastic GRN model by investigating incorporation of various standard noise measurements in the deterministic S-system model. Experimental evaluations performed for varying sizes of synthetic network, representing different stochastic processes, demonstrate the effect of noise on the accuracy of genetic network modeling and the significance of stochastic modeling for GRN reconstruction . The proposed stochastic model is subsequently applied to infer the regulations among genes in two real life networks: (1) the well-studied IRMA network, a real-life in-vivo synthetic network constructed within the Saccharomyces cerevisiae yeast, and (2) the SOS DNA repair network in Escherichia coli.
Metrics for evaluating performance and uncertainty of Bayesian network models
Bruce G. Marcot
2012-01-01
This paper presents a selected set of existing and new metrics for gauging Bayesian network model performance and uncertainty. Selected existing and new metrics are discussed for conducting model sensitivity analysis (variance reduction, entropy reduction, case file simulation); evaluating scenarios (influence analysis); depicting model complexity (numbers of model...
System-level Modeling of Wireless Integrated Sensor Networks
DEFF Research Database (Denmark)
Virk, Kashif M.; Hansen, Knud; Madsen, Jan
2005-01-01
Wireless integrated sensor networks have emerged as a promising infrastructure for a new generation of monitoring and tracking applications. In order to efficiently utilize the extremely limited resources of wireless sensor nodes, accurate modeling of the key aspects of wireless sensor networks...... is necessary so that system-level design decisions can be made about the hardware and the software (applications and real-time operating system) architecture of sensor nodes. In this paper, we present a SystemC-based abstract modeling framework that enables system-level modeling of sensor network behavior...... by modeling the applications, real-time operating system, sensors, processor, and radio transceiver at the sensor node level and environmental phenomena, including radio signal propagation, at the sensor network level. We demonstrate the potential of our modeling framework by simulating and analyzing a small...
Agent Based Modeling on Organizational Dynamics of Terrorist Network
Directory of Open Access Journals (Sweden)
Bo Li
2015-01-01
Full Text Available Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.
Electron correlations in narrow energy bands: modified polar model approach
Directory of Open Access Journals (Sweden)
L. Didukh
2008-09-01
Full Text Available The electron correlations in narrow energy bands are examined within the framework of the modified form of polar model. This model permits to analyze the effect of strong Coulomb correlation, inter-atomic exchange and correlated hopping of electrons and explain some peculiarities of the properties of narrow-band materials, namely the metal-insulator transition with an increase of temperature, nonlinear concentration dependence of Curie temperature and peculiarities of transport properties of electronic subsystem. Using a variant of generalized Hartree-Fock approximation, the single-electron Green's function and quasi-particle energy spectrum of the model are calculated. Metal-insulator transition with the change of temperature is investigated in a system with correlated hopping. Processes of ferromagnetic ordering stabilization in the system with various forms of electronic DOS are studied. The static conductivity and effective spin-dependent masses of current carriers are calculated as a function of electron concentration at various DOS forms. The correlated hopping is shown to cause the electron-hole asymmetry of transport and ferromagnetic properties of narrow band materials.
Towards a Social Networks Model for Online Learning & Performance
Chung, Kon Shing Kenneth; Paredes, Walter Christian
2015-01-01
In this study, we develop a theoretical model to investigate the association between social network properties, "content richness" (CR) in academic learning discourse, and performance. CR is the extent to which one contributes content that is meaningful, insightful and constructive to aid learning and by social network properties we…
Energy Model of Networks-on-Chip and a Bus
Wolkotte, P.T.; Smit, Gerardus Johannes Maria; Kavaldjiev, N.K.; Becker, Jens E.; Becker, Jürgen; Nurmi, J.; Takala, J.; Hamalainen, T.D.
2005-01-01
A Network-on-Chip (NoC) is an energy-efficient onchip communication architecture for Multi-Processor Systemon-Chip (MPSoC) architectures. In earlier papers we proposed two Network-on-Chip architectures based on packet-switching and circuit-switching. In this paper we derive an energy model for both
Modeling and optimization of an electric power distribution network ...
African Journals Online (AJOL)
EDNEPP) was solved by a mixed binary integer programming (MBIP) formulation of the network, where the steady-state operation of the network was modelled with non-linear mathematical expressions. The non-linear terms are linearized, using ...
Modelling flow dynamics in water distribution networks using ...
African Journals Online (AJOL)
Computational approaches can be used to detect leakages in water distribution networks. One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can ...
Water distribution network modelling of a small community using ...
African Journals Online (AJOL)
In this study a network model was constructed for the hydraulic analysis and design of a small community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using WaterCAD simulator. The analysis included a review of pressures, velocities and head loss gradients under steady state average ...
An artificial neural network based fast radiative transfer model for ...
Indian Academy of Sciences (India)
In the present study, a fast radiative transfer model using neural networks is proposed to simulate radiances corresponding to the wavenumbers of INSAT-3D. Realistic atmospheric temperature and humidity profiles have been used for training the network. Spectral response functions of GOES-13, a satellite similar in ...
Radio Channel Modelling for UAV Communication over Cellular Networks
DEFF Research Database (Denmark)
Amorim, Rafhael Medeiros de; Nguyen, Huan Cong; Mogensen, Preben Elgaard
2017-01-01
The main goal of this paper is to obtain models for path loss exponents and shadowing for the radio channel between airborne Unmanned Aerial Vehicles (UAVs) and cellular networks. In this pursuit, field measurements were conducted in live LTE networks at the 800 MHz frequency band, using a commer...
Distribution Of Wealth In A Network Model Of The Economy
Tao Ma; Holden, John G.; Serota, R. A.
2012-01-01
We show, analytically and numerically, that wealth distribution in the Bouchaud-M\\'ezard network model of the economy is described by a three-parameter generalized inverse gamma distribution. In the mean-field limit of a network with any two agents linked, it reduces to the inverse gamma distribution.
Water Distribution Network Modelling of a Small Community using ...
African Journals Online (AJOL)
Water Distribution Network Modelling of a Small Community using Watercad Simulator. ... Global Journal of Engineering Research ... with respect to pressure or available fire flow for the proposed service area and also that flow velocities are not excessive while head loss gradients in the network are within acceptable limits.
Capacity allocation in wireless communication networks - models and analyses
Litjens, Remco
2003-01-01
This monograph has concentrated on capacity allocation in cellular and Wireless Local Area Networks, primarily with a network operator’s perspective. In the introduc- tory chapter, a reference model has been proposed for the extensive suite of capacity allocation mechanisms that can be applied at
Line and lattice networks under deterministic interference models
Goseling, Jasper; Gastpar, Michael; Weber, Jos H.
Capacity bounds are compared for four different deterministic models of wireless networks, representing four different ways of handling broadcast and superposition in the physical layer. In particular, the transport capacity under a multiple unicast traffic pattern is studied for a 1-D network of
Energy Model of Networks-on-Chip and a Bus
Wolkotte, P.T.; Smit, Gerardus Johannes Maria; Kavaldjiev, N.K.; Becker, Jens E.; Becker, Jurgen
A Network-on-Chip (NoC) is an energy-efficient onchip communication architecture for Multi-Processor Systemon- Chip (MPSoC) architectures. In earlier papers we proposed two Network-on-Chip architectures based on packet-switching and circuit-switching. In this paper we derive an energy model for both
Modeling Verdict Outcomes Using Social Network Measures: The Watergate and Caviar Network Cases
2016-01-01
Modelling criminal trial verdict outcomes using social network measures is an emerging research area in quantitative criminology. Few studies have yet analyzed which of these measures are the most important for verdict modelling or which data classification techniques perform best for this application. To compare the performance of different techniques in classifying members of a criminal network, this article applies three different machine learning classifiers–Logistic Regression, Naïve Bayes and Random Forest–with a range of social network measures and the necessary databases to model the verdicts in two real–world cases: the U.S. Watergate Conspiracy of the 1970’s and the now–defunct Canada–based international drug trafficking ring known as the Caviar Network. In both cases it was found that the Random Forest classifier did better than either Logistic Regression or Naïve Bayes, and its superior performance was statistically significant. This being so, Random Forest was used not only for classification but also to assess the importance of the measures. For the Watergate case, the most important one proved to be betweenness centrality while for the Caviar Network, it was the effective size of the network. These results are significant because they show that an approach combining machine learning with social network analysis not only can generate accurate classification models but also helps quantify the importance social network variables in modelling verdict outcomes. We conclude our analysis with a discussion and some suggestions for future work in verdict modelling using social network measures. PMID:26824351
Non-consensus Opinion Models on Complex Networks
Li, Qian; Braunstein, Lidia A.; Wang, Huijuan; Shao, Jia; Stanley, H. Eugene; Havlin, Shlomo
2013-04-01
Social dynamic opinion models have been widely studied to understand how interactions among individuals cause opinions to evolve. Most opinion models that utilize spin interaction models usually produce a consensus steady state in which only one opinion exists. Because in reality different opinions usually coexist, we focus on non-consensus opinion models in which above a certain threshold two opinions coexist in a stable relationship. We revisit and extend the non-consensus opinion (NCO) model introduced by Shao et al. (Phys. Rev. Lett. 103:01870, 2009). The NCO model in random networks displays a second order phase transition that belongs to regular mean field percolation and is characterized by the appearance (above a certain threshold) of a large spanning cluster of the minority opinion. We generalize the NCO model by adding a weight factor W to each individual's original opinion when determining their future opinion (NCO W model). We find that as W increases the minority opinion holders tend to form stable clusters with a smaller initial minority fraction than in the NCO model. We also revisit another non-consensus opinion model based on the NCO model, the inflexible contrarian opinion (ICO) model (Li et al. in Phys. Rev. E 84:066101, 2011), which introduces inflexible contrarians to model the competition between two opinions in a steady state. Inflexible contrarians are individuals that never change their original opinion but may influence the opinions of others. To place the inflexible contrarians in the ICO model we use two different strategies, random placement and one in which high-degree nodes are targeted. The inflexible contrarians effectively decrease the size of the largest rival-opinion cluster in both strategies, but the effect is more pronounced under the targeted method. All of the above models have previously been explored in terms of a single network, but human communities are usually interconnected, not isolated. Because opinions propagate not
Implementing partnership-driven clinical federated electronic health record data sharing networks.
Stephens, Kari A; Anderson, Nicholas; Lin, Ching-Ping; Estiri, Hossein
2016-09-01
Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results. Two federated data sharing pilot architectures developed to support network-based research associated with the University of Washington's Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages. We found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility. An iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Talukdar, M.S.; Torsaeter, O. [Department of Petroleum Engineering and Applied Geophysics, Norwegian University of Science and Technology, Trondheim (Norway)
2002-05-01
We report the stochastic reconstruction of chalk pore networks from limited morphological information that may be readily extracted from 2D backscatter electron (BSE) images of the pore space. The reconstruction technique employs a simulated annealing (SA) algorithm, which can be constrained by an arbitrary number of morphological descriptors. Backscatter electron images of a high-porosity North Sea chalk sample are analyzed and the morphological descriptors of the pore space are determined. The morphological descriptors considered are the void-phase two-point probability function and lineal path function computed with or without the application of periodic boundary conditions (PBC). 2D and 3D samples have been reconstructed with different combinations of the descriptors and the reconstructed pore networks have been analyzed quantitatively to evaluate the quality of reconstructions. The results demonstrate that simulated annealing technique may be used to reconstruct chalk pore networks with reasonable accuracy using the void-phase two-point probability function and/or void-phase lineal path function. Void-phase two-point probability function produces slightly better reconstruction than the void-phase lineal path function. Imposing void-phase lineal path function results in slight improvement over what is achieved by using the void-phase two-point probability function as the only constraint. Application of periodic boundary conditions appears to be not critically important when reasonably large samples are reconstructed.
Hybrid modeling and empirical analysis of automobile supply chain network
Sun, Jun-yan; Tang, Jian-ming; Fu, Wei-ping; Wu, Bing-ying
2017-05-01
Based on the connection mechanism of nodes which automatically select upstream and downstream agents, a simulation model for dynamic evolutionary process of consumer-driven automobile supply chain is established by integrating ABM and discrete modeling in the GIS-based map. Firstly, the rationality is proved by analyzing the consistency of sales and changes in various agent parameters between the simulation model and a real automobile supply chain. Second, through complex network theory, hierarchical structures of the model and relationships of networks at different levels are analyzed to calculate various characteristic parameters such as mean distance, mean clustering coefficients, and degree distributions. By doing so, it verifies that the model is a typical scale-free network and small-world network. Finally, the motion law of this model is analyzed from the perspective of complex self-adaptive systems. The chaotic state of the simulation system is verified, which suggests that this system has typical nonlinear characteristics. This model not only macroscopically illustrates the dynamic evolution of complex networks of automobile supply chain but also microcosmically reflects the business process of each agent. Moreover, the model construction and simulation of the system by means of combining CAS theory and complex networks supplies a novel method for supply chain analysis, as well as theory bases and experience for supply chain analysis of auto companies.
When individual behaviour matters: homogeneous and network models in epidemiology.
Bansal, Shweta; Grenfell, Bryan T; Meyers, Lauren Ancel
2007-10-22
Heterogeneity in host contact patterns profoundly shapes population-level disease dynamics. Many epidemiological models make simplifying assumptions about the patterns of disease-causing interactions among hosts. In particular, homogeneous-mixing models assume that all hosts have identical rates of disease-causing contacts. In recent years, several network-based approaches have been developed to explicitly model heterogeneity in host contact patterns. Here, we use a network perspective to quantify the extent to which real populations depart from the homogeneous-mixing assumption, in terms of both the underlying network structure and the resulting epidemiological dynamics. We find that human contact patterns are indeed more heterogeneous than assumed by homogeneous-mixing models, but are not as variable as some have speculated. We then evaluate a variety of methodologies for incorporating contact heterogeneity, including network-based models and several modifications to the simple SIR compartmental model. We conclude that the homogeneous-mixing compartmental model is appropriate when host populations are nearly homogeneous, and can be modified effectively for a few classes of non-homogeneous networks. In general, however, network models are more intuitive and accurate for predicting disease spread through heterogeneous host populations.
Destabilization of Terrorist Networks through Argument Driven Hypothesis Model
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar
2007-01-01
Social network analysis has been used for quite some time to analyze and understand the behavior of nodes in the network. Theses nodes could be individuals or group of persons, events or organizations etc. Infact these nodes could be any thing importantly, these nodes propagate and obviously ha......) to predict a path for its destabilization. This network is selected to benchmark our proposed model framework. The results obtained with various network analysis shows that it works better than other analysis measures for example based on degree, betweeness and closeness etc. ...
Mutual Interference Models for CDMA Mobile Communication Networks
Directory of Open Access Journals (Sweden)
K. Hrudkay
2002-12-01
Full Text Available Nowadays we are witnesses of a huge development one of the mostprogressive communication technology - mobile networks. The mainproblem in these networks is an elimination of the mutual interference,which, mainly in non-orthogonal CDMA networks, is the principalobstacle for reaching high transmission rates The aim of thiscontribution is to give simplified view to mutual interference modelsfor orthogonal and non-orthogonal CDMA networks. The contribution isintended mainly for PhD. students to help them to obtain an orientationin such a complicated areas, as the interference models for CDMAnetworks are.
Neural network models: Insights and prescriptions from practical applications
Energy Technology Data Exchange (ETDEWEB)
Samad, T. [Honeywell Technology Center, Minneapolis, MN (United States)
1995-12-31
Neural networks are no longer just a research topic; numerous applications are now testament to their practical utility. In the course of developing these applications, researchers and practitioners have been faced with a variety of issues. This paper briefly discusses several of these, noting in particular the rich connections between neural networks and other, more conventional technologies. A more comprehensive version of this paper is under preparation that will include illustrations on real examples. Neural networks are being applied in several different ways. Our focus here is on neural networks as modeling technology. However, much of the discussion is also relevant to other types of applications such as classification, control, and optimization.
Chen, Duan; Wei, Guo-Wei
2010-01-01
The miniaturization of nano-scale electronic devices, such as metal oxide semiconductor field effect transistors (MOSFETs), has given rise to a pressing demand in the new theoretical understanding and practical tactic for dealing with quantum mechanical effects in integrated circuits. Modeling and simulation of this class of problems have emerged as an important topic in applied and computational mathematics. This work presents mathematical models and computational algorithms for the simulation of nano-scale MOSFETs. We introduce a unified two-scale energy functional to describe the electrons and the continuum electrostatic potential of the nano-electronic device. This framework enables us to put microscopic and macroscopic descriptions in an equal footing at nano scale. By optimization of the energy functional, we derive consistently-coupled Poisson-Kohn-Sham equations. Additionally, layered structures are crucial to the electrostatic and transport properties of nano transistors. A material interface model is proposed for more accurate description of the electrostatics governed by the Poisson equation. Finally, a new individual dopant model that utilizes the Dirac delta function is proposed to understand the random doping effect in nano electronic devices. Two mathematical algorithms, the matched interface and boundary (MIB) method and the Dirichlet-to-Neumann mapping (DNM) technique, are introduced to improve the computational efficiency of nano-device simulations. Electronic structures are computed via subband decomposition and the transport properties, such as the I-V curves and electron density, are evaluated via the non-equilibrium Green's functions (NEGF) formalism. Two distinct device configurations, a double-gate MOSFET and a four-gate MOSFET, are considered in our three-dimensional numerical simulations. For these devices, the current fluctuation and voltage threshold lowering effect induced by the discrete dopant model are explored. Numerical convergence
DEFF Research Database (Denmark)
Sindbæk, Søren Michael
2015-01-01
Long-distance communication has emerged as a particular focus for archaeologicalexploration using network theory, analysis, and modelling. The promise is apparentlyobvious: communication in the past doubtlessly had properties of complex, dynamicnetworks, and archaeological datasets almost certainly...... preserve patterns of thisinteraction. Formal network analysis and modelling holds the potential to identify anddemonstrate such patterns, where traditional methods often prove inadequate. Thearchaeological study of communication networks in the past, however, calls for radically different analytical......,and use patterns. This point is demonstrated with reference to a study of Viking-period communication in the North Sea region...
A Simplified Network Model for Travel Time Reliability Analysis in a Road Network
Directory of Open Access Journals (Sweden)
Kenetsu Uchida
2017-01-01
Full Text Available This paper proposes a simplified network model which analyzes travel time reliability in a road network. A risk-averse driver is assumed in the simplified model. The risk-averse driver chooses a path by taking into account both a path travel time variance and a mean path travel time. The uncertainty addressed in this model is that of traffic flows (i.e., stochastic demand flows. In the simplified network model, the path travel time variance is not calculated by considering all travel time covariance between two links in the network. The path travel time variance is calculated by considering all travel time covariance between two adjacent links in the network. Numerical experiments are carried out to illustrate the applicability and validity of the proposed model. The experiments introduce the path choice behavior of a risk-neutral driver and several types of risk-averse drivers. It is shown that the mean link flows calculated by introducing the risk-neutral driver differ as a whole from those calculated by introducing several types of risk-averse drivers. It is also shown that the mean link flows calculated by the simplified network model are almost the same as the flows calculated by using the exact path travel time variance.
Liu, Zugang
Network systems, including transportation and logistic systems, electric power generation and distribution networks as well as financial networks, provide the critical infrastructure for the functioning of our societies and economies. The understanding of the dynamic behavior of such systems is also crucial to national security and prosperity. The identification of new connections between distinct network systems is the inspiration for the research in this dissertation. In particular, I answer two questions raised by Beckmann, McGuire, and Winsten (1956) and Copeland (1952) over half a century ago, which are, respectively, how are electric power flows related to transportation flows and does money flow like water or electricity? In addition, in this dissertation, I achieve the following: (1) I establish the relationships between transportation networks and three other classes of complex network systems: supply chain networks, electric power generation and transmission networks, and financial networks with intermediation. The establishment of such connections provides novel theoretical insights as well as new pricing mechanisms, and efficient computational methods. (2) I develop new modeling frameworks based on evolutionary variational inequality theory that capture the dynamics of such network systems in terms of the time-varying flows and incurred costs, prices, and, where applicable, profits. This dissertation studies the dynamics of such network systems by addressing both internal competition and/or cooperation, and external changes, such as varying costs and demands. (3) I focus, in depth, on electric power supply chains. By exploiting the relationships between transportation networks and electric power supply chains, I develop a large-scale network model that integrates electric power supply chains and fuel supply markets. The model captures both the economic transactions as well as the physical transmission constraints. The model is then applied to the New
A DUAL NETWORK MODEL OF INTERLOCKING DIRECTORATES
Directory of Open Access Journals (Sweden)
Humphry Hung
2003-01-01
Full Text Available The article proposes an integrative framework for the study of interlocking directorates by using an approach that encompasses the concepts of multiple networks and resource endowment. This serves to integrate the traditional views of interorganizational linkages and intra-class cohesion. Through appropriate strategic analysis of relevant resource endowment of internal environment and external networks of organizations and corporate elites, this article argues that the selection of directors, if used effectively, can be adopted as a strategic device to enhance the corporation's overall performance.
ARCHITECTURES AND ALGORITHMS FOR COGNITIVE NETWORKS ENABLED BY QUALITATIVE MODELS
DEFF Research Database (Denmark)
Balamuralidhar, P.
2013-01-01
Complexity of communication networks is ever increasing and getting complicated by their heterogeneity and dynamism. Traditional techniques are facing challenges in network performance management. Cognitive networking is an emerging paradigm to make networks more intelligent, thereby overcoming...... traditional limitations and potentially achieving better performance. The vision is that, networks should be able to monitor themselves, reason upon changes in self and environment, act towards the achievement of specific goals and learn from experience. The concept of a Cognitive Engine (CE) supporting...... cognitive functions, as part of network elements, enabling above said autonomic capabilities is gathering attention. Awareness of the self and the world is an important aspect of the cognitive engine to be autonomic. This is achieved through embedding their models in the engine, but the complexity...
A Gaussian graphical model approach to climate networks
Energy Technology Data Exchange (ETDEWEB)
Zerenner, Tanja, E-mail: tanjaz@uni-bonn.de [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Friederichs, Petra; Hense, Andreas [Meteorological Institute, University of Bonn, Auf dem Hügel 20, 53121 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany); Lehnertz, Klaus [Department of Epileptology, University of Bonn, Sigmund-Freud-Straße 25, 53105 Bonn (Germany); Helmholtz Institute for Radiation and Nuclear Physics, University of Bonn, Nussallee 14-16, 53115 Bonn (Germany); Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Straße 7, 53119 Bonn (Germany)
2014-06-15
Distinguishing between direct and indirect connections is essential when interpreting network structures in terms of dynamical interactions and stability. When constructing networks from climate data the nodes are usually defined on a spatial grid. The edges are usually derived from a bivariate dependency measure, such as Pearson correlation coefficients or mutual information. Thus, the edges indistinguishably represent direct and indirect dependencies. Interpreting climate data fields as realizations of Gaussian Random Fields (GRFs), we have constructed networks according to the Gaussian Graphical Model (GGM) approach. In contrast to the widely used method, the edges of GGM networks are based on partial correlations denoting direct dependencies. Furthermore, GRFs can be represented not only on points in space, but also by expansion coefficients of orthogonal basis functions, such as spherical harmonics. This leads to a modified definition of network nodes and edges in spectral space, which is motivated from an atmospheric dynamics perspective. We construct and analyze networks from climate data in grid point space as well as in spectral space, and derive the edges from both Pearson and partial correlations. Network characteristics, such as mean degree, average shortest path length, and clustering coefficient, reveal that the networks posses an ordered and strongly locally interconnected structure rather than small-world properties. Despite this, the network structures differ strongly depending on the construction method. Straightforward approaches to infer networks from climate data while not regarding any physical processes may contain too strong simplifications to describe the dynamics of the climate system appropriately.
Quebec mental health services networks: models and implementation
Directory of Open Access Journals (Sweden)
Marie-Josée Fleury
2005-06-01
Full Text Available Purpose: In the transformation of health care systems, the introduction of integrated service networks is considered to be one of the main solutions for enhancing efficiency. In the last few years, a wealth of literature has emerged on the topic of services integration. However, the question of how integrated service networks should be modelled to suit different implementation contexts has barely been touched. To fill that gap, this article presents four models for the organization of mental health integrated networks. Data sources: The proposed models are drawn from three recently published studies on mental health integrated services in the province of Quebec (Canada with the author as principal investigator. Description: Following an explanation of the concept of integrated service network and a description of the Quebec context for mental health networks, the models, applicable in all settings: rural, urban or semi-urban, and metropolitan, and summarized in four figures, are presented. Discussion and conclusion: To apply the models successfully, the necessity of rallying all the actors of a system, from the strategic, tactical and operational levels, according to the type of integration involved: functional/administrative, clinical and physician-system is highlighted. The importance of formalizing activities among organizations and actors in a network and reinforcing the governing mechanisms at the local level is also underlined. Finally, a number of integration strategies and key conditions of success to operationalize integrated service networks are suggested.
Modeling the customer in electronic commerce.
Helander, M G; Khalid, H M
2000-12-01
This paper reviews interface design of web pages for e-commerce. Different tasks in e-commerce are contrasted. A systems model is used to illustrate the information flow between three subsystems in e-commerce: store environment, customer, and web technology. A customer makes several decisions: to enter the store, to navigate, to purchase, to pay, and to keep the merchandize. This artificial environment must be designed so that it can support customer decision-making. To retain customers it must be pleasing and fun, and create a task with natural flow. Customers have different needs, competence and motivation, which affect decision-making. It may therefore be important to customize the design of the e-store environment. Future ergonomics research will have to investigate perceptual aspects, such as presentation of merchandize, and cognitive issues, such as product search and navigation, as well as decision making while considering various economic parameters. Five theories on e-commerce research are presented.
Numerical modeling of electron-beam welding of dissimilar metals
Krektuleva, R. A.; Cherepanov, O. I.; Cherepanov, R. O.
2016-11-01
This paper is devoted to numerical modeling of heat transfer processes and estimation of thermal stresses in weld seams created by electron beam welding of heterogeneous metals. The mathematical model is based on a system of equations that includes the Lagrange's variational equation of theory of plasticity and variational equation of M. Biot's principle to simulate the heat transfer processes. The two-dimensional problems (plane strain and plane stress) are considered for estimation of thermal stresses in welds considering differences of mechanical properties of welded materials. The model is developed for simulation of temperature fields and stresses during electron beam welding.
Local electronic structure in the Peyrard-Bishop-Holstein model
Energy Technology Data Exchange (ETDEWEB)
Zhu Jianxin; Rasmussen, K Oe; Balatsky, A V; Bishop, A R [Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States)
2007-04-04
There is increasing evidence for polaronic effects on charge localization and dynamics in DNA. The Peyrard-Bishop-Holstein model has been previously suggested as an appropriate model for the description of such effects. Here we report a self-consistent study of local electronic structure within this model for both homopolymer and realistic viral P5 promoter segments. Our results indicate that both the inter-base-pair stacking interaction and the electron filling can qualitatively influence the polaronic properties in a specific DNA sequence, including features of two distinct length scales and competition with sequence-disorder induced localization.
Relativistic models for quasielastic electron and neutrino-nucleus scattering
Directory of Open Access Journals (Sweden)
Meucci Andrea
2012-12-01
Full Text Available Relativistic models developed within the framework of the impulse approximation for quasielastic (QE electron scattering and successfully tested in comparison with electron-scattering data have been extended to neutrino-nucleus scattering. Different descriptions of final-state interactions (FSI in the inclusive scattering are compared. In the relativistic Green’s function (RGF model FSI are described consistently with the exclusive scattering using a complex optical potential. In the relativistic mean field (RMF model FSI are described by the same RMF potential which gives the bound states. The results of the models are compared for electron and neutrino scattering and, for neutrino scattering, with the recently measured charged-current QE (CCQE MiniBooNE cross sections.
Modified binary encounter Bethe model for electron-impact ionization
Guerra, M; Indelicato, P; Santos, J P
2013-01-01
Theoretical expressions for ionization cross sections by electron impact based on the binary encounter Bethe (BEB) model, valid from ionization threshold up to relativistic energies, are proposed. The new modified BEB (MBEB) and its relativistic counterpart (MRBEB) expressions are simpler than the BEB (nonrelativistic and relativistic) expressions because they require only one atomic parameter, namely the binding energy of the electrons to be ionized, and use only one scaling term for the ionization of all sub-shells. The new models are used to calculate the K-, L- and M-shell ionization cross sections by electron impact for several atoms with Z from 6 to 83. Comparisons with all, to the best of our knowledge, available experimental data show that this model is as good or better than other models, with less complexity.
A New Perspective for Modeling Power Electronics Converters : Complementarity Framework
Vasca, Francesco; Iannelli, Luigi; Camlibel, M. Kanat; Frasca, Roberto
2009-01-01
The switching behavior of power converters with "ideal" electronic devices (EDs) makes it difficult to define a switched model that describes the dynamics of the converter in all possible operating conditions, i.e., a "complete" model. Indeed, simplifying assumptions on the sequences of modes are
Modeling paraxial wave propagation in free-electron laser oscillators
Karssenberg, J.G.; van der Slot, Petrus J.M.; Volokhine, I.; Verschuur, Jeroen W.J.; Boller, Klaus J.
2006-01-01
Modeling free-electron laser (FEL) oscillators requires calculation of both the light-beam interaction within the undulator and the light propagation outside the undulator. We have developed a paraxial optical propagation code that can be combined with various existing models of gain media, for
Byłak, Michał; RóŻański, Grzegorz
2017-04-01
The article presents the concept of ensuring the security of network information infrastructure for the management of Electronic Warfare (EW) systems. The concept takes into account the reactive and proactive tools against threats. An overview of the methods used to support the safety of IT networks and information sources about threats is presented. Integration of mechanisms that allow for effective intrusion detection and rapid response to threats in a network has been proposed. The architecture of the research environment is also presented.
A simple model for electron dissipation in trapped ion turbulence
Lesur, M.; Cartier-Michaud, T.; Drouot, T.; Diamond, P. H.; Kosuga, Y.; Réveillé, T.; Gravier, E.; Garbet, X.; Itoh, S.-I.; Itoh, K.
2017-01-01
Trapped ion resonance-driven turbulence is investigated in the presence of electron dissipation in a simplified tokamak geometry. A reduced gyrokinetic bounce-averaged model for trapped ions is adopted. Electron dissipation is modeled by a simple phase-shift δ between density and electric potential perturbations. The linear eigenfunction features a peak at the resonant energy, which becomes stronger with increasing electron dissipation. Accurately resolving this narrow peak in numerical simulation of the initial-value problem yields a stringent lower bound on the number of grid points in the energy space. Further, the radial particle flux is investigated in the presence of electron dissipation, including kinetic effects. When the density gradient is higher than the temperature gradient, and the phase-shift is finite but moderate ( δ≈0.02 ), the particle flux peaks at an order-of-magnitude above the gyro-Bohm estimate. Slight particle pinch is observed for δ<0.003 .
Kinetic modelling of runaway electrons in dynamic scenarios
Stahl, A; Papp, G; Landreman, M; Fülöp, T
2016-01-01
Improved understanding of runaway-electron formation and decay processes are of prime interest for the safe operation of large tokamaks, and the dynamics of the runaway electrons during dynamical scenarios such as disruptions are of particular concern. In this paper, we present kinetic modelling of scenarios with time-dependent plasma parameters; in particular, we investigate hot-tail runaway generation during a rapid drop in plasma temperature. With the goal of studying runaway-electron generation with a self-consistent electric-field evolution, we also discuss the implementation of a conservative collision operator and demonstrate its properties. An operator for avalanche runaway-electron generation, which takes the energy dependence of the scattering cross section and the runaway distribution into account, is investigated. We show that the simpler avalanche model of Rosenbluth & Putvinskii [Nucl. Fusion 37, 1355 (1997)] can give very inaccurate results for the avalanche growth rate (either lower or hig...
MODEL OF ELECTRON CLOUD INSTABILITY IN FERMILAB RECYCLER
Energy Technology Data Exchange (ETDEWEB)
Antipov, Sergey A. [Chicago U.; Burov, A. [Fermilab; Nagaitsev, S. [Fermilab
2016-10-04
An electron cloud instability might limit the intensity in the Fermilab Recycler after the PIP-II upgrade. A multibunch instability typically develops in the horizontal plane within a hundred turns and, in certain conditions, leads to beam loss. Recent studies have indicated that the instability is caused by an electron cloud, trapped in the Recycler index dipole magnets. We developed an analytical model of an electron cloud driven instability with the electrons trapped in combined function dipoles. The resulting instability growth rate of about 30 revolutions is consistent with experimental observations and qualitatively agrees with the simulation in the PEI code. The model allows an estimation of the instability rate for the future intensity upgrades.
Modelling hot electron generation in short pulse target heating experiments
Directory of Open Access Journals (Sweden)
Sircombe N.J.
2013-11-01
Full Text Available Target heating experiments planned for the Orion laser facility, and electron beam driven fast ignition schemes, rely on the interaction of a short pulse high intensity laser with dense material to generate a flux of energetic electrons. It is essential that the characteristics of this electron source are well known in order to inform transport models in radiation hydrodynamics codes and allow effective evaluation of experimental results and forward modelling of future campaigns. We present results obtained with the particle in cell (PIC code EPOCH for realistic target and laser parameters, including first and second harmonic light. The hot electron distributions are characterised and their implications for onward transport and target heating are considered with the aid of the Monte-Carlo transport code THOR.
Modeling and simulation of the USAVRE network and radiology operations
Martinez, Ralph; Bradford, Daniel Q.; Hatch, Jay; Sochan, John; Chimiak, William J.
1998-07-01
The U.S. Army Medical Command, lead by the Brooke Army Medical Center, has embarked on a visionary project. The U.S. Army Virtual Radiology Environment (USAVRE) is a CONUS-based network that connects all the Army's major medical centers and Regional Medical Commands (RMC). The purpose of the USAVRE is to improve the quality, access, and cost of radiology services in the Army via the use of state-of-the-art medical imaging, computer, and networking technologies. The USAVRE contains multimedia viewing workstations; database archive systems are based on a distributed computing environment using Common Object Request Broker Architecture (CORBA) middleware protocols. The underlying telecommunications network is an ATM-based backbone network that connects the RMC regional networks and PACS networks at medical centers and RMC clinics. This project is a collaborative effort between Army, university, and industry centers with expertise in teleradiology and Global PACS applications. This paper describes a model and simulation of the USAVRE for performance evaluation purposes. As a first step the results of a Technology Assessment and Requirements Analysis (TARA) -- an analysis of the workload in Army radiology departments, their equipment and their staffing. Using the TARA data and other workload information, we have developed a very detailed analysis of the workload and workflow patterns of our Medical Treatment Facilities. We are embarking on modeling and simulation strategies, which will form the foundation for the VRE network. The workload analysis is performed for each radiology modality in a RMC site. The workload consists of the number of examinations per modality, type of images per exam, number of images per exam, and size of images. The frequency for store and forward cases, second readings, and interactive consultation cases are also determined. These parameters are translated into the model described below. The model for the USAVRE is hierarchical in nature
A regional GNSS-VTEC model over Nigeria using neural networks: A novel approach
Directory of Open Access Journals (Sweden)
Daniel Okoh
2016-01-01
Full Text Available A neural network model of the Global Navigation Satellite System – vertical total electron content (GNSS-VTEC over Nigeria is developed. A new approach that has been utilized in this work is the consideration of the International Reference Ionosphere's (IRI's critical plasma frequency (foF2 parameter as an additional neuron for the network's input layer. The work also explores the effects of using various other input layer neurons like disturbance storm time (DST and sunspot number. All available GNSS data from the Nigerian Permanent GNSS Network (NIGNET were used, and these cover the period from 2011 to 2015, for 14 stations. Asides increasing the learning accuracy of the networks, the inclusion of the IRI's foF2 parameter as an input neuron is ideal for making the networks to learn long-term solar cycle variations. This is important especially for regions, like in this work, where the GNSS data is available for less than the period of a solar cycle. The neural network model developed in this work has been tested for time-varying and spatial performances. The latest 10% of the GNSS observations from each of the stations were used to test the forecasting ability of the networks, while data from 2 of the stations were entirely used for spatial performance testing. The results show that root-mean-squared-errors were generally less than 8.5 TEC units for all modes of testing performed using the optimal network. When compared to other models, the model developed in this work was observed to reduce the prediction errors to about half those of the NeQuick and the IRI model.
Numerical analysis of modeling based on improved Elman neural network.
Jie, Shao; Li, Wang; WeiSong, Zhao; YaQin, Zhong; Malekian, Reza
2014-01-01
A modeling based on the improved Elman neural network (IENN) is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE) varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA) with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL) model, Chebyshev neural network (CNN) model, and basic Elman neural network (BENN) model, the proposed model has better performance.
Numerical Analysis of Modeling Based on Improved Elman Neural Network
Directory of Open Access Journals (Sweden)
Shao Jie
2014-01-01
Full Text Available A modeling based on the improved Elman neural network (IENN is proposed to analyze the nonlinear circuits with the memory effect. The hidden layer neurons are activated by a group of Chebyshev orthogonal basis functions instead of sigmoid functions in this model. The error curves of the sum of squared error (SSE varying with the number of hidden neurons and the iteration step are studied to determine the number of the hidden layer neurons. Simulation results of the half-bridge class-D power amplifier (CDPA with two-tone signal and broadband signals as input have shown that the proposed behavioral modeling can reconstruct the system of CDPAs accurately and depict the memory effect of CDPAs well. Compared with Volterra-Laguerre (VL model, Chebyshev neural network (CNN model, and basic Elman neural network (BENN model, the proposed model has better performance.
Neural network models of learning and categorization in multigame experiments
Directory of Open Access Journals (Sweden)
Davide eMarchiori
2011-12-01
Full Text Available Previous research has shown that regret-driven neural networks predict behavior in repeated completely mixed games remarkably well, substantially equating the performance of the most accurate established models of learning. This result prompts the question of what is the added value of modeling learning through neural networks. We submit that this modeling approach allows for models that are able to distinguish among and respond differently to different payoff structures. Moreover, the process of categorization of a game is implicitly carried out by these models, thus without the need of any external explicit theory of similarity between games. To validate our claims, we designed and ran two multigame experiments in which subjects faced, in random sequence, different instances of two completely mixed 2x2 games. Then, we tested on our experimental data two regret-driven neural network models, and compared their performance with that of other established models of learning and Nash equilibrium.
SCYNet. Testing supersymmetric models at the LHC with neural networks
Energy Technology Data Exchange (ETDEWEB)
Bechtle, Philip; Belkner, Sebastian; Hamer, Matthias [Universitaet Bonn, Bonn (Germany); Dercks, Daniel [Universitaet Hamburg, Hamburg (Germany); Keller, Tim; Kraemer, Michael; Sarrazin, Bjoern; Schuette-Engel, Jan; Tattersall, Jamie [RWTH Aachen University, Institute for Theoretical Particle Physics and Cosmology, Aachen (Germany)
2017-10-15
SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model. (orig.)
SCYNet: testing supersymmetric models at the LHC with neural networks
Bechtle, Philip; Belkner, Sebastian; Dercks, Daniel; Hamer, Matthias; Keller, Tim; Krämer, Michael; Sarrazin, Björn; Schütte-Engel, Jan; Tattersall, Jamie
2017-10-01
SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model.
Modelling the self-organization and collapse of complex networks
Indian Academy of Sciences (India)
Modelling the self-organization and collapse of complex networks. Sanjay Jain Department of Physics and Astrophysics, University of Delhi Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore Santa Fe Institute, Santa Fe, New Mexico.
A model reduction method for biochemical reaction networks
National Research Council Canada - National Science Library
Rao, Shodhan; van der Schaft, Arjan; van Eunen, Karen; Bakker, Barbara; Jayawardhana, Bayu
2014-01-01
Background: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics...
Water distribution network modelling of a small community using ...
African Journals Online (AJOL)
Water distribution network modelling of a small community using watercad simulator. ... Global Journal of Engineering Research ... Pipes P-6, P-12, P-15 and P-19 expectedly have relatively low flow velocities due to the low average day ...
Computational modeling of signal transduction networks: a pedagogical exposition.
Prasad, Ashok
2012-01-01
We give a pedagogical introduction to computational modeling of signal transduction networks, starting from explaining the representations of chemical reactions by differential equations via the law of mass action. We discuss elementary biochemical reactions such as Michaelis-Menten enzyme kinetics and cooperative binding, and show how these allow the representation of large networks as systems of differential equations. We discuss the importance of looking for simpler or reduced models, such as network motifs or dynamical motifs within the larger network, and describe methods to obtain qualitative behavior by bifurcation analysis, using freely available continuation software. We then discuss stochastic kinetics and show how to implement easy-to-use methods of rule-based modeling for stochastic simulations. We finally suggest some methods for comprehensive parameter sensitivity analysis, and discuss the insights that it could yield. Examples, including code to try out, are provided based on a paper that modeled Ras kinetics in thymocytes.
Modelling and predicting biogeographical patterns in river networks
Directory of Open Access Journals (Sweden)
Sabela Lois
2016-04-01
Full Text Available Statistical analysis and interpretation of biogeographical phenomena in rivers is now possible using a spatially explicit modelling framework, which has seen significant developments in the past decade. I used this approach to identify a spatial extent (geostatistical range in which the abundance of the parasitic freshwater pearl mussel (Margaritifera margaritifera L. is spatially autocorrelated in river networks. I show that biomass and abundance of host fish are a likely explanation for the autocorrelation in mussel abundance within a 15-km spatial extent. The application of universal kriging with the empirical model enabled precise prediction of mussel abundance within segments of river networks, something that has the potential to inform conservation biogeography. Although I used a variety of modelling approaches in my thesis, I focus here on the details of this relatively new spatial stream network model, thus advancing the study of biogeographical patterns in river networks.
A Bayesian network model for predicting aquatic toxicity mode ...
The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity but MoA classification in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity mode of action using a recently published dataset containing over one thousand chemicals with MoA assignments for aquatic animal toxicity. Two dimensional theoretical chemical descriptors were generated for each chemical using the Toxicity Estimation Software Tool. The model was developed through augmented Markov blanket discovery from the data set with the MoA broad classifications as a target node. From cross validation, the overall precision for the model was 80.2% with a R2 of 0.959. The best precision was for the AChEI MoA (93.5%) where 257 chemicals out of 275 were correctly classified. Model precision was poorest for the reactivity MoA (48.5%) where 48 out of 99 reactive chemicals were correctly classified. Narcosis represented the largest class within the MoA dataset and had a precision and reliability of 80.0%, reflecting the global precision across all of the MoAs. False negatives for narcosis most often fell into electron transport inhibition, neurotoxicity or reactivity MoAs. False negatives for all other MoAs were most often narcosis. A probabilistic sensitivity analysis was undertaken for each MoA to examine the sensitivity to individual and multiple descriptor findings. The results show that the Markov blanket of a structurally
Bayesian network models for error detection in radiotherapy plans.
Kalet, Alan M; Gennari, John H; Ford, Eric C; Phillips, Mark H
2015-04-07
The purpose of this study is to design and develop a probabilistic network for detecting errors in radiotherapy plans for use at the time of initial plan verification. Our group has initiated a multi-pronged approach to reduce these errors. We report on our development of Bayesian models of radiotherapy plans. Bayesian networks consist of joint probability distributions that define the probability of one event, given some set of other known information. Using the networks, we find the probability of obtaining certain radiotherapy parameters, given a set of initial clinical information. A low probability in a propagated network then corresponds to potential errors to be flagged for investigation. To build our networks we first interviewed medical physicists and other domain experts to identify the relevant radiotherapy concepts and their associated interdependencies and to construct a network topology. Next, to populate the network's conditional probability tables, we used the Hugin Expert software to learn parameter distributions from a subset of de-identified data derived from a radiation oncology based clinical information database system. These data represent 4990 unique prescription cases over a 5 year period. Under test case scenarios with approximately 1.5% introduced error rates, network performance produced areas under the ROC curve of 0.88, 0.98, and 0.89 for the lung, brain and female breast cancer error detection networks, respectively. Comparison of the brain network to human experts performance (AUC of 0.90 ± 0.01) shows the Bayes network model performs better than domain experts under the same test conditions. Our results demonstrate the feasibility and effectiveness of comprehensive probabilistic models as part of decision support systems for improved detection of errors in initial radiotherapy plan verification procedures.
Infection dynamics on spatial small-world network models
Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario
2017-11-01
The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.
Modelling, Synthesis, and Configuration of Networks-on-Chips
DEFF Research Database (Denmark)
Stuart, Matthias Bo
This thesis presents three contributions in two different areas of network-on-chip and system-on-chip research: Application modelling and identifying and solving different optimization problems related to two specific network-on-chip architectures. The contribution related to application modelling...... is an analytical method for deriving the worst-case traffic pattern caused by an application and the cache-coherence protocol in a cache-coherent shared-memory system. The contributions related to network-on-chip optimization problems consist of two parts: The development and evaluation of six heuristics...... for solving the network synthesis problem in the MANGO network-on-chip, and the identification and formalization of the ReNoC configuration problem together with three heuristics for solving it....
A Neural Network Model for Prediction of Sound Quality
DEFF Research Database (Denmark)
Nielsen,, Lars Bramsløw
An artificial neural network structure has been specified, implemented and optimized for the purpose of predicting the perceived sound quality for normal-hearing and hearing-impaired subjects. The network was implemented by means of commercially available software and optimized to predict results...... error on the test set. The overall concept proved functional, but further testing with data obtained from a new rating experiment is necessary to better assess the utility of this measure. The weights in the trained neural networks were analyzed to qualitatively interpret the relation between...... obtained in subjective sound quality rating experiments based on input data from an auditory model. Various types of input data and data representations from the auditory model were used as input data for the chosen network structure, which was a three-layer perceptron. This network was trained by means...
Molecular modeling of amorphous, non-woven polymer networks.
Krausse, Constantin A; Milek, Theodor; Zahn, Dirk
2015-10-01
We outline a simple and efficient approach to generating molecular models of amorphous polymer networks. Similar to established techniques of preparing woven polymer networks from quenching high-temperature molecular simulation runs, we use a molecular dynamics simulations of a generic melt as starting points. This generic melt is however only used to describe parts of the polymers, namely the cross-linker units which positions are adopted from particle positions of the quenched melt. Specific degrees of network connectivity are tuned by geometric criteria for linker-linker connections and by suitable multi-body interaction potentials applied to the generic melt simulations. Using this technique we demonstrate adjusting fourfold linker coordination in amorphous polymer networks comprising 10-20% under-coordinated linkers. Graphical Abstract Molecular modeling of amorphous, non-woven polymer networks.
Modeling and Analysis of New Products Diffusion on Heterogeneous Networks
Directory of Open Access Journals (Sweden)
Shuping Li
2014-01-01
Full Text Available We present a heterogeneous networks model with the awareness stage and the decision-making stage to explain the process of new products diffusion. If mass media is neglected in the decision-making stage, there is a threshold whether the innovation diffusion is successful or not, or else it is proved that the network model has at least one positive equilibrium. For networks with the power-law degree distribution, numerical simulations confirm analytical results, and also at the same time, by numerical analysis of the influence of the network structure and persuasive advertisements on the density of adopters, we give two different products propagation strategies for two classes of nodes in scale-free networks.
Modeling pedestrian's conformity violation behavior: a complex network based approach.
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.
Optimization of recurrent neural networks for time series modeling
DEFF Research Database (Denmark)
Pedersen, Morten With
1997-01-01
series. The overall objective s are to improve training by application of second-order methods and to improve generalization ability by architecture optimization accomplished by pruning. The major topics covered in the thesis are: 1. The problem of training recurrent networks is analyzed from a numerical...... of solution obtained as well as computation time required. 3. A theoretical definition of the generalization error for recurrent networks is provided. This definition justifies a commonly adopted approach for estimating generalization ability. 4. The viability of pruning recurrent networks by the Optimal...... networks is proposed. The tool allows for assessment of the length of the effe ctive memory of previous inputs built up in the recurrent network during application. Time series modeling is also treated from a more general point of view, namely modeling of the joint probability distribution function...
USign--a security enhanced electronic consent model.
Li, Yanyan; Xie, Mengjun; Bian, Jiang
2014-01-01
Electronic consent becomes increasingly popular in the healthcare sector given the many benefits it provides. However, security concerns, e.g., how to verify the identity of a person who is remotely accessing the electronic consent system in a secure and user-friendly manner, also arise along with the popularity of electronic consent. Unfortunately, existing electronic consent systems do not pay sufficient attention to those issues. They mainly rely on conventional password based authentication to verify the identity of an electronic consent user, which is far from being sufficient given that identity theft threat is real and significant in reality. In this paper, we present a security enhanced electronic consent model called USign. USign enhances the identity protection and authentication for electronic consent systems by leveraging handwritten signatures everyone is familiar with and mobile computing technologies that are becoming ubiquitous. We developed a prototype of USign and conducted preliminary evaluation on accuracy and usability of signature verification. Our experimental results show the feasibility of the proposed model.
Message Integrity Model for Wireless Sensor Networks
Qleibo, Haider W.
2009-01-01
WSNs are susceptible to a variety of attacks. These attacks vary in the way they are performed and executed; they include but not limited to node capture, physical tampering, denial of service, and message alteration. It is of paramount importance to protect gathered data by WSNs and defend the network against illegal access and malicious…
Modeling integrated cellular machinery using hybrid Petri-Boolean networks.
Directory of Open Access Journals (Sweden)
Natalie Berestovsky
Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them
Reconstruction of social group networks from friendship networks using a tag-based model
Guan, Yuan-Pan; You, Zhi-Qiang; Han, Xiao-Pu
2016-12-01
Social group is a type of mesoscopic structure that connects human individuals in microscopic level and the global structure of society. In this paper, we propose a tag-based model considering that social groups expand along the edge that connects two neighbors with a similar tag of interest. The model runs on a real-world friendship network, and its simulation results show that various properties of simulated group network can well fit the empirical analysis on real-world social groups, indicating that the model catches the major mechanism driving the evolution of social groups and successfully reconstructs the social group network from a friendship network and throws light on digging of relationships between social functional organizations.