Neutral theory of chemical reaction networks

International Nuclear Information System (INIS)

Lee, Sang Hoon; Holme, Petter; Minnhagen, Petter; Bernhardsson, Sebastian; Kim, Beom Jun

2012-01-01

To what extent do the characteristic features of a chemical reaction network reflect its purpose and function? In general, one argues that correlations between specific features and specific functions are key to understanding a complex structure. However, specific features may sometimes be neutral and uncorrelated with any system-specific purpose, function or causal chain. Such neutral features are caused by chance and randomness. Here we compare two classes of chemical networks: one that has been subjected to biological evolution (the chemical reaction network of metabolism in living cells) and one that has not (the atmospheric planetary chemical reaction networks). Their degree distributions are shown to share the very same neutral system-independent features. The shape of the broad distributions is to a large extent controlled by a single parameter, the network size. From this perspective, there is little difference between atmospheric and metabolic networks; they are just different sizes of the same random assembling network. In other words, the shape of the degree distribution is a neutral characteristic feature and has no functional or evolutionary implications in itself; it is not a matter of life and death. (paper)

Reaction network modelling for kinetic parameters of pyrolytic reactions of CHON extractants in nuclear fuel processing waste management. Contributed Paper IT-07

International Nuclear Information System (INIS)

Gaikar, Vilas G.; Thaore, Vaishali

2014-01-01

The recovery and purification of plutonium (Pu) from uranium (U) and of U from Thorium (Th) in spent nuclear fuel reprocessing is accomplished by processes that employ organophosphorous compounds as extractants.The main objective of the present work was to develop a suitable kinetic model and to determine the kinetic parameters of the set of reactions involved in the pyrolysis of amides by fitting the experimental data in the reaction network model. The experimental data and analysis are expected to be useful in the steam pyrolysis of amide waste in fuel reprocessing in the nuclear industry. The basic approach was to understand the reaction mechanism of the steam pyrolysis of amides and then to estimate the reaction rate constants for the generation and consumption of different species by solving the model equations, allowing for the determination of important species in the reaction network

A computational framework for the automated construction of glycosylation reaction networks.

Science.gov (United States)

Liu, Gang; Neelamegham, Sriram

2014-01-01

Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS) data. The features described above are illustrated using three case studies that examine: i) O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii) automated N-linked glycosylation pathway construction; and iii) the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme biochemistry. All

A computational framework for the automated construction of glycosylation reaction networks.

Directory of Open Access Journals (Sweden)

Gang Liu

Full Text Available Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS data. The features described above are illustrated using three case studies that examine: i O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii automated N-linked glycosylation pathway construction; and iii the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme

An Efficient Forward-Reverse EM Algorithm for Statistical Inference in Stochastic Reaction Networks

KAUST Repository

Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2016-01-01

In this work [1], we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem

On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

Energy Technology Data Exchange (ETDEWEB)

Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Zunino, Roberto, E-mail: roberto.zunino@unitn.it [Department of Mathematics, University of Trento, Trento (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Department of Mathematics, University of Trento, Trento (Italy)

2015-06-28

Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.

Open complex-balanced mass action chemical reaction networks

NARCIS (Netherlands)

Rao, Shodhan; van der Schaft, Arjan; Jayawardhana, Bayu

We consider open chemical reaction networks, i.e. ones with inflows and outflows. We assume that all the inflows to the network are constant and all outflows obey the mass action kinetics rate law. We define a complex-balanced open reaction network as one that admits a complex-balanced steady state.

Switching dynamics in reaction networks induced by molecular discreteness

International Nuclear Information System (INIS)

Togashi, Yuichi; Kaneko, Kunihiko

2007-01-01

To study the fluctuations and dynamics in chemical reaction processes, stochastic differential equations based on the rate equation involving chemical concentrations are often adopted. When the number of molecules is very small, however, the discreteness in the number of molecules cannot be neglected since the number of molecules must be an integer. This discreteness can be important in biochemical reactions, where the total number of molecules is not significantly larger than the number of chemical species. To elucidate the effects of such discreteness, we study autocatalytic reaction systems comprising several chemical species through stochastic particle simulations. The generation of novel states is observed; it is caused by the extinction of some molecular species due to the discreteness in their number. We demonstrate that the reaction dynamics are switched by a single molecule, which leads to the reconstruction of the acting network structure. We also show the strong dependence of the chemical concentrations on the system size, which is caused by transitions to discreteness-induced novel states

On the Complexity of Reconstructing Chemical Reaction Networks

DEFF Research Database (Denmark)

Fagerberg, Rolf; Flamm, Christoph; Merkle, Daniel

2013-01-01

The analysis of the structure of chemical reaction networks is crucial for a better understanding of chemical processes. Such networks are well described as hypergraphs. However, due to the available methods, analyses regarding network properties are typically made on standard graphs derived from...... the full hypergraph description, e.g. on the so-called species and reaction graphs. However, a reconstruction of the underlying hypergraph from these graphs is not necessarily unique. In this paper, we address the problem of reconstructing a hypergraph from its species and reaction graph and show NP...

Graphical reduction of reaction networks by linear elimination of species

DEFF Research Database (Denmark)

Saez Cornellana, Meritxell; Wiuf, Carsten; Feliu, Elisenda

2017-01-01

We give a graphically based procedure to reduce a reaction network to a smaller reaction network with fewer species after linear elimination of a set of noninteracting species. We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties...

Consumer Activities and Reactions to Social Network Marketing

OpenAIRE

Bistra Vassileva

2017-01-01

The purpose of this paper is to understand consumer behavioural models with respect to their reactions to social network marketing. Theoretical background is focused on online and social network usage, motivations and behaviour. The research goal is to explore consumer reactions to the exposure of social network marketing based on the following criteria: level of brand engagement, word-of-mouth (WOM) referral behaviour, and purchase intentions. Consumers are investigated ...

A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2016-01-01

In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.

A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro

2016-07-07

In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.

The Forward-Reverse Algorithm for Stochastic Reaction Networks

KAUST Repository

Bayer, Christian

2015-01-07

In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which we solve a set of deterministic optimization problems where the SRNs are replaced by the classical ODE rates; then, during the second phase, the Monte Carlo version of the EM algorithm is applied starting from the output of the previous phase. Starting from a set of over-dispersed seeds, the output of our two-phase method is a cluster of maximum likelihood estimates obtained by using convergence assessment techniques from the theory of Markov chain Monte Carlo.

Doubly Periodic Traveling Waves in a Cellular Neural Network with Linear Reaction

Directory of Open Access Journals (Sweden)

Lin JianJhong

2009-01-01

Full Text Available Szekeley observed that the dynamic pattern of the locomotion of salamanders can be explained by periodic vector sequences generated by logical neural networks. Such sequences can mathematically be described by "doubly periodic traveling waves" and therefore it is of interest to propose dynamic models that may produce such waves. One such dynamic network model is built here based on reaction-diffusion principles and a complete discussion is given for the existence of doubly periodic waves as outputs. Since there are 2 parameters in our model and 4 a priori unknown parameters involved in our search of solutions, our results are nontrivial. The reaction term in our model is a linear function and hence our results can also be interpreted as existence criteria for solutions of a nontrivial linear problem depending on 6 parameters.

Generating Seismograms with Deep Neural Networks

Science.gov (United States)

Krischer, L.; Fichtner, A.

2017-12-01

The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of

Conditions for extinction events in chemical reaction networks with discrete state spaces.

Science.gov (United States)

Johnston, Matthew D; Anderson, David F; Craciun, Gheorghe; Brijder, Robert

2018-05-01

We study chemical reaction networks with discrete state spaces and present sufficient conditions on the structure of the network that guarantee the system exhibits an extinction event. The conditions we derive involve creating a modified chemical reaction network called a domination-expanded reaction network and then checking properties of this network. Unlike previous results, our analysis allows algorithmic implementation via systems of equalities and inequalities and suggests sequences of reactions which may lead to extinction events. We apply the results to several networks including an EnvZ-OmpR signaling pathway in Escherichia coli.

An Efficient Forward-Reverse EM Algorithm for Statistical Inference in Stochastic Reaction Networks

KAUST Repository

Bayer, Christian

2016-01-06

In this work [1], we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce an efficient two-phase algorithm in which the first phase is deterministic and it is intended to provide a starting point for the second phase which is the Monte Carlo EM Algorithm.

Characterizing multistationarity regimes in biochemical reaction networks.

Directory of Open Access Journals (Sweden)

Irene Otero-Muras

Full Text Available Switch like responses appear as common strategies in the regulation of cellular systems. Here we present a method to characterize bistable regimes in biochemical reaction networks that can be of use to both direct and reverse engineering of biological switches. In the design of a synthetic biological switch, it is important to study the capability for bistability of the underlying biochemical network structure. Chemical Reaction Network Theory (CRNT may help at this level to decide whether a given network has the capacity for multiple positive equilibria, based on their structural properties. However, in order to build a working switch, we also need to ensure that the bistability property is robust, by studying the conditions leading to the existence of two different steady states. In the reverse engineering of biological switches, knowledge collected about the bistable regimes of the underlying potential model structures can contribute at the model identification stage to a drastic reduction of the feasible region in the parameter space of search. In this work, we make use and extend previous results of the CRNT, aiming not only to discriminate whether a biochemical reaction network can exhibit multiple steady states, but also to determine the regions within the whole space of parameters capable of producing multistationarity. To that purpose we present and justify a condition on the parameters of biochemical networks for the appearance of multistationarity, and propose an efficient and reliable computational method to check its satisfaction through the parameter space.

Nonequilibrium thermodynamics and a fluctuation theorem for individual reaction steps in a chemical reaction network

International Nuclear Information System (INIS)

Pal, Krishnendu; Das, Biswajit; Banerjee, Kinshuk; Gangopadhyay, Gautam

2015-01-01

We have introduced an approach to nonequilibrium thermodynamics of an open chemical reaction network in terms of the propensities of the individual elementary reactions and the corresponding reverse reactions. The method is a microscopic formulation of the dissipation function in terms of the relative entropy or Kullback-Leibler distance which is based on the analogy of phase space trajectory with the path of elementary reactions in a network of chemical process. We have introduced here a fluctuation theorem valid for each opposite pair of elementary reactions which is useful in determining the contribution of each sub-reaction on the nonequilibrium thermodynamics of overall reaction. The methodology is applied to an oligomeric enzyme kinetics at a chemiostatic condition that leads the reaction to a nonequilibrium steady state for which we have estimated how each step of the reaction is energy driven or entropy driven to contribute to the overall reaction. (paper)

Next Generation Social Networks

DEFF Research Database (Denmark)

Sørensen, Lene Tolstrup; Skouby, Knud Erik

2008-01-01

different online networks for communities of people who share interests or individuals who presents themselves through user produced content is what makes up the social networking of today. The purpose of this paper is to discuss perceived user requirements to the next generation social networks. The paper...

SkyNet: Modular nuclear reaction network library

Science.gov (United States)

Lippuner, Jonas; Roberts, Luke F.

2017-10-01

The general-purpose nuclear reaction network SkyNet evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. Any list of isotopes can be evolved and SkyNet supports various different types of nuclear reactions. SkyNet is modular, permitting new or existing physics, such as nuclear reactions or equations of state, to be easily added or modified.

Molecular codes in biological and chemical reaction networks.

Directory of Open Access Journals (Sweden)

Dennis Görlich

Full Text Available Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio- chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries, biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades, an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.

Estimation of parameter sensitivities for stochastic reaction networks

KAUST Repository

Gupta, Ankit

2016-01-07

Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.

Network Restoration for Next-Generation Communication and Computing Networks

Directory of Open Access Journals (Sweden)

B. S. Awoyemi

2018-01-01

Full Text Available Network failures are undesirable but inevitable occurrences for most modern communication and computing networks. A good network design must be robust enough to handle sudden failures, maintain traffic flow, and restore failed parts of the network within a permissible time frame, at the lowest cost achievable and with as little extra complexity in the network as possible. Emerging next-generation (xG communication and computing networks such as fifth-generation networks, software-defined networks, and internet-of-things networks have promises of fast speeds, impressive data rates, and remarkable reliability. To achieve these promises, these complex and dynamic xG networks must be built with low failure possibilities, high network restoration capacity, and quick failure recovery capabilities. Hence, improved network restoration models have to be developed and incorporated in their design. In this paper, a comprehensive study on network restoration mechanisms that are being developed for addressing network failures in current and emerging xG networks is carried out. Open-ended problems are identified, while invaluable ideas for better adaptation of network restoration to evolving xG communication and computing paradigms are discussed.

Do-it-yourself networks: a novel method of generating weighted networks.

Science.gov (United States)

Shanafelt, D W; Salau, K R; Baggio, J A

2017-11-01

Network theory is finding applications in the life and social sciences for ecology, epidemiology, finance and social-ecological systems. While there are methods to generate specific types of networks, the broad literature is focused on generating unweighted networks. In this paper, we present a framework for generating weighted networks that satisfy user-defined criteria. Each criterion hierarchically defines a feature of the network and, in doing so, complements existing algorithms in the literature. We use a general example of ecological species dispersal to illustrate the method and provide open-source code for academic purposes.

Consumer Activities and Reactions to Social Network Marketing

Directory of Open Access Journals (Sweden)

Bistra Vassileva

2017-06-01

Full Text Available The purpose of this paper is to understand consumer behavioural models with respect to their reactions to social network marketing. Theoretical background is focused on online and social network usage, motivations and behaviour. The research goal is to explore consumer reactions to the exposure of social network marketing based on the following criteria: level of brand engagement, word-of-mouth (WOM referral behaviour, and purchase intentions. Consumers are investigated based on their attitudes toward social network marketing and basic socio-demographic covariates using data from a sample size of 700 Bulgarian respondents (age group 21–54 years, Internet users, urban inhabitants. Factor and cluster analyses are applied. It is found that consumers are willing to receive information about brands and companies through social networks. They like to talk in social networks about these brands and companies and to share information as well (factor 2, brand engagement. Internet users are willing to share information received through social network advertising (factor 1, wom referral behaviour but they would not buy a certain brand as a result of brand communication activities in social networks (factor 3, purchase intention. Several practical implications regarding marketing activities through social networks are drawn.

Entropy Generation in a Chemical Reaction

Science.gov (United States)

Miranda, E. N.

2010-01-01

Entropy generation in a chemical reaction is analysed without using the general formalism of non-equilibrium thermodynamics at a level adequate for advanced undergraduates. In a first approach to the problem, the phenomenological kinetic equation of an elementary first-order reaction is used to show that entropy production is always positive. A…

Generative adversarial networks for brain lesion detection

Science.gov (United States)

Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

2017-02-01

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

CERN Document Server

Santi, Paolo

2012-01-01

Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

Network information provision to potential generators: Appendices

Energy Technology Data Exchange (ETDEWEB)

NONE

2001-07-01

This Code of Practice (CoP) has been prepared to outline the standard of information that Distribution Network Operators (DNOs) should be required to produce in relation to the provision of network maps, schematic diagrams and specific network data. Network information from DNOs may be required by generators (and other customers) in order to assess the potential opportunities available for the connection of new generation plant. Seven Year Statements are published annually by the Transmission Licensees operating in Great Britain, i.e. The National Grid Company, Scottish Power and Scottish Hydro Electric, and contain all the network information relating to each transmission system, e.g. Generation Capacities, System Parameters and Plant Fault Levels. A similar arrangement for DNOs has been outlined in the Electricity Distribution Licence published by Ofgem. Under Condition 25 of the licence, 'The Long Term Development Statement', distribution licence holders are required to make available historic and planned network data. By providing sufficient network information, competition in generation will be improved. At the time of writing, any party interested in assessing distribution network information needs to make contact with the appropriate DNO, identifying the correct department and person. Written applications are then sent to that person, describing the type of network information that is required. Information required from embedded generators by DNOs is specified in detail in both of The Distribution Codes of England and Wales, and Scotland. However, there are no guidelines or details of network information to be provided by DNOs. This Code of Practise is designed to balance this situation and help DNOs, prospective generators and other applicants for information to achieve satisfaction by clarifying expectations. (Author)

On the network thermodynamics of mass action chemical reaction networks

NARCIS (Netherlands)

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

In this paper we elaborate on the mathematical formulation of mass action chemical reaction networks as recently given in van der Schaft, Rao, Jayawardhana (2012). We show how the reference chemical potentials define a specific thermodynamical equilibrium, and we discuss the port-Hamiltonian

Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

Science.gov (United States)

Gao, Connie W.; Allen, Joshua W.; Green, William H.; West, Richard H.

2016-06-01

Reaction Mechanism Generator (RMG) constructs kinetic models composed of elementary chemical reaction steps using a general understanding of how molecules react. Species thermochemistry is estimated through Benson group additivity and reaction rate coefficients are estimated using a database of known rate rules and reaction templates. At its core, RMG relies on two fundamental data structures: graphs and trees. Graphs are used to represent chemical structures, and trees are used to represent thermodynamic and kinetic data. Models are generated using a rate-based algorithm which excludes species from the model based on reaction fluxes. RMG can generate reaction mechanisms for species involving carbon, hydrogen, oxygen, sulfur, and nitrogen. It also has capabilities for estimating transport and solvation properties, and it automatically computes pressure-dependent rate coefficients and identifies chemically-activated reaction paths. RMG is an object-oriented program written in Python, which provides a stable, robust programming architecture for developing an extensible and modular code base with a large suite of unit tests. Computationally intensive functions are cythonized for speed improvements.

Integration of metabolome data with metabolic networks reveals reporter reactions

DEFF Research Database (Denmark)

Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen

2006-01-01

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...... network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from...... is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through...

Formal balancing of chemical reaction networks

NARCIS (Netherlands)

van der Schaft, Abraham; Rao, S.; Jayawardhana, B.

2016-01-01

In this paper we recall and extend the main results of Van der Schaft, Rao, Jayawardhana (2015) concerning the use of Kirchhoff’s Matrix Tree theorem in the explicit characterization of complex-balanced reaction networks and the notion of formal balancing. The notion of formal balancing corresponds

SkyNet: A Modular Nuclear Reaction Network Library

Science.gov (United States)

Lippuner, Jonas; Roberts, Luke F.

2017-12-01

Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there are dozens to thousands of nuclear species that interact with each other to produce successively heavier elements. In this paper, we present SkyNet, a new general-purpose nuclear reaction network that evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. SkyNet is free and open source, and aims to be easy to use and flexible. Any list of isotopes can be evolved, and SkyNet supports different types of nuclear reactions. SkyNet is modular so that new or existing physics, like nuclear reactions or equations of state, can easily be added or modified. Here, we present in detail the physics implemented in SkyNet with a focus on a self-consistent transition to and from nuclear statistical equilibrium to non-equilibrium nuclear burning, our implementation of electron screening, and coupling of the network to an equation of state. We also present comprehensive code tests and comparisons with existing nuclear reaction networks. We find that SkyNet agrees with published results and other codes to an accuracy of a few percent. Discrepancies, where they exist, can be traced to differences in the physics implementations.

Metal phosphonate coordination networks and frameworks as precursors of electrocatalysts for the hydrogen and oxygen evolution reactions

Science.gov (United States)

Zhang, Rui; El-Refaei, Sayed M.; Russo, Patrícia A.; Pinna, Nicola

2018-05-01

The hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) play key roles in the conversion of energy derived from renewable energy sources into chemical energy. Efficient, robust, and inexpensive electrocatalysts are necessary for driving these reactions at high rates at low overpotentials and minimize energetic losses. Recently, electrocatalysts derived from hybrid metal phosphonate compounds have shown high activity for the HER or OER. We review here the utilization of metal phosphonate coordination networks and metal-organic frameworks as precursors/templates for transition-metal phosphides, phosphates, or oxyhydroxides generated in situ in alkaline solutions, and their electrocatalytic performance in HER or OER.

Stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters

International Nuclear Information System (INIS)

Wang Linshan; Zhang Zhe; Wang Yangfan

2008-01-01

Some criteria for the global stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters are presented. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish some easy-to-test criteria of global exponential stability in the mean square for the stochastic neural networks. The criteria are computationally efficient, since they are in the forms of some linear matrix inequalities

Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control.

Science.gov (United States)

Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

2016-04-01

In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.

Optical Subsystems for Next Generation Access Networks

DEFF Research Database (Denmark)

Lazaro, J.A; Polo, V.; Schrenk, B.

2011-01-01

Recent optical technologies are providing higher flexibility to next generation access networks: on the one hand, providing progressive FTTx and specifically FTTH deployment, progressively shortening the copper access network; on the other hand, also opening fixed-mobile convergence solutions...... in next generation PON architectures. It is provided an overview of the optical subsystems developed for the implementation of the proposed NG-Access Networks....

Mean field interaction in biochemical reaction networks

KAUST Repository

Tembine, Hamidou; Tempone, Raul; Vilanova, Pedro

2011-01-01

In this paper we establish a relationship between chemical dynamics and mean field game dynamics. We show that chemical reaction networks can be studied using noisy mean field limits. We provide deterministic, noisy and switching mean field limits

Micro-generation network connection (renewables)

Energy Technology Data Exchange (ETDEWEB)

Thornycroft, J.; Russell, T.; Curran, J.

2003-07-01

The drive to reduce emissions of carbon dioxide will result in an increase in the number of small generation units seeking connection to the electric power distribution network. The objectives of this study were to consider connection issues relating to micro-generation from renewables and their integration into the UK distribution network. The document is divided into two sections. The first section describes the present system which includes input from micro-generation, the technical impacts and the financial considerations. The second part discusses technical, financial and governance options for the future. A summary of preferred options and recommendations is given. The study was carried out by the Halcrow Group Ltd under contract to the DTI.

The guitar chord-generating algorithm based on complex network

Science.gov (United States)

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

2016-02-01

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

A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks

OpenAIRE

Harirchi, Farshad; Khalil, Omar A.; Liu, Sijia; Elvati, Paolo; Violi, Angela; Hero, Alfred O.

2017-01-01

In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational comple...

NASA's Next Generation Space Geodesy Network

Science.gov (United States)

Desai, S. D.; Gross, R. S.; Hilliard, L.; Lemoine, F. G.; Long, J. L.; Ma, C.; McGarry, J. F.; Merkowitz, S. M.; Murphy, D.; Noll, C. E.;

Building next-generation converged networks theory and practice

CERN Document Server

Pathan, Al-Sakib Khan

2013-01-01

Supplying a comprehensive introduction to next-generation networks, Building Next-Generation Converged Networks: Theory and Practice strikes a balance between how and why things work and how to make them work. It compiles recent advancements along with basic issues from the wide range of fields related to next generation networks. Containing the contributions of 56 industry experts and researchers from 16 different countries, the book presents relevant theoretical frameworks and the latest research. It investigates new technologies such as IPv6 over Low Power Wireless Personal Area Network (6L

Network information provision to potential generators

Energy Technology Data Exchange (ETDEWEB)

Nicholson, G.

2001-07-01

At the time of finalising this report, an Ofgem consultation is underway on the form of Distribution Licence Condition 25, which will state the requirements for Distribution Network Operators to provide and publish data. This report is also relevant to the DTI Ofgem Embedded Generation Working Group (EGWG), which has recently completed its report and recommendations. It is hoped that this document will provide an overview of the status, importance, role and benefits of network information, which can be utilised by Generators, Network Operators and other industry players in framing their responses to this and future consultations. (Authors)

Elimination of intermediate species in multiscale stochastic reaction networks

DEFF Research Database (Denmark)

Cappelletti, Daniele; Wiuf, Carsten

2016-01-01

such as the substrate-enzyme complex in the Michaelis-Menten mechanism. Such species are virtually in all real-world networks, they are typically short-lived, degraded at a fast rate and hard to observe experimentally. We provide conditions under which the Markov process of a multiscale reaction network...

Random catalytic reaction networks

Science.gov (United States)

Stadler, Peter F.; Fontana, Walter; Miller, John H.

1993-03-01

We study networks that are a generalization of replicator (or Lotka-Volterra) equations. They model the dynamics of a population of object types whose binary interactions determine the specific type of interaction product. Such a system always reduces its dimension to a subset that contains production pathways for all of its members. The network equation can be rewritten at a level of collectives in terms of two basic interaction patterns: replicator sets and cyclic transformation pathways among sets. Although the system contains well-known cases that exhibit very complicated dynamics, the generic behavior of randomly generated systems is found (numerically) to be extremely robust: convergence to a globally stable rest point. It is easy to tailor networks that display replicator interactions where the replicators are entire self-sustaining subsystems, rather than structureless units. A numerical scan of random systems highlights the special properties of elementary replicators: they reduce the effective interconnectedness of the system, resulting in enhanced competition, and strong correlations between the concentrations.

Carrier ethernet network control plane based on the Next Generation Network

DEFF Research Database (Denmark)

Fu, Rong; Wang, Yanmeng; Berger, Michael Stubert

2008-01-01

This paper contributes on presenting a step towards the realization of Carrier Ethernet control plane based on the next generation network (NGN). Specifically, transport MPLS (T-MPLS) is taken as the transport technology in Carrier Ethernet. It begins with providing an overview of the evolving...... architecture of the next generation network (NGN). As an essential candidate among the NGN transport technologies, the definition of Carrier Ethernet (CE) is also introduced here. The second part of this paper depicts the contribution on the T-MPLS based Carrier Ethernet network with control plane based on NGN...... at illustrating the improvement of the Carrier Ethernet network with the NGN control plane....

Model reduction of detailed-balanced reaction networks by clustering linkage classes

NARCIS (Netherlands)

Rao, Shodhan; Jayawardhana, Bayu; van der Schaft, Abraham; Findeisen, Rolf; Bullinger, Eric; Balsa-Canto, Eva; Bernaerts, Kristel

2016-01-01

We propose a model reduction method that involves sequential application of clustering of linkage classes and Kron reduction. This approach is specifically useful for chemical reaction networks with each linkage class having less number of reactions. In case of detailed balanced chemical reaction

Mean field interaction in biochemical reaction networks

KAUST Repository

Tembine, Hamidou

2011-09-01

In this paper we establish a relationship between chemical dynamics and mean field game dynamics. We show that chemical reaction networks can be studied using noisy mean field limits. We provide deterministic, noisy and switching mean field limits and illustrate them with numerical examples. © 2011 IEEE.

Safe design and operation of tank reactors for multiple-reaction networks: uniqueness and multiplicity

NARCIS (Netherlands)

Westerterp, K.R.; Westerink, E.J.

1990-01-01

A method is developed to design a tank reactor in which a network of reactions is carried out. The network is a combination of parallel and consecutive reactions. The method ensures unique operation. Dimensionless groups are used which are either representative of properties of the reaction system

Physical Configuration of the Next Generation Home Network

Science.gov (United States)

Terada, Shohei; Kakishima, Yu; Hanawa, Dai; Oguchi, Kimio

The number of broadband users is rapidly increasing worldwide. Japan already has over 10 million FTTH users. Another trend is the rapid digitalization of home electrical equipment e. g. digital cameras and hard disc recorders. These trends will encourage the emergence of the next generation home network. In this paper, we introduce the next generation home network image and describe the five domains into which home devices can be classified. We then clarify the optimum medium with which to configure the network given the requirements imposed by the home environment. Wiring cable lengths for three network topologies are calculated. The results gained from the next generation home network implemented on the first phase testbed are shown. Finally, our conclusions are given.

Recent developments in research on catalytic reaction networks

Directory of Open Access Journals (Sweden)

Roberto Serra

2013-09-01

Full Text Available Over the last years, analyses performed on a stochastic model of catalytic reaction networks have provided some indications about the reasons why wet-lab experiments hardly ever comply with the phase transition typically predicted by theoretical models with regard to the emergence of collectively self-replicating sets of molecule (also defined as autocatalytic sets, ACSs, a phenomenon that is often observed in nature and that is supposed to have played a major role in the emergence of the primitive forms of life. The model at issue has allowed to reveal that the emerging ACSs are characterized by a general dynamical fragility, which might explain the difficulty to observe them in lab experiments. In this work, the main results of the various analyses are reviewed, with particular regard to the factors able to affect the generic properties of catalytic reactions network, for what concerns, not only the probability of ACSs to be observed, but also the overall activity of the system, in terms of production of new species, reactions and matter.

Comparing chemical reaction networks

DEFF Research Database (Denmark)

Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max

2017-01-01

We study chemical reaction networks (CRNs) as a kernel model of concurrency provided with semantics based on ordinary differential equations. We investigate the problem of comparing two CRNs, i.e., to decide whether the solutions of a source and of a target CRN can be matched for an appropriate...... choice of initial conditions. Using a categorical framework, we extend and unify model-comparison approaches based on dynamical (semantic) and structural (syntactic) properties of CRNs. Then, we provide an algorithm to compare CRNs, running linearly in time with respect to the cardinality of all possible...... comparisons. Finally, using a prototype implementation, CAGE, we apply our results to biological models from the literature....

Exact model reduction of combinatorial reaction networks

Directory of Open Access Journals (Sweden)

Fey Dirk

2008-08-01

Full Text Available Abstract Background Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models. Results We introduce methods that extend and complete the already introduced approach. For instance, we provide techniques to handle the formation of multi-scaffold complexes as well as receptor dimerization. Furthermore, we discuss a new modeling approach that allows the direct generation of exactly reduced model structures. The developed methods are used to reduce a model of EGF and insulin receptor crosstalk comprising 5,182 ordinary differential equations (ODEs to a model with 87 ODEs. Conclusion The methods, presented in this contribution, significantly enhance the available methods to exactly reduce models of combinatorial reaction networks.

The Adaptive QSE-reduced Nuclear Reaction Network for Silicon Burning

International Nuclear Information System (INIS)

Parete-Koon, Suzanne; Hix, William Raphael; Thielemann, Friedrich-Karl W.

2008-01-01

The nuclei of the 'iron peak' are formed in massive stars shortly before core collapse and during their supernova outbursts as well as during thermonuclear supernovae. Complete and incomplete silicon burning during these events are responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. Because of the large number of nuclei involved, accurate modeling of silicon burning is computationally expensive. However, examination of the physics of silicon burning has revealed that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present an improvement on our hybrid equilibrium-network scheme which takes advantage of this quasi-equilibrium in order to reduce the number of independent variables calculated. Because the size and membership of these groups vary as the temperature, density and electron faction change, achieving maximal efficiency requires dynamic adjustment of group number and membership. Toward this end, we are implementing a scheme beginning with 2 QSE groups at appropriately high temperature, then progressing through, 3 and 3* group stages (with successively more independent variables) as temperature declines. This combination allows accurate prediction of the nuclear abundance evolution, deleptonization and energy generation at a further reduced computational cost when compared to a conventional nuclear reaction network or our previous 3 fixed group QSE-reduced network. During silicon burning, the resultant QSE-reduced network is up to 20 times faster than the full network it replaces without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multi-dimensional applications

Flow network QSAR for the prediction of physicochemical properties by mapping an electrical resistance network onto a chemical reaction poset.

Science.gov (United States)

Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J

2013-06-01

Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset

Estimation of parameter sensitivities for stochastic reaction networks

KAUST Repository

Gupta, Ankit

2016-01-01

Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a

Stochastic analysis of complex reaction networks using binomial moment equations.

Science.gov (United States)

Barzel, Baruch; Biham, Ofer

2012-09-01

The stochastic analysis of complex reaction networks is a difficult problem because the number of microscopic states in such systems increases exponentially with the number of reactive species. Direct integration of the master equation is thus infeasible and is most often replaced by Monte Carlo simulations. While Monte Carlo simulations are a highly effective tool, equation-based formulations are more amenable to analytical treatment and may provide deeper insight into the dynamics of the network. Here, we present a highly efficient equation-based method for the analysis of stochastic reaction networks. The method is based on the recently introduced binomial moment equations [Barzel and Biham, Phys. Rev. Lett. 106, 150602 (2011)]. The binomial moments are linear combinations of the ordinary moments of the probability distribution function of the population sizes of the interacting species. They capture the essential combinatorics of the reaction processes reflecting their stoichiometric structure. This leads to a simple and transparent form of the equations, and allows a highly efficient and surprisingly simple truncation scheme. Unlike ordinary moment equations, in which the inclusion of high order moments is prohibitively complicated, the binomial moment equations can be easily constructed up to any desired order. The result is a set of equations that enables the stochastic analysis of complex reaction networks under a broad range of conditions. The number of equations is dramatically reduced from the exponential proliferation of the master equation to a polynomial (and often quadratic) dependence on the number of reactive species in the binomial moment equations. The aim of this paper is twofold: to present a complete derivation of the binomial moment equations; to demonstrate the applicability of the moment equations for a representative set of example networks, in which stochastic effects play an important role.

Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.

Science.gov (United States)

Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán

2014-03-11

While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.

COEL: A Cloud-based Reaction Network Simulator

Directory of Open Access Journals (Sweden)

Peter eBanda

2016-04-01

Full Text Available Chemical Reaction Networks (CRNs are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework that does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL's key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm-based optimization toolbox for rate constants.COEL is an open-source project hosted on GitHub (http://dx.doi.org/10.5281/zenodo.46544, which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at http://coel-sim.org. The framework is ideally suited for a collaborative use in both research and education.

Synchronization of Reaction-Diffusion Neural Networks With Dirichlet Boundary Conditions and Infinite Delays.

Science.gov (United States)

Sheng, Yin; Zhang, Hao; Zeng, Zhigang

2017-10-01

This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

OpenAIRE

Zhang, Han; Xu, Tao; Li, Hongsheng; Zhang, Shaoting; Wang, Xiaogang; Huang, Xiaolei; Metaxas, Dimitris

2017-01-01

Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) aiming at generating high-resolution photo-realistic images. First, we propose a two-stage generative adversarial network architecture, StackGAN-v1, for text-to-image synthesis. The Stage-I GAN sketches the primitive shape and colors of the object based on given...

Injectivity, multiple zeros, and multistationarity in reaction networks

DEFF Research Database (Denmark)

Feliu, Elisenda

2015-01-01

Polynomial dynamical systems are widely used to model and study real phenomena. In biochemistry, they are the preferred choice for modelling the concentration of chemical species in reaction networks with mass-action kinetics. These systems are typically parametrized by many (unknown) parameters...

Sodium-Water Reaction approach and mastering for ASTRID Steam Generator design

International Nuclear Information System (INIS)

Saez, Manuel; Allou, Alexandre; Beauchamp, François; Bertrand, Carole; Rodriguez, Gilles; Menou, Sylvain; Prele, Gérard

2013-01-01

Conclusions: • Modular Steam Generator concept selected for ASTRID: → Brings flexibility for the expertise of failed modules after their removal; → Intrinsically limit the mechanical consequences of a postulated large Sodium-Water Reaction. • Sodium-Water-Air Reaction studies include both prevention and mitigation aspects, with dedicated tools to be developed through R&D. • Regarding Safety analysis, the possibility to move from the scenario of instantaneous failure of the whole Steam Generator tube bundle toward a scenario with sequenced failure needs to be investigated. • The Steam Generator is one of the key components in the Sodium-cooled Fast Reactor system for it provides an interface between sodium and water. The design objective for the Steam Generator is related to the improvement of mastering of Sodium-Water Reaction. • Potential Sodium-Water Reactions can be eliminated by adopting a Gas based Power Conversion System

Toward a self-consistent and unitary reaction network for big bang nucleosynthesis

International Nuclear Information System (INIS)

Paris, Mark W.; Brown, Lowell S.; Hale, Gerald M.; Hayes-Sterbenz, Anna C.; Jungman, Gerard; Kawano, Toshihiko; Fuller, George M.; Grohs, Evan B.; Kunieda, Satoshi

2014-01-01

Unitarity, the mathematical expression of the conservation of probability in multichannel reactions, is an essential ingredient in the development of accurate nuclear reaction networks appropriate for nucleosynthesis in a variety of environments. We describe our ongoing program to develop a 'unitary reaction network' for the big-bang nucleosynthesis environment and look at an example of the need and power of unitary parametrizations of nuclear scattering and reaction data. Recent attention has been focused on the possible role of the 9 B compound nuclear system in the resonant destruction of 7 Li during primordial nucleosynthesis. We have studied reactions in the 9 B compound system with a multichannel, two-body unitary R-matrix code (EDA) using the known elastic and reaction data, in a four-channel treatment. The data include elastic 6 Li( 3 He, 3 He) 6 Li differential cross sections from 0.7 to 2.0 MeV, integrated reaction cross sections for energies from 0.7 to 5.0 MeV for 6 Li( 3 He,p) 8 Be* and from 0.4 to 5.0 MeV for the 6 Li( 3 He,γ) 7 Be reaction. Capture data have been added to the previous analysis with integrated cross section measurements from 0.7 to 0.825 MeV for 6 Li( 3 He,γ) 9 B. The resulting resonance parameters are compared with tabulated values from TUNL Nuclear Data Group analyses. Previously unidentified resonances are noted and the relevance of this analysis and a unitary reaction network for big-bang nucleosynthesis are emphasized. (author)

Lyapunov Functions, Stationary Distributions, and Non-equilibrium Potential for Reaction Networks

DEFF Research Database (Denmark)

Anderson, David F; Craciun, Gheorghe; Gopalkrishnan, Manoj

2015-01-01

We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non-equilibrium potent...

On the Mathematical Structure of Balanced Chemical Reaction Networks Governed by Mass Action Kinetics

NARCIS (Netherlands)

Schaft, Arjan van der; Rao, Shodhan; Jayawardhana, Bayu

2013-01-01

Motivated by recent progress on the interplay between graph theory, dynamics, and systems theory, we revisit the analysis of chemical reaction networks described by mass action kinetics. For reaction networks possessing a thermodynamic equilibrium we derive a compact formulation exhibiting at the

Deep brain stimulation: custom-made silicone-coated pulse-generator implantation after allergic reaction to generator compounds.

Science.gov (United States)

Anthofer, Judith; Herbst, Andreas; Janzen, Annettte; Lange, Max; Brawanski, Alexander; Schlaier, Juergen

2018-02-01

Deep brain stimulation for Parkinson's disease has become an established treatment option in recent years. The method and its application in clinical practice has proved to be safe and effective. Nevertheless, procedure-related and hardware-related complications occur. We present a rare case of a patient with an allergic reaction to the impulse generator. The patient suffered from delayed wound-healing deficits with several wound revisions and generator repositionings. After diagnosis of an allergic reaction to components of the generator, a custom-made silicon-coated model was implanted. Hereafter, no wound healing-deficit occurred throughout long-term follow-up. Allergic reaction to hardware components may lead to wound-healing deficits. In such cases, custom-made silicon-coated models may be an effective treatment option.

To address surface reaction network complexity using scaling relations machine learning and DFT calculations

International Nuclear Information System (INIS)

Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; Nørskov, Jens K.

2017-01-01

Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Lastly, propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.

Learning Orthographic Structure With Sequential Generative Neural Networks.

Science.gov (United States)

Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

2016-04-01

Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

Deep Convolutional Generative Adversarial Network for Procedural 3D Landscape Generation Based on DEM

OpenAIRE

Wulff-Jensen, Andreas; Rant, Niclas Nerup; Møller, Tobias Nordvig; Billeskov, Jonas Aksel

2018-01-01

This paper proposes a novel framework for improving procedural generation of 3D landscapes using machine learning. We utilized a Deep Convolutional Generative Adversarial Network (DC-GAN) to generate heightmaps. The network was trained on a dataset consisting of Digital Elevation Maps (DEM) of the alps. During map generation, the batch size and learning rate were optimized for the most efficient and satisfying map production. The diversity of the final output was tested against Perlin noise u...

Preclusion of switch behavior in reaction networks with mass-action kinetics

DEFF Research Database (Denmark)

Feliu, Elisenda; Wiuf, C.

2012-01-01

We study networks taken with mass-action kinetics and provide a Jacobian criterion that applies to an arbitrary network to preclude the existence of multiple positive steady states within any stoichiometric class for any choice of rate constants. We are concerned with the characterization...... precludes the existence of degenerate steady states. Further, we relate injectivity of a network to that of the network obtained by adding outflow, or degradation, reactions for all species....

Large-leak sodium-water reaction analysis for steam generators

International Nuclear Information System (INIS)

Sakano, K.; Shindo, Y.; Hori, M.

1975-01-01

The guillotine rupture of 4 tubes is assumed as a design basis regarding the large-leak sodium-water reaction in the system of the MONJU steam generator. Three kinds of analyses were performed with the view to showing the integrity of the steam generator system on the reaction. The first one is the analysis of the initial pressure spike, assuming the initial guillotine rupture of 1 tube. The analysis was performed by utilizing one-dimensional sphere-cylinder model code SWAC-7 and two-dimensional axisymmetric code PISCES 2DL. The second one is the analysis of the secondary peak pressure and its propagation in the system, assuming the instantaneous guillotine rupture of 4 tubes. The third one is the analysis of the dynamic deformation of the steam generator shell. The integrity of the steam generator system was shown by the analyses. (author)

Large-leak sodium-water reaction analysis for steam generators

Energy Technology Data Exchange (ETDEWEB)

Sakano, K; Shindo, Y; Hori, M

1975-07-01

The guillotine rupture of 4 tubes is assumed as a design basis regarding the large-leak sodium-water reaction in the system of the MONJU steam generator. Three kinds of analyses were performed with the view to showing the integrity of the steam generator system on the reaction. The first one is the analysis of the initial pressure spike, assuming the initial guillotine rupture of 1 tube. The analysis was performed by utilizing one-dimensional sphere-cylinder model code SWAC-7 and two-dimensional axisymmetric code PISCES 2DL. The second one is the analysis of the secondary peak pressure and its propagation in the system, assuming the instantaneous guillotine rupture of 4 tubes. The third one is the analysis of the dynamic deformation of the steam generator shell. The integrity of the steam generator system was shown by the analyses. (author)

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

Directory of Open Access Journals (Sweden)

Ankit Gupta

2014-06-01

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

Toward a self-consistent and unitary reaction network for big bang nucleosynthesis

Energy Technology Data Exchange (ETDEWEB)

Paris, Mark W.; Brown, Lowell S.; Hale, Gerald M.; Hayes-Sterbenz, Anna C.; Jungman, Gerard; Kawano, Toshihiko, E-mail: mparis@lanl.gov [Los Alamos National Laboratory, Los Alamos, New Mexico (United States); Fuller, George M.; Grohs, Evan B. [Department of Physics, University of California, San Diego, La Jolla, CA (United States); Kunieda, Satoshi [Nuclear Data Center, Japan Atomic Energy Agency, Tokai-mura Naka-gun, Ibaraki (Japan)

2014-07-01

Unitarity, the mathematical expression of the conservation of probability in multichannel reactions, is an essential ingredient in the development of accurate nuclear reaction networks appropriate for nucleosynthesis in a variety of environments. We describe our ongoing program to develop a 'unitary reaction network' for the big-bang nucleosynthesis environment and look at an example of the need and power of unitary parametrizations of nuclear scattering and reaction data. Recent attention has been focused on the possible role of the {sup 9}B compound nuclear system in the resonant destruction of {sup 7}Li during primordial nucleosynthesis. We have studied reactions in the {sup 9}B compound system with a multichannel, two-body unitary R-matrix code (EDA) using the known elastic and reaction data, in a four-channel treatment. The data include elastic {sup 6}Li({sup 3}He,{sup 3}He){sup 6}Li differential cross sections from 0.7 to 2.0 MeV, integrated reaction cross sections for energies from 0.7 to 5.0 MeV for {sup 6}Li({sup 3}He,p){sup 8}Be* and from 0.4 to 5.0 MeV for the {sup 6}Li({sup 3}He,γ){sup 7}Be reaction. Capture data have been added to the previous analysis with integrated cross section measurements from 0.7 to 0.825 MeV for {sup 6}Li({sup 3}He,γ){sup 9}B. The resulting resonance parameters are compared with tabulated values from TUNL Nuclear Data Group analyses. Previously unidentified resonances are noted and the relevance of this analysis and a unitary reaction network for big-bang nucleosynthesis are emphasized. (author)

Modeling documents with Generative Adversarial Networks

OpenAIRE

Glover, John

2016-01-01

This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.

Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory.

Science.gov (United States)

Pantazis, Yannis; Katsoulakis, Markos A; Vlachos, Dionisios G

2013-10-22

Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks. Typically, such mathematical models involve networks of coupled jump stochastic processes with a large number of parameters that need to be suitably calibrated against experimental data. In this direction, the parameter sensitivity analysis of reaction networks is an essential mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, existing sensitivity analysis approaches such as variants of the finite difference method can have an overwhelming computational cost in models with a high-dimensional parameter space. We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters. The proposed approach is based on Information Theory methods and relies on the quantification of information loss due to parameter perturbations between time-series distributions. For this reason, we need to work on path-space, i.e., the set consisting of all stochastic trajectories, hence the proposed approach is referred to as "pathwise". The pathwise sensitivity analysis method is realized by employing the rigorously-derived Relative Entropy Rate, which is directly computable from the propensity functions. A key aspect of the method is that an associated pathwise Fisher Information Matrix (FIM) is defined, which in turn constitutes a gradient-free approach to quantifying parameter sensitivities. The structure of the FIM turns out to be block-diagonal, revealing hidden parameter dependencies and sensitivities in reaction networks. As a gradient-free method, the proposed sensitivity analysis provides a significant advantage when dealing with complex stochastic systems with a large number of parameters. In addition, the knowledge of the structure of the FIM can allow to efficiently address

Creating Turbulent Flow Realizations with Generative Adversarial Networks

Science.gov (United States)

King, Ryan; Graf, Peter; Chertkov, Michael

2017-11-01

Generating valid inflow conditions is a crucial, yet computationally expensive, step in unsteady turbulent flow simulations. We demonstrate a new technique for rapid generation of turbulent inflow realizations that leverages recent advances in machine learning for image generation using a deep convolutional generative adversarial network (DCGAN). The DCGAN is an unsupervised machine learning technique consisting of two competing neural networks that are trained against each other using backpropagation. One network, the generator, tries to produce samples from the true distribution of states, while the discriminator tries to distinguish between true and synthetic samples. We present results from a fully-trained DCGAN that is able to rapidly draw random samples from the full distribution of possible inflow states without needing to solve the Navier-Stokes equations, eliminating the costly process of spinning up inflow turbulence. This suggests a new paradigm in physics informed machine learning where the turbulence physics can be encoded in either the discriminator or generator. Finally, we also propose additional applications such as feature identification and subgrid scale modeling.

The European Nuclear Society Young Generation Network: Five years of networking experience

International Nuclear Information System (INIS)

Meskens, Gaston

2000-01-01

In 1995, Mr Jan Runermark (Sweden), aware of a need for an exchange of knowledge from the older to the younger generation, came up with the idea of starting a European Nuclear Society Young Generation Network. A first network was formed with Sweden, the Netherlands, Spain, Finland, Germany and Belgium. The ENSYGN is now affiliated to the European Nuclear Society and brings together young students and professionals from 21 member countries Belgium, Bulgaria, Croatia, Czech Republic, Denmark Finland, France, Germany, Hungary, Italy, Netherlands, Poland, Romania, Russia, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Ukraine, and United Kingdom, The ENSYGN Core group meets (at least) twice a year and elects its own chair and co chair for a term of two years. The ENSYGN chair has a seat in the ENS Steering Committee and in the ENS Board. The ENSYGN works closely together with other young generation networks from the US, Australia, Japan and South America. ENSYGN organises workshops and courses on European level, takes part in international meetings (fl. UNFCCC, OECD) and stimulates networking on national level

High yield neutron generators using the DD reaction

Energy Technology Data Exchange (ETDEWEB)

Vainionpaa, J. H.; Harris, J. L.; Piestrup, M. A.; Gary, C. K.; Williams, D. L.; Apodaca, M. D.; Cremer, J. T. [Adelphi technology, 2003 E. Bayshore Rd. 94061, Redwood City, CA (United States); Ji, Qing; Ludewigt, B. A. [Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, CA 94720 (United States); Jones, G. [G and J Enterprise, 1258 Quary Ln, Suite F, Pleasanton California 94566 (United States)

2013-04-19

A product line of high yield neutron generators has been developed at Adelphi technology inc. The generators use the D-D fusion reaction and are driven by an ion beam supplied by a microwave ion source. Yields of up to 5 Multiplication-Sign 10{sup 9} n/s have been achieved, which are comparable to those obtained using the more efficient D-T reaction. The microwave-driven plasma uses the electron cyclotron resonance (ECR) to produce a high plasma density for high current and high atomic ion species. These generators have an actively pumped vacuum system that allows operation at reduced pressure in the target chamber, increasing the overall system reliability. Since no radioactive tritium is used, the generators can be easily serviced, and components can be easily replaced, providing essentially an unlimited lifetime. Fast neutron source size can be adjusted by selecting the aperture and target geometries according to customer specifications. Pulsed and continuous operation has been demonstrated. Minimum pulse lengths of 50 {mu}s have been achieved. Since the generators are easily serviceable, they offer a long lifetime neutron generator for laboratories and commercial systems requiring continuous operation. Several of the generators have been enclosed in radiation shielding/moderator structures designed for customer specifications. These generators have been proven to be useful for prompt gamma neutron activation analysis (PGNAA), neutron activation analysis (NAA) and fast neutron radiography. Thus these generators make excellent fast, epithermal and thermal neutron sources for laboratories and industrial applications that require neutrons with safe operation, small footprint, low cost and small regulatory burden.

A computational approach to extinction events in chemical reaction networks with discrete state spaces.

Science.gov (United States)

Johnston, Matthew D

2017-12-01

Recent work of Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature. We also run the program on 458 models from the European Bioinformatics Institute's BioModels Database and report our results. Copyright © 2017 Elsevier Inc. All rights reserved.

Achieving universal access to next generation networks

DEFF Research Database (Denmark)

Falch, Morten; Henten, Anders

The paper examines investment dimensions of next generation networks in a universal service perspective in a European context. The question is how new network infrastructures for getting access to communication, information and entertainment services in the present and future information society...

Young generation network: facing the future

International Nuclear Information System (INIS)

Berk, R.

1997-01-01

The future of the nuclear industry lies with the young generation. That's why in 1995, ENS supported the creation of the Young Generation Network (YGN). The YGN aims to fulfill the needs and interests of young people working in the nuclear business by organizing special programs with interesting opportunities and activities. (author)

Effects of traffic generation patterns on the robustness of complex networks

Science.gov (United States)

Wu, Jiajing; Zeng, Junwen; Chen, Zhenhao; Tse, Chi K.; Chen, Bokui

2018-02-01

Cascading failures in communication networks with heterogeneous node functions are studied in this paper. In such networks, the traffic dynamics are highly dependent on the traffic generation patterns which are in turn determined by the locations of the hosts. The data-packet traffic model is applied to Barabási-Albert scale-free networks to study the cascading failures in such networks and to explore the effects of traffic generation patterns on network robustness. It is found that placing the hosts at high-degree nodes in a network can make the network more robust against both intentional attacks and random failures. It is also shown that the traffic generation pattern plays an important role in network design.

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

Science.gov (United States)

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

2017-08-01

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

Global exponential stability and periodicity of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions

International Nuclear Information System (INIS)

Lu Junguo

2008-01-01

In this paper, the global exponential stability and periodicity for a class of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are addressed by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential converge to 0 of the difference between any two solutions of the original reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Furthermore, we prove periodicity of the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Sufficient conditions ensuring the global exponential stability and the existence of periodic oscillatory solutions for the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are given. These conditions are easy to check and have important leading significance in the design and application of reaction-diffusion recurrent neural networks with delays. Finally, two numerical examples are given to show the effectiveness of the obtained results

Global exponential stability of reaction-diffusion recurrent neural networks with time-varying delays

International Nuclear Information System (INIS)

Liang Jinling; Cao Jinde

2003-01-01

Employing general Halanay inequality, we analyze the global exponential stability of a class of reaction-diffusion recurrent neural networks with time-varying delays. Several new sufficient conditions are obtained to ensure existence, uniqueness and global exponential stability of the equilibrium point of delayed reaction-diffusion recurrent neural networks. The results extend and improve the earlier publications. In addition, an example is given to show the effectiveness of the obtained result

Nuclear Forensics and Radiochemistry: Reaction Networks

Energy Technology Data Exchange (ETDEWEB)

Rundberg, Robert S. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

2017-11-22

In the intense neutron flux of a nuclear explosion the production of isotopes may occur through successive neutron induced reactions. The pathway to these isotopes illustrates both the complexity of the problem and the need for high quality nuclear data. The growth and decay of radioactive isotopes can follow a similarly complex network. The Bateman equation will be described and modified to apply to the transmutation of isotopes in a high flux reactor. A alternative model of growth and decay, the GD code, that can be applied to fission products will also be described.

Optimizing the next generation optical access networks

DEFF Research Database (Denmark)

Amaya Fernández, Ferney Orlando; Soto, Ana Cardenas; Tafur Monroy, Idelfonso

2009-01-01

Several issues in the design and optimization of the next generation optical access network (NG-OAN) are presented. The noise, the distortion and the fiber optic nonlinearities are considered to optimize the video distribution link in a passive optical network (PON). A discussion of the effect...

Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks

OpenAIRE

Jalalifar, Seyed Ali; Hasani, Hosein; Aghajan, Hamid

2018-01-01

We present a novel approach to generating photo-realistic images of a face with accurate lip sync, given an audio input. By using a recurrent neural network, we achieved mouth landmarks based on audio features. We exploited the power of conditional generative adversarial networks to produce highly-realistic face conditioned on a set of landmarks. These two networks together are capable of producing a sequence of natural faces in sync with an input audio track.

The US nuclear reaction data network. Summary of the first meeting, March 13 ampersand 14 1996

International Nuclear Information System (INIS)

1996-03-01

The first meeting of the US Nuclear Reaction Data Network (USNRDN) was held at the Colorado School of Mines, March 13-14, 1996 chaired by F. Edward Cecil. The Agenda of the meeting is attached. The Network, its mission, products and services; related nuclear data and data networks, members, and organization are described in Attachment 1. The following progress reports from the members of the USNRDN were distributed prior to the meeting and are given as Attachment 2. (1) Measurements and Development of Analytic Techniques for Basic Nuclear Physics and Nuclear Applications; (2) Nuclear Reaction Data Activities at the National Nuclear Data Center; (3) Studies of nuclear reactions at very low energies; (4) Nuclear Reaction Data Activities, Nuclear Data Group; (5) Progress in Neutron Physics at Los Alamos - Experiments; (6) Nuclear Reaction Data Activities in Group T2; (7) Progress Report for the US Nuclear Reaction Data Network Meeting; (8) Nuclear Astrophysics Research Group (ORNL); (9) Progress Report from Ohio University; (10) Exciton Model Phenomenology; and (11) Progress Report for Coordination Meeting USNRDN

Interaction Networks: Generating High Level Hints Based on Network Community Clustering

Science.gov (United States)

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

Reaction networks as systems for resource allocation: a variational principle for their non-equilibrium steady states.

Directory of Open Access Journals (Sweden)

Andrea De Martino

Full Text Available Within a fully microscopic setting, we derive a variational principle for the non-equilibrium steady states of chemical reaction networks, valid for time-scales over which chemical potentials can be taken to be slowly varying: at stationarity the system minimizes a global function of the reaction fluxes with the form of a Hopfield Hamiltonian with hebbian couplings, that is explicitly seen to correspond to the rate of decay of entropy production over time. Guided by this analogy, we show that reaction networks can be formally re-cast as systems of interacting reactions that optimize the use of the available compounds by competing for substrates, akin to agents competing for a limited resource in an optimal allocation problem. As an illustration, we analyze the scenario that emerges in two simple cases: that of toy (random reaction networks and that of a metabolic network model of the human red blood cell.

Sodium/water reactions in steam generators of liquid metal fast breeder reactors

International Nuclear Information System (INIS)

Hori, M.

1980-01-01

The status of the research and development on sodium/water reactions resulting from the leakage of water into sodium in LMFBR steam generators is reviewed. The importance of sodium/water reaction phenomena in the design and operation of steam generators is discussed. The effects of sodium/water reactions are evaluated and methods of protection against these phenomena are surveyed. The products of chemical reactions between sodium and water under steam generator conditions are H 2 , NaOH, Na 2 O and NaH. Together with the temperature rise due to the associated exothermic reaction, these reaction products cause effects such as self-wastage, single- and multi-target wastage, and rapid pressure increase, depending on the size of the leak hole or the magnitude of leak rate. As for the wastage phenomena of small leaks, the effects of various factors have been studied and experimental correlations, as well as some theoretical work, have been performed. To investigate the pressure phenomena of a large leak, large-scale tests have been conducted by various organizations, and the computer codes to analyse these phenomena have been developed and verified by experiments. In the design of steam generators, an initial failure up to a hypothetical double-ended guillotine rupture of a single heat transfer tube is widely used as the design basis leak. Protection systems for LMFBR plants consist of leak detection devices, leak termination devices, and reaction pressure relief devices. From analyses based on research and development activities, as well as from experience with leaks in steam generator test loops and reactor plants, it has been confirmed that protection systems can satisfactorily be designed to accommodate leak incidents in LMFBR plants. (author)

Implications of small water leak reactions on sodium heated steam generator design

Energy Technology Data Exchange (ETDEWEB)

Smedley, J A

1975-07-01

Various types of sodium water reactions have been looked on as possibly causing hazard conditions in sodium heated steam generator units ranging from the very improbable boiler tube double ended guillotine fracture to the almost certain occurrence of micro-leaks. Within this range small water leaks reactions have attracted particular interest and the present paper looks at the principles of associating the reactions with detection and protection systems for Commercial Fast Reactors. A method is developed for assessing whether adequate protection has been provided against the effects of small water leak reactions in a steam generator unit. (author)

Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation.

Directory of Open Access Journals (Sweden)

Andrea Ciliberto

2007-03-01

Full Text Available In metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the Michaelis-Menten rate law is perfectly valid. This rate law assumes that the concentration of enzyme-substrate complex (C is much less than the free substrate concentration (S0. However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0. We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated Goldbeter-Koshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three Goldbeter-Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1 it unveils the modular structure of the enzymatic reactions, (2 it suggests a simple algorithm to formulate correct kinetic equations, and (3 contrary to classical Michaelis-Menten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively.

A two-stage flow-based intrusion detection model for next-generation networks.

Science.gov (United States)

Umer, Muhammad Fahad; Sher, Muhammad; Bi, Yaxin

2018-01-01

The next-generation network provides state-of-the-art access-independent services over converged mobile and fixed networks. Security in the converged network environment is a major challenge. Traditional packet and protocol-based intrusion detection techniques cannot be used in next-generation networks due to slow throughput, low accuracy and their inability to inspect encrypted payload. An alternative solution for protection of next-generation networks is to use network flow records for detection of malicious activity in the network traffic. The network flow records are independent of access networks and user applications. In this paper, we propose a two-stage flow-based intrusion detection system for next-generation networks. The first stage uses an enhanced unsupervised one-class support vector machine which separates malicious flows from normal network traffic. The second stage uses a self-organizing map which automatically groups malicious flows into different alert clusters. We validated the proposed approach on two flow-based datasets and obtained promising results.

A cascade reaction network mimicking the basic functional steps of acquired immune response

Science.gov (United States)

Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

2015-01-01

Biological systems use complex ‘information processing cores’ composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS which we call Adaptive Immune Response Simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system which responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner which is superficially similar to the most basic responses of the vertebrate acquired immune system, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices. PMID:26391084

A cascade reaction network mimicking the basic functional steps of adaptive immune response.

Science.gov (United States)

Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

2015-10-01

Biological systems use complex 'information-processing cores' composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS that we call an adaptive immune response simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system that responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner that is superficially similar to the most basic responses of the vertebrate AIS, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices.

Application of Integrated Neural Network Method to Fault Diagnosis of Nuclear Steam Generator

International Nuclear Information System (INIS)

Zhou Gang; Yang Li

2009-01-01

A new fault diagnosis method based on integrated neural networks for nuclear steam generator (SG) was proposed in view of the shortcoming of the conventional fault monitoring and diagnosis method. In the method, two neural networks (ANNs) were employed for the fault diagnosis of steam generator. A neural network, which was used for predicting the values of steam generator operation parameters, was taken as the dynamics model of steam generator. The principle of fault monitoring method using the neural network model is to detect the deviations between process signals measured from an operating steam generator and corresponding output signals from the neural network model of steam generator. When the deviation exceeds the limit set in advance, the abnormal event is thought to occur. The other neural network as a fault classifier conducts the fault classification of steam generator. So, the fault types of steam generator are given by the fault classifier. The clear information on steam generator faults was obtained by fusing the monitoring and diagnosis results of two neural networks. The simulation results indicate that employing integrated neural networks can improve the capacity of fault monitoring and diagnosis for the steam generator. (authors)

Synthetic aperture radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks

CSIR Research Space (South Africa)

Schwegmann, Colin P

2017-07-01

Full Text Available such as Synthetic Aperture Radar imagery. To aid in the creation of improved machine learning-based ship detection and discrimination methods this paper applies a type of neural network known as an Information Maximizing Generative Adversarial Network. Generative...

A palladium-doped ceria@carbon core-sheath nanowire network: a promising catalyst support for alcohol electrooxidation reactions

Science.gov (United States)

Tan, Qiang; Du, Chunyu; Sun, Yongrong; Du, Lei; Yin, Geping; Gao, Yunzhi

2015-08-01

A novel palladium-doped ceria and carbon core-sheath nanowire network (Pd-CeO2@C CSNWN) is synthesized by a template-free and surfactant-free solvothermal process, followed by high temperature carbonization. This hierarchical network serves as a new class of catalyst support to enhance the activity and durability of noble metal catalysts for alcohol oxidation reactions. Its supported Pd nanoparticles, Pd/(Pd-CeO2@C CSNWN), exhibit >9 fold increase in activity toward the ethanol oxidation over the state-of-the-art Pd/C catalyst, which is the highest among the reported Pd systems. Moreover, stability tests show a virtually unchanged activity after 1000 cycles. The high activity is mainly attributed to the superior oxygen-species releasing capability of Pd-doped CeO2 nanowires by accelerating the removal of the poisoning intermediate. The unique interconnected one-dimensional core-sheath structure is revealed to facilitate immobilization of the metal catalysts, leading to the improved durability. This core-sheath nanowire network opens up a new strategy for catalyst performance optimization for next-generation fuel cells.A novel palladium-doped ceria and carbon core-sheath nanowire network (Pd-CeO2@C CSNWN) is synthesized by a template-free and surfactant-free solvothermal process, followed by high temperature carbonization. This hierarchical network serves as a new class of catalyst support to enhance the activity and durability of noble metal catalysts for alcohol oxidation reactions. Its supported Pd nanoparticles, Pd/(Pd-CeO2@C CSNWN), exhibit >9 fold increase in activity toward the ethanol oxidation over the state-of-the-art Pd/C catalyst, which is the highest among the reported Pd systems. Moreover, stability tests show a virtually unchanged activity after 1000 cycles. The high activity is mainly attributed to the superior oxygen-species releasing capability of Pd-doped CeO2 nanowires by accelerating the removal of the poisoning intermediate. The unique

Transient porosity pulses and microfracturing during a stress-generating retrograde metamorphic reaction

Science.gov (United States)

Renard, F.; Zheng, X.; Cordonnier, B.; Zhu, W.; Jamtveit, B.

2017-12-01

Several geological processes involve mineral transformations where nominally dry rocks transform into hydrated ones when left in contact with water (i.e. eclogitization, serpentinization). In these systems, the transformation induces stress if the rock is confined, and the new minerals create a so-called force of crystallization. Here, we study a model retrograde metamorphic reaction, the hydration of periclase, MgO, into brucite, Mg(OH)2, to quantify the coupling between reaction, stress generation, porosity evolution and fracturing. This hydration reaction generates a volume increase of 110%, and a density decrease of 33.8% of the solid. Samples of a microporous MgO ceramics were reacted at 170-211°C, 5-80 MPa confining pressure, 6-95 MPa differential stress and 5 MPa pore fluid pressure. They were installed into an X-ray transparent triaxial deformation rig, called Hades, and mounted on a synchrotron microtomography stage. Each experiment lasted between 2 and 5 hours, during which between 35 and 130 three-dimensional images were acquired, allowing to follow the chemical transformation and the deformation of the sample. Below 30 MPa mean pressure, the hydration reaction was coupled to fracturing of the MgO ceramics, and the transformation rate followed a sigmoidal kinetics curve with a slow initiation, a fast reaction coupled to fracturing and the generation of a transient porosity pulse, and a slow-down until an almost complete transformation of periclase into brucite.. Conversely, above 30 MPa, the reaction kinetics was very slow, without fracturing over the time scale of the experiment. When considering the driving force of the hydration reaction, stress generation should be several hundreds MPa, whereas the present experiments show that fracturing occurred only below 30 MPa. This indicates that the potential energy due to phase transformation generates much lower stress than what is estimated from non-equilibrium thermodynamics. A possible interpretation of

BIG-10 fission product generation and reaction rates

International Nuclear Information System (INIS)

Rogers, J.W.

1976-01-01

Fission product generation rates for high quality fission foils and reaction rates of nonfission foils have been measured by gamma ray activation analyses. These foils were irradiated in the BIG-10 facility and the activities were measured by NaI counting techniques

Energy balance and flow in steam generator part with sodium-water reaction

International Nuclear Information System (INIS)

Matal, O.

1980-01-01

Relations were derived for the calculation of heat liberated during the sodium water reaction in a tube failure in different parts of a steam generator. The results are graphically shown in i-T diagrams. Heat removal is described from the reaction zone to water and steam in undisturbed tubes and to the steam generator metal structure. (author)

Simulation and Statistical Inference of Stochastic Reaction Networks with Applications to Epidemic Models

KAUST Repository

Moraes, Alvaro

2015-01-01

Networks (SRNs), that are intended to describe the time evolution of interacting particle systems where one particle interacts with the others through a finite set of reaction channels. SRNs have been mainly developed to model biochemical reactions

Design Guidelines for New Generation Network Architecture

Science.gov (United States)

Harai, Hiroaki; Fujikawa, Kenji; Kafle, Ved P.; Miyazawa, Takaya; Murata, Masayuki; Ohnishi, Masaaki; Ohta, Masataka; Umezawa, Takeshi

Limitations are found in the recent Internet because a lot of functions and protocols are patched to the original suite of layered protocols without considering global optimization. This reveals that end-to-end argument in the original Internet was neither sufficient for the current societal network and nor for a sustainable network of the future. In this position paper, we present design guidelines for a future network, which we call the New Generation Network, which provides the inclusion of diverse human requirements, reliable connection between the real-world and virtual network space, and promotion of social potentiality for human emergence. The guidelines consist of the crystal synthesis, the reality connection, and the sustainable & evolutional guidelines.

An efficient forward–reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Bayer, Christian

2016-02-20

© 2016 Taylor & Francis Group, LLC. ABSTRACT: In this work, we present an extension of the forward–reverse representation introduced by Bayer and Schoenmakers (Annals of Applied Probability, 24(5):1994–2032, 2014) to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, that is, SRNs conditional on their values in the extremes of given time intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the expectation-maximization algorithm to the phase I output. By selecting a set of overdispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.

An efficient forward-reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Vilanova, Pedro

2016-01-07

In this work, we present an extension of the forward-reverse representation introduced in Simulation of forward-reverse stochastic representations for conditional diffusions , a 2014 paper by Bayer and Schoenmakers to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the Expectation-Maximization algorithm to the phase I output. By selecting a set of over-dispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis

Science.gov (United States)

Wang, Ting; Plecháč, Petr

2017-12-01

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

Parallel replica dynamics method for bistable stochastic reaction networks: Simulation and sensitivity analysis.

Science.gov (United States)

Wang, Ting; Plecháč, Petr

2017-12-21

Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.

TopoGen: A Network Topology Generation Architecture with application to automating simulations of Software Defined Networks

CERN Document Server

Laurito, Andres; The ATLAS collaboration

2017-01-01

Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...

TopoGen: A Network Topology Generation Architecture with application to automating simulations of Software Defined Networks

CERN Document Server

Laurito, Andres; The ATLAS collaboration

2018-01-01

Simulation is an important tool to validate the performance impact of control decisions in Software Defined Networks (SDN). Yet, the manual modeling of complex topologies that may change often during a design process can be a tedious error-prone task. We present TopoGen, a general purpose architecture and tool for systematic translation and generation of network topologies. TopoGen can be used to generate network simulation models automatically by querying information available at diverse sources, notably SDN controllers. The DEVS modeling and simulation framework facilitates a systematic translation of structured knowledge about a network topology into a formal modular and hierarchical coupling of preexisting or new models of network entities (physical or logical). TopoGen can be flexibly extended with new parsers and generators to grow its scope of applicability. This permits to design arbitrary workflows of topology transformations. We tested TopoGen in a network engineering project for the ATLAS detector ...

Transient stability analysis of a distribution network with distributed generators

NARCIS (Netherlands)

Xyngi, I.; Ishchenko, A.; Popov, M.; Sluis, van der L.

2009-01-01

This letter describes the transient stability analysis of a 10-kV distribution network with wind generators, microturbines, and CHP plants. The network being modeled in Matlab/Simulink takes into account detailed dynamic models of the generators. Fault simulations at various locations are

Global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Jian; Lu Junguo

2008-01-01

In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits

Traffic Management for Next Generation Transport Networks

DEFF Research Database (Denmark)

Yu, Hao

required by the next generation transport network to provide Quality-of-Service (QoS) guaranteed video services. Augmenting network capacity and upgrading network nodes indicate long deployment period, replacement of equipment and thus significant cost to the network service providers. This challenge may...... slacken the steps of some network operators towards providing IPTV services. In this dissertation, the topology-based hierarchical scheduling scheme is proposed to tackle the problem addressed. The scheme simplifies the deployment process by placing an intelligent switch with centralized traffic...... management functions at the edge of the network, scheduling traffic on behalf of the other nodes. The topology-based hierarchical scheduling scheme is able to provide outstanding flow isolation due to its centralized scheduling ability, which is essential for providing IPTV services. In order to reduce...

Flows, scaling, and the control of moment hierarchies for stochastic chemical reaction networks

Science.gov (United States)

Smith, Eric; Krishnamurthy, Supriya

2017-12-01

Stochastic chemical reaction networks (CRNs) are complex systems that combine the features of concurrent transformation of multiple variables in each elementary reaction event and nonlinear relations between states and their rates of change. Most general results concerning CRNs are limited to restricted cases where a topological characteristic known as deficiency takes a value 0 or 1, implying uniqueness and positivity of steady states and surprising, low-information forms for their associated probability distributions. Here we derive equations of motion for fluctuation moments at all orders for stochastic CRNs at general deficiency. We show, for the standard base case of proportional sampling without replacement (which underlies the mass-action rate law), that the generator of the stochastic process acts on the hierarchy of factorial moments with a finite representation. Whereas simulation of high-order moments for many-particle systems is costly, this representation reduces the solution of moment hierarchies to a complexity comparable to solving a heat equation. At steady states, moment hierarchies for finite CRNs interpolate between low-order and high-order scaling regimes, which may be approximated separately by distributions similar to those for deficiency-zero networks and connected through matched asymptotic expansions. In CRNs with multiple stable or metastable steady states, boundedness of high-order moments provides the starting condition for recursive solution downward to low-order moments, reversing the order usually used to solve moment hierarchies. A basis for a subset of network flows defined by having the same mean-regressing property as the flows in deficiency-zero networks gives the leading contribution to low-order moments in CRNs at general deficiency, in a 1 /n expansion in large particle numbers. Our results give a physical picture of the different informational roles of mean-regressing and non-mean-regressing flows and clarify the dynamical

Bioactive Properties of Maillard Reaction Products Generated From Food Protein-derived Peptides.

Science.gov (United States)

Arihara, K; Zhou, L; Ohata, M

Food protein-derived peptides are promising food ingredients for developing functional foods, since various bioactive peptides are released from food proteins. The Maillard reaction, which plays an important role in most processed foods, generates various chemical components during processing. Although changes of amino acids or proteins and reduced sugars by the Maillard reaction have been studied extensively, such changes of peptides by the Maillard reaction are still not resolved enough. Since food protein-derived peptides are widely utilized in many processed foods, it deserves concern and research on the changes of peptides by the Maillard reaction in foods during processing or storage. This chapter initially overviewed food protein-derived bioactive peptides. Then, Maillard reaction products generated from peptides are discussed. We focused particularly on their bioactivities. © 2017 Elsevier Inc. All rights reserved.

Thermonuclear reaction generation method and device

International Nuclear Information System (INIS)

Imazaki, Kazuo

1998-01-01

The present invention provides a method of and a device for causing thermonuclear reaction capable of obtaining extremely high profits (about 1000 times), capable of forming a target which is strong against instability upon implosion as a problem of an inertia process and capable of realizing utilization of nuclear fusion. Namely, elementary particles such as pion, muon and K particles are deposited a portion or some portion of thermonuclear fuel materials by using high energy ions and highly brilliant γ rays generated from a high energy accelerator. The thermonuclear fuel materials are compressed to high density. The nuclear fusion reaction is promoted to ignite and burn thermonuclear fuels. A portion of nuclear fuels is ignited selectively by the means. High profits can be obtained. Since there is no need to attain implosion rate required for self ignition of nuclear fuels, a target of low aspect ratio can be used. (I.S.)

Robust network topologies for generating switch-like cellular responses.

Directory of Open Access Journals (Sweden)

Najaf A Shah

2011-06-01

Full Text Available Signaling networks that convert graded stimuli into binary, all-or-none cellular responses are critical in processes ranging from cell-cycle control to lineage commitment. To exhaustively enumerate topologies that exhibit this switch-like behavior, we simulated all possible two- and three-component networks on random parameter sets, and assessed the resulting response profiles for both steepness (ultrasensitivity and extent of memory (bistability. Simulations were used to study purely enzymatic networks, purely transcriptional networks, and hybrid enzymatic/transcriptional networks, and the topologies in each class were rank ordered by parametric robustness (i.e., the percentage of applied parameter sets exhibiting ultrasensitivity or bistability. Results reveal that the distribution of network robustness is highly skewed, with the most robust topologies clustering into a small number of motifs. Hybrid networks are the most robust in generating ultrasensitivity (up to 28% and bistability (up to 18%; strikingly, a purely transcriptional framework is the most fragile in generating either ultrasensitive (up to 3% or bistable (up to 1% responses. The disparity in robustness among the network classes is due in part to zero-order ultrasensitivity, an enzyme-specific phenomenon, which repeatedly emerges as a particularly robust mechanism for generating nonlinearity and can act as a building block for switch-like responses. We also highlight experimentally studied examples of topologies enabling switching behavior, in both native and synthetic systems, that rank highly in our simulations. This unbiased approach for identifying topologies capable of a given response may be useful in discovering new natural motifs and in designing robust synthetic gene networks.

Intrinsically-generated fluctuating activity in excitatory-inhibitory networks

Science.gov (United States)

Mastrogiuseppe, Francesca; Ostojic, Srdjan

2017-01-01

Recurrent networks of non-linear units display a variety of dynamical regimes depending on the structure of their synaptic connectivity. A particularly remarkable phenomenon is the appearance of strongly fluctuating, chaotic activity in networks of deterministic, but randomly connected rate units. How this type of intrinsically generated fluctuations appears in more realistic networks of spiking neurons has been a long standing question. To ease the comparison between rate and spiking networks, recent works investigated the dynamical regimes of randomly-connected rate networks with segregated excitatory and inhibitory populations, and firing rates constrained to be positive. These works derived general dynamical mean field (DMF) equations describing the fluctuating dynamics, but solved these equations only in the case of purely inhibitory networks. Using a simplified excitatory-inhibitory architecture in which DMF equations are more easily tractable, here we show that the presence of excitation qualitatively modifies the fluctuating activity compared to purely inhibitory networks. In presence of excitation, intrinsically generated fluctuations induce a strong increase in mean firing rates, a phenomenon that is much weaker in purely inhibitory networks. Excitation moreover induces two different fluctuating regimes: for moderate overall coupling, recurrent inhibition is sufficient to stabilize fluctuations; for strong coupling, firing rates are stabilized solely by the upper bound imposed on activity, even if inhibition is stronger than excitation. These results extend to more general network architectures, and to rate networks receiving noisy inputs mimicking spiking activity. Finally, we show that signatures of the second dynamical regime appear in networks of integrate-and-fire neurons. PMID:28437436

Constrained approximation of effective generators for multiscale stochastic reaction networks and application to conditioned path sampling

Energy Technology Data Exchange (ETDEWEB)

Cotter, Simon L., E-mail: simon.cotter@manchester.ac.uk

2016-10-15

Efficient analysis and simulation of multiscale stochastic systems of chemical kinetics is an ongoing area for research, and is the source of many theoretical and computational challenges. In this paper, we present a significant improvement to the constrained approach, which is a method for computing effective dynamics of slowly changing quantities in these systems, but which does not rely on the quasi-steady-state assumption (QSSA). The QSSA can cause errors in the estimation of effective dynamics for systems where the difference in timescales between the “fast” and “slow” variables is not so pronounced. This new application of the constrained approach allows us to compute the effective generator of the slow variables, without the need for expensive stochastic simulations. This is achieved by finding the null space of the generator of the constrained system. For complex systems where this is not possible, or where the constrained subsystem is itself multiscale, the constrained approach can then be applied iteratively. This results in breaking the problem down into finding the solutions to many small eigenvalue problems, which can be efficiently solved using standard methods. Since this methodology does not rely on the quasi steady-state assumption, the effective dynamics that are approximated are highly accurate, and in the case of systems with only monomolecular reactions, are exact. We will demonstrate this with some numerics, and also use the effective generators to sample paths of the slow variables which are conditioned on their endpoints, a task which would be computationally intractable for the generator of the full system.

Generative Adversarial Networks: An Overview

Science.gov (United States)

Creswell, Antonia; White, Tom; Dumoulin, Vincent; Arulkumaran, Kai; Sengupta, Biswa; Bharath, Anil A.

2018-01-01

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image super-resolution and classification. The aim of this review paper is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

Neural network based control of Doubly Fed Induction Generator in wind power generation

Science.gov (United States)

Barbade, Swati A.; Kasliwal, Prabha

2012-07-01

To complement the other types of pollution-free generation wind energy is a viable option. Previously wind turbines were operated at constant speed. The evolution of technology related to wind systems industry leaded to the development of a generation of variable speed wind turbines that present many advantages compared to the fixed speed wind turbines. In this paper the phasor model of DFIG is used. This paper presents a study of a doubly fed induction generator driven by a wind turbine connected to the grid, and controlled by artificial neural network ANN controller. The behaviour of the system is shown with PI control, and then as controlled by ANN. The effectiveness of the artificial neural network controller is compared to that of a PI controller. The SIMULINK/MATLAB simulation for Doubly Fed Induction Generator and corresponding results and waveforms are displayed.

Event-triggered synchronization for reaction-diffusion complex networks via random sampling

Science.gov (United States)

Dong, Tao; Wang, Aijuan; Zhu, Huiyun; Liao, Xiaofeng

2018-04-01

In this paper, the synchronization problem of the reaction-diffusion complex networks (RDCNs) with Dirichlet boundary conditions is considered, where the data is sampled randomly. An event-triggered controller based on the sampled data is proposed, which can reduce the number of controller and the communication load. Under this strategy, the synchronization problem of the diffusion complex network is equivalently converted to the stability of a of reaction-diffusion complex dynamical systems with time delay. By using the matrix inequality technique and Lyapunov method, the synchronization conditions of the RDCNs are derived, which are dependent on the diffusion term. Moreover, it is found the proposed control strategy can get rid of the Zeno behavior naturally. Finally, a numerical example is given to verify the obtained results.

Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms

International Nuclear Information System (INIS)

Sheng Li; Yang Huizhong; Lou Xuyang

2009-01-01

This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.

Generating prior probabilities for classifiers of brain tumours using belief networks

Directory of Open Access Journals (Sweden)

Arvanitis Theodoros N

2007-09-01

Full Text Available Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET, germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

Generating random networks and graphs

CERN Document Server

Coolen, Ton; Roberts, Ekaterina

2017-01-01

This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of rand...

Embedded generation connection incentives for distribution network operators

Energy Technology Data Exchange (ETDEWEB)

Williams, P.; Andrews, S.

2002-07-01

This is the final report with respect to work commissioned by the Department of Trade and Industry (DTI) as part of the New and Renewable Energy Programme into incentives for distribution network operators (DNOs) for the connection of embedded generation. This report, which incorporates the contents of the interim report submitted in February 2002, considers the implications of changes in the structure and regulation in the UK electricity industry on the successful technical and commercial integrated of embedded generation into distribution networks. The report examines: the obligations of public electricity suppliers (PESs); current DNO practices regarding the connection of embedded generation; the changes introduced by the Utilities Act 2000, including the impact of new obligations placed on DNOs on the connection of embedded generation and the requirements of the new Electricity Distribution Standard Licence conditions; and problems and prospects for DNO incentives.

Mobility management techniques for the next-generation wireless networks

Science.gov (United States)

Sun, Junzhao; Howie, Douglas P.; Sauvola, Jaakko J.

2001-10-01

The tremendous demands from social market are pushing the booming development of mobile communications faster than ever before, leading to plenty of new advanced techniques emerging. With the converging of mobile and wireless communications with Internet services, the boundary between mobile personal telecommunications and wireless computer networks is disappearing. Wireless networks of the next generation need the support of all the advances on new architectures, standards, and protocols. Mobility management is an important issue in the area of mobile communications, which can be best solved at the network layer. One of the key features of the next generation wireless networks is all-IP infrastructure. This paper discusses the mobility management schemes for the next generation mobile networks through extending IP's functions with mobility support. A global hierarchical framework model for the mobility management of wireless networks is presented, in which the mobility management is divided into two complementary tasks: macro mobility and micro mobility. As the macro mobility solution, a basic principle of Mobile IP is introduced, together with the optimal schemes and the advances in IPv6. The disadvantages of the Mobile IP on solving the micro mobility problem are analyzed, on the basis of which three main proposals are discussed as the micro mobility solutions for mobile communications, including Hierarchical Mobile IP (HMIP), Cellular IP, and Handoff-Aware Wireless Access Internet Infrastructure (HAWAII). A unified model is also described in which the different micro mobility solutions can coexist simultaneously in mobile networks.

Modeling urbanization patterns with generative adversarial networks

OpenAIRE

Albert, Adrian; Strano, Emanuele; Kaur, Jasleen; Gonzalez, Marta

2018-01-01

In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory. We generated a synthetic urban "universe" that qualitatively reproduces the complex spatial organization observed in global urban patterns, while being able to quantitatively recover certain key high-level urban spatial metrics.

International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Otsuka, Naohiko; Dunaeva, Svetlana

2010-11-01

The activities of fourteen nuclear data centres are summarized, and their cooperation under the auspices of the International Atomic Energy Agency is described. Each of the centres provides coverage for different geographical zones and/or specific types of nuclear data, thus together providing a complete service for users worldwide. The International Network of Nuclear Reaction Data Centres (NRDC) was established with the objective of providing nuclear physics databases that are required for nuclear technology (encompassing energy and non-energy applications) by coordinating the collection, compilation and dissemination of nuclear data on an international scale. (author)

HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks

Directory of Open Access Journals (Sweden)

Luca Marchetti

2017-01-01

Full Text Available HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA.

Voltage regulation in distribution networks with distributed generation

Science.gov (United States)

Blažič, B.; Uljanić, B.; Papič, I.

2012-11-01

The paper deals with the topic of voltage regulation in distribution networks with relatively high distributed energy resources (DER) penetration. The problem of voltage rise is described and different options for voltage regulation are given. The influence of DER on voltage profile and the effectiveness of the investigated solutions are evaluated by means of simulation in DIgSILENT. The simulated network is an actual distribution network in Slovenia with a relatively high penetration of distributed generation. Recommendations for voltage control in networks with DER penetration are given at the end.

Unified Model for Generation Complex Networks with Utility Preferential Attachment

International Nuclear Information System (INIS)

Wu Jianjun; Gao Ziyou; Sun Huijun

2006-01-01

In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics of this new network are given.

Variable elimination in chemical reaction networks with mass-action kinetics

DEFF Research Database (Denmark)

Feliu, Elisenda; Wiuf, C.

2012-01-01

We consider chemical reaction networks taken with mass-action kinetics. The steady states of such a system are solutions to a system of polynomial equations. Even for small systems the task of finding the solutions is daunting. We develop an algebraic framework and procedure for linear elimination...

Fiber to the home: next generation network

Science.gov (United States)

Yang, Chengxin; Guo, Baoping

2006-07-01

Next generation networks capable of carrying converged telephone, television (TV), very high-speed internet, and very high-speed bi-directional data services (like video-on-demand (VOD), Game etc.) strategy for Fiber To The Home (FTTH) is presented. The potential market is analyzed. The barriers and some proper strategy are also discussed. Several technical problems like various powering methods, optical fiber cables, and different network architecture are discussed too.

Big Data Perspective and Challenges in Next Generation Networks

Directory of Open Access Journals (Sweden)

Kashif Sultan

2018-06-01

Full Text Available With the development towards the next generation cellular networks, i.e., 5G, the focus has shifted towards meeting the higher data rate requirements, potential of micro cells and millimeter wave spectrum. The goals for next generation networks are very high data rates, low latency and handling of big data. The achievement of these goals definitely require newer architecture designs, upgraded technologies with possible backward support, better security algorithms and intelligent decision making capability. In this survey, we identify the opportunities which can be provided by 5G networks and discuss the underlying challenges towards implementation and realization of the goals of 5G. This survey also provides a discussion on the recent developments made towards standardization, the architectures which may be potential candidates for deployment and the energy concerns in 5G networks. Finally, the paper presents a big data perspective and the potential of machine learning for optimization and decision making in 5G networks.

Address Translation Problems in IMS Based Next Generation Networks

Directory of Open Access Journals (Sweden)

Balazs Godor

2006-01-01

Full Text Available The development of packed based multimedia networks reached a turning point when the ITU-T and the ETSIhave incorporated the IMS to the NGN. With the fast development of mobile communication more and more services andcontent are available. In contrast with fix network telephony both the services and the devices are personalized in the “mobileworld”. Services, known from the Internet - like e-mail, chat, browsing, presence, etc. – are already available via mobiledevices as well. The IMS originally wanted to exploit both the benefits of mobile networks and the fancy services of theInternet. But today it is already more than that. IMS is the core of the next generation telecommunication networks and abasis for fix-mobile convergent services. The fact however that IMS was originally a “mobile” standard, where IPv6 was notoddity generated some problems for the fix networks, where IPv4 is used. In this article I give an overview of these problemsand mention some solutions as well.

Harmonics: Generation and Suppression in AC System Networks ...

African Journals Online (AJOL)

However, reactive power flow in electrical networks has adverse effects depending on their magnitude and the nature of the supply network. How these harmonics are generated by nonlinear loads and the means by which they can be kept low are the focus of this paper. Keywords: non-linear loads, harmonics, reactive ...

Modeling of steam generator in nuclear power plant using neural network ensemble

International Nuclear Information System (INIS)

Lee, S. K.; Lee, E. C.; Jang, J. W.

2003-01-01

Neural network is now being used in modeling the steam generator is known to be difficult due to the reverse dynamics. However, Neural network is prone to the problem of overfitting. This paper investigates the use of neural network combining methods to model steam generator water level and compares with single neural network. The results show that neural network ensemble is effective tool which can offer improved generalization, lower dependence of the training set and reduced training time

Towards Third-Generation Living Lab Networks in Cities

Directory of Open Access Journals (Sweden)

Seppo Leminen

2017-11-01

Full Text Available Many cities engage in diverse experimentation, innovation, and development activities with a broad variety of environments and stakeholders to the benefit of citizens, companies, municipalities, and other organizations. Hence, this article discusses such engagement in terms of next-generation living lab networks in the city context. In so doing, the study contributes to the discussion on living labs by introducing a framework of collaborative innovation networks in cities and suggesting a typology of third-generation living labs. Our framework is characterized by diverse platforms and participation approaches, resulting in four distinctive modes of collaborative innovation networks where the city is: i a provider, ii a neighbourhood participator, iii a catalyst, or iv a rapid experimenter. The typology is based on an analysis of 118 interviews with participants in six Finnish cities and reveals various ways to organize innovation activities in the city context. In particular, cities can benefit from innovation networks by simultaneously exploiting multiple platforms such as living labs for innovation. We conclude by discussing implications to theory and practice, and suggesting directions for future research.

A moment-convergence method for stochastic analysis of biochemical reaction networks.

Science.gov (United States)

Zhang, Jiajun; Nie, Qing; Zhou, Tianshou

2016-05-21

Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.

A moment-convergence method for stochastic analysis of biochemical reaction networks

Energy Technology Data Exchange (ETDEWEB)

Zhang, Jiajun [School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China); Nie, Qing [Department of Mathematics, University of California at Irvine, Irvine, California 92697 (United States); Zhou, Tianshou, E-mail: mcszhtsh@mail.sysu.edu.cn [School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China); Guangdong Province Key Laboratory of Computational Science and School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China)

2016-05-21

Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.

Generation of clusters in complex dynamical networks via pinning control

International Nuclear Information System (INIS)

Li Kezan; Fu Xinchu; Small, Michael

2008-01-01

Many real-world networks show community structure, i.e., groups (or clusters) of nodes that have a high density of links within them but with a lower density of links between them. In this paper, by applying feedback injections to a fraction of network nodes, various clusters are synchronized independently according to the community structure generated by the group partition of the network (cluster synchronization). This control is achieved by pinning (i.e. applying linear feedback control) to a subset of the network nodes. Those pinned nodes are selected not randomly but according to the topological structure of communities of a given network. Specifically, for a given group partition of a network, those nodes with direct connections between groups must be pinned in order to achieve cluster synchronization. Both the local stability and global stability of cluster synchronization are investigated. Taking the tree-shaped network and the most modular network as two particular examples, we illustrate in detail how the pinning strategy influences the generation of clusters. The simulations verify the efficiency of the pinning schemes used in this paper

Next-generation science information network for leading-edge applications

International Nuclear Information System (INIS)

Urushidani, S.; Matsukata, J.

2008-01-01

High-speed networks are definitely essential tools for leading-edge applications in many research areas, including nuclear fusion research. This paper describes a number of advanced features in the Japanese next-generation science information network, called SINET3, and gives researchers clues on the uses of advanced high-speed network for their applications. The network services have four categories, multiple layer transfer, enriched virtual private network, enhanced quality-of-service, and bandwidth on demand services, and comprise a versatile service platform. The paper also describes the network architecture and advanced networking capabilities that enable economical service accommodation and flexible network resource assignment as well as effective use of Japan's first 40-Gbps lines

Next-generation science information network for leading-edge applications

Energy Technology Data Exchange (ETDEWEB)

Urushidani, S. [National Institute of Informatics, 2-1-2 Hitotsubashi Chiyoda-ku, Tokyo 101-8430 (Japan)], E-mail: urushi@nii.ac.jp; Matsukata, J. [National Institute of Informatics, 2-1-2 Hitotsubashi Chiyoda-ku, Tokyo 101-8430 (Japan)

2008-04-15

High-speed networks are definitely essential tools for leading-edge applications in many research areas, including nuclear fusion research. This paper describes a number of advanced features in the Japanese next-generation science information network, called SINET3, and gives researchers clues on the uses of advanced high-speed network for their applications. The network services have four categories, multiple layer transfer, enriched virtual private network, enhanced quality-of-service, and bandwidth on demand services, and comprise a versatile service platform. The paper also describes the network architecture and advanced networking capabilities that enable economical service accommodation and flexible network resource assignment as well as effective use of Japan's first 40-Gbps lines.

Innovation and networking among entrepreneurs across generations of Asian tigers

DEFF Research Database (Denmark)

Brambini-Pedersen, Jan Vang; Jensen, Kent W; Schøtt, Thomas

2017-01-01

entrepreneurship monitor (GEM) data, this paper aims at reducing this research gap by conducting an analysis of the generational differences between the tiger economies entrepreneurs in respect to their innovative performance, their inclination to network and the importance of the quality of the network......Much attention has been paid to analysing the determinants of the economic development in the different generations of Asian tiger economies. This stream of research has provided valuable insights on the particular generational challenges, the tigers face in implementing successful catching up...

Next Generation Campus Network Deployment Project Based on Softswitch

OpenAIRE

HU Feng; LIU Ziyan

2011-01-01

After analyzing the current networks of Guizhou University，we brought forward a scheme of next generation campus networks based on softswitch technology by choosing SoftX3000 switching system of HuaWei and provided the specific solution of accessing campus networks in this paper. It is proved that this scheme is feasible by using OPNET, which not only accomplished the integration of the PSTN and IP networks but also achieved the combining of voice services and data services.

Convergence of wireless, wireline, and photonics next generation networks

CERN Document Server

Iniewski, Krzysztof

2010-01-01

Filled with illustrations and practical examples from industry, this book provides a brief but comprehensive introduction to the next-generation wireless networks that will soon replace more traditional wired technologies. Written by a mixture of top industrial experts and key academic professors, it is the only book available that covers both wireless networks (such as wireless local area and personal area networks) and optical networks (such as long-haul and metropolitan networks) in one volume. It gives engineers and engineering students the necessary knowledge to meet challenges of next-ge

Embedded generation and network management issues

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-07-01

This report focuses on the characteristics of power generators that are important to accommodation in a distribution system. Part 1 examines the differences between transmission and distribution systems, and issues such as randomness, diversity, predictability, and controllability associated with accommodation in a distribution system. Part 2 concentrates on technical and operational issues relating to embedded generation, and the possible impact of the New Electricity Trading Arrangements. Commercial issues, contractual relationships for network charging and provision of services, and possible ways forward are examined in the last three parts of the report.

ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

Directory of Open Access Journals (Sweden)

Zhihuai Xiao

2015-01-01

Full Text Available Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO- initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the frequency components of the hydroturbine generating unit vibration signals are used as feature vectors for wavelet neural network training to realize mapping relationship from vibration features to fault types. A real vibration fault diagnosis case result of a hydroturbine generating unit shows that the proposed method has faster convergence speed and stronger generalization ability than the traditional wavelet neural network and ACO wavelet neural network. Thus it can provide an effective solution for online vibration fault diagnosis of a hydroturbine generating unit.

A method of generating moving objects on the constrained network

Science.gov (United States)

Zhang, Jie; Ma, Linbing

2008-10-01

Moving objects databases have become an important research issue in recent years. In case large real data sets acquired by GPS, PDA or other mobile devices are not available, benchmarking requires the generation of artificial data sets following the real-world behavior of spatial objects that change their locations over time. In the field of spatiotemporal databases, a number of publications about the generation of test data are restricted to few papers. However, most of the existing moving-object generators assume a fixed and often unrealistic mobility model and do not consider several important characteristics of the network. In this paper, a new generator is presented to solve these problems. First of all, the network is realistic transportation network of Guangzhou. Second, the observation records of vehicle flow are available. Third, in order to simplify the whole simulation process and to help us visualize the process, this framework is built under .Net development platform of Microsoft and ArcEngine9 environment.

Neural network application to diesel generator diagnostics

International Nuclear Information System (INIS)

Logan, K.P.

1990-01-01

Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

Distributed network generation based on preferential attachment in ABS

NARCIS (Netherlands)

K. Azadbakht (Keyvan); N. Bezirgiannis (Nikolaos); F.S. de Boer (Frank)

2017-01-01

textabstractGeneration of social networks using Preferential Attachment (PA) mechanism is proposed in the Barabasi-Albert model. In this mechanism, new nodes are introduced to the network sequentially and they attach to the existing nodes preferentially where the preference can be based on the

Identification of alterations in the Jacobian of biochemical reaction networks from steady state covariance data at two conditions.

Science.gov (United States)

Kügler, Philipp; Yang, Wei

2014-06-01

Model building of biochemical reaction networks typically involves experiments in which changes in the behavior due to natural or experimental perturbations are observed. Computational models of reaction networks are also used in a systems biology approach to study how transitions from a healthy to a diseased state result from changes in genetic or environmental conditions. In this paper we consider the nonlinear inverse problem of inferring information about the Jacobian of a Langevin type network model from covariance data of steady state concentrations associated to two different experimental conditions. Under idealized assumptions on the Langevin fluctuation matrices we prove that relative alterations in the network Jacobian can be uniquely identified when comparing the two data sets. Based on this result and the premise that alteration is locally confined to separable parts due to network modularity we suggest a computational approach using hybrid stochastic-deterministic optimization for the detection of perturbations in the network Jacobian using the sparsity promoting effect of [Formula: see text]-penalization. Our approach is illustrated by means of published metabolomic and signaling reaction networks.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

International Nuclear Information System (INIS)

Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong

2016-01-01

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

Energy Technology Data Exchange (ETDEWEB)

Marchetti, Luca, E-mail: marchetti@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); University of Trento, Department of Mathematics (Italy); Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)

2016-07-15

This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

Impact of distributed generation units with power electronic converters on distribution network protection

NARCIS (Netherlands)

Morren, J.; Haan, de S.W.H.

2008-01-01

An increasing number of distributed generation units (DG units) are connected to the distribution network. These generators affect the operation and coordination of the distribution network protection. The influence from DG units that are coupled to the network with a power electronic converter

Small leak detection by measuring surface oscillation during sodium-water reaction in steam generator

International Nuclear Information System (INIS)

Nei, Hiromichi; Hori, Masao

1977-01-01

Small leak sodium-water reaction tests were conducted to develop various kinds of leak detectors for the sodium-heated steam generator in FBR. The super-heated steam was injected into sodium in a reaction vessel having a sodium free surface, simulating the steam generator. The level gauge in the reaction vessel generated the most reliable signal among detectors, as long as the leak rates were relatively high. The level gauge signal was estimated to be the sodium surface oscillation caused by hydrogen bubbles produced in sodium-water reaction. Experimental correlation was derived, predicting the amplitude as a function of leak rate, hydrogen dissolution ratio, bubble rise velocity and other parameters concerned, assuming that the surface oscillation is in proportion to the gas hold-up. The noise amplitude under normal operation without water leak was increased with sodium flow rate and found to be well correlated with Froud number. These two correlations predict that a water leak in a ''MONJU'' class (300 MWe) steam generator could possibly be detected by level gauges at a leak rate above 2 g/sec. (auth.)

Application of genetic neural network in steam generator fault diagnosing

International Nuclear Information System (INIS)

Lin Xiaogong; Jiang Xingwei; Liu Tao; Shi Xiaocheng

2005-01-01

In the paper, a new algorithm which neural network and genetic algorithm are mixed is adopted, aiming at the problems of slow convergence rate and easily falling into part minimums in network studying of traditional BP neural network, and used in the fault diagnosis of steam generator. The result shows that this algorithm can solve the convergence problem in the network trains effectively. (author)

Toward green next-generation passive optical networks

Science.gov (United States)

Srivastava, Anand

2015-01-01

Energy efficiency has become an increasingly important aspect of designing access networks, due to both increased concerns for global warming and increased network costs related to energy consumption. Comparing access, metro, and core, the access constitutes a substantial part of the per subscriber network energy consumption and is regarded as the bottleneck for increased network energy efficiency. One of the main opportunities for reducing network energy consumption lies in efficiency improvements of the customer premises equipment. Access networks in general are designed for low utilization while supporting high peak access rates. The combination of large contribution to overall network power consumption and low Utilization implies large potential for CPE power saving modes where functionality is powered off during periods of idleness. Next-generation passive optical network, which is considered one of the most promising optical access networks, has notably matured in the past few years and is envisioned to massively evolve in the near future. This trend will increase the power requirements of NG-PON and make it no longer coveted. This paper will first provide a comprehensive survey of the previously reported studies on tackling this problem. A novel solution framework is then introduced, which aims to explore the maximum design dimensions and achieve the best possible power saving while maintaining the QoS requirements for each type of service.

Deuteron-induced reactions generated by intense lasers for PET isotope production

Science.gov (United States)

Kimura, Sachie; Bonasera, Aldo

2011-05-01

We investigate the feasibility of using laser accelerated protons/deuterons for positron emission tomography (PET) isotope production by means of the nuclear reactions 11B(p, n) 11C and 10B(d, n) 11C. The second reaction has a positive Q-value and no energy threshold. One can, therefore, make use of the lower energy part of the laser-generated deuterons, which includes the majority of the accelerated deuterons. By assuming that the deuteron spectra are similar to the proton spectra, the 11C produced from the reaction 10B(d, n) 11C is estimated to be 7.4×10 9 per laser-shot at the Titan laser at Lawrence Livermore National Laboratory. Meanwhile a high-repetition table-top laser irradiation is estimated to generate 3.5×10 711C per shot from the same reaction. In terms of the 11C activity, it is about 2×10 4 Bq per shot. If this laser delivers kHz, the activity is integrated to 1 GBq after 3 min. The number is sufficient for the practical application in medical imaging for PET.

Critical regimes driven by recurrent mobility patterns of reaction-diffusion processes in networks

Science.gov (United States)

Gómez-Gardeñes, J.; Soriano-Paños, D.; Arenas, A.

2018-04-01

Reaction-diffusion processes1 have been widely used to study dynamical processes in epidemics2-4 and ecology5 in networked metapopulations. In the context of epidemics6, reaction processes are understood as contagions within each subpopulation (patch), while diffusion represents the mobility of individuals between patches. Recently, the characteristics of human mobility7, such as its recurrent nature, have been proven crucial to understand the phase transition to endemic epidemic states8,9. Here, by developing a framework able to cope with the elementary epidemic processes, the spatial distribution of populations and the commuting mobility patterns, we discover three different critical regimes of the epidemic incidence as a function of these parameters. Interestingly, we reveal a regime of the reaction-diffussion process in which, counter-intuitively, mobility is detrimental to the spread of disease. We analytically determine the precise conditions for the emergence of any of the three possible critical regimes in real and synthetic networks.

Traveling and Pinned Fronts in Bistable Reaction-Diffusion Systems on Networks

Science.gov (United States)

Kouvaris, Nikos E.; Kori, Hiroshi; Mikhailov, Alexander S.

2012-01-01

Traveling fronts and stationary localized patterns in bistable reaction-diffusion systems have been broadly studied for classical continuous media and regular lattices. Analogs of such non-equilibrium patterns are also possible in networks. Here, we consider traveling and stationary patterns in bistable one-component systems on random Erdös-Rényi, scale-free and hierarchical tree networks. As revealed through numerical simulations, traveling fronts exist in network-organized systems. They represent waves of transition from one stable state into another, spreading over the entire network. The fronts can furthermore be pinned, thus forming stationary structures. While pinning of fronts has previously been considered for chains of diffusively coupled bistable elements, the network architecture brings about significant differences. An important role is played by the degree (the number of connections) of a node. For regular trees with a fixed branching factor, the pinning conditions are analytically determined. For large Erdös-Rényi and scale-free networks, the mean-field theory for stationary patterns is constructed. PMID:23028746

A vacuum--generated inertia reaction force

International Nuclear Information System (INIS)

Rueda, Alfonso; Haisch, Bernard

2001-01-01

A clear and succinct covariant approach shows that, in principle, there must be a contribution to the inertia reaction force on an accelerated object by the surrounding vacuum electromagnetic field in which the object is embedded. No details of the vacuum to object electromagnetic interaction need to be specified other than the fact that the object is made of electromagnetically interacting particles. Some interesting consequences of this feature are discussed. This analysis strongly supports the concept that inertia is indeed an opposition of the vacuum fields to any attempt to change the uniform state of motion of material bodies. This also definitely shows that inertia should be viewed as extrinsic to mass and that causing agents and/or mechanisms responsible for the inertia reaction force are neither intrinsic to the notion of mass nor to the entities responsible for the existence of mass in elementary particles (as, e.g., the Higgs field). In other words the mechanism that produces the inertia-reaction-force requires an explicit explanation. This explicit explanation is that inertia is an opposition of the vacuum fields to the accelerated motion of any material entities, i.e., of entities that possess mass. It is briefly commented why the existence of a Higgs field responsible for the generation of mass in elementary particles does not contradict the view presented here. It is also briefly discussed why a strict version of Mach's Principle does really contradict this view, though a broad sense version of Mach's Principle may be in agreement

Reduced linear noise approximation for biochemical reaction networks with time-scale separation: The stochastic tQSSA+

Science.gov (United States)

Herath, Narmada; Del Vecchio, Domitilla

2018-03-01

Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.

Application of Generative Adversarial Networks (GANs) to jet images

CERN Multimedia

CERN. Geneva

2017-01-01

https://arxiv.org/abs/1701.05927 We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of jet images -- 2D representations of energy depositions from particles interacting with a calorimeter. We propose a simple architecture, the Location-Aware Generative Adversarial Network, that learns to produce realistic radiation patterns from simulated high energy particle collisions. The pixel intensities of GAN-generated images faithfully span over many orders of magnitude and exhibit the desired low-dimensional physical properties (i.e., jet mass, n-subjettiness, etc.). We shed light on limitations, and provide a novel empirical validation of image quality and validity of GAN-produced simulations of the natural world. This work provides a base for further explorations of GANs for use in faster simulation in High Energy Particle Physics.

Technical guide to the connection of generation to the distribution network

Energy Technology Data Exchange (ETDEWEB)

Jarrett, K.; Hedgecock, J.; Gregory, R.; Warham, T.

2003-07-01

This guide provides a 'route map' of the processes of getting a generation scheme connected to the network and is intended to help developers of any form of distributed generation connected to the UK's local electricity networks, eg: renewable energy schemes; waste-to-energy schemes; on-site generation and combined heat and power (CHP) schemes; and peak lopping schemes using back-up generators. Where necessary, the guide distinguishes between arrangements that apply in Scotland and those that apply in England and Wales. The guide aims to: provide background information about the electricity industry; highlight common technical issues that arise during connection negotiation and their implications for distribution network operators (DNOs) and developers; examine the main factors affecting connection costs and timescales for achieving connections; and identify the different types of contracts relating to connection. The report considers the connection process, the connection application process and timescales, costs and charges, competition in connection, the structure of the UK electricity industry, the statutory framework, the effects of distributed generation of the distribution system, earthing and protection design, safety issues and DNO network information. It includes a glossary, checklists, useful contact details and information about standards and other useful documents.

Stability analysis of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Li Zuoan; Li Kelin

2009-01-01

In this paper, we investigate a class of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. By employing the delay differential inequality with impulsive initial conditions and M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. In particular, the estimate of the exponential converging index is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.

S-Nitroglutathione, a product of the reaction between peroxynitrite and glutathione that generates nitric oxide.

Science.gov (United States)

Balazy, M; Kaminski, P M; Mao, K; Tan, J; Wolin, M S

1998-11-27

Peroxynitrite (ONOO-) has been shown in studies on vascular relaxation and guanylate cyclase activation to react with glutathione (GSH), generating an intermediate product that promotes a time-dependent production of nitric oxide (NO). In this study, reactions of ONOO- with GSH produced a new substance, which was characterized by liquid chromatography, ultraviolet spectroscopy, and electrospray tandem mass spectrometry. The mass spectrometric data provided evidence that the product of this reaction was S-nitroglutathione (GSNO2) and that S-nitrosoglutathione (GSNO) was not a detectable product of this reaction. Further evidence was obtained by comparison of the spectral and chromatographic properties with synthetic standards prepared by reaction of GSH with nitrosonium or nitronium borofluorates. Both the synthetic and ONOO-/GSH-derived GSNO2 generated a protonated ion, GSNO2H+, at m/z 353, which was unusually resistant to decomposition under collision activation, and no fragmentation was observed at collision energy of 25 eV. In contrast, an ion at m/z 337 (GSNOH+), generated from the synthetic GSNO, readily fragmented with the abundant loss of NO at 9 eV. Reactions of ONOO- with GSH resulted in the generation of NO, which was detected by the head space/NO-chemiluminescence analyzer method. The generation of NO was inhibited by the presence of glucose and/or CO2 in the buffers employed. Synthetic GSNO2 spontaneously generated NO in a manner that was not significantly altered by glucose or CO2. Thus, ONOO- reacts with GSH to form GSNO2, and GSNO2 decomposes in a manner that generates NO.

Mobile location services over the next generation IP core network

DEFF Research Database (Denmark)

Thongthammachart, Saowanee; Olesen, Henning

2003-01-01

network is changing from circuit-switched to packet-switched technology and evolving to an IP core network based on IPv6. The IP core network will allow all IP devices to be connected seamlessly. Due to the movement detection mechanism of Mobile IPv6, mobile terminals will periodically update....... The concept of mobile location services over the next generation IP networks is described. We also discuss the effectiveness of the short-range wireless network regarding a mobile user's position inside buildings and hotspot areas....

Overcoming barriers to scheduling embedded generation to support distribution networks

Energy Technology Data Exchange (ETDEWEB)

Wright, A.J.; Formby, J.R.

2000-07-01

Current scheduling of embedded generation for distribution in the UK is limited and patchy. Some DNOs actively schedule while others do none. The literature on the subject is mainly about accommodating volatile wind output, and optimising island systems, for both cost of supply and network stability. The forthcoming NETA will lower prices, expose unpredictable generation to imbalance markets and could introduce punitive constraint payments on DNOs, but at the same time create a dynamic market for both power and ancillary services from embedded generators. Most renewable generators either run as base load (e.g. waste ) or according to the vagaries of the weather (e.g. wind, hydro), so offer little scope for scheduling other than 'off'. CHP plant is normally heat- led for industrial processes or building needs, but supplementary firing or thermal storage often allow considerable scope for scheduling. Micro-CHP with thermal storage could provide short-term scheduling, but tends to be running anyway during the evening peak. Standby generation appears to be ideal for scheduling, but in practice operators may be unwilling to run parallel with the network, and noise and pollution problems may preclude frequent operation. Statistical analysis can be applied to calculate the reliability of several generators compared to one; with a large number of generators such as micro-CHP reliability of a proportion of load is close to unity. The type of communication for generation used will depend on requirements for bandwidth, cost, reliability and whether it is bundled with other services. With high levels of deeply embedded, small-scale generation using induction machines, voltage control and black start capability will become important concerns on 11 kV and LV networks. This will require increased generation monitoring and remote control of switchgear. Examples of cost benefits from scheduling are given, including deferred reinforcement, increased exports on non

Novel mechanism of network protection against the new generation of cyber attacks

Science.gov (United States)

Milovanov, Alexander; Bukshpun, Leonid; Pradhan, Ranjit

2012-06-01

A new intelligent mechanism is presented to protect networks against the new generation of cyber attacks. This mechanism integrates TCP/UDP/IP protocol stack protection and attacker/intruder deception to eliminate existing TCP/UDP/IP protocol stack vulnerabilities. It allows to detect currently undetectable, highly distributed, low-frequency attacks such as distributed denial-of-service (DDoS) attacks, coordinated attacks, botnet, and stealth network reconnaissance. The mechanism also allows insulating attacker/intruder from the network and redirecting the attack to a simulated network acting as a decoy. As a result, network security personnel gain sufficient time to defend the network and collect the attack information. The presented approach can be incorporated into wireless or wired networks that require protection against known and the new generation of cyber attacks.

Unbundling in Current Broadband and Next-Generation Ultra-Broadband Access Networks

Science.gov (United States)

Gaudino, Roberto; Giuliano, Romeo; Mazzenga, Franco; Valcarenghi, Luca; Vatalaro, Francesco

2014-05-01

This article overviews the methods that are currently under investigation for implementing multi-operator open-access/shared-access techniques in next-generation access ultra-broadband architectures, starting from the traditional "unbundling-of-the-local-loop" techniques implemented in legacy twisted-pair digital subscriber line access networks. A straightforward replication of these copper-based unbundling-of-the-local-loop techniques is usually not feasible on next-generation access networks, including fiber-to-the-home point-to-multipoint passive optical networks. To investigate this issue, the article first gives a concise description of traditional copper-based unbundling-of-the-local-loop solutions, then focalizes on both next-generation access hybrid fiber-copper digital subscriber line fiber-to-the-cabinet scenarios and on fiber to the home by accounting for the mix of regulatory and technological reasons driving the next-generation access migration path, focusing mostly on the European situation.

Wavelet network controller for nuclear steam generators

International Nuclear Information System (INIS)

Habibiyan, H; Sayadian, A; Ghafoori-Fard, H

2005-01-01

Poor control of steam generator water level is the main cause of unexpected shutdowns in nuclear power plants. Particularly at low powers, it is a difficult task due to shrink and swell phenomena and flow measurement errors. In addition, the steam generator is a highly complex, nonlinear and time-varying system and its parameters vary with operating conditions. Therefore, it seems that design of a suitable controller is a necessary step to enhance plant availability factor. The purpose of this paper is to design, analyze and evaluate a water level controller for U-tube steam generators using wavelet neural networks. Computer simulations show that the proposed controller improves transient response of steam generator water level and demonstrate its superiority to existing controllers

Impact parameter determination for 40Ca + 40Ca reactions using a neural network

International Nuclear Information System (INIS)

Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

1995-01-01

A neural network is used for the impact parameter determination in 40 Ca + 40 Ca reactions at energies between 35 and 70 AMeV. A special attention is devoted to the effect of experimental constraints such as the detection efficiency. An overall improvement of the impact parameter determination of 25% is obtained with the neural network. The neural network technique is then used in the analysis of the Ca+Ca data at 35 AMeV and allows separation of three different class of events among the selected 'complete' events. (authors). 8 refs., 5 figs

BGen: A UML Behavior Network Generator Tool

Science.gov (United States)

Huntsberger, Terry; Reder, Leonard J.; Balian, Harry

2010-01-01

BGen software was designed for autogeneration of code based on a graphical representation of a behavior network used for controlling automatic vehicles. A common format used for describing a behavior network, such as that used in the JPL-developed behavior-based control system, CARACaS ["Control Architecture for Robotic Agent Command and Sensing" (NPO-43635), NASA Tech Briefs, Vol. 32, No. 10 (October 2008), page 40] includes a graph with sensory inputs flowing through the behaviors in order to generate the signals for the actuators that drive and steer the vehicle. A computer program to translate Unified Modeling Language (UML) Freeform Implementation Diagrams into a legacy C implementation of Behavior Network has been developed in order to simplify the development of C-code for behavior-based control systems. UML is a popular standard developed by the Object Management Group (OMG) to model software architectures graphically. The C implementation of a Behavior Network is functioning as a decision tree.

On the graph and systems analysis of reversible chemical reaction networks with mass action kinetics

NARCIS (Netherlands)

Rao, Shodhan; Jayawardhana, Bayu; Schaft, Arjan van der

2012-01-01

Motivated by the recent progresses on the interplay between the graph theory and systems theory, we revisit the analysis of reversible chemical reaction networks described by mass action kinetics by reformulating it using the graph knowledge of the underlying networks. Based on this formulation, we

Generation of Complex Karstic Conduit Networks with a Hydro-chemical Model

Science.gov (United States)

De Rooij, R.; Graham, W. D.

2016-12-01

The discrete-continuum approach is very well suited to simulate flow and solute transport within karst aquifers. Using this approach, discrete one-dimensional conduits are embedded within a three-dimensional continuum representative of the porous limestone matrix. Typically, however, little is known about the geometry of the karstic conduit network. As such the discrete-continuum approach is rarely used for practical applications. It may be argued, however, that the uncertainty associated with the geometry of the network could be handled by modeling an ensemble of possible karst conduit networks within a stochastic framework. We propose to generate stochastically realistic karst conduit networks by simulating the widening of conduits as caused by the dissolution of limestone over geological relevant timescales. We illustrate that advanced numerical techniques permit to solve the non-linear and coupled hydro-chemical processes efficiently, such that relatively large and complex networks can be generated in acceptable time frames. Instead of specifying flow boundary conditions on conduit cells to recharge the network as is typically done in classical speleogenesis models, we specify an effective rainfall rate over the land surface and let model physics determine the amount of water entering the network. This is advantageous since the amount of water entering the network is extremely difficult to reconstruct, whereas the effective rainfall rate may be quantified using paleoclimatic data. Furthermore, we show that poorly known flow conditions may be constrained by requiring a realistic flow field. Using our speleogenesis model we have investigated factors that influence the geometry of simulated conduit networks. We illustrate that our model generates typical branchwork, network and anastomotic conduit systems. Flow, solute transport and water ages in karst aquifers are simulated using a few illustrative networks.

Reconstruction of three-dimensional porous media using generative adversarial neural networks

Science.gov (United States)

Mosser, Lukas; Dubrule, Olivier; Blunt, Martin J.

2017-10-01

To evaluate the variability of multiphase flow properties of porous media at the pore scale, it is necessary to acquire a number of representative samples of the void-solid structure. While modern x-ray computer tomography has made it possible to extract three-dimensional images of the pore space, assessment of the variability in the inherent material properties is often experimentally not feasible. We present a method to reconstruct the solid-void structure of porous media by applying a generative neural network that allows an implicit description of the probability distribution represented by three-dimensional image data sets. We show, by using an adversarial learning approach for neural networks, that this method of unsupervised learning is able to generate representative samples of porous media that honor their statistics. We successfully compare measures of pore morphology, such as the Euler characteristic, two-point statistics, and directional single-phase permeability of synthetic realizations with the calculated properties of a bead pack, Berea sandstone, and Ketton limestone. Results show that generative adversarial networks can be used to reconstruct high-resolution three-dimensional images of porous media at different scales that are representative of the morphology of the images used to train the neural network. The fully convolutional nature of the trained neural network allows the generation of large samples while maintaining computational efficiency. Compared to classical stochastic methods of image reconstruction, the implicit representation of the learned data distribution can be stored and reused to generate multiple realizations of the pore structure very rapidly.

Formal Specification Based Automatic Test Generation for Embedded Network Systems

Directory of Open Access Journals (Sweden)

Eun Hye Choi

2014-01-01

Full Text Available Embedded systems have become increasingly connected and communicate with each other, forming large-scaled and complicated network systems. To make their design and testing more reliable and robust, this paper proposes a formal specification language called SENS and a SENS-based automatic test generation tool called TGSENS. Our approach is summarized as follows: (1 A user describes requirements of target embedded network systems by logical property-based constraints using SENS. (2 Given SENS specifications, test cases are automatically generated using a SAT-based solver. Filtering mechanisms to select efficient test cases are also available in our tool. (3 In addition, given a testing goal by the user, test sequences are automatically extracted from exhaustive test cases. We’ve implemented our approach and conducted several experiments on practical case studies. Through the experiments, we confirmed the efficiency of our approach in design and test generation of real embedded air-conditioning network systems.

Conception of Next Generation Networks

Directory of Open Access Journals (Sweden)

Slavko Šarić

2004-11-01

tool for the realization ofadditional se1vices and for enabling the control in NGN. Theproblem of JP routers for NGN has also been mentioned, aswell as the importance of the new core generation of optical networks.The conceptual framework of NGN is based today onIP/ATM transport technology, which is at this level of developmentgenerally accepted as the optimal transp011 solution. The problem of addressing caused by the insufficient address spaceof Ipv4 has been stressed and the solution of that problem hasbeen anticipated with the introduction of lpv6 technology,which, due to its complexity and high costs, would be graduallyintroduced by a dual approach into the system.The differentiating elements of NGN in relation to the existingnetworks have been specially pointed out. The modulm;that is, plane nature of the NGN conception in relation to thevertical and hierarchical conception of PSTN has beenstressed, as well as the pdvileges that this open conception offerswhen choosing the equipment of the highest quality by differentmanufacturers. Both existing, voice (TDM and data(NGN (ATM/IP, networks will act parallel in the next yearsuntil new solutions to NGN will have been introduced.

Report on the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Forrest, R.; Dunaeva, S.; Otsuka, N.

2010-07-01

This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Japan Nuclear Reaction Data Centre, Hokkaido University, Sapporo, Japan, from 20 - 23 April 2010. The meeting was attended by 27 participants from 12 cooperating data centres of seven Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Japan Nuclear Reaction Data Centre, Hokkaido University, Sapporo, Japan, from 20 - 23 April 2010. The meeting was attended by 27 participants from 12 cooperating data centres of seven Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. (author)

Variable elimination in post-translational modification reaction networks with mass-action kinetics

DEFF Research Database (Denmark)

Feliu, Elisenda; Wiuf, Carsten

2013-01-01

We define a subclass of chemical reaction networks called post-translational modification systems. Important biological examples of such systems include MAPK cascades and two-component systems which are well-studied experimentally as well as theoretically. The steady states of such a system...

Game-theoretic modeling of curtailment rules and network investments with distributed generation

International Nuclear Information System (INIS)

Andoni, Merlinda; Robu, Valentin; Früh, Wolf-Gerrit; Flynn, David

2017-01-01

Highlights: •Comparative study on curtailment rules and their effects on RES profitability. •Proposal of novel fair curtailment rule which minimises generators’ disruption. •Modeling of private network upgrade as leader-follower (Stackelberg) game. •New model incorporating stochastic generation and variable demand. •New methodology for setting transmission charges in private network upgrade. -- Abstract: Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of several curtailment rules widely used in UK renewable energy projects, and their effect on the viability of renewable generation investment. Moreover, we propose a new curtailment rule which guarantees fair allocation of curtailment amongst all generators with minimal disruption. Another key knowledge gap faced by DNOs is how to incentivise private network upgrades, especially in settings where several generators can use the same line against the payment of a transmission fee. In this work, we provide a solution to this problem by using tools from algorithmic game theory. Specifically, this setting can be modelled as a Stackelberg game between the private transmission line investor and local renewable generators, who are required to pay a transmission fee to access the line. We provide a method for computing the equilibrium of this game, using a model that captures the stochastic nature of renewable energy generation and demand. Finally, we use the practical setting of a grid reinforcement project from the UK and a large dataset of wind speed measurements and demand

Pinning Synchronization of Linear Complex Coupling Synchronous Generators Network of Hydroelectric Generating Set

Directory of Open Access Journals (Sweden)

Xuefei Wu

2014-01-01

Full Text Available A novel linear complex system for hydroturbine-generator sets in multimachine power systems is suggested in this paper and synchronization of the power-grid networks is studied. The advanced graph theory and stability theory are combined to solve the problem. Here we derive a sufficient condition under which the synchronous state of power-grid networks is stable in disturbance attenuation. Finally, numerical simulations are provided to illustrate the effectiveness of the results by the IEEE 39 bus system.

Noise-Induced Modulation of the Relaxation Kinetics around a Non-Equilibrium Steady State of Non-Linear Chemical Reaction Networks

OpenAIRE

Ramaswamy, Rajesh; Sbalzarini, Ivo F; González-Segredo, Nélido

2011-01-01

Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confi...

Loss optimization in distribution networks with distributed generation

DEFF Research Database (Denmark)

Pokhrel, Basanta Raj; Nainar, Karthikeyan; Bak-Jensen, Birgitte

2017-01-01

This paper presents a novel power loss minimization approach in distribution grids considering network reconfiguration, distributed generation and storage installation. Identification of optimum configuration in such scenario is one of the main challenges faced by distribution system operators...... in highly active distribution grids. This issue is tackled by formulating a hybrid loss optimization problem and solved using the Interior Point Method. Sensitivity analysis is used to identify the optimum location of storage units. Different scenarios of reconfiguration, storage and distributed generation...... penetration are created to test the proposed algorithm. It is tested in a benchmark medium voltage network to show the effectiveness and performance of the algorithm. Results obtained are found to be encouraging for radial distribution system. It shows that we can reduce the power loss by more than 30% using...

Converged Wireless Networking and Optimization for Next Generation Services

Directory of Open Access Journals (Sweden)

J. Rodriguez

2010-01-01

Full Text Available The Next Generation Network (NGN vision is tending towards the convergence of internet and mobile services providing the impetus for new market opportunities in combining the appealing services of internet with the roaming capability of mobile networks. However, this convergence does not go far enough, and with the emergence of new coexistence scenarios, there is a clear need to evolve the current architecture to provide cost-effective end-to-end communication. The LOOP project, a EUREKA-CELTIC driven initiative, is one piece in the jigsaw by helping European industry to sustain a leading role in telecommunications and manufacturing of high-value products and machinery by delivering pioneering converged wireless networking solutions that can be successfully demonstrated. This paper provides an overview of the LOOP project and the key achievements that have been tunneled into first prototypes for showcasing next generation services for operators and process manufacturers.

The effect of increasing levels of embedded generation on the distribution network. Final report

Energy Technology Data Exchange (ETDEWEB)

Collinson, A; Earp, G K; Howson, D; Owen, R D; Wright, A J

1999-10-01

This report was commissioned as part of the EA Technology Strategic Technology Programme under guidance of the Module 5 (Embedded Generation) Steering Group. This report aims to provide information related to the distribution and supply of electricity in the context of increasing levels of embedded generation. There is a brief description of the operating environment within which electricity companies in the UK must operate. Technical issues related to the connection of generation to the existing distribution infrastructure are highlighted and the design philosophy adopted by network designers in accommodating applications for the connection of embedded generation to the network is discussed. The effects embedded generation has on the network and the issues raised are presented as many of them present barriers to the connection of embedded generators. The final chapters cover the forecast of required connection to 2010 and solutions to restrictions preventing the connection of more embedded generation to the network. (author)

MTGAN: Speaker Verification through Multitasking Triplet Generative Adversarial Networks

OpenAIRE

Ding, Wenhao; He, Liang

2018-01-01

In this paper, we propose an enhanced triplet method that improves the encoding process of embeddings by jointly utilizing generative adversarial mechanism and multitasking optimization. We extend our triplet encoder with Generative Adversarial Networks (GANs) and softmax loss function. GAN is introduced for increasing the generality and diversity of samples, while softmax is for reinforcing features about speakers. For simplification, we term our method Multitasking Triplet Generative Advers...

Stationary patterns in star networks of bistable units: Theory and application to chemical reactions.

Science.gov (United States)

Kouvaris, Nikos E; Sebek, Michael; Iribarne, Albert; Díaz-Guilera, Albert; Kiss, István Z

2017-04-01

We present theoretical and experimental studies on pattern formation with bistable dynamical units coupled in a star network configuration. By applying a localized perturbation to the central or the peripheral elements, we demonstrate the subsequent spreading, pinning, or retraction of the activations; such analysis enables the characterization of the formation of stationary patterns of localized activity. The results are interpreted with a theoretical analysis of a simplified bistable reaction-diffusion model. Weak coupling results in trivial pinned states where the activation cannot propagate. At strong coupling, a uniform state is expected with active or inactive elements at small or large degree networks, respectively. A nontrivial stationary spatial pattern, corresponding to an activation pinning, is predicted to occur at an intermediate number of peripheral elements and at intermediate coupling strengths, where the central activation of the network is pinned, but the peripheral activation propagates toward the center. The results are confirmed in experiments with star networks of bistable electrochemical reactions. The experiments confirm the existence of the stationary spatial patterns and the dependence of coupling strength on the number of peripheral elements for transitions between pinned and retreating or spreading fronts in forced network configurations (where the central or periphery elements are forced to maintain their states).

Generation of arbitrary two-point correlated directed networks with given modularity

International Nuclear Information System (INIS)

Zhou Jie; Xiao Gaoxi; Wong, Limsoon; Fu Xiuju; Ma, Stefan; Cheng, Tee Hiang

2010-01-01

In this Letter, we introduce measures of correlation in directed networks and develop an efficient algorithm for generating directed networks with arbitrary two-point correlation. Furthermore, a method is proposed for adjusting community structure in directed networks without changing the correlation. Effectiveness of both methods is verified by numerical results.

Thermal analysis experiment for elucidating sodium-water chemical reaction mechanism in steam generator of sodium-cooled fast reactor

International Nuclear Information System (INIS)

Kikuchi, Shin; Kurihara, Akikazu; Ohshima, Hiroyuki

2012-01-01

For the purpose of elucidating the mechanism of the sodium-water surface reaction in a steam generator of sodium-cooled fast reactors, kinetic study of the sodium (Na)-sodium hydroxide (NaOH) reaction has been carried out by using Differential Thermal Analysis (DTA) technique. The parameters, including melting points of Na and NaOH, phase transition temperature of NaOH, Na-NaOH reaction temperature, and decomposition temperature of sodium hydride (NaH) have been identified from DTA curves. Based on the measured reaction temperature, rate constant of sodium monoxide (Na 2 O) generation was obtained. Thermal analysis results indicated that Na 2 O generation at the secondary overall reaction should be considered during the sodium-water reaction. (author)

Sum Frequency Generation Studies of Hydrogenation Reactions on Platinum Nanoparticles

Energy Technology Data Exchange (ETDEWEB)

Krier, James M. [Univ. of California, Berkeley, CA (United States)

2013-08-31

Sum Frequency Generation (SFG) vibrational spectroscopy is used to characterize intermediate species of hydrogenation reactions on the surface of platinum nanoparticle catalysts. In contrast to other spectroscopy techniques which operate in ultra-high vacuum or probe surface species after reaction, SFG collects information under normal conditions as the reaction is taking place. Several systems have been studied previously using SFG on single crystals, notably alkene hydrogenation on Pt(111). In this thesis, many aspects of SFG experiments on colloidal nanoparticles are explored for the first time. To address spectral interference by the capping agent (PVP), three procedures are proposed: UV cleaning, H2 induced disordering and calcination (core-shell nanoparticles). UV cleaning and calcination physically destroy organic capping while disordering reduces SFG signal through a reversible structural change by PVP.

Dynamic Network Drivers of Seizure Generation, Propagation and Termination in Human Neocortical Epilepsy

Science.gov (United States)

Khambhati, Ankit N.; Davis, Kathryn A.; Oommen, Brian S.; Chen, Stephanie H.; Lucas, Timothy H.; Litt, Brian; Bassett, Danielle S.

2015-01-01

The epileptic network is characterized by pathologic, seizure-generating ‘foci’ embedded in a web of structural and functional connections. Clinically, seizure foci are considered optimal targets for surgery. However, poor surgical outcome suggests a complex relationship between foci and the surrounding network that drives seizure dynamics. We developed a novel technique to objectively track seizure states from dynamic functional networks constructed from intracranial recordings. Each dynamical state captures unique patterns of network connections that indicate synchronized and desynchronized hubs of neural populations. Our approach suggests that seizures are generated when synchronous relationships near foci work in tandem with rapidly changing desynchronous relationships from the surrounding epileptic network. As seizures progress, topographical and geometrical changes in network connectivity strengthen and tighten synchronous connectivity near foci—a mechanism that may aid seizure termination. Collectively, our observations implicate distributed cortical structures in seizure generation, propagation and termination, and may have practical significance in determining which circuits to modulate with implantable devices. PMID:26680762

Modeling stochasticity in biochemical reaction networks

International Nuclear Information System (INIS)

Constantino, P H; Vlysidis, M; Smadbeck, P; Kaznessis, Y N

2016-01-01

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. (topical review)

Identifying options for regulating the coordination of network investments with investments in distributed electricity generation

International Nuclear Information System (INIS)

Nisten, E.

2010-02-01

The increase in the distributed generation of electricity, with wind turbines and solar panels, necessitates investments in the distribution network. The current tariff regulation in the Dutch electricity industry, with its ex post evaluation of the efficiency of investments and the frontier shift in the x-factor, delays these investments. In the unbundled electricity industry, the investments in the network need to be coordinated with those in the distributed generation of electricity to enable the DSOs to build enough network capacity. The current Dutch regulations do not provide for a sufficient information exchange between the generators and the system operators to coordinate the investments. This paper analyses these two effects of the Dutch regulation, and suggests improvements to the regulation of the network connection and transportation tariffs to allow for sufficient network capacity and coordination between the investments in the network and in the generation of electricity. These improvements include locally differentiated tariffs that increase with an increasing concentration of distributed generators.

Network investments and the integration of distributed generation: Regulatory recommendations for the Dutch electricity industry

International Nuclear Information System (INIS)

Niesten, Eva

2010-01-01

An increase in the distributed generation of electricity necessitates investments in the distribution network. The current tariff regulation in the Dutch electricity industry, with its ex post evaluation of the efficiency of investments, average benchmarking and a frontier shift in the x-factor, delays these investments. In the unbundled electricity industry, the investments in the network need to be coordinated with those in the distributed generation of electricity to enable the system operators to build enough network capacity. The current Dutch regulations do not provide for a sufficient information exchange between the generators and the system operators to coordinate the investments. This paper analyses these two effects of the Dutch regulations, and suggests improvements to the regulation of the network connection and transportation tariffs to allow for sufficient network capacity and coordination between the investments in the network and in the generation of electricity. These improvements include locally differentiated tariffs that increase with an increasing concentration of distributed generation.

Hierarchical feedback modules and reaction hubs in cell signaling networks.

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

Hierarchical feedback modules and reaction hubs in cell signaling networks.

Directory of Open Access Journals (Sweden)

Jianfeng Xu

Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

Science.gov (United States)

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms

International Nuclear Information System (INIS)

Wang Xiaohu; Xu Daoyi

2009-01-01

In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.

A review on the impact of embedded generation to network fault level

Science.gov (United States)

Yahaya, M. S.; Basar, M. F.; Ibrahim, Z.; Nasir, M. N. N.; Lada, M. Y.; Bukhari, W. M.

2015-05-01

The line of Embedded Generation (EG) in power systems especially for renewable energy has increased markedly in recent years. The interconnection of EG has a technical impact which needs to considered. One of the technical challenges faced by the Distribution Network Operator (DNO) is the network fault level. In this paper, the different methods of interconnection with and without EG on the network is analyze by looking at the impact of network fault level. This comparative study made to determine the most effective method to reduce fault level or fault current. This paper will gives basic understanding on the fault level effect when synchronous generator connected to network by different method of interconnection. A three phase fault is introduced at one network bus bar. By employ it to simple network configuration of network configurations which is normal interconnection and splitting network connection with and without EG, the fault level has been simulated and analyzed. Developing the network model by using PSS-Viper™ software package, the fault level for both networks will be showed and the difference is defines. From the review, network splitting was found the best interconnection method and greatest potential for reducing the fault level in the network.

Facilitate generation connections on Orkney by automatic distribution network management

Energy Technology Data Exchange (ETDEWEB)

NONE

2004-07-01

This report summarises the results of a study assessing the capability and limitations of the Orkney Network under a variety of conditions of demand, generation connections, network configuration, and reactive compensation). A conceptual active management scheme (AMS) suitable for the conditions on Orkney is developed and evaluated. Details are given of a proposed framework for the design and evaluation of future active management schemes, logic control sequences for managed generation units, and a proposed evaluation method for the active management scheme. Implications of introducing the proposed AMS are examined, and the commercial aspects of an AMS and system security are considered. The existing Orkney network is described; and an overview of the SHEPDL (Scottish Hydro Electric Power Distribution Ltd.) SCADA system is presented with a discussion of AMS identification, selection, and development.

Next generation satellite communications networks

Science.gov (United States)

Garland, P. J.; Osborne, F. J.; Streibl, I.

The paper introduces two potential uses for new space hardware to permit enhanced levels of signal handling and switching in satellite communication service for Canada. One application involves increased private-sector services in the Ku band; the second supports new personal/mobile services by employing higher levels of handling and switching in the Ka band. First-generation satellite regeneration and switching experiments involving the NASA/ACTS spacecraft are described, where the Ka band and switching satellite network problems are emphasized. Second-generation satellite development is outlined based on demand trends for more packet-based switching, low-cost earth stations, and closed user groups. A demonstration mission for new Ka- and Ku-band technologies is proposed, including the payload configuration. The half ANIK E payload is shown to meet the demonstration objectives, and projected to maintain a fully operational payload for at least 10 years.

A reaction-diffusion model of ROS-induced ROS release in a mitochondrial network.

Directory of Open Access Journals (Sweden)

Lufang Zhou

2010-01-01

Full Text Available Loss of mitochondrial function is a fundamental determinant of cell injury and death. In heart cells under metabolic stress, we have previously described how the abrupt collapse or oscillation of the mitochondrial energy state is synchronized across the mitochondrial network by local interactions dependent upon reactive oxygen species (ROS. Here, we develop a mathematical model of ROS-induced ROS release (RIRR based on reaction-diffusion (RD-RIRR in one- and two-dimensional mitochondrial networks. The nodes of the RD-RIRR network are comprised of models of individual mitochondria that include a mechanism of ROS-dependent oscillation based on the interplay between ROS production, transport, and scavenging; and incorporating the tricarboxylic acid (TCA cycle, oxidative phosphorylation, and Ca(2+ handling. Local mitochondrial interaction is mediated by superoxide (O2.- diffusion and the O2.(--dependent activation of an inner membrane anion channel (IMAC. In a 2D network composed of 500 mitochondria, model simulations reveal DeltaPsi(m depolarization waves similar to those observed when isolated guinea pig cardiomyocytes are subjected to a localized laser-flash or antioxidant depletion. The sensitivity of the propagation rate of the depolarization wave to O(2.- diffusion, production, and scavenging in the reaction-diffusion model is similar to that observed experimentally. In addition, we present novel experimental evidence, obtained in permeabilized cardiomyocytes, confirming that DeltaPsi(m depolarization is mediated specifically by O2.-. The present work demonstrates that the observed emergent macroscopic properties of the mitochondrial network can be reproduced in a reaction-diffusion model of RIRR. Moreover, the findings have uncovered a novel aspect of the synchronization mechanism, which is that clusters of mitochondria that are oscillating can entrain mitochondria that would otherwise display stable dynamics. The work identifies the

Computer-assisted design for scaling up systems based on DNA reaction networks.

Science.gov (United States)

Aubert, Nathanaël; Mosca, Clément; Fujii, Teruo; Hagiya, Masami; Rondelez, Yannick

2014-04-06

In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the

Comparing Generative Adversarial Network Techniques for Image Creation and Modification

NARCIS (Netherlands)

Pieters, Mathijs; Wiering, Marco

2018-01-01

Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder

Data-Driven Handover Optimization in Next Generation Mobile Communication Networks

Directory of Open Access Journals (Sweden)

Po-Chiang Lin

2016-01-01

Full Text Available Network densification is regarded as one of the important ingredients to increase capacity for next generation mobile communication networks. However, it also leads to mobility problems since users are more likely to hand over to another cell in dense or even ultradense mobile communication networks. Therefore, supporting seamless and robust connectivity through such networks becomes a very important issue. In this paper, we investigate handover (HO optimization in next generation mobile communication networks. We propose a data-driven handover optimization (DHO approach, which aims to mitigate mobility problems including too-late HO, too-early HO, HO to wrong cell, ping-pong HO, and unnecessary HO. The key performance indicator (KPI is defined as the weighted average of the ratios of these mobility problems. The DHO approach collects data from the mobile communication measurement results and provides a model to estimate the relationship between the KPI and features from the collected dataset. Based on the model, the handover parameters, including the handover margin and time-to-trigger, are optimized to minimize the KPI. Simulation results show that the proposed DHO approach could effectively mitigate mobility problems.

Development and testing of a compartmentalized reaction network model for redox zones in contaminated aquifers

Science.gov (United States)

Abrams , Robert H.; Loague, Keith; Kent, Douglas B.

1998-01-01

The work reported here is the first part of a larger effort focused on efficient numerical simulation of redox zone development in contaminated aquifers. The sequential use of various electron acceptors, which is governed by the energy yield of each reaction, gives rise to redox zones. The large difference in energy yields between the various redox reactions leads to systems of equations that are extremely ill-conditioned. These equations are very difficult to solve, especially in the context of coupled fluid flow, solute transport, and geochemical simulations. We have developed a general, rational method to solve such systems where we focus on the dominant reactions, compartmentalizing them in a manner that is analogous to the redox zones that are often observed in the field. The compartmentalized approach allows us to easily solve a complex geochemical system as a function of time and energy yield, laying the foundation for our ongoing work in which we couple the reaction network, for the development of redox zones, to a model of subsurface fluid flow and solute transport. Our method (1) solves the numerical system without evoking a redox parameter, (2) improves the numerical stability of redox systems by choosing which compartment and thus which reaction network to use based upon the concentration ratios of key constituents, (3) simulates the development of redox zones as a function of time without the use of inhibition factors or switching functions, and (4) can reduce the number of transport equations that need to be solved in space and time. We show through the use of various model performance evaluation statistics that the appropriate compartment choice under different geochemical conditions leads to numerical solutions without significant error. The compartmentalized approach described here facilitates the next phase of this effort where we couple the redox zone reaction network to models of fluid flow and solute transport.

Optimization Models for Reaction Networks: Information Divergence, Quadratic Programming and Kirchhoff’s Laws

Directory of Open Access Journals (Sweden)

Julio Michael Stern

2014-03-01

Full Text Available This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

Passivity analysis for uncertain BAM neural networks with time delays and reaction-diffusions

Science.gov (United States)

Zhou, Jianping; Xu, Shengyuan; Shen, Hao; Zhang, Baoyong

2013-08-01

This article deals with the problem of passivity analysis for delayed reaction-diffusion bidirectional associative memory (BAM) neural networks with weight uncertainties. By using a new integral inequality, we first present a passivity condition for the nominal networks, and then extend the result to the case with linear fractional weight uncertainties. The proposed conditions are expressed in terms of linear matrix inequalities, and thus can be checked easily. Examples are provided to demonstrate the effectiveness of the proposed results.

FERN - a Java framework for stochastic simulation and evaluation of reaction networks.

Science.gov (United States)

Erhard, Florian; Friedel, Caroline C; Zimmer, Ralf

2008-08-29

Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a) do not provide the most efficient simulation algorithms and are difficult to extend, b) cannot be easily integrated into other applications or c) do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. In this article, we present FERN (Framework for Evaluation of Reaction Networks), a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment. FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new

Applicant reactions to social network web use in personnel selection and assessment

Directory of Open Access Journals (Sweden)

David Aguado

2016-12-01

Full Text Available Human Resource (HR professionals are increasingly using Social Networking Websites (SNWs for personnel recruitment and selection processes. However, evidence is required regarding their psychometric properties and their impact on applicant reactions. In this paper we present and discuss the results of exploring applicant reactions to either the use of a professional SNW (such as LinkedIn or a non-professional SNW (such as Facebook. A scale for assessing applicant reactions was applied to 124 professionals. The results showed more positive attitudes to the use of professional SNWs compared with non-professional SNWs. Both gender and age moderated these results, with females and young applicants having a less positive attitude than males and older participants towards the use of non-professional SNWs.

SUPECA kinetics for scaling redox reactions in networks of mixed substrates and consumers and an example application to aerobic soil respiration

Science.gov (United States)

Tang, Jin-Yun; Riley, William J.

2017-09-01

Several land biogeochemical models used for studying carbon-climate feedbacks have begun explicitly representing microbial dynamics. However, to our knowledge, there has been no theoretical work on how to achieve a consistent scaling of the complex biogeochemical reactions from microbial individuals to populations, communities, and interactions with plants and mineral soils. We focus here on developing a mathematical formulation of the substrate-consumer relationships for consumer-mediated redox reactions of the form A + BE→ products, where products could be, e.g., microbial biomass or bioproducts. Under the quasi-steady-state approximation, these substrate-consumer relationships can be formulated as the computationally difficult full equilibrium chemistry problem or approximated analytically with the dual Monod (DM) or synthesizing unit (SU) kinetics. We find that DM kinetics is scaling inconsistently for reaction networks because (1) substrate limitations are not considered, (2) contradictory assumptions are made regarding the substrate processing rate when transitioning from single- to multi-substrate redox reactions, and (3) the product generation rate cannot be scaled from one to multiple substrates. In contrast, SU kinetics consistently scales the product generation rate from one to multiple substrates but predicts unrealistic results as consumer abundances reach large values with respect to their substrates. We attribute this deficit to SU's failure to incorporate substrate limitation in its derivation. To address these issues, we propose SUPECA (SU plus the equilibrium chemistry approximation - ECA) kinetics, which consistently imposes substrate and consumer mass balance constraints. We show that SUPECA kinetics satisfies the partition principle, i.e., scaling invariance across a network of an arbitrary number of reactions (e.g., as in Newton's law of motion and Dalton's law of partial pressures). We tested SUPECA kinetics with the equilibrium chemistry

Regulatory Improvements for Effective Integration of Distributed Generation into Electricity Distribution Networks

International Nuclear Information System (INIS)

Scheepers, M.J.J.; Jansen, J.C.; De Joode, J.; Bauknecht, D.; Gomez, T.; Pudjianto, D.; Strbac, G.; Ropenus, S.

2007-11-01

The growth of distributed electricity supply of renewable energy sources (RES-E) and combined heat and power (CHP) - so called distributed generation (DG) - can cause technical problems for electricity distribution networks. These integration problems can be overcome by reinforcing the network. Many European Member States apply network regulation that does not account for the impact of DG growth on the network costs. Passing on network integration costs to the DG-operator who is responsible for these extra costs may result in discrimination between different DG plants and between DG and large power generation. Therefore, in many regulatory systems distribution system operators (DSOs) are not being compensated for the DG integration costs. The DG-GRID project analysed technical and economical barriers for integration of distributed generation into electricity distribution networks. The project looked into the impact of a high DG deployment on the electricity distribution system costs and the impact on the financial position of the DSO. Several ways for improving network regulation in order to compensate DSOs for the increasing DG penetration were identified and tested. The DG-GRID project looked also into stimulating network innovations through economic regulation. The project was co-financed by the European Commission and carried out by nine European universities and research institutes. This report summarises the project results and is based on a number of DG-GRID reports that describe the conducted analyses and their results

Synchronous ethernet and IEEE 1588 in telecoms next generation synchronization networks

CERN Document Server

2013-01-01

This book addresses the multiple technical aspects of the distribution of synchronization in new generation telecommunication networks, focusing in particular on synchronous Ethernet and IEEE1588 technologies. Many packet network engineers struggle with understanding the challenges that precise synchronization distribution can impose on networks. The usual “why”, “when” and particularly “how” can cause problems for many engineers. In parallel to this, some other markets have identical synchronization requirements, but with their own design requirements, generating further questions. This book attempts to respond to the different questions by providing background technical information. Invaluable information on state of-the-art packet network synchronization and timing architectures is provided, as well as an unbiased view on the synchronization technologies that have been internationally standardized over recent years, with the aim of providing the average reader (who is not skilled in the art) wi...

Drift-Implicit Multi-Level Monte Carlo Tau-Leap Methods for Stochastic Reaction Networks

KAUST Repository

Ben Hammouda, Chiheb

2015-01-01

-space and deterministic ones. These stochastic models constitute the theory of stochastic reaction networks (SRNs). Furthermore, in some cases, the dynamics of fast and slow time scales can be well separated and this is characterized by what is called sti

Generating private recommendations in a social trust network

NARCIS (Netherlands)

Erkin, Z.; Veugen, P.J.M.; Lagendijk, R.L.

2011-01-01

Recommender systems have become increasingly important in e-commerce as they can guide customers with finding personalized services and products. A variant of recommender systems that generates recommendations from a set of trusted people is recently getting more attention in social networks.

Pricing of embedded generation: Incorporation of externalities and avoided network losses

International Nuclear Information System (INIS)

Rodrigo, Asanka S.; Wijayatunga, Priyantha D.C.

2007-01-01

Traditionally, the electricity purchase tariff of embedded generators reflected only the cost of production and delivery of electricity to the consumers, which includes the costs of labor, capital, operation, taxes and insurance. However, the production of electricity causes adverse impacts on the environment. At present, this issue has not been widely addressed by the existing pricing methodologies. This paper proposes a pricing methodology for renewable energy based embedded electricity generation, incorporating the cost of externalities with a case study on the Sri Lanka power system. It recommends that the embedded generation tariff be based on the principle of 'avoided cost', considering the cost of energy production, cost of externalities and the cost of network losses. While the 'impact path way' approach is proposed for calculation of the cost of externalities of energy, the nodal-based cost calculation is proposed for the avoided cost of network losses calculation. The pricing methodology proposed in the paper provides important information for investors when choosing the most economical site for their development. It can also be used to optimize the network use. These will allow the developers of embedded generation facilities and the utilities operating the national grid to maximize the potential of embedded generation. (author)

Global exponential stability for reaction-diffusion recurrent neural networks with multiple time varying delays

International Nuclear Information System (INIS)

Lou, X.; Cui, B.

2008-01-01

In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)

Neural network approach to time-dependent dividing surfaces in classical reaction dynamics

Science.gov (United States)

Schraft, Philippe; Junginger, Andrej; Feldmaier, Matthias; Bardakcioglu, Robin; Main, Jörg; Wunner, Günter; Hernandez, Rigoberto

2018-04-01

In a dynamical system, the transition between reactants and products is typically mediated by an energy barrier whose properties determine the corresponding pathways and rates. The latter is the flux through a dividing surface (DS) between the two corresponding regions, and it is exact only if it is free of recrossings. For time-independent barriers, the DS can be attached to the top of the corresponding saddle point of the potential energy surface, and in time-dependent systems, the DS is a moving object. The precise determination of these direct reaction rates, e.g., using transition state theory, requires the actual construction of a DS for a given saddle geometry, which is in general a demanding methodical and computational task, especially in high-dimensional systems. In this paper, we demonstrate how such time-dependent, global, and recrossing-free DSs can be constructed using neural networks. In our approach, the neural network uses the bath coordinates and time as input, and it is trained in a way that its output provides the position of the DS along the reaction coordinate. An advantage of this procedure is that, once the neural network is trained, the complete information about the dynamical phase space separation is stored in the network's parameters, and a precise distinction between reactants and products can be made for all possible system configurations, all times, and with little computational effort. We demonstrate this general method for two- and three-dimensional systems and explain its straightforward extension to even more degrees of freedom.

Systems biology and the origins of life? part II. Are biochemical networks possible ancestors of living systems? networks of catalysed chemical reactions: non-equilibrium, self-organization and evolution.

Science.gov (United States)

Ricard, Jacques

2010-01-01

The present article discusses the possibility that catalysed chemical networks can evolve. Even simple enzyme-catalysed chemical reactions can display this property. The example studied is that of a two-substrate proteinoid, or enzyme, reaction displaying random binding of its substrates A and B. The fundamental property of such a system is to display either emergence or integration depending on the respective values of the probabilities that the enzyme has bound one of its substrate regardless it has bound the other substrate, or, specifically, after it has bound the other substrate. There is emergence of information if p(A)>p(AB) and p(B)>p(BA). Conversely, if p(A)equilibrium. Moreover, in such systems, emergence results in an increase of the energy level of the ternary EAB complex that becomes closer to the transition state of the reaction, thus leading to the enhancement of catalysis. Hence a drift from quasi-equilibrium is, to a large extent, responsible for the production of information and enhancement of catalysis. Non-equilibrium of these simple systems must be an important aspect that leads to both self-organization and evolutionary processes. These conclusions can be extended to networks of catalysed chemical reactions. Such networks are, in fact, networks of networks, viz. meta-networks. In this formal representation, nodes are chemical reactions catalysed by poorly specific proteinoids, and links can be identified to the transport of metabolites from proteinoid to proteinoid. The concepts of integration and emergence can be applied to such situations and can be used to define the identity of these networks and therefore their evolution. Defined as open non-equilibrium structures, such biochemical networks possess two remarkable properties: (1) the probability of occurrence of their nodes is dependant upon the input and output of matter in, and from, the system and (2) the probability of occurrence of the nodes is strictly linked to their degree of

Probabilistic generation of random networks taking into account information on motifs occurrence.

Science.gov (United States)

Bois, Frederic Y; Gayraud, Ghislaine

2015-01-01

Because of the huge number of graphs possible even with a small number of nodes, inference on network structure is known to be a challenging problem. Generating large random directed graphs with prescribed probabilities of occurrences of some meaningful patterns (motifs) is also difficult. We show how to generate such random graphs according to a formal probabilistic representation, using fast Markov chain Monte Carlo methods to sample them. As an illustration, we generate realistic graphs with several hundred nodes mimicking a gene transcription interaction network in Escherichia coli.

Acoustic sodium-water reaction detection of the Phenix steam generators

International Nuclear Information System (INIS)

Carminati, M.; Martin, L.; Sauzaret, A.

1990-01-01

The systems for acoustic sodium-water reaction detection and hydrogen detection of the Phenix steam generators as well as systems for monitoring signals analysis and processing are described. It is reported that the results obtained during operation and calibration phases are very encouraging and that industrial equipment showing the same performance are being examined. 6 figs

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

Science.gov (United States)

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

2010-01-01

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

Next Generation Access Network Deployment in Croatia: Optical Access Networks and Current IoT/5G Status

Science.gov (United States)

Breskovic, Damir; Sikirica, Mladen; Begusic, Dinko

2018-05-01

This paper gives an overview and background of optical access network deployment in Croatia. Optical access network development in Croatia has been put into a global as well as in the European Union context. All the challenges and the driving factors for optical access networks deployment are considered. Optical access network architectures that have been deployed by most of the investors in Croatian telecommunication market are presented, as well as the architectures that are in early phase of deployment. Finally, an overview on current status of mobile networks of the fifth generation and Internet of Things is given.

Translation-aware semantic segmentation via conditional least-square generative adversarial networks

Science.gov (United States)

Zhang, Mi; Hu, Xiangyun; Zhao, Like; Pang, Shiyan; Gong, Jinqi; Luo, Min

2017-10-01

Semantic segmentation has recently made rapid progress in the field of remote sensing and computer vision. However, many leading approaches cannot simultaneously translate label maps to possible source images with a limited number of training images. The core issue is insufficient adversarial information to interpret the inverse process and proper objective loss function to overcome the vanishing gradient problem. We propose the use of conditional least squares generative adversarial networks (CLS-GAN) to delineate visual objects and solve these problems. We trained the CLS-GAN network for semantic segmentation to discriminate dense prediction information either from training images or generative networks. We show that the optimal objective function of CLS-GAN is a special class of f-divergence and yields a generator that lies on the decision boundary of discriminator that reduces possible vanished gradient. We also demonstrate the effectiveness of the proposed architecture at translating images from label maps in the learning process. Experiments on a limited number of high resolution images, including close-range and remote sensing datasets, indicate that the proposed method leads to the improved semantic segmentation accuracy and can simultaneously generate high quality images from label maps.

Wastage of Steam Generator Tubes by Sodium-Water Reaction

International Nuclear Information System (INIS)

Jeong, Ji Young; Kim, Jong Man; Kim, Tae Joon; Choi, Jong Hyeun; Kim, Byung Ho; Lee, Yong Bum; Park, Nam Cook

2010-01-01

The Korea Advanced LIquid MEtal Reactor (KALIMER) steam generator is a helical coil, vertically oriented, shell-and-tube type heat exchanger with fixed tube-sheet. The conceptual design and outline drawing of the steam generator are shown in Figure 1. Flow is counter-current, with sodium on the shell side and water/steam on the tube side. Sodium flow enters the steam generator through the upper inlet nozzles and then flows down through the tube bundle. Feedwater enters the steam generator through the feedwater nozzles at the bottom of steam generator. Therefore, if there is a hole or a crack in a heat transfer tube, a leakage of water/steam into the sodium may occur, resulting in a sodium-water reaction. When such a leak occurs, so-called 'wastage' is the result which may cause damage to or a failure of the adjacent tubes. If a steam generator is operated for some time in this condition, it is possible that it might create an intermediate leak state which would then give rise to the problems of a multi-target wastage in a very short time. Therefore, it is very important to predict these phenomena quantitatively from the view of designing a steam generator and its leak detection systems. For this, multi-target wastage tests for modified 9Cr-1Mo steel tube bundle by intermediate leaks are being prepared

Tri-generation in urban networks; Trigeneration en reseau urbain

Energy Technology Data Exchange (ETDEWEB)

Malahieude, J.M. [Trigen Energy Corp., New-York (United States)

1996-12-31

The concepts of tri-generation (simultaneous production of heat, electric power and refrigerating energy) and thermal energy distribution networks, are presented. The different components of the tri-generation system from Trigen Energy Corp. are ammonia as a refrigerant for the production of cooled water, screw compressors, gas turbines and an induction motor-generator in order to optimize the combined gas turbine and compressor utilization. The energy efficiency and pollution reduction of the system are evaluated; the system has been enhanced through re-powering and post combustion

Network integration of distributed power generation

Science.gov (United States)

Dondi, Peter; Bayoumi, Deia; Haederli, Christoph; Julian, Danny; Suter, Marco

The world-wide move to deregulation of the electricity and other energy markets, concerns about the environment, and advances in renewable and high efficiency technologies has led to major emphasis being placed on the use of small power generation units in a variety of forms. The paper reviews the position of distributed generation (DG, as these small units are called in comparison with central power plants) with respect to the installation and interconnection of such units with the classical grid infrastructure. In particular, the status of technical standards both in Europe and USA, possible ways to improve the interconnection situation, and also the need for decisions that provide a satisfactory position for the network operator (who remains responsible for the grid, its operation, maintenance and investment plans) are addressed.

Report on the IAEA technical meeting on network of nuclear reaction data centres

Energy Technology Data Exchange (ETDEWEB)

Schwerer, O [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria); Henriksson, H [NEA Data Bank, Issy-les-Moulineaux (France)

2005-01-15

This report summarizes the IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Brookhaven National Laboratory, Upton, NY, USA from 4-7 October 2004. The meeting was attended by 20 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, status reports of the participating data centres, and a revised technical protocol for the cooperation of the network. (author)

Report on the IAEA technical meeting on network of nuclear reaction data centres

International Nuclear Information System (INIS)

Schwerer, O.; Henriksson, H.

2005-01-01

This report summarizes the IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Brookhaven National Laboratory, Upton, NY, USA from 4-7 October 2004. The meeting was attended by 20 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, status reports of the participating data centres, and a revised technical protocol for the cooperation of the network. (author)

The generation of random directed networks with prescribed 1-node and 2-node degree correlations

International Nuclear Information System (INIS)

Zamora-Lopez, Gorka; Kurths, Juergen; Zhou Changsong; Zlatic, Vinko

2008-01-01

The generation of random networks is a very common problem in complex network research. In this paper, we have studied the correlation nature of several real networks and found that, typically, a large number of links are deterministic, i.e. they cannot be randomized. This finding permits fast generation of ensembles of maximally random networks with prescribed 1-node and 2-node degree correlations. When the introduction of self-loops or multiple-links are not desired, random network generation methods typically reach blocked states. Here, a mechanism is proposed, the 'force-and-drop' method, to overcome such states. Our algorithm can be easily simplified for undirected graphs and reduced to account for any subclass of 2-node degree correlations

Automatic Generation of Network Protocol Gateways

DEFF Research Database (Denmark)

Bromberg, Yérom-David; Réveillère, Laurent; Lawall, Julia

2009-01-01

for describing protocol behaviors, message structures, and the gateway logic.  Z2z includes a compiler that checks essential correctness properties and produces efficient code. We have used z2z to develop a number of gateways, including SIP to RTSP, SLP to UPnP, and SMTP to SMTP via HTTP, involving a range......The emergence of networked devices in the home has made it possible to develop applications that control a variety of household functions. However, current devices communicate via a multitude of incompatible protocols, and thus gateways are needed to translate between them.  Gateway construction......, however, requires an intimate knowledge of the relevant protocols and a substantial understanding of low-level network programming, which can be a challenge for many application programmers. This paper presents a generative approach to gateway construction, z2z, based on a domain-specific language...

Gender roles in social network sites from generation Y

Directory of Open Access Journals (Sweden)

F. Javier Rondan-Cataluña

2017-12-01

Full Text Available One of the fundamental and most commonly used communication tools by the generation Y or Millennials are online social networks. The first objective of this study is to model the effects that exercise social participation, community integration and trust in community satisfaction, as an antecedent of routinization. Besides, we propose as a second objective checking if gender roles proposed to underlie the different behaviors that develop social network users. An empirical study was carried out on a sample of 1,448 undergraduate students that are SNS users from Generation Y. First, we applied a structural equation modeling approach to test the proposed model. Second, we followed a methodology using a scale of masculinity and femininity to categorize the sample obtaining three groups: feminine, masculine, and androgynous.

Effects of network dissolution changes on pore-to-core upscaled reaction rates for kaolinite and anorthite reactions under acidic conditions

KAUST Repository

Kim, Daesang

2013-11-01

We have extended reactive flow simulation in pore-network models to include geometric changes in the medium from dissolution effects. These effects include changes in pore volume and reactive surface area, as well as topological changes that open new connections. The computed changes were based upon a mineral map from an X-ray computed tomography image of a sandstone core. We studied the effect of these changes on upscaled (pore-scale to core-scale) reaction rates and compared against the predictions of a continuum model. Specifically, we modeled anorthite and kaolinite reactions under acidic flow conditions during which the anorthite reactions remain far from equilibrium (dissolution only), while the kaolinite reactions can be near-equilibrium. Under dissolution changes, core-scale reaction rates continuously and nonlinearly evolved in time. At higher injection rates, agreement with predictions of the continuum model degraded significantly. For the far-from-equilibrium reaction, our results indicate that the ability to correctly capture the heterogeneity in dissolution changes in the reactive mineral surface area is critical to accurately predict upscaled reaction rates. For the near-equilibrium reaction, the ability to correctly capture the heterogeneity in the saturation state remains critical. Inclusion of a Nernst-Planck term to ensure neutral ionic currents under differential diffusion resulted in at most a 9% correction in upscaled rates.

Fluid power network for centralized electricity generation in offshore wind farms

NARCIS (Netherlands)

Jarquin-Laguna, A.

2014-01-01

An innovative and completely different wind-energy conversion system is studied where a centralized electricity generation within a wind farm is proposed by means of a hydraulic network. This paper presents the dynamic interaction of two turbines when they are coupled to the same hydraulic network.

Satellite communications for the next generation telecommunication services and networks

Science.gov (United States)

Chitre, D. M.

1991-01-01

Satellite communications can play an important role in provisioning the next-generation telecommunication services and networks, provided the protocols specifying these services and networks are satellite-compatible and the satellite subnetworks, consisting of earth stations interconnected by the processor and the switch on board the satellite, interwork effectively with the terrestrial networks. The specific parameters and procedures of frame relay and broadband integrated services digital network (B-ISDN) protocols which are impacted by a satellite delay. Congestion and resource management functions for frame relay and B-ISDN are discussed in detail, describing the division of these functions between earth stations and on board the satellite. Specific onboard and ground functions are identified as potential candidates for their implementation via neural network technology.

Dynamical Behaviors of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Delays

Directory of Open Access Journals (Sweden)

Li Wan

2012-01-01

Full Text Available This paper investigates dynamical behaviors of stochastic Cohen-Grossberg neural network with delays and reaction diffusion. By employing Lyapunov method, Poincaré inequality and matrix technique, some sufficient criteria on ultimate boundedness, weak attractor, and asymptotic stability are obtained. Finally, a numerical example is given to illustrate the correctness and effectiveness of our theoretical results.

Design and testing of an armature-reaction-compensated permanent magnet synchronous generator for island operation

Energy Technology Data Exchange (ETDEWEB)

Kamiev, K.

2013-11-01

At present, permanent magnet synchronous generators (PMSGs) are of great interest. Since they do not have electrical excitation losses, the highly efficient, lightweight and compact PMSGs equipped with damper windings work perfectly when connected to a network. However, in island operation, the generator (or parallel generators) alone is responsible for the building up of the network and maintaining its voltage and reactive power level. Thus, in island operation, a PMSG faces very tight constraints, which are difficult to meet, because the flux produced by the permanent magnets (PMs) is constant and the voltage of the generator cannot be controlled. Traditional electrically excited synchronous generators (EESGs) can easily meet these constraints, because the field winding current is controllable. The main drawback of the conventional EESG is the relatively high excitation loss. This doctoral thesis presents a study of an alternative solution termed as a hybrid excitation synchronous generator (HESG). HESGs are a special class of electrical machines, where the total rotor current linkage is produced by the simultaneous action of two different excitation sources: the electrical and permanent magnet (PM) excitation. An overview of the existing HESGs is given. Several HESGs are introduced and compared with the conventional EESG from technical and economic points of view. In the study, the armature-reaction-compensated permanent magnet synchronous generator with alternated current linkages (ARC-PMSG with ACL) showed a better performance than the other options. Therefore, this machine type is studied in more detail. An electromagnetic design and a thermal analysis are presented. To verify the operation principle and the electromagnetic design, a down-sized prototype of 69 kVA apparent power was built. The experimental results are demonstrated and compared with the predicted ones. A prerequisite for an ARC-PMSG with ACL is an even number of pole pairs (p = 2, 4, 6

Cascading Generative Adversarial Networks for Targeted

KAUST Repository

Hamdi, Abdullah

2018-01-01

Abundance of labelled data played a crucial role in the recent developments in computer vision, but that faces problems like scalability and transferability to the wild. One alternative approach is to utilize the data without labels, i.e. unsupervised learning, in learning valuable information and put it in use to tackle vision problems. Generative Adversarial Networks (GANs) have gained momentum for their ability to model image distributions in unsupervised manner. They learn to emulate the training set and that enables sampling from that domain and using the knowledge learned for useful applications. Several methods proposed enhancing GANs, including regularizing the loss with some feature matching. We seek to push GANs beyond the data in the training and try to explore unseen territory in the image manifold. We first propose a new regularizer for GAN based on K-Nearest Neighbor (K-NN) selective feature matching to a target set Y in high-level feature space, during the adversarial training of GAN on the base set X, and we call this novel model K-GAN. We show that minimizing the added term follows from cross-entropy minimization between the distributions of GAN and set Y. Then, we introduce a cascaded framework for GANs that try to address the task of imagining a new distribution that combines the base set X and target set Y by cascading sampling GANs with translation GANs, and we dub the cascade of such GANs as the Imaginative Adversarial Network (IAN). Several cascades are trained on a collected dataset Zoo-Faces and generated innovative samples are shown, including from K-GAN cascade. We conduct an objective and subjective evaluation for different IAN setups in the addressed task of generating innovative samples and we show the effect of regularizing GAN on different scores. We conclude with some useful applications for these IANs, like multi-domain manifold traversing.

Cascading Generative Adversarial Networks for Targeted

KAUST Repository

Hamdi, Abdullah

2018-04-09

Abundance of labelled data played a crucial role in the recent developments in computer vision, but that faces problems like scalability and transferability to the wild. One alternative approach is to utilize the data without labels, i.e. unsupervised learning, in learning valuable information and put it in use to tackle vision problems. Generative Adversarial Networks (GANs) have gained momentum for their ability to model image distributions in unsupervised manner. They learn to emulate the training set and that enables sampling from that domain and using the knowledge learned for useful applications. Several methods proposed enhancing GANs, including regularizing the loss with some feature matching. We seek to push GANs beyond the data in the training and try to explore unseen territory in the image manifold. We first propose a new regularizer for GAN based on K-Nearest Neighbor (K-NN) selective feature matching to a target set Y in high-level feature space, during the adversarial training of GAN on the base set X, and we call this novel model K-GAN. We show that minimizing the added term follows from cross-entropy minimization between the distributions of GAN and set Y. Then, we introduce a cascaded framework for GANs that try to address the task of imagining a new distribution that combines the base set X and target set Y by cascading sampling GANs with translation GANs, and we dub the cascade of such GANs as the Imaginative Adversarial Network (IAN). Several cascades are trained on a collected dataset Zoo-Faces and generated innovative samples are shown, including from K-GAN cascade. We conduct an objective and subjective evaluation for different IAN setups in the addressed task of generating innovative samples and we show the effect of regularizing GAN on different scores. We conclude with some useful applications for these IANs, like multi-domain manifold traversing.

Artificial earthquake record generation using cascade neural network

Directory of Open Access Journals (Sweden)

Bani-Hani Khaldoon A.

2017-01-01

Full Text Available This paper presents the results of using artificial neural networks (ANN in an inverse mapping problem for earthquake accelerograms generation. This study comprises of two parts: 1-D site response analysis; performed for Dubai Emirate at UAE, where eight earthquakes records are selected and spectral matching are performed to match Dubai response spectrum using SeismoMatch software. Site classification of Dubai soil is being considered for two classes C and D based on shear wave velocity of soil profiles. Amplifications factors are estimated to quantify Dubai soil effect. Dubai’s design response spectra are developed for site classes C & D according to International Buildings Code (IBC -2012. In the second part, ANN is employed to solve inverse mapping problem to generate time history earthquake record. Thirty earthquakes records and their design response spectrum with 5% damping are used to train two cascade forward backward neural networks (ANN1, ANN2. ANN1 is trained to map the design response spectrum to time history and ANN2 is trained to map time history records to the design response spectrum. Generalized time history earthquake records are generated using ANN1 for Dubai’s site classes C and D, and ANN2 is used to evaluate the performance of ANN1.

Column Generation for Transmission Switching of Electricity Networks with Unit Commitment

DEFF Research Database (Denmark)

Villumsen, Jonas Christoffer; Philpott, Andy B.

2011-01-01

This paper presents the problem of finding the minimum cost dispatch and commitment of power generation units in a transmission network with active switching.We use the term active switching to denote the use of switches to optimize network topology in an operational context. We propose a Dantzig...

Privacy and Generation Y: Applying Library Values to Social Networking Sites

Science.gov (United States)

Fernandez, Peter

2010-01-01

Librarians face many challenges when dealing with issues of privacy within the mediated space of social networking sites. Conceptually, social networking sites differ from libraries on privacy as a value. Research about Generation Y students, the primary clientele of undergraduate libraries, can inform librarians' relationship to this important…

The generation of random directed networks with prescribed 1-node and 2-node degree correlations

Energy Technology Data Exchange (ETDEWEB)

Zamora-Lopez, Gorka; Kurths, Juergen [Institute of Physics, University of Potsdam, PO Box 601553, 14415 Potsdam (Germany); Zhou Changsong [Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong (China); Zlatic, Vinko [Rudjer Boskovic Institute, PO Box 180, HR-10002 Zagreb (Croatia)

2008-06-06

The generation of random networks is a very common problem in complex network research. In this paper, we have studied the correlation nature of several real networks and found that, typically, a large number of links are deterministic, i.e. they cannot be randomized. This finding permits fast generation of ensembles of maximally random networks with prescribed 1-node and 2-node degree correlations. When the introduction of self-loops or multiple-links are not desired, random network generation methods typically reach blocked states. Here, a mechanism is proposed, the 'force-and-drop' method, to overcome such states. Our algorithm can be easily simplified for undirected graphs and reduced to account for any subclass of 2-node degree correlations.

Ultrasonic inspection for wastage in the LMFBR steam generator due to sodium--water reactions

International Nuclear Information System (INIS)

Neely, H.H.; Renger, L.

1977-01-01

As part of a program to study the results of large sodium-water reactions in the LMFBR Steam Generator, a boreside ultrasonic inspection device was developed to measure the wall thickness and diameter of the 2- 1 / 4 Cr-1 Mo, 0.397 in. I.D. steam tubes. The reaction was created in a near prototype steam generator by guillotine-type rupture of a steam tube, while the generator was at operating conditions. Wastage occurred on the surrounding tubes due to the high temperature reaction. The UT test instrument was designed to operate with a 15 MHz transducer in the pulse-echo shear-wave mode, with a sampling rate of 10 4 /sec. System outputs are diameter, wall thickness, attitude and axial position of the transducer. All are displayed digitally and may be recorded. Measurements are fed into a computer for later retrieval, and/or cascaded outputs into an x-y recorded displaying either out-of-limit or thickness data. The UT data taken in this experiment were consistent with physical measurements on a tube which was removed from the generator after the test. A machined flat 1 / 8 -inch long and 0.002-inch deep could readily be detected

An autonomous organic reaction search engine for chemical reactivity

Science.gov (United States)

Dragone, Vincenza; Sans, Victor; Henson, Alon B.; Granda, Jaroslaw M.; Cronin, Leroy

2017-06-01

The exploration of chemical space for new reactivity, reactions and molecules is limited by the need for separate work-up-separation steps searching for molecules rather than reactivity. Herein we present a system that can autonomously evaluate chemical reactivity within a network of 64 possible reaction combinations and aims for new reactivity, rather than a predefined set of targets. The robotic system combines chemical handling, in-line spectroscopy and real-time feedback and analysis with an algorithm that is able to distinguish and select the most reactive pathways, generating a reaction selection index (RSI) without need for separate work-up or purification steps. This allows the automatic navigation of a chemical network, leading to previously unreported molecules while needing only to do a fraction of the total possible reactions without any prior knowledge of the chemistry. We show the RSI correlates with reactivity and is able to search chemical space using the most reactive pathways.

Definition of Distribution Network Tariffs Considering Distribution Generation and Demand Response

DEFF Research Database (Denmark)

Soares, Tiago; Faria, Pedro; Vale, Zita

2014-01-01

The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits for the wh......The use of distribution networks in the current scenario of high penetration of Distributed Generation (DG) is a problem of great importance. In the competitive environment of electricity markets and smart grids, Demand Response (DR) is also gaining notable impact with several benefits...... the determination of topological distribution factors, and consequent application of the MW-mile method. The application of the proposed tariffs definition methodology is illustrated in a distribution network with 33 buses, 66 DG units, and 32 consumers with DR capacity...

Effects of odor generated from the glycine/glucose Maillard reaction on human mood and brainwaves.

Science.gov (United States)

Zhou, Lanxi; Ohata, Motoko; Arihara, Keizo

2016-06-15

Effects of the odor generated from the glycine/glucose Maillard reaction on human mood and brainwaves were investigated in the present study. Equimolar solutions of glucose and glycine were adjusted to pH 7 and pH 9 and heated at 90 °C for 30 min. The odor generated from the glycine/glucose Maillard reaction significantly decreased negative moods. Its effects on brainwaves differed according to pH; alpha brainwave distribution was increased after inhalation of the odor generated at pH 7, whereas it was decreased by the odor generated at pH 9. The effects on mood and brainwaves were also measured after inhalation of model solutions, which comprised of potent odorants determined by aroma extract dilution analysis (AEDA), and the results were similar to those obtained with the Maillard reaction samples. Therefore, odors constructed by potent odorants could influence human mood and brainwaves. Among all potent odorants, 2,3-dimethylpyrazine and 2,5-dimethyl-4-hydroxy-3(2H)-furanone (DMHF) were identified as the strongest, and high pH values resulted in higher yields of these odorants. Furthermore, DMHF was identified as the putative agent responsible for the decrease in alpha brainwave distribution after smelling the pH-9 Maillard reaction sample since higher concentrations of DMHF resulted in a similar effect.

An efficient forward-reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Vilanova, Pedro

2016-01-01

reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ

Social network indices in the Generations and Gender Survey: An appraisal

Directory of Open Access Journals (Sweden)

Pearl A. Dykstra

2016-06-01

Full Text Available Background: In this contribution we critically appraise the social network indices in the Generations and Gender Survey (GGS. Objective: After discussing the rationale for including social network indices in the GGS, we provide descriptive information on social network characteristics and an overview of substantive questions that have been addressed using GGS social network data: antecedents and consequences of demographic behaviour, care, and differences in well-being. We identify topics that have received relatively little attention in GGS research so far, despite the availability of novel and appropriate social network data. We end with a discussion of what is unique about the social network indices in the GGS. Methods: The descriptive information on social network characteristics is based on empirical analyses of GGS data, and an experimental pilot study. The overview of GGS research using social network indices is based on a library search. The identification of what is unique about the social network indices in the GGS is based on a comparison with the European Quality of Life Survey (EQLS, the Survey of Health, Ageing and Retirement (SHARE, and the International Social Survey Program (ISSP. Results: Results show a high representation of family members in the social networks, and confirm the adequacy of using a cap of five names for network-generating questions. GGS research using the social network indices has largely focused on determinants of fertility behaviour, intergenerational linkages in families, and downward care transfers. Conclusions: Topics that have received relatively little attention are demographic behaviours other than those related to parenthood, upward transfers of practical support, ties with siblings, and stepfamily ties. Social network indices in the GGS show a high degree of overlap with those in other international surveys. The unique features are the inventory of family ties ever born and still living, and the

Network Characteristics and the Value of Collaborative User-Generated Content

OpenAIRE

Sam Ransbotham; Gerald C. Kane; Nicholas H. Lurie

2012-01-01

User-generated content is increasingly created through the collaborative efforts of multiple individuals. In this paper, we argue that the value of collaborative user-generated content is a function both of the direct efforts of its contributors and of its embeddedness in the content-contributor network that creates it. An analysis of Wikipedia's WikiProject Medicine reveals a curvilinear relationship between the number of distinct contributors to user-generated content and viewership. A two-...

Assessment of energy supply and continuity of service in distribution network with renewable distributed generation

International Nuclear Information System (INIS)

Abdullah, M.A.; Agalgaonkar, A.P.; Muttaqi, K.M.

2014-01-01

Highlights: • Difficulties in assessing distribution network adequacy with DG are addressed. • Indices are proposed to assess adequacy of energy supply and service continuity. • Analytical methodology is developed to assess the proposed indices. • Concept of joint probability distribution of demand and generation is applied. - Abstract: Continuity of electricity supply with renewable distributed generation (DG) is a topical issue for distribution system planning and operation, especially due to the stochastic nature of power generation and time varying load demand. The conventional adequacy and reliability analysis methods related to bulk generation systems cannot be applied directly for the evaluation of adequacy criteria such as ‘energy supply’ and ‘continuity of service’ for distribution networks embedded with renewable DG. In this paper, new indices highlighting ‘available supply capacity’ and ‘continuity of service’ are proposed for ‘energy supply’ and ‘continuation of service’ evaluation of generation-rich distribution networks, and analytical techniques are developed for their quantification. A probability based analytical method has been developed using the joint probability of the demand and generation, and probability distributions of the proposed indices have been used to evaluate the network adequacy in energy supply and service continuation. A data clustering technique has been used to evaluate the joint probability between coincidental demand and renewable generation. Time sequential Monte Carlo simulation has been used to compare the results obtained using the proposed analytical method. A standard distribution network derived from Roy Billinton test system and a practical radial distribution network have been used to test the proposed method and demonstrate the estimation of the well-being of a system for hosting renewable DG units. It is found that renewable DG systems improve the ‘energy supply’ and �

Elucidation of Diels-Alder Reaction Network of 2,5-Dimethylfuran and Ethylene on HY Zeolite Catalyst

Energy Technology Data Exchange (ETDEWEB)

Do, Phuong T. M. [Univ. of Delaware, Newark, DE (United States); McAtee, Jesse R. [Univ. of Delaware, Newark, DE (United States); Watson, Donald A. [Univ. of Delaware, Newark, DE (United States); Lobo, Raul F. [Univ. of Delaware, Newark, DE (United States)

2012-12-12

The reaction of 2,5-dimethylfuran and ethylene to produce p-xylene represents a potentially important route for the conversion of biomass to high-value organic chemicals. Current preparation methods suffer from low selectivity and produce a number of byproducts. Using modern separation and analytical techniques, the structures of many of the byproducts produced in this reaction when HY zeolite is employed as a catalyst have been identified. From these data, a detailed reaction network is proposed, demonstrating that hydrolysis and electrophilic alkylation reactions compete with the desired Diels–Alder/dehydration sequence. This information will allow the rational identification of more selective catalysts and more selective reaction conditions.

Limits for Stochastic Reaction Networks

DEFF Research Database (Denmark)

Cappelletti, Daniele

Reaction systems have been introduced in the 70s to model biochemical systems. Nowadays their range of applications has increased and they are fruitfully used in dierent elds. The concept is simple: some chemical species react, the set of chemical reactions form a graph and a rate function...... is associated with each reaction. Such functions describe the speed of the dierent reactions, or their propensities. Two modelling regimes are then available: the evolution of the dierent species concentrations can be deterministically modelled through a system of ODE, while the counts of the dierent species...... at a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along a path...

Apprenticeship effect on a neural network

International Nuclear Information System (INIS)

Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.

1997-01-01

Utilization of a neural network for determining the value of the impact parameter in heavy ion reactions is a two stage process: a apprenticeship stage followed by a utilization stage. During the first stage one determines the network parameters by means of a trial game for which the inputs and outputs are known. To realize the trial game a numerical simulation code was used. In order to estimate the biases of this procedure we generated trial games by resorting to two different models: a transport dynamical model (QMD) coupled to a deexcitation code (GEMINI) and a event generator based on a statistical approach (EUGENE). The procedure was applied to determine the impact parameter in 40 Ca + 40 Ca reactions. The effect of the model dependence of the apprenticeship procedure is studied thoroughly. In the two cases the outputs provided by the network are smaller then 7 fm which can be expected in case of complete events. The network trained with QMD gives outputs for input parameters within a larger range of values which is due to the fact that EUGENE does not contain 'Deep Inelastic'

Explicit integration of extremely stiff reaction networks: partial equilibrium methods

International Nuclear Information System (INIS)

Guidry, M W; Hix, W R; Billings, J J

2013-01-01

In two preceding papers (Guidry et al 2013 Comput. Sci. Disc. 6 015001 and Guidry and Harris 2013 Comput. Sci. Disc. 6 015002), we have shown that when reaction networks are well removed from equilibrium, explicit asymptotic and quasi-steady-state approximations can give algebraically stabilized integration schemes that rival standard implicit methods in accuracy and speed for extremely stiff systems. However, we also showed that these explicit methods remain accurate but are no longer competitive in speed as the network approaches equilibrium. In this paper, we analyze this failure and show that it is associated with the presence of fast equilibration timescales that neither asymptotic nor quasi-steady-state approximations are able to remove efficiently from the numerical integration. Based on this understanding, we develop a partial equilibrium method to deal effectively with the approach to equilibrium and show that explicit asymptotic methods, combined with the new partial equilibrium methods, give an integration scheme that can plausibly deal with the stiffest networks, even in the approach to equilibrium, with accuracy and speed competitive with that of implicit methods. Thus we demonstrate that such explicit methods may offer alternatives to implicit integration of even extremely stiff systems and that these methods may permit integration of much larger networks than have been possible before in a number of fields. (paper)

SNR-steam generator design with respect to large sodium water reactions

International Nuclear Information System (INIS)

Jong, J.J. de; Kellner, A.; Florie, C.J.L.

1984-01-01

This paper deals with the experiences gained during the licensing procedure for the steam generators for the SNR 300 LMFBR regarding large sodium-water reactions. A description is given of the different calculations executed to investigate the effects of large leaks on the 85 MW helical coiled and straight tube steam generators. The investigations on the helical coiled steam generators are divided in the formulations of fluid behaviour, dynamic force calculations, dynamic response calculation and finally stress analyses. Several results are shown. The investigations on the straight tube steam generators are performed using models describing fluid-structure interaction, coupled with stress analyses. Several results are presented. A description is given of the problems and necessary construction changes during the licensing process. Advises are given for future analyses and design concepts for second generation commercial size LMFBR steam generators with respect to large leaks; based on the experience, gained with SNR 300, and using some new calculations for SNR 2. (author)

Covalently crosslinked diels-alder polymer networks.

Energy Technology Data Exchange (ETDEWEB)

Bowman, Christopher (University of Colorado, Boulder, CO); Adzima, Brian J. (University of Colorado, Boulder, CO); Anderson, Benjamin John

2011-09-01

This project examines the utility of cycloaddition reactions for the synthesis of polymer networks. Cycloaddition reactions are desirable because they produce no unwanted side reactions or small molecules, allowing for the formation of high molecular weight species and glassy crosslinked networks. Both the Diels-Alder reaction and the copper-catalyzed azide-alkyne cycloaddition (CuAAC) were studied. Accomplishments include externally triggered healing of a thermoreversible covalent network via self-limited hysteresis heating, the creation of Diels-Alder based photoresists, and the successful photochemical catalysis of CuAAC as an alternative to the use of ascorbic acid for the generation of Cu(I) in click reactions. An analysis of the results reveals that these new methods offer the promise of efficiently creating robust, high molecular weight species and delicate three dimensional structures that incorporate chemical functionality in the patterned material. This work was performed under a Strategic Partnerships LDRD during FY10 and FY11 as part of a Sandia National Laboratories/University of Colorado-Boulder Excellence in Science and Engineering Fellowship awarded to Brian J. Adzima, a graduate student at UC-Boulder. Benjamin J. Anderson (Org. 1833) was the Sandia National Laboratories point-of-contact for this fellowship.

Optimizing Low Speed VoIP Network for Rural Next Generation Network (R-NGN

Directory of Open Access Journals (Sweden)

Yoanes Bandung

2007-11-01

Full Text Available In this research, we propose an optimization method based-on E-Model for designing an efficient low speed VoIP network for Rural Next Generation Network (R-NGN. We are choosing 128 kbps and 256 kbps bandwidth as the typical community link to be used in the designing of R-NGN infrastructure. The method is based on selection of some VoIP network parameters such as voice coder, communication protocol, packet loss level, network utilization and resource allocation. We draw analytic approach for achieving rating value (R of E-model that represent level of quality of service. In this approach, we focus on delay and packet loss calculation to find the rating value. We state the rating value = 70 as minimum level of quality of service for each call, equivalent to 3.6 of Mean Opinion Score (MOS. In our experiments, either G.723.1 5.3 kbps or G.729 is chosen for maximizing the number of VoIP calls, it depends on link utilization and level of packet loss.

Contribution of Histidine and Lysine to the Generation of Volatile Compounds in Jinhua Ham Exposed to Ripening Conditions Via Maillard Reaction.

Science.gov (United States)

Zhu, Chao-Zhi; Zhao, Jing-Li; Tian, Wei; Liu, Yan-Xia; Li, Miao-Yun; Zhao, Gai-Ming

2018-01-01

To evaluate the role of Maillard reactions in the generation of flavor compounds in Jinhua ham, the reactions of glucose and ethanal with histidine and lysine, respectively, were studied by simulating the ripening conditions of Jinhua ham. The volatile products produced were analyzed using solid phase microextraction-gas chromatography/mass spectrometry. The results showed that 8 volatile compounds were generated by the reaction of glucose and histidine and 10 volatile compounds were generated by the reaction of glucose and lysine. Reactions of ethanal with lysine and with histidine both generated 31 volatile compounds that contributed to the flavor of Jinhua ham. This indicates that histidine and lysine related to Maillard reactions possibly play important roles in the generation of the unique flavor compounds in Jinhua ham. This research demonstrates that free amino acids participate in the generation of volatile compounds from Jinhua ham via the Maillard reaction and provides a basic mechanism to explain flavor formation in Jinhua ham. Jinhua ham is a well-known traditional Chinese dry-cured meat product. However, the formation of the compounds comprising its special flavor is not well understood. Our results indicate that Maillard reactions occur in Jinhua ham under ripening conditions. This work illustrates the contribution of Maillard reactions to the flavor of Jinhua ham. © 2017 Institute of Food Technologists®.

Exploiting Mobile Ad Hoc Networking and Knowledge Generation to Achieve Ambient Intelligence

Directory of Open Access Journals (Sweden)

Anna Lekova

2012-01-01

Full Text Available Ambient Intelligence (AmI joins together the fields of ubiquitous computing and communications, context awareness, and intelligent user interfaces. Energy, fault-tolerance, and mobility are newly added dimensions of AmI. Within the context of AmI the concept of mobile ad hoc networks (MANETs for “anytime and anywhere” is likely to play larger roles in the future in which people are surrounded and supported by small context-aware, cooperative, and nonobtrusive devices that will aid our everyday life. The connection between knowledge generation and communication ad hoc networking is symbiotic—knowledge generation utilizes ad hoc networking to perform their communication needs, and MANETs will utilize the knowledge generation to enhance their network services. The contribution of the present study is a distributed evolving fuzzy modeling framework (EFMF to observe and categorize relationships and activities in the user and application level and based on that social context to take intelligent decisions about MANETs service management. EFMF employs unsupervised online one-pass fuzzy clustering method to recognize nodes' mobility context from social scenario traces and ubiquitously learn “friends” and “strangers” indirectly and anonymously.

A Methodology for Physical Interconnection Decisions of Next Generation Transport Networks

DEFF Research Database (Denmark)

Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Madsen, Ole Brun

2011-01-01

of possibilities when designing the physical network interconnection. This paper develops and presents a methodology in order to deal with aspects related to the interconnection problem of optical transport networks. This methodology is presented as independent puzzle pieces, covering diverse topics going from......The physical interconnection for optical transport networks has critical relevance in the overall network performance and deployment costs. As telecommunication services and technologies evolve, the provisioning of higher capacity and reliability levels is becoming essential for the proper...... development of Next Generation Networks. Currently, there is a lack of specific procedures that describe the basic guidelines to design such networks better than "best possible performance for the lowest investment". Therefore, the research from different points of view will allow a broader space...

Prioritizing Signaling Information Transmission in Next Generation Networks

Directory of Open Access Journals (Sweden)

Jasmina Baraković

2011-01-01

Full Text Available Next generation transport network is characterized by the use of in-band signaling, where Internet Protocol (IP packets carrying signaling or media information are mixed in transmission. Since transport resources are limited, when any segment of access or core network is congested, IP packets carrying signaling information may be discarded. As a consequence, it may be impossible to implement reachability and quality of service (QoS. Since present approaches are insufficient to completely address this problem, a novel approach is proposed, which is based on prioritizing signaling information transmission. To proof the concept, a simulation study was performed using Network Simulator version 2 (ns-2 and independently developed Session Initiation Protocol (SIP module. The obtained results were statistically processed using Statistical Package for the Social Sciences (SPSS version 15.0. Summarizing our research results, several issues are identified for future work.

MSU SINP CDFE nuclear data activities in the nuclear reaction data centres network

International Nuclear Information System (INIS)

Boboshin, I.N.; Varlamov, V.V.; Komarov, S.Yu.; Peskov, N.N.; Semin, S.B.; Stepanov, M.E.; Chesnokov, V.V.

2002-01-01

This paper is the progress report of the Centre for Photonuclear Experiments Data, Moscow. It is a short review of the works carried out by the CDFE concerning the IAEA nuclear reaction data centers network activities from May 2001 until May 2002. and the description of the main results obtained. (a.n.)

Generative Adversarial Networks for Improving Face Classification

OpenAIRE

Natten, Jonas

2017-01-01

Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Facial recognition can be applied in a wide variety of cases, including entertainment purposes and biometric security. In this thesis we take a look at improving the results of an existing facial recognition approach by utilizing generative adversarial networks to improve the existing dataset. The training data was taken from the LFW dataset[4] and was preprocessed using OpenCV[2] for...

Generative adversarial networks for anomaly detection in images

OpenAIRE

Batiste Ros, Guillem

2018-01-01

Anomaly detection is used to identify abnormal observations that don t follow a normal pattern. Inthis work, we use the power of Generative Adversarial Networks in sampling from image distributionsto perform anomaly detection with images and to identify local anomalous segments within thisimages. Also, we explore potential application of this method to support pathological analysis ofbiological tissues

Impulsive Synchronization of Reaction-Diffusion Neural Networks With Mixed Delays and Its Application to Image Encryption.

Science.gov (United States)

Chen, Wu-Hua; Luo, Shixian; Zheng, Wei Xing

2016-12-01

This paper presents a new impulsive synchronization criterion of two identical reaction-diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov functional combined with the use of a new type of integral inequality for treating the reaction-diffusion terms. The impulse-time-dependent feature of the proposed Lyapunov functional can capture more hybrid dynamical behaviors of the impulsive reaction-diffusion neural networks than the conventional impulse-time-independent Lyapunov functions/functionals, while the new integral inequality, which is derived from Wirtinger's inequality, overcomes the conservatism introduced by the integral inequality used in the previous results. Numerical examples demonstrate the effectiveness of the proposed method. Later, the developed impulsive synchronization method is applied to build a spatiotemporal chaotic cryptosystem that can transmit an encrypted image. The experimental results verify that the proposed image-encrypting cryptosystem has the advantages of large key space and high security against some traditional attacks.

Determination of Plasma Screening Effects for Thermonuclear Reactions in Laser-generated Plasmas

Energy Technology Data Exchange (ETDEWEB)

Wu, Yuanbin; Pálffy, Adriana, E-mail: yuanbin.wu@mpi-hd.mpg.de, E-mail: Palffy@mpi-hd.mpg.de [Max-Planck-Institut für Kernphysik, Saupfercheckweg 1, D-69117 Heidelberg (Germany)

2017-03-20

Due to screening effects, nuclear reactions in astrophysical plasmas may behave differently than in the laboratory. The possibility to determine the magnitude of these screening effects in colliding laser-generated plasmas is investigated theoretically, having as a starting point a proposed experimental setup with two laser beams at the Extreme Light Infrastructure facility. A laser pulse interacting with a solid target produces a plasma through the Target Normal Sheath Acceleration scheme, and this rapidly streaming plasma (ion flow) impacts a secondary plasma created by the interaction of a second laser pulse on a gas jet target. We model this scenario here and calculate the reaction events for the astrophysically relevant reaction {sup 13}C({sup 4}He, n ){sup 16}O. We find that it should be experimentally possible to determine the plasma screening enhancement factor for fusion reactions by detecting the difference in reaction events between two scenarios of ion flow interacting with the plasma target and a simple gas target. This provides a way to evaluate nuclear reaction cross-sections in stellar environments and can significantly advance the field of nuclear astrophysics.

Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

Directory of Open Access Journals (Sweden)

St Laurent Georges

2010-03-01

Full Text Available Abstract Background Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors. Methods In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology. Results Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4

Next generation network based carrier ethernet test bed for IPTV traffic

DEFF Research Database (Denmark)

Fu, Rong; Berger, Michael Stübert; Zheng, Yu

2009-01-01

This paper presents a Carrier Ethernet (CE) test bed based on the Next Generation Network (NGN) framework. After the concept of CE carried out by Metro Ethernet Forum (MEF), the carrier-grade Ethernet are obtaining more and more interests and being investigated as the low cost and high performanc...... services of transport network to carry the IPTV traffic. This test bed is approaching to support the research on providing a high performance carrier-grade Ethernet transport network for IPTV traffic....

A Reaction-Diffusion-Based Coding Rate Control Mechanism for Camera Sensor Networks

Directory of Open Access Journals (Sweden)

Naoki Wakamiya

2010-08-01

Full Text Available A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.

A reaction-diffusion-based coding rate control mechanism for camera sensor networks.

Science.gov (United States)

Yamamoto, Hiroshi; Hyodo, Katsuya; Wakamiya, Naoki; Murata, Masayuki

2010-01-01

A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.

Generating functional analysis of complex formation and dissociation in large protein interaction networks

International Nuclear Information System (INIS)

Coolen, A C C; Rabello, S

2009-01-01

We analyze large systems of interacting proteins, using techniques from the non-equilibrium statistical mechanics of disordered many-particle systems. Apart from protein production and removal, the most relevant microscopic processes in the proteome are complex formation and dissociation, and the microscopic degrees of freedom are the evolving concentrations of unbound proteins (in multiple post-translational states) and of protein complexes. Here we only include dimer-complexes, for mathematical simplicity, and we draw the network that describes which proteins are reaction partners from an ensemble of random graphs with an arbitrary degree distribution. We show how generating functional analysis methods can be used successfully to derive closed equations for dynamical order parameters, representing an exact macroscopic description of the complex formation and dissociation dynamics in the infinite system limit. We end this paper with a discussion of the possible routes towards solving the nontrivial order parameter equations, either exactly (in specific limits) or approximately.

Cisco Networking Academy: Next-Generation Assessments and Their Implications for K-12 Education

Science.gov (United States)

Liu, Meredith

2014-01-01

To illuminate the possibilities for next-generation assessments in K-12 schools, this case study profiles the Cisco Networking Academy, which creates comprehensive online training curriculum to teach networking skills. Since 1997, the Cisco Networking Academy has served more than five million high school and college students and now delivers…

Advanced optical components for next-generation photonic networks

Science.gov (United States)

Yoo, S. J. B.

2003-08-01

Future networks will require very high throughput, carrying dominantly data-centric traffic. The role of Photonic Networks employing all-optical systems will become increasingly important in providing scalable bandwidth, agile reconfigurability, and low-power consumptions in the future. In particular, the self-similar nature of data traffic indicates that packet switching and burst switching will be beneficial in the Next Generation Photonic Networks. While the natural conclusion is to pursue Photonic Packet Switching and Photonic Burst Switching systems, there are significant challenges in realizing such a system due to practical limitations in optical component technologies. Lack of a viable all-optical memory technology will continue to drive us towards exploring rapid reconfigurability in the wavelength domain. We will introduce and discuss the advanced optical component technologies behind the Photonic Packet Routing system designed and demonstrated at UC Davis. The system is capable of packet switching and burst switching, as well as circuit switching with 600 psec switching speed and scalability to 42 petabit/sec aggregated switching capacity. By utilizing a combination of rapidly tunable wavelength conversion and a uniform-loss cyclic frequency (ULCF) arrayed waveguide grating router (AWGR), the system is capable of rapidly switching the packets in wavelength, time, and space domains. The label swapping module inside the Photonic Packet Routing system containing a Mach-Zehnder wavelength converter and a narrow-band fiber Bragg-grating achieves all-optical label swapping with optical 2R (potentially 3R) regeneration while maintaining optical transparency for the data payload. By utilizing the advanced optical component technologies, the Photonic Packet Routing system successfully demonstrated error-free, cascaded, multi-hop photonic packet switching and routing with optical-label swapping. This paper will review the advanced optical component technologies

Modeling generator power plant portfolios and pollution taxes in electric power supply chain networks: a transportation network equilibrium transformation

International Nuclear Information System (INIS)

Kai Wu; Nagurney, A.; University of Massachusetts, Amherst, MA; Zugang Liu; Stranlund, J.K.

2006-01-01

Global climate change and fuel security risks have encouraged international and regional adoption of pollution/carbon taxes. A major portion of such policy interventions is directed at the electric power industry with taxes applied according to the type of fuel used by the power generators in their power plants. This paper proposes an electric power supply chain network model that captures the behavior of power generators faced with a portfolio of power plant options and subject to pollution taxes. We demonstrate that this general model can be reformulated as a transportation network equilibrium model with elastic demands and qualitatively analyzed and solved as such. The connections between these two different modeling schemas is done through finite-dimensional variational inequality theory. The numerical examples illustrate how changes in the pollution/carbon taxes affect the equilibrium electric power supply chain network production outputs, the transactions between the various decision-makers the demand market prices, as well as the total amount of carbon emissions generated. (author)

Integrated Power Flow and Short Circuit Calculation Method for Distribution Network with Inverter Based Distributed Generation

Directory of Open Access Journals (Sweden)

Shan Yang

2016-01-01

Full Text Available Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverter based distributed generation is proposed. The proposed method let the inverter based distributed generation be equivalent to Iθ bus, which makes it suitable to calculate the power flow of distribution network with a current limited inverter based distributed generation. And the low voltage ride through capability of inverter based distributed generation can be considered as well in this paper. Finally, some tests of power flow and short circuit current calculation are performed on a 33-bus distribution network. The calculated results from the proposed method in this paper are contrasted with those by the traditional method and the simulation method, whose results have verified the effectiveness of the integrated method suggested in this paper.

Saliency detection by conditional generative adversarial network

Science.gov (United States)

Cai, Xiaoxu; Yu, Hui

2018-04-01

Detecting salient objects in images has been a fundamental problem in computer vision. In recent years, deep learning has shown its impressive performance in dealing with many kinds of vision tasks. In this paper, we propose a new method to detect salient objects by using Conditional Generative Adversarial Network (GAN). This type of network not only learns the mapping from RGB images to salient regions, but also learns a loss function for training the mapping. To the best of our knowledge, this is the first time that Conditional GAN has been used in salient object detection. We evaluate our saliency detection method on 2 large publicly available datasets with pixel accurate annotations. The experimental results have shown the significant and consistent improvements over the state-of-the-art method on a challenging dataset, and the testing speed is much faster.

Biogenic Methane Generation Potential in the Eastern Nankai Trough, Japan: Effect of Reaction Temperature and Total Organic Carbon

Science.gov (United States)

Aung, T. T.; Fujii, T.; Amo, M.; Suzuki, K.

2017-12-01

Understanding potential of methane flux from the Pleistocene fore-arc basin filled turbiditic sedimentary formation along the eastern Nankai Trough is important in the quantitative assessment of gas hydrate resources. We considered generated methane could exist in sedimentary basin in the forms of three major components, and those are methane in methane hydrate, free gas and methane dissolved in water. Generation of biomethane strongly depends on microbe activity and microbes in turn survive in diverse range of temperature, salinity and pH. This study aims to understand effect of reaction temperature and total organic carbon on generation of biomethane and its components. Biomarker analysis and cultural experiment results of the core samples from the eastern Nankai Trough reveal that methane generation rate gets peak at various temperature ranging12.5°to 35°. Simulation study of biomethane generation was made using commercial basin scale simulator, PetroMod, with different reaction temperature and total organic carbon to predict how these effect on generation of biomethane. Reaction model is set by Gaussian distribution with constant hydrogen index and standard deviation of 1. Series of simulation cases with peak reaction temperature ranging 12.5°to 35° and total organic carbon of 0.6% to 3% were conducted and analyzed. Simulation results show that linear decrease in generation potential while increasing reaction temperature. But decreasing amount becomes larger in the model with higher total organic carbon. At higher reaction temperatures, >30°, extremely low generation potential was found. This is due to the fact that the source formation modeled is less than 1 km in thickness and most of formation do not reach temperature more than 30°. In terms of the components, methane in methane hydrate and free methane increase with increasing TOC. Drastic increase in free methane was observed in the model with 3% of TOC. Methane amount dissolved in water shows almost

Segmenting Costumers Based on Their Reactions to Social Networks Marketing on Instagram

Directory of Open Access Journals (Sweden)

rashin ghahreman

2017-09-01

Full Text Available Since customers react differently to business and marketing on social networks, the researcher is looking for segmenting customers into different categories according to their reaction to marketing in social networks. The present study is a descriptive-exploratory research and the data were collected through a questionnaire. The population of 14,000 follower of the researcher’s personal page on Instagram were analyzed and a sample 224 members were randomly selected. To analyze the data, a two-step clustering method was applied. As a result, five distinct clusters (the active, the talker, the hesitant, the passive and the averse were identified. Two segments were reported to be highly influenced by social networks marketing in terms of brand engagement, purchase intention and word of mouth advertisement (WOM. The "Active" are the most influenced group including 18.3% of the population most of whom are single girls or women. The next group that are influenced the most by social networks marketing is the "Talker". This group represents 24.1% of the population, the most populated group. The "Talker" are different from the "Active" in term of their intention to purchase. Totally, 42.2% of the population are reported to be influenced by social networks marketing.

Modeling the video distribution link in the Next Generation Optical Access Networks

International Nuclear Information System (INIS)

Amaya, F; Cardenas, A; Tafur, I

2011-01-01

In this work we present a model for the design and optimization of the video distribution link in the next generation optical access network. We analyze the video distribution performance in a SCM-WDM link, including the noise, the distortion and the fiber optic nonlinearities. Additionally, we consider in the model the effect of distributed Raman amplification, used to extent the capacity and the reach of the optical link. In the model, we use the nonlinear Schroedinger equation with the purpose to obtain capacity limitations and design constrains of the next generation optical access networks.

Generation of artificial accelerograms using neural networks for data of Iran

International Nuclear Information System (INIS)

Bargi, Kh.; Loux, C.; Rohani, H.

2002-01-01

A new method for generation of artificial earthquake accelerograms from response spectra is proposed by Ghaboussi and Lin in 1997 using neural networks. In this paper the methodology has been extended and enhanced for data of Iran. For this purpose, first 40 records of Iran acceleration is chosen, then an RBF neural network which called generalized regression neural network learn the inverse mapping directly from the response spectrum to the Discrete Cosine Transform of accelerograms. Discrete Cosine Transform has been used as an assisting device to extract the content of frequency domain. Learning of network is reasonable and a generalized regression neural network learns it in a few second. Outputs are presented to demonstrate the performance of this method and show its capabilities

How Irish Political Parties are Using Social Networking Sites to Reach Generation Z: an Insight into a New Online Social Network in a Small Democracy

OpenAIRE

Lynch, Kevin; Hogan, John

2016-01-01

This study, using in-depth interviews and focus groups, examines perceptions of social networking sites as a means of communicating with Generation Z, from the perspectives of the major Irish political parties using these online resources and the perspective of their young target audience. There are two research questions: (1) How do political parties perceive social networking sites’ role in communicating with Generation Z? and (2) How do members of Generation Z perceive social networking si...

State of the art of the virtual utility: the smart distributed generation network

International Nuclear Information System (INIS)

Coll-Mayor, D.; Picos, R.; Garcia-Moreno, E.

2004-01-01

The world of energy has lately experienced a revolution, and new rules are being defined. The climate change produced by the greenhouse gases, the inefficiency of the energy system or the lack of power supply infrastructure in most of the poor countries, the liberalization of the energy market and the development of new technologies in the field of distributed generation (DG) are the key factors of this revolution. It seems clear that the solution at the moment is the DG. The advantage of DG is the energy generation close to the demand point. It means that DG can lower costs, reduce emissions, or expand the energy options of the consumers. DG may add redundancy that increases grid security even while powering emergency lighting or other critical systems and reduces power losses in the electricity distribution. After the development of the different DG and high efficiency technologies such as co-generation and tri-generation, the next step in the DG world is the interconnection of different small distributed generation facilities which act together in a DG network as a large power plant controlled by a centralized energy management system (EMS). The main aim of the EMS is to reach the targets of low emissions and high efficiency. The EMS gives priority to renewable energy sources instead of the use of fossil fuels. This new concept of energy infrastructure is referred to as virtual utility (VU). The VU can be defined as a new model of energy infrastructure which consists of integrating different kind of distributed generation utilities in an energy (electricity and heat) generation network controlled by a central energy management system (EMS). The electricity production in the network is subordinated to the heat necessity of every user. The thermal energy is consumed on site; the electricity is generated and distributed in the entire network. The network is composed of one centralized control with the EMS and different clusters of distributed generation utilities

Probing Rubber Cross-Linking Generation of Industrial Polymer Networks at Nanometer Scale.

Science.gov (United States)

Gabrielle, Brice; Gomez, Emmanuel; Korb, Jean-Pierre

2016-06-23

We present improved analyses of rheometric torque measurements as well as (1)H double-quantum (DQ) nuclear magnetic resonance (NMR) buildup data on polymer networks of industrial compounds. This latter DQ NMR analysis allows finding the distribution of an orientation order parameter (Dres) resulting from the noncomplete averaging of proton dipole-dipole couplings within the cross-linked polymer chains. We investigate the influence of the formulation (filler and vulcanization systems) as well as the process (curing temperature) ending to the final polymer network. We show that DQ NMR follows the generation of the polymer network during the vulcanization process from a heterogeneous network to a very homogeneous one. The time variations of microscopic Dres and macroscopic rheometric torques present power-law behaviors above a threshold time scale with characteristic exponents of the percolation theory. We observe also a very good linear correlation between the kinetics of Dres and rheometric data routinely performed in industry. All these observations confirm the description of the polymer network generation as a critical phenomenon. On the basis of all these results, we believe that DQ NMR could become a valuable tool for investigating in situ the cross-linking of industrial polymer networks at the nanometer scale.

Flavylium network of chemical reactions in confined media: modulation of 3',4',7-trihydroxyflavilium reactions by host-guest interactions with cucurbit[7]uril.

Science.gov (United States)

Basílio, Nuno; Pina, Fernando

2014-08-04

In moderately acidic aqueous solutions, flavylium compounds undergo a pH-, and in some cases, light-dependent array of reversible chemical reactions. This network can be described as a single acid-base reaction involving a flavylium cation (acidic form) and a mixture of basic forms (quinoidal base, hemiketal and cis and trans chalcones). The apparent pK'a of the system and the relative mole fractions of the basic forms can be modulated by the interaction with cucurbit[7]uril. The system is studied by using (1) H NMR spectroscopy, UV/Vis spectroscopy, flash photolysis, and steady-state irradiation. Of all the network species, the flavylium cation possesses the highest affinity for cucurbit[7]uril. The rate of interconversion between flavylium cation and the basic species (where trans-chalcone is dominant) is approximately nine times lower inside the cucurbit[7]uril. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Experimental (Network) and Evaluated Nuclear Reaction Data at NDS

International Nuclear Information System (INIS)

Otsuka, N.; Semkova, V.; Simakov, S.P.; Zerkin, V.

2011-01-01

Dr Simakov of Nuclear Data Services Unit in the Nuclear Data Section (NDS) gave a brief overview of the data compilation and evaluation activities in the nuclear data community: experimental nuclear reaction data (EXFOR, http://www-nds.iaea.org/exfor/) and evaluated nuclear reaction data (ENDF, http://www-nds.iaea.org/endf). The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by NDS includes 14 Centres in 8 Countries (China, Hungary, India, Japan, Korea, Russian, Ukraine, USA) and 2 International Organizations (NEA, IAEA). It had the first meeting of four core centres (Brookhaven, Saclay, Obninsk, Vienna) in 1966 and the EXFOR was adopted as an official data exchange format. In 2000, IAEA implemented the EXFOR database as a relational multiform database and the EXFOR is a trusted, increasing and living database with 19100 experimental works (as of September 2011) and 141600 data tables. The EXFOR provides a compilation control system for selection of articles and compilation of data and the NRDC home page provides manuals, documents and codes. The nuclear data can be retrieved by the web-retrieval system or distributed on a DVD on request. The EXFOR data play a critical role in the development of evaluated nuclear reaction data. There are several major general purpose libraries: ENDF (US), CENDL (China), JEFF (EU), JENDL (Japan) and RUSFOND (Russia). In addition, there are special libraries for particular applications: EAF (European Activation File), FENDL (Fusion Evaluated Nuclear Data Library for ITER neutronics), IBANDL (Ion Beam Analysis Nuclear Data Library for surface analysis of solids), IRDF, DXS (Dosimetry, radiation damage and gas production data) and Medical portal. Dr V. Zerkin of NDS demonstrated the data retrieval from the EXFOR database and the ENDF library.

Experimental (Network) and Evaluated Nuclear Reaction Data at NDS

Energy Technology Data Exchange (ETDEWEB)

Otsuka, N; Semkova, V; Simakov, S P; Zerkin, V [Nuclear Data Services Unit, Nuclear Data Section, IAEA, Vienna (Austria)

2011-11-15

Dr Simakov of Nuclear Data Services Unit in the Nuclear Data Section (NDS) gave a brief overview of the data compilation and evaluation activities in the nuclear data community: experimental nuclear reaction data (EXFOR, http://www-nds.iaea.org/exfor/) and evaluated nuclear reaction data (ENDF, http://www-nds.iaea.org/endf). The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by NDS includes 14 Centres in 8 Countries (China, Hungary, India, Japan, Korea, Russian, Ukraine, USA) and 2 International Organizations (NEA, IAEA). It had the first meeting of four core centres (Brookhaven, Saclay, Obninsk, Vienna) in 1966 and the EXFOR was adopted as an official data exchange format. In 2000, IAEA implemented the EXFOR database as a relational multiform database and the EXFOR is a trusted, increasing and living database with 19100 experimental works (as of September 2011) and 141600 data tables. The EXFOR provides a compilation control system for selection of articles and compilation of data and the NRDC home page provides manuals, documents and codes. The nuclear data can be retrieved by the web-retrieval system or distributed on a DVD on request. The EXFOR data play a critical role in the development of evaluated nuclear reaction data. There are several major general purpose libraries: ENDF (US), CENDL (China), JEFF (EU), JENDL (Japan) and RUSFOND (Russia). In addition, there are special libraries for particular applications: EAF (European Activation File), FENDL (Fusion Evaluated Nuclear Data Library for ITER neutronics), IBANDL (Ion Beam Analysis Nuclear Data Library for surface analysis of solids), IRDF, DXS (Dosimetry, radiation damage and gas production data) and Medical portal. Dr V. Zerkin of NDS demonstrated the data retrieval from the EXFOR database and the ENDF library.

Small Distributed Renewable Energy Generation for Low Voltage Distribution Networks

Directory of Open Access Journals (Sweden)

Chindris M.

2016-08-01

Full Text Available Driven by the existing energy policies, the use of renewable energy has increased considerably all over the world in order to respond to the increasing energy consumption and to reduce the environmental impact of the electricity generation. Although most policy makers and companies are focusing on large applications, the use of cheap small generation units, based on local renewable resources, has become increasingly attractive for the general public, small farms and remote communities. The paper presents several results of a research project aiming to identify the power quality issues and the impact of RES based distributed generation (DG or other non-linear loads on low voltage (LV distribution networks in Romania; the final goal is to develop a Universal Power Quality Conditioner (UPQC able to diminish the existing disturbances. Basically, the work analyses the existing DG technologies and identifies possible solutions for their integration in Romania; taking into account the existent state of the art, the attention was paid on small systems, using wind and solar energy, and on possibility to integrate them into suburban and rural LV distribution networks. The presence of DG units at distribution voltage level means the transition from traditional passive to active distribution networks. In general, the relatively low penetration levels of DG does not produce problems; however, the nowadays massive increase of local power generation have led to new integration challenges in order to ensure the reliability and quality of the power supply. Power quality issues are identified and their assessment is the key element in the design of measures aiming to diminish all existing disturbances.

Universal Intelligent Small Cell (UnISCell for next generation cellular networks

Directory of Open Access Journals (Sweden)

Mohammad Patwary

2016-11-01

Full Text Available Exploring innovative cellular architectures to achieve enhanced system capacity and good coverage has become a critical issue towards realizing the next generation of wireless communications. In this context, this paper proposes a novel concept of Universal Intelligent Small Cell (UnISCell for enabling the densification of the next generation of cellular networks. The proposed novel concept envisions an integrated platform of providing a strong linkage between different stakeholders such as street lighting networks, landline telephone networks and future wireless networks, and is universal in nature being independent of the operating frequency bands and traffic types. The main motivating factors for the proposed small cell concept are the need of public infrastructure re-engineering, and the recent advances in several enabling technologies. First, we highlight the main concepts of the proposed UnISCell platform. Subsequently, we present two deployment scenarios for the proposed UnISCell concept considering infrastructure sharing and service sharing as important aspects. We then describe the key future technologies for enabling the proposed UnISCell concept and present a use case example with the help of numerical results. Finally, we conclude this article by providing some interesting future recommendations.

Prediction of municipal solid waste generation using nonlinear autoregressive network.

Science.gov (United States)

Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A

2015-12-01

Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.

Neural network based daily precipitation generator (NNGEN-P)

Energy Technology Data Exchange (ETDEWEB)

Boulanger, Jean-Philippe [LODYC, UMR CNRS/IRD/UPMC, Paris (France); University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Martinez, Fernando; Segura, Enrique C. [University of Buenos Aires, Departamento de Computacion, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina); Penalba, Olga [University of Buenos Aires, Departamento de Ciencias de la Atmosfera y los Oceanos, Facultad de Ciencias Exactas y Naturales, Buenos Aires (Argentina)

2007-02-15

Daily weather generators are used in many applications and risk analyses. The present paper explores the potential of neural network architectures to design daily weather generator models. Focusing this first paper on precipitation, we design a collection of neural networks (multi-layer perceptrons in the present case), which are trained so as to approximate the empirical cumulative distribution (CDF) function for the occurrence of wet and dry spells and for the precipitation amounts. This approach contributes to correct some of the biases of the usual two-step weather generator models. As compared to a rainfall occurrence Markov model, NNGEN-P represents fairly well the mean and standard deviation of the number of wet days per month, and it significantly improves the simulation of the longest dry and wet periods. Then, we compared NNGEN-P to three parametric distribution functions usually applied to fit rainfall cumulative distribution functions (Gamma, Weibull and double-exponential). A data set of 19 Argentine stations was used. Also, data corresponding to stations in the United States, in Europe and in the Tropics were included to confirm the results. One of the advantages of NNGEN-P is that it is non-parametric. Unlike other parametric function, which adapt to certain types of climate regimes, NNGEN-P is fully adaptive to the observed cumulative distribution functions, which, on some occasions, may present complex shapes. On-going works will soon produce an extended version of NNGEN to temperature and radiation. (orig.)

Noise-induced modulation of the relaxation kinetics around a non-equilibrium steady state of non-linear chemical reaction networks.

Science.gov (United States)

Ramaswamy, Rajesh; Sbalzarini, Ivo F; González-Segredo, Nélido

2011-01-28

Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confinement increases the lifetimes of all species that are involved in any non-linear reaction as a reactant. Burst monotonically increases or decreases lifetimes. Competition between burst-induced and confinement-induced modulation may hence lead to a non-monotonic modulation. We quantify lifetime as the integral of the time autocorrelation function (ACF) of concentration fluctuations around a non-equilibrium steady state of the reaction network. Furthermore, we look at the first and second derivatives of the ACF, each of which is affected in opposite ways by burst and confinement. This allows discriminating between these two noise sources. We analytically derive the ACF from the linear Fokker-Planck approximation of the chemical master equation in order to establish a baseline for the burst-induced modulation at low confinement. Effects of higher confinement are then studied using a partial-propensity stochastic simulation algorithm. The results presented here may help understand the mechanisms that deviate stochastic kinetics from its deterministic counterpart. In addition, they may be instrumental when using fluorescence-lifetime imaging microscopy (FLIM) or fluorescence-correlation spectroscopy (FCS) to measure confinement and burst in systems with known reaction rates, or, alternatively, to correct for the effects of confinement and burst when experimentally measuring reaction rates.

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.

ACO-Initialized Wavelet Neural Network for Vibration Fault Diagnosis of Hydroturbine Generating Unit

OpenAIRE

Xiao, Zhihuai; He, Xinying; Fu, Xiangqian; Malik, O. P.

2015-01-01

Considering the drawbacks of traditional wavelet neural network, such as low convergence speed and high sensitivity to initial parameters, an ant colony optimization- (ACO-) initialized wavelet neural network is proposed in this paper for vibration fault diagnosis of a hydroturbine generating unit. In this method, parameters of the wavelet neural network are initialized by the ACO algorithm, and then the wavelet neural network is trained by the gradient descent algorithm. Amplitudes of the fr...

Training algorithms evaluation for artificial neural network to temporal prediction of photovoltaic generation

International Nuclear Information System (INIS)

Arantes Monteiro, Raul Vitor; Caixeta Guimarães, Geraldo; Rocio Castillo, Madeleine; Matheus Moura, Fabrício Augusto; Tamashiro, Márcio Augusto

2016-01-01

Current energy policies are encouraging the connection of power generation based on low-polluting technologies, mainly those using renewable sources, to distribution networks. Hence, it becomes increasingly important to understand technical challenges, facing high penetration of PV systems at the grid, especially considering the effects of intermittence of this source on the power quality, reliability and stability of the electric distribution system. This fact can affect the distribution networks on which they are attached causing overvoltage, undervoltage and frequency oscillations. In order to predict these disturbs, artificial neural networks are used. This article aims to analyze 3 training algorithms used in artificial neural networks for temporal prediction of the generated active power thru photovoltaic panels. As a result it was concluded that the algorithm with the best performance among the 3 analyzed was the Levenberg-Marquadrt.

Application of clustering analysis in the prediction of photovoltaic power generation based on neural network

Science.gov (United States)

Cheng, K.; Guo, L. M.; Wang, Y. K.; Zafar, M. T.

2017-11-01

In order to select effective samples in the large number of data of PV power generation years and improve the accuracy of PV power generation forecasting model, this paper studies the application of clustering analysis in this field and establishes forecasting model based on neural network. Based on three different types of weather on sunny, cloudy and rainy days, this research screens samples of historical data by the clustering analysis method. After screening, it establishes BP neural network prediction models using screened data as training data. Then, compare the six types of photovoltaic power generation prediction models before and after the data screening. Results show that the prediction model combining with clustering analysis and BP neural networks is an effective method to improve the precision of photovoltaic power generation.

Modeling the video distribution link in the Next Generation Optical Access Networks

DEFF Research Database (Denmark)

Amaya, F.; Cárdenas, A.; Tafur Monroy, Idelfonso

2011-01-01

In this work we present a model for the design and optimization of the video distribution link in the next generation optical access network. We analyze the video distribution performance in a SCM-WDM link, including the noise, the distortion and the fiber optic nonlinearities. Additionally, we...... consider in the model the effect of distributed Raman amplification, used to extent the capacity and the reach of the optical link. In the model, we use the nonlinear Schrödinger equation with the purpose to obtain capacity limitations and design constrains of the next generation optical access networks....

Fiber-wireless convergence in next-generation communication networks systems, architectures, and management

CERN Document Server

Chang, Gee-Kung; Ellinas, Georgios

2017-01-01

This book investigates new enabling technologies for Fi-Wi convergence. The editors discuss Fi-Wi technologies at the three major network levels involved in the path towards convergence: system level, network architecture level, and network management level. The main topics will be: a. At system level: Radio over Fiber (digitalized vs. analogic, standardization, E-band and beyond) and 5G wireless technologies; b. Network architecture level: NGPON, WDM-PON, BBU Hotelling, Cloud Radio Access Networks (C-RANs), HetNets. c. Network management level: SDN for convergence, Next-generation Point-of-Presence, Wi-Fi LTE Handover, Cooperative MultiPoint. • Addresses the Fi-Wi convergence issues at three different levels, namely at the system level, network architecture level, and network management level • Provides approaches in communication systems, network architecture, and management that are expected to steer the evolution towards fiber-wireless convergence • Contributions from leading experts in the field of...

A new building block for DNA network formation by self-assembly and polymerase chain reaction.

Science.gov (United States)

Bußkamp, Holger; Keller, Sascha; Robotta, Marta; Drescher, Malte; Marx, Andreas

2014-01-01

The predictability of DNA self-assembly is exploited in many nanotechnological approaches. Inspired by naturally existing self-assembled DNA architectures, branched DNA has been developed that allows self-assembly to predesigned architectures with dimensions on the nanometer scale. DNA is an attractive material for generation of nanostructures due to a plethora of enzymes which modify DNA with high accuracy, providing a toolbox for many different manipulations to construct nanometer scaled objects. We present a straightforward synthesis of a rigid DNA branching building block successfully used for the generation of DNA networks by self-assembly and network formation by enzymatic DNA synthesis. The Y-shaped 3-armed DNA construct, bearing 3 primer strands is accepted by Taq DNA polymerase. The enzyme uses each arm as primer strand and incorporates the branched construct into large assemblies during PCR. The networks were investigated by agarose gel electrophoresis, atomic force microscopy, dynamic light scattering, and electron paramagnetic resonance spectroscopy. The findings indicate that rather rigid DNA networks were formed. This presents a new bottom-up approach for DNA material formation and might find applications like in the generation of functional hydrogels.

Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks

KAUST Repository

Moraes, Alvaro

2015-01-07

Stochastic reaction networks (SRNs) is a class of continuous-time Markov chains intended to describe, from the kinetic point of view, the time-evolution of chemical systems in which molecules of different chemical species undergo a finite set of reaction channels. This talk is based on articles [4, 5, 6], where we are interested in the following problem: given a SRN, X, defined though its set of reaction channels, and its initial state, x0, estimate E (g(X(T))); that is, the expected value of a scalar observable, g, of the process, X, at a fixed time, T. This problem lead us to define a series of Monte Carlo estimators, M, such that, with high probability can produce values close to the quantity of interest, E (g(X(T))). More specifically, given a user-selected tolerance, TOL, and a small confidence level, η, find an estimator, M, based on approximate sampled paths of X, such that, P (|E (g(X(T))) − M| ≤ TOL) ≥ 1 − η; even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.

Robust transient stabilisation problem for a synchronous generator in a power network

Science.gov (United States)

Verrelli, C. M.; Damm, G.

2010-04-01

The robust transient stabilisation problem (with stability proof€) of a synchronous generator in an uncertain power network with transfer conductances is rigorously formulated and solved. The generator angular speed and electrical power are required to be kept close, when mechanical and electrical perturbations occur, to the synchronous speed and mechanical input power, respectively, while the generator terminal voltage is to be regulated, when perturbations are removed, to its pre-fault reference constant value. A robust adaptive nonlinear feedback control algorithm is designed on the basis of a third-order model of the synchronous machine: only two system parameters (synchronous machine damping and inertia constants) along with upper and lower bounds on the remaining uncertain ones are supposed to be known. The conditions to be satisfied by the remote network dynamics for guaranteeing ℒ2 and ℒ∞ robustness and asymptotic relative speed and voltage regulation to zero are weaker than those required by the single machine-infinite bus approximation: dynamic interactions between the local deviations of the generator states from the corresponding equilibrium values and the remote generators states are allowed.

Biology Question Generation from a Semantic Network

Science.gov (United States)

Zhang, Lishan

Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions. To boost students' learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student's current competence so that a suitable question could be selected based on the student's previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group. To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators. A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from

Implementing Value Added Applications in Next Generation Networks

Directory of Open Access Journals (Sweden)

Yuan-Kuang Tu

2010-08-01

Full Text Available One of the major issues in the future Internet is the integration of telecom networks with the Internet. In many countries, large Internet Service Providers (ISPs are also telecom operators that have been focusing on providing Internet services through their telecom networks with telecom-grade mechanisms. In this article, we show that IP Multimedia Subsystem (IMS is a telecom-grade mechanism that addresses this important issue. In Next Generation Network (NGN, IMS supports IP-based multimedia services that can be accessed from various wireless and wired access technologies through fixed-mobile convergence. We show how to integrate Internet Protocol Television (IPTV with NGN/IMS to offer enhanced IPTV services for subscribers with set-top boxes or mobile phones. We specifically describe the implementations of three services: weather forecasts, short messages on TV screens and TV shopping/food ordering for mobile users. Although these services can be directly implemented in the Internet, our commercial operation experiences indicate that the NGN/IMS implementation has advantages in terms of telecom-grade security, Quality of Service (QoS, and flexible service creation.

Disorder generated by interacting neural networks: application to econophysics and cryptography

International Nuclear Information System (INIS)

Kinzel, Wolfgang; Kanter, Ido

2003-01-01

When neural networks are trained on their own output signals they generate disordered time series. In particular, when two neural networks are trained on their mutual output they can synchronize; they relax to a time-dependent state with identical synaptic weights. Two applications of this phenomenon are discussed for (a) econophysics and (b) cryptography. (a) When agents competing in a closed market (minority game) are using neural networks to make their decisions, the total system relaxes to a state of good performance. (b) Two partners communicating over a public channel can find a common secret key

Integrated Power Flow and Short Circuit Calculation Method for Distribution Network with Inverter Based Distributed Generation

OpenAIRE

Yang, Shan; Tong, Xiangqian

2016-01-01

Power flow calculation and short circuit calculation are the basis of theoretical research for distribution network with inverter based distributed generation. The similarity of equivalent model for inverter based distributed generation during normal and fault conditions of distribution network and the differences between power flow and short circuit calculation are analyzed in this paper. Then an integrated power flow and short circuit calculation method for distribution network with inverte...

Neural Network Control of CSTR for Reversible Reaction Using Reverence Model Approach

Directory of Open Access Journals (Sweden)

Duncan ALOKO

2007-01-01

Full Text Available In this work, non-linear control of CSTR for reversible reaction is carried out using Neural Network as design tool. The Model Reverence approach in used to design ANN controller. The idea is to have a control system that will be able to achieve improvement in the level of conversion and to be able to track set point change and reject load disturbance. We use PID control scheme as benchmark to study the performance of the controller. The comparison shows that ANN controller out perform PID in the extreme range of non-linearity.This paper represents a preliminary effort to design a simplified neutral network control scheme for a class of non-linear process. Future works will involve further investigation of the effectiveness of thin approach for the real industrial chemical process

Noise-induced modulation of the relaxation kinetics around a non-equilibrium steady state of non-linear chemical reaction networks.

Directory of Open Access Journals (Sweden)

Rajesh Ramaswamy

2011-01-01

Full Text Available Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confinement increases the lifetimes of all species that are involved in any non-linear reaction as a reactant. Burst monotonically increases or decreases lifetimes. Competition between burst-induced and confinement-induced modulation may hence lead to a non-monotonic modulation. We quantify lifetime as the integral of the time autocorrelation function (ACF of concentration fluctuations around a non-equilibrium steady state of the reaction network. Furthermore, we look at the first and second derivatives of the ACF, each of which is affected in opposite ways by burst and confinement. This allows discriminating between these two noise sources. We analytically derive the ACF from the linear Fokker-Planck approximation of the chemical master equation in order to establish a baseline for the burst-induced modulation at low confinement. Effects of higher confinement are then studied using a partial-propensity stochastic simulation algorithm. The results presented here may help understand the mechanisms that deviate stochastic kinetics from its deterministic counterpart. In addition, they may be instrumental when using fluorescence-lifetime imaging microscopy (FLIM or fluorescence-correlation spectroscopy (FCS to measure confinement and burst in systems with known reaction rates, or, alternatively, to correct for the effects of confinement and burst when experimentally measuring reaction rates.

Distribution network planning method considering distributed generation for peak cutting

International Nuclear Information System (INIS)

Ouyang Wu; Cheng Haozhong; Zhang Xiubin; Yao Liangzhong

2010-01-01

Conventional distribution planning method based on peak load brings about large investment, high risk and low utilization efficiency. A distribution network planning method considering distributed generation (DG) for peak cutting is proposed in this paper. The new integrated distribution network planning method with DG implementation aims to minimize the sum of feeder investments, DG investments, energy loss cost and the additional cost of DG for peak cutting. Using the solution techniques combining genetic algorithm (GA) with the heuristic approach, the proposed model determines the optimal planning scheme including the feeder network and the siting and sizing of DG. The strategy for the site and size of DG, which is based on the radial structure characteristics of distribution network, reduces the complexity degree of solving the optimization model and eases the computational burden substantially. Furthermore, the operation schedule of DG at the different load level is also provided.

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

DEFF Research Database (Denmark)

Hald, Bjørn Olav

2016-01-01

The physiology of biological structures is inherently dynamic and emerges from the interaction and assembly of large collections of small entities. The extent of coupled entities complicates modeling and increases computational load. Here, microvascular networks are used to present a novel...... to synchronize vessel tone across the vast distances within a network. We hypothesize that electrical conduction capacity is delimited by the size of vascular structures and connectivity of the network. Generation and simulation of series of dynamical models of electrical spread within vascular networks...... of different size and composition showed that (1) Conduction is enhanced in models harboring long and thin endothelial cells that couple preferentially along the longitudinal axis. (2) Conduction across a branch point depends on endothelial connectivity between branches. (3) Low connectivity sub...

Custom Topology Generation for Network-on-Chip

DEFF Research Database (Denmark)

Stuart, Matthias Bo; Sparsø, Jens

2007-01-01

This paper compares simulated annealing and tabu search for generating custom topologies for applications with periodic behaviour executing on a network-on-chip. The approach differs from previous work by starting from a fixed mapping of IP-cores to routers and performing design space exploration...... around an initial topology. The tabu search has been modified from its normally encountered form to allow easier escaping from local minima. A number of synthetic benchmarks are used for tuning the parameters of both heuristics and for testing the quality of the solutions each heuristic produces...

Robust Power Supply Restoration for Self-Healing Active Distribution Networks Considering the Availability of Distributed Generation

Directory of Open Access Journals (Sweden)

Qiang Yang

2018-01-01

Full Text Available The increasing penetration of distributed generations (DGs with intermittent and stochastic characteristics into current power distribution networks can lead to increased fault levels and degradation in network protection. As one of the key requirements of active network management (ANM, efficient power supply restoration solution to guarantee network self-healing capability with full consideration of DG uncertainties is demanded. This paper presents a joint power supply restoration through combining the DG local restoration and switcher operation-based restoration to enhance the self-healing capability in active distribution networks considering the availability of distributed generation. The restoration algorithmic solution is designed to be able to carry out power restoration in parallel upon multiple simultaneous faults to maximize the load restoration while additionally minimizing power loss, topology variation and power flow changes due to switcher operations. The performance of the proposed solution is validated based on a 53-bus distribution network with wind power generators through extensive simulation experiments for a range of fault cases and DG scenarios generated based on Heuristic Moment Matching (HMM method to fully consider the DG randomness. The numerical result in comparison with the existing solutions demonstrates the effectiveness of the proposed power supply restoration solution.

The generation of radiolabeled DNA and RNA probes with polymerase chain reaction

International Nuclear Information System (INIS)

Schowalter, D.B.; Sommer, S.S.

1989-01-01

By including a radioactive triphosphate during polymerase chain reaction (PCR), probes of very high specific activity can be generated. The advantages of PCR labeling include (1) uniform labeling with a specific activity of 5 X 10(9) cpm/micrograms or higher (sensitivity of detection: 0.028 pg of target DNA per 24 h); (2) ease of regulation of both the specific activity and the amount of labeled probe produced; (3) efficient labeling of fragments less than 500 bp; (4) efficient incorporation over a wide range of input DNA template; (5) labeling with subnanogram amounts of input DNA; and (6) direct labeling of genomic DNA. The minimal amount of input DNA allows a virtually unlimited number of PCR labeling reactions to be performed on DNA generated by one amplification under the previously described nonlabeling conditions. This obviates the need for CsCl gradients or other large scale methods of DNA preparation. The above advantages except for the very high specific activity can also be achieved by transcript labeling after an amplification where one or both of PCR primers contain a phage promoter sequence

Computational methodology of sodium-water reaction phenomenon in steam generator of sodium-cooled fast reactor

International Nuclear Information System (INIS)

Takata, Takashi; Yamaguchi, Akira; Uchibori, Akihiro; Ohshima, Hiroyuki

2009-01-01

A new computational methodology of sodium-water reaction (SWR), which occurs in a steam generator of a liquid-sodium-cooled fast reactor when a heat transfer tube in the steam generator fails, has been developed considering multidimensional and multiphysics thermal hydraulics. Two kinds of reaction models are proposed in accordance with a phase of sodium as a reactant. One is the surface reaction model in which water vapor reacts directly with liquid sodium at the interface between the liquid sodium and the water vapor. The reaction heat will lead to a vigorous evaporation of liquid sodium, resulting in a reaction of gas-phase sodium. This is designated as the gas-phase reaction model. These two models are coupled with a multidimensional, multicomponent gas, and multiphase thermal hydraulics simulation method with compressibility (named the 'SERAPHIM' code). Using the present methodology, a numerical investigation of the SWR under a pin-bundle configuration (a benchmark analysis of the SWAT-1R experiment) has been carried out. As a result, the maximum gas temperature of approximately 1,300degC is predicted stably, which lies within the range of previous experimental observations. It is also demonstrated that the maximum temperature of the mass weighted average in the analysis agrees reasonably well with the experimental result measured by thermocouples. The present methodology will be promising to establish a theoretical and mechanical modeling of secondary failure propagation of heat transfer tubes due to such as an overheating rupture and a wastage. (author)

Energy Management Optimization for Cellular Networks under Renewable Energy Generation Uncertainty

KAUST Repository

Rached, Nadhir B.

2017-03-28

The integration of renewable energy (RE) as an alternative power source for cellular networks has been deeply investigated in literature. However, RE generation is often assumed to be deterministic; an impractical assumption for realistic scenarios. In this paper, an efficient energy procurement strategy for cellular networks powered simultaneously by the smart grid (SG) and locally deployed RE sources characterized by uncertain processes is proposed. For a one-day operation cycle, the mobile operator aims to reduce its total energy cost by optimizing the amounts of energy to be procured from the local RE sources and SG at each time period. Additionally, it aims to determine the amount of extra generated RE to be sold back to SG. A chance constrained optimization is first proposed to deal with the RE generation uncertainty. Then, two convex approximation approaches: Chernoff and Chebyshev methods, characterized by different levels of knowledge about the RE generation, are developed to determine the energy procurement strategy for different risk levels. In addition, their performances are analyzed for various daily scenarios through selected simulation results. It is shown that the higher complex Chernoff method outperforms the Chebyshev one for different risk levels set by the operator.

Energy Management Optimization for Cellular Networks under Renewable Energy Generation Uncertainty

KAUST Repository

Rached, Nadhir B.; Ghazzai, Hakim; Kadri, Abdullah; Alouini, Mohamed-Slim

2017-01-01

The integration of renewable energy (RE) as an alternative power source for cellular networks has been deeply investigated in literature. However, RE generation is often assumed to be deterministic; an impractical assumption for realistic scenarios. In this paper, an efficient energy procurement strategy for cellular networks powered simultaneously by the smart grid (SG) and locally deployed RE sources characterized by uncertain processes is proposed. For a one-day operation cycle, the mobile operator aims to reduce its total energy cost by optimizing the amounts of energy to be procured from the local RE sources and SG at each time period. Additionally, it aims to determine the amount of extra generated RE to be sold back to SG. A chance constrained optimization is first proposed to deal with the RE generation uncertainty. Then, two convex approximation approaches: Chernoff and Chebyshev methods, characterized by different levels of knowledge about the RE generation, are developed to determine the energy procurement strategy for different risk levels. In addition, their performances are analyzed for various daily scenarios through selected simulation results. It is shown that the higher complex Chernoff method outperforms the Chebyshev one for different risk levels set by the operator.

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Directory of Open Access Journals (Sweden)

Qiankun Song

2007-06-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Directory of Open Access Journals (Sweden)

Cao Jinde

2007-01-01

Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

Large scale sodium-water reaction tests for Monju steam generators

International Nuclear Information System (INIS)

Sato, M.; Hiroi, H.; Hori, M.

1976-01-01

To demonstrate the safe design of the steam generator system of the prototype fast reactor Monju against the postulated large leak sodium-water reaction, a large scale test facility SWAT-3 was constructed. SWAT-3 is a 1/2.5 scale model of the Monju secondary loop on the basis of the iso-velocity modeling. Two tests have been conducted in SWAT-3 since its construction. The test items using SWAT-3 are discussed, and the description of the facility and the test results are presented

Simulation and Statistical Inference of Stochastic Reaction Networks with Applications to Epidemic Models

KAUST Repository

Moraes, Alvaro

2015-01-01

Epidemics have shaped, sometimes more than wars and natural disasters, demo- graphic aspects of human populations around the world, their health habits and their economies. Ebola and the Middle East Respiratory Syndrome (MERS) are clear and current examples of potential hazards at planetary scale. During the spread of an epidemic disease, there are phenomena, like the sudden extinction of the epidemic, that can not be captured by deterministic models. As a consequence, stochastic models have been proposed during the last decades. A typical forward problem in the stochastic setting could be the approximation of the expected number of infected individuals found in one month from now. On the other hand, a typical inverse problem could be, given a discretely observed set of epidemiological data, infer the transmission rate of the epidemic or its basic reproduction number. Markovian epidemic models are stochastic models belonging to a wide class of pure jump processes known as Stochastic Reaction Networks (SRNs), that are intended to describe the time evolution of interacting particle systems where one particle interacts with the others through a finite set of reaction channels. SRNs have been mainly developed to model biochemical reactions but they also have applications in neural networks, virus kinetics, and dynamics of social networks, among others. 4 This PhD thesis is focused on novel fast simulation algorithms and statistical inference methods for SRNs. Our novel Multi-level Monte Carlo (MLMC) hybrid simulation algorithms provide accurate estimates of expected values of a given observable of SRNs at a prescribed final time. They are designed to control the global approximation error up to a user-selected accuracy and up to a certain confidence level, and with near optimal computational work. We also present novel dual-weighted residual expansions for fast estimation of weak and strong errors arising from the MLMC methodology. Regarding the statistical inference

Question Generation and Adaptation Using a Bayesian Network of the Learner’s Achievements

NARCIS (Netherlands)

Wißner, M.; Linnebank, F.; Liem, J.; Bredeweg, B.; André, E.; Lane, H.C.; Yacef, K.; Mostow, J.; Pavlik, P.

2013-01-01

This paper presents a domain independent question generation and interaction procedure that automatically generates multiple-choice questions for conceptual models created with Qualitative Reasoning vocabulary. A Bayesian Network is deployed that captures the learning progress based on the answers

Ultrasound-mediated drug delivery by gas bubbles generated from a chemical reaction.

Science.gov (United States)

Lee, Sungmun; Al-Kaabi, Leena; Mawart, Aurélie; Khandoker, Ahsan; Alsafar, Habiba; Jelinek, Herbert F; Khalaf, Kinda; Park, Ji-Ho; Kim, Yeu-Chun

2018-02-01

Highly echogenic and ultrasound-responsive microbubbles such as nitrogen and perfluorocarbons have been exploited as ultrasound-mediated drug carriers. Here, we propose an innovative method for drug delivery using microbubbles generated from a chemical reaction. In a novel drug delivery system, luminol encapsulated in folate-conjugated bovine serum albumin nanoparticles (Fol-BSAN) can generate nitrogen gas (N 2 ) by chemical reaction when it reacts with hydrogen peroxide (H 2 O 2 ), one of reactive oxygen species (ROS). ROS plays an important role in the initiation and progression of cancer and elevated ROS have been observed in cancer cells both in vitro and in vivo. High-intensity focussed ultrasound (HIFU) is used to burst the N 2 microbubbles, causing site-specific delivery of anticancer drugs such as methotrexate. In this research, the drug delivery system was optimised by using water-soluble luminol and Mobil Composition of Matter-41 (MCM-41), a mesoporous material, so that the delivery system was sensitive to micromolar concentrations of H 2 O 2 . HIFU increased the drug release from Fol-BSAN by 52.9 ± 2.9% in 10 minutes. The cytotoxicity of methotrexate was enhanced when methotrexate is delivered to MDA-MB-231, a metastatic human breast cancer cell line, using Fol-BSAN with HIFU. We anticipate numerous applications of chemically generated microbubbles for ultrasound-mediated drug delivery.

Building a mechanistic biogeochemical reaction network for upscaling : Characterization of mass transport limitation between regions of hydrolysis and methanogenesis

NARCIS (Netherlands)

Van Turnhout, A.G.; Kleerebezem, R.; Heimovaara, T.J.

2015-01-01

In this study, we aim to validate the reaction network with an idealized experiment. We want to show that 1) rate controlling parameters are identifiable from the measured data by inverse modeling, and 2) that this network is able to predict the measured emissions in the experiment given the initial

Comprehensive evaluation of impacts of distributed generation integration in distribution network

Science.gov (United States)

Peng, Sujiang; Zhou, Erbiao; Ji, Fengkun; Cao, Xinhui; Liu, Lingshuang; Liu, Zifa; Wang, Xuyang; Cai, Xiaoyu

2018-04-01

All Distributed generation (DG) as the supplement to renewable energy centralized utilization, is becoming the focus of development direction of renewable energy utilization. With the increasing proportion of DG in distribution network, the network power structure, power flow distribution, operation plans and protection are affected to some extent. According to the main impacts of DG, a comprehensive evaluation model of distributed network with DG is proposed in this paper. A comprehensive evaluation index system including 7 aspects, along with their corresponding index calculation method is established for quantitative analysis. The indices under different access capacity of DG in distribution network are calculated based on the IEEE RBTS-Bus 6 system and the evaluation result is calculated by analytic hierarchy process (AHP). The proposed model and method are verified effective and validity through case study.

Design of an Adaptive-Neural Network Attitude Controller of a Satellite using Reaction Wheels

Directory of Open Access Journals (Sweden)

Abbas Ajorkar

2015-04-01

Full Text Available In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. Then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. In order to eliminate the effect of these uncertainties, a multilayer neural network with back-propagation law is designed. In this structure, the parameters of the moment of inertia matrix and external disturbances are estimated and used in feedback linearization control law. Finally, the performance of the designed attitude controller is investigated by several simulations.

Metadislocation reactions and metadislocation networks in the complex metallic alloy ξ'-Al-Pd-Mn

International Nuclear Information System (INIS)

Heggen, Marc; Feuerbacher, Michael

2005-01-01

Metadislocations are novel structural defects firstly observed in the complex metallic alloy ξ'-Al-Pd-Mn. We present a transmission electron microscopy study on metadislocation reactions and networks. It is shown that metadislocations can dissociate into partials, which leads to a decrease of the elastic line energy. Connected groups of metadislocations can assume large and complex network structures with large total Burgers vectors. However, the local elastic strain at the individual metadislocation cores as well as the fault-plane energies remain small. By this mechanism, effective large Burgers vectors, contributing massively to plastic strain, can be distributed over a large portion of the material

An artificial neural network model for periodic trajectory generation

Science.gov (United States)

Shankar, S.; Gander, R. E.; Wood, H. C.

A neural network model based on biological systems was developed for potential robotic application. The model consists of three interconnected layers of artificial neurons or units: an input layer subdivided into state and plan units, an output layer, and a hidden layer between the two outer layers which serves to implement nonlinear mappings between the input and output activation vectors. Weighted connections are created between the three layers, and learning is effected by modifying these weights. Feedback connections between the output and the input state serve to make the network operate as a finite state machine. The activation vector of the plan units of the input layer emulates the supraspinal commands in biological central pattern generators in that different plan activation vectors correspond to different sequences or trajectories being recalled, even with different frequencies. Three trajectories were chosen for implementation, and learning was accomplished in 10,000 trials. The fault tolerant behavior, adaptiveness, and phase maintenance of the implemented network are discussed.

Helium generation reaction rates for 6Li and 10B in benchmark facilities

International Nuclear Information System (INIS)

Farrar, Harry IV; Oliver, B.M.; Lippincott, E.P.

1980-01-01

The helium generation rates for 10 B and 6 Li have been measured in two benchmark reactor facilities having neutron spectra similar to those found in a breeder reactor. The irradiations took place in the Coupled Fast Reactivity Measurements Facility (CFRMF) and in the 10% enriched 235 U critical assembly, BIG-10. The helium reaction rates were obtained by precise high-sensitivity gas mass spectrometric analyses of the helium content of numerous small samples. Comparison of these reaction rates with other reaction rates measured in the same facilities, and with rates calculated from published cross sections and from best estimates of the neutron spectral shapes, indicate significant discrepancies in the calculated values. Additional irradiations in other benchmark facilities have been undertaken to better determine the energy ranges where the discrepancies lie

Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

KAUST Repository

Ghazzai, Hakim

2016-09-16

Over the last decade, mobile communications have been witnessing a noteworthy increase of data traffic demand that is causing an enormous energy consumption in cellular networks. The reduction of their fossil fuel consumption in addition to the huge energy bills paid by mobile operators is considered as the most important challenges for the next-generation cellular networks. Although most of the proposed studies were focusing on individual physical layer power optimizations, there is a growing necessity to meet the green objective of fifth-generation cellular networks while respecting the user\\'s quality of service. This paper investigates four important techniques that could be exploited separately or together in order to enable wireless operators achieve significant economic benefits and environmental savings: 1) the base station sleeping strategy; 2) the optimized energy procurement from the smart grid; 3) the base station energy sharing; and 4) the green networking collaboration between competitive mobile operators. The presented simulation results measure the gain that could be obtained using these techniques compared with that of traditional scenarios. Finally, this paper discusses the issues and challenges related to the implementations of these techniques in real environments. © 2016 IEEE.

Mittag-Leffler synchronization of fractional neural networks with time-varying delays and reaction-diffusion terms using impulsive and linear controllers.

Science.gov (United States)

Stamova, Ivanka; Stamov, Gani

2017-12-01

In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fractional Lyapunov method sufficient conditions are given. We also study the global Mittag-Leffler synchronization of two identical fractional impulsive reaction-diffusion neural networks using linear controllers, which was an open problem even for integer-order models. Since the Mittag-Leffler stability notion is a generalization of the exponential stability concept for fractional-order systems, our results extend and improve the exponential impulsive control theory of neural network system with time-varying delays and reaction-diffusion terms to the fractional-order case. The fractional-order derivatives allow us to model the long-term memory in the neural networks, and thus the present research provides with a conceptually straightforward mathematical representation of rather complex processes. Illustrative examples are presented to show the validity of the obtained results. We show that by means of appropriate impulsive controllers we can realize the stability goal and to control the qualitative behavior of the states. An image encryption scheme is extended using fractional derivatives. Copyright © 2017 Elsevier Ltd. All rights reserved.

Mitigation of Hydrogen Gas Generation from the Reaction of Water with Uranium Metal in K Basins Sludge

International Nuclear Information System (INIS)

Sinkov, Sergey I.; Delegard, Calvin H.; Schmidt, Andrew J.

2010-01-01

Means to decrease the rate of hydrogen gas generation from the chemical reaction of uranium metal with water were identified by surveying the technical literature. The underlying chemistry and potential side reactions were explored by conducting 61 principal experiments. Several methods achieved significant hydrogen gas generation rate mitigation. Gas-generating side reactions from interactions of organics or sludge constituents with mitigating agents were observed. Further testing is recommended to develop deeper knowledge of the underlying chemistry and to advance the technology aturation level. Uranium metal reacts with water in K Basin sludge to form uranium hydride (UH3), uranium dioxide or uraninite (UO2), and diatomic hydrogen (H2). Mechanistic studies show that hydrogen radicals (H·) and UH3 serve as intermediates in the reaction of uranium metal with water to produce H2 and UO2. Because H2 is flammable, its release into the gas phase above K Basin sludge during sludge storage, processing, immobilization, shipment, and disposal is a concern to the safety of those operations. Findings from the technical literature and from experimental investigations with simple chemical systems (including uranium metal in water), in the presence of individual sludge simulant components, with complete sludge simulants, and with actual K Basin sludge are presented in this report. Based on the literature review and intermediate lab test results, sodium nitrate, sodium nitrite, Nochar Acid Bond N960, disodium hydrogen phosphate, and hexavalent uranium [U(VI)] were tested for their effects in decreasing the rate of hydrogen generation from the reaction of uranium metal with water. Nitrate and nitrite each were effective, decreasing hydrogen generation rates in actual sludge by factors of about 100 to 1000 when used at 0.5 molar (M) concentrations. Higher attenuation factors were achieved in tests with aqueous solutions alone. Nochar N960, a water sorbent, decreased hydrogen

Mitigation of Hydrogen Gas Generation from the Reaction of Water with Uranium Metal in K Basins Sludge

Energy Technology Data Exchange (ETDEWEB)

Sinkov, Sergey I.; Delegard, Calvin H.; Schmidt, Andrew J.

2010-01-29

Means to decrease the rate of hydrogen gas generation from the chemical reaction of uranium metal with water were identified by surveying the technical literature. The underlying chemistry and potential side reactions were explored by conducting 61 principal experiments. Several methods achieved significant hydrogen gas generation rate mitigation. Gas-generating side reactions from interactions of organics or sludge constituents with mitigating agents were observed. Further testing is recommended to develop deeper knowledge of the underlying chemistry and to advance the technology aturation level. Uranium metal reacts with water in K Basin sludge to form uranium hydride (UH3), uranium dioxide or uraninite (UO2), and diatomic hydrogen (H2). Mechanistic studies show that hydrogen radicals (H·) and UH3 serve as intermediates in the reaction of uranium metal with water to produce H2 and UO2. Because H2 is flammable, its release into the gas phase above K Basin sludge during sludge storage, processing, immobilization, shipment, and disposal is a concern to the safety of those operations. Findings from the technical literature and from experimental investigations with simple chemical systems (including uranium metal in water), in the presence of individual sludge simulant components, with complete sludge simulants, and with actual K Basin sludge are presented in this report. Based on the literature review and intermediate lab test results, sodium nitrate, sodium nitrite, Nochar Acid Bond N960, disodium hydrogen phosphate, and hexavalent uranium [U(VI)] were tested for their effects in decreasing the rate of hydrogen generation from the reaction of uranium metal with water. Nitrate and nitrite each were effective, decreasing hydrogen generation rates in actual sludge by factors of about 100 to 1000 when used at 0.5 molar (M) concentrations. Higher attenuation factors were achieved in tests with aqueous solutions alone. Nochar N960, a water sorbent, decreased hydrogen

Reactions of carbon radicals generated by 1,5-transposition of reactive centers

Directory of Open Access Journals (Sweden)

ZIVORAD CEKOVIC

2005-03-01

Full Text Available Radical intermediates can undergo specific reactions, such as intramolecular rearrangements, i.e., the transpositions of radical centers, which are not known in classical ionic organic reactions. 1,5-Transposition of a radical center to a non-activated carbon atom are of great synthetic importance. It can be successfully applied for the introduction of different functional groups (oxygen, nitrogen, sulfur, halogens onto a carbon atom remote from the present functional group. In addition to functionalization of a remote non-activated carbon atom, the formation of new C-C bonds on the d-carbon atom have also been achieved. 1,5-Transposition of the radical centers takes place from alkoxyl, aminyl and carbon radicals to a remote carbon atom. Relocation of the radical centers preferentially involves 1,5-transfer of a hydrogen atom, although migrations of some other groups are known. The reactions of the carbon radical generated by 1,5-relocation of the radical center are presented and their synthetic applications are reviewed.

3D conditional generative adversarial networks for high-quality PET image estimation at low dose.

Science.gov (United States)

Wang, Yan; Yu, Biting; Wang, Lei; Zu, Chen; Lalush, David S; Lin, Weili; Wu, Xi; Zhou, Jiliu; Shen, Dinggang; Zhou, Luping

2018-07-01

Positron emission tomography (PET) is a widely used imaging modality, providing insight into both the biochemical and physiological processes of human body. Usually, a full dose radioactive tracer is required to obtain high-quality PET images for clinical needs. This inevitably raises concerns about potential health hazards. On the other hand, dose reduction may cause the increased noise in the reconstructed PET images, which impacts the image quality to a certain extent. In this paper, in order to reduce the radiation exposure while maintaining the high quality of PET images, we propose a novel method based on 3D conditional generative adversarial networks (3D c-GANs) to estimate the high-quality full-dose PET images from low-dose ones. Generative adversarial networks (GANs) include a generator network and a discriminator network which are trained simultaneously with the goal of one beating the other. Similar to GANs, in the proposed 3D c-GANs, we condition the model on an input low-dose PET image and generate a corresponding output full-dose PET image. Specifically, to render the same underlying information between the low-dose and full-dose PET images, a 3D U-net-like deep architecture which can combine hierarchical features by using skip connection is designed as the generator network to synthesize the full-dose image. In order to guarantee the synthesized PET image to be close to the real one, we take into account of the estimation error loss in addition to the discriminator feedback to train the generator network. Furthermore, a concatenated 3D c-GANs based progressive refinement scheme is also proposed to further improve the quality of estimated images. Validation was done on a real human brain dataset including both the normal subjects and the subjects diagnosed as mild cognitive impairment (MCI). Experimental results show that our proposed 3D c-GANs method outperforms the benchmark methods and achieves much better performance than the state

Phoebus: Network Middleware for Next-Generation Network Computing

Energy Technology Data Exchange (ETDEWEB)

Martin Swany

2012-06-16

The Phoebus project investigated algorithms, protocols, and middleware infrastructure to improve end-to-end performance in high speed, dynamic networks. The Phoebus system essentially serves as an adaptation point for networks with disparate capabilities or provisioning. This adaptation can take a variety of forms including acting as a provisioning agent across multiple signaling domains, providing transport protocol adaptation points, and mapping between distributed resource reservation paradigms and the optical network control plane. We have successfully developed the system and demonstrated benefits. The Phoebus system was deployed in Internet2 and in ESnet, as well as in GEANT2, RNP in Brazil and over international links to Korea and Japan. Phoebus is a system that implements a new protocol and associated forwarding infrastructure for improving throughput in high-speed dynamic networks. It was developed to serve the needs of large DOE applications on high-performance networks. The idea underlying the Phoebus model is to embed Phoebus Gateways (PGs) in the network as on-ramps to dynamic circuit networks. The gateways act as protocol translators that allow legacy applications to use dedicated paths with high performance.

Report on the IAEA technical meeting on network of nuclear reaction data centres

Energy Technology Data Exchange (ETDEWEB)

Schwerer, O [IAEA Nuclear Data Section, Vienna (Austria)

2006-12-15

An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting) was held at IAEA Headquarters, Vienna, Austria, from 25 to 28 September 2006. The meeting was attended by 19 participants from 10 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)

Report on the IAEA technical meeting on network of nuclear reaction data centres

International Nuclear Information System (INIS)

Schwerer, O.

2006-12-01

An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting) was held at IAEA Headquarters, Vienna, Austria, from 25 to 28 September 2006. The meeting was attended by 19 participants from 10 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)

Generative Recurrent Networks for De Novo Drug Design.

Science.gov (United States)

Gupta, Anvita; Müller, Alex T; Huisman, Berend J H; Fuchs, Jens A; Schneider, Petra; Schneider, Gisbert

2018-01-01

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Lewis Research Center studies of multiple large wind turbine generators on a utility network

Science.gov (United States)

Gilbert, L. J.; Triezenberg, D. M.

1979-01-01

A NASA-Lewis program to study the anticipated performance of a wind turbine generator farm on an electric utility network is surveyed. The paper describes the approach of the Lewis Wind Energy Project Office to developing analysis capabilities in the area of wind turbine generator-utility network computer simulations. Attention is given to areas such as, the Lewis Purdue hybrid simulation, an independent stability study, DOE multiunit plant study, and the WEST simulator. Also covered are the Lewis mod-2 simulation including analog simulation of a two wind turbine system and comparison with Boeing simulation results, and gust response of a two machine model. Finally future work to be done is noted and it is concluded that the study shows little interaction between the generators and between the generators and the bus.

Distributed generation of shared RSA keys in mobile ad hoc networks

Science.gov (United States)

Liu, Yi-Liang; Huang, Qin; Shen, Ying

2005-12-01

Mobile Ad Hoc Networks is a totally new concept in which mobile nodes are able to communicate together over wireless links in an independent manner, independent of fixed physical infrastructure and centralized administrative infrastructure. However, the nature of Ad Hoc Networks makes them very vulnerable to security threats. Generation and distribution of shared keys for CA (Certification Authority) is challenging for security solution based on distributed PKI(Public-Key Infrastructure)/CA. The solutions that have been proposed in the literature and some related issues are discussed in this paper. The solution of a distributed generation of shared threshold RSA keys for CA is proposed in the present paper. During the process of creating an RSA private key share, every CA node only has its own private security. Distributed arithmetic is used to create the CA's private share locally, and that the requirement of centralized management institution is eliminated. Based on fully considering the Mobile Ad Hoc network's characteristic of self-organization, it avoids the security hidden trouble that comes by holding an all private security share of CA, with which the security and robustness of system is enhanced.

Reaction-Diffusion Automata Phenomenology, Localisations, Computation

CERN Document Server

Adamatzky, Andrew

2013-01-01

Reaction-diffusion and excitable media are amongst most intriguing substrates. Despite apparent simplicity of the physical processes involved the media exhibit a wide range of amazing patterns: from target and spiral waves to travelling localisations and stationary breathing patterns. These media are at the heart of most natural processes, including morphogenesis of living beings, geological formations, nervous and muscular activity, and socio-economic developments.   This book explores a minimalist paradigm of studying reaction-diffusion and excitable media using locally-connected networks of finite-state machines: cellular automata and automata on proximity graphs. Cellular automata are marvellous objects per se because they show us how to generate and manage complexity using very simple rules of dynamical transitions. When combined with the reaction-diffusion paradigm the cellular automata become an essential user-friendly tool for modelling natural systems and designing future and emergent computing arch...

Synthesis of sp3-rich scaffolds for molecular libraries through complexity-generating cascade reactions

DEFF Research Database (Denmark)

Flagstad, Thomas; Min, Geanna; Bonnet, K.

2016-01-01

An efficient strategy for the synthesis of complex small molecules from simple building blocks is presented. Key steps of the strategy include tandem Petasis and Diels–Alder reactions, and divergent complexity-generating cyclization cascades from a key dialdehyde intermediate. The methodology...

Design of an embedded inverse-feedforward biomolecular tracking controller for enzymatic reaction processes.

Science.gov (United States)

Foo, Mathias; Kim, Jongrae; Sawlekar, Rucha; Bates, Declan G

2017-04-06

Feedback control is widely used in chemical engineering to improve the performance and robustness of chemical processes. Feedback controllers require a 'subtractor' that is able to compute the error between the process output and the reference signal. In the case of embedded biomolecular control circuits, subtractors designed using standard chemical reaction network theory can only realise one-sided subtraction, rendering standard controller design approaches inadequate. Here, we show how a biomolecular controller that allows tracking of required changes in the outputs of enzymatic reaction processes can be designed and implemented within the framework of chemical reaction network theory. The controller architecture employs an inversion-based feedforward controller that compensates for the limitations of the one-sided subtractor that generates the error signals for a feedback controller. The proposed approach requires significantly fewer chemical reactions to implement than alternative designs, and should have wide applicability throughout the fields of synthetic biology and biological engineering.

Predicting adverse drug reaction profiles by integrating protein interaction networks with drug structures.

Science.gov (United States)

Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y

2013-01-01

The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Looking for chemical reaction networks exhibiting a drift along a manifold of marginally stable states.

Science.gov (United States)

Brogioli, Doriano

2013-02-07

I recently reported some examples of mass-action equations that have a continuous manifold of marginally stable stationary states [Brogioli, D., 2010. Marginally stable chemical systems as precursors of life. Phys. Rev. Lett. 105, 058102; Brogioli, D., 2011. Marginal stability in chemical systems and its relevance in the origin of life. Phys. Rev. E 84, 031931]. The corresponding chemical reaction networks show nonclassical effects, i.e. a violation of the mass-action equations, under the effect of the concentration fluctuations: the chemical system drifts along the marginally stable states. I proposed that this effect is potentially involved in abiogenesis. In the present paper, I analyze the mathematical properties of mass-action equations of marginally stable chemical reaction networks. The marginal stability implies that the mass-action equations obey some conservation law; I show that the mathematical properties of the conserved quantity characterize the motion along the marginally stable stationary state manifold, i.e. they allow to predict if the fluctuations give rise to a random walk or a drift under the effect of concentration fluctuations. Moreover, I show that the presence of the drift along the manifold of marginally stable stationary-states is a critical property, i.e. at least one of the reaction constants must be fine tuned in order to obtain the drift. Copyright © 2012 Elsevier Ltd. All rights reserved.

Understanding the Generation of Network Bursts by Adaptive Oscillatory Neurons

Directory of Open Access Journals (Sweden)

Tanguy Fardet

2018-02-01

Full Text Available Experimental and numerical studies have revealed that isolated populations of oscillatory neurons can spontaneously synchronize and generate periodic bursts involving the whole network. Such a behavior has notably been observed for cultured neurons in rodent's cortex or hippocampus. We show here that a sufficient condition for this network bursting is the presence of an excitatory population of oscillatory neurons which displays spike-driven adaptation. We provide an analytic model to analyze network bursts generated by coupled adaptive exponential integrate-and-fire neurons. We show that, for strong synaptic coupling, intrinsically tonic spiking neurons evolve to reach a synchronized intermittent bursting state. The presence of inhibitory neurons or plastic synapses can then modulate this dynamics in many ways but is not necessary for its appearance. Thanks to a simple self-consistent equation, our model gives an intuitive and semi-quantitative tool to understand the bursting behavior. Furthermore, it suggests that after-hyperpolarization currents are sufficient to explain bursting termination. Through a thorough mapping between the theoretical parameters and ion-channel properties, we discuss the biological mechanisms that could be involved and the relevance of the explored parameter-space. Such an insight enables us to propose experimentally-testable predictions regarding how blocking fast, medium or slow after-hyperpolarization channels would affect the firing rate and burst duration, as well as the interburst interval.

The Influence of Social Network on Consumer Purchase Intention of Young Generation in Manado

OpenAIRE

Tumewu, Ferdinand; Korompis, Prycilia Novita

2014-01-01

Social network now is very prevalent in the society. Today, many small enterprises sell and promote their product through social network and also many people are likely to make an online purchase especially for young generation. Social network is play a vital role in increasing someone intention to buy a product. This research is designed because there are some factor in social network that influence someone purchase intention. The original purpose of this research is to know the influence of...

Planning of distributed generation in distribution network based on improved particle swarm optimization algorithm

Science.gov (United States)

Li, Jinze; Qu, Zhi; He, Xiaoyang; Jin, Xiaoming; Li, Tie; Wang, Mingkai; Han, Qiu; Gao, Ziji; Jiang, Feng

2018-02-01

Large-scale access of distributed power can improve the current environmental pressure, at the same time, increasing the complexity and uncertainty of overall distribution system. Rational planning of distributed power can effectively improve the system voltage level. To this point, the specific impact on distribution network power quality caused by the access of typical distributed power was analyzed and from the point of improving the learning factor and the inertia weight, an improved particle swarm optimization algorithm (IPSO) was proposed which could solve distributed generation planning for distribution network to improve the local and global search performance of the algorithm. Results show that the proposed method can well reduce the system network loss and improve the economic performance of system operation with distributed generation.

Network reconfiguration and neuronal plasticity in rhythm-generating networks.

Science.gov (United States)

Koch, Henner; Garcia, Alfredo J; Ramirez, Jan-Marino

2011-12-01

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.

Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Otsuka, Naohiko

2013-07-01

This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres, held at the IAEA Headquarters in Vienna, Austria from 23 to 25 April 2013. The meeting was attended by 24 participants representing 13 cooperative centres from 8 Member States and 2 International Organisations. A summary of the meeting is given in this report along with the conclusions and actions. (author)

A Rural Next Generation Network (R-NGN and Its Testbed

Directory of Open Access Journals (Sweden)

Armein Z. R. Langi

2007-05-01

Full Text Available Rural Next Generation Networks (R-NGN technology allows Internet protocol (IP based systems to be used in rural areas. This paper reports a testbed of R-NGN that uses low cost Ethernet radio links, combined with media gateways and a softswitch. The network consists of point-to-point IP Ethernet 2.4 GHz wireless link, IP switches and gateways in each community, standard copper wires and telephone sets for users. It uses low power consumption, and suitable for low density users. This combination allows low cost systems as well as multiservices (voice, data, and multimedia for rural communications. An infrastructure has been deployed in two communities in Cipicung Girang, a village 10 km outside Bandung city, Indonesia. Two towers link the communities with a network of Institut Teknologi Bandung (ITB campus. In addition, local wirelines connect community houses to the network. Currently there are four houses connected to each community node (for a total of eight house, upon which we can perform various tests and measurements.

A Rural Next Generation Network (R-NGN and Its Testbed

Directory of Open Access Journals (Sweden)

Armein Z. R. Langi

2013-09-01

Full Text Available Rural Next Generation Networks (R-NGN technology allows Internet protocol (IP based systems to be used in rural areas. This paper reports a testbed of R-NGN that uses low cost Ethernet radio links, combined with media gateways and a softswitch. The network consists of point-to-point IP Ethernet 2.4 GHz wireless link, IP switches and gateways in each community, standard copper wires and telephone sets for users. It uses low power consumption, and suitable for low density users. This combination allows low cost systems as well as multiservices (voice, data, and multimedia for rural communications. An infrastructure has been deployed in two communities in Cipicung Girang, a village 10 km outside Bandung city, Indonesia. Two towers link the communities with a network of Institut Teknologi Bandung (ITB campus. In addition, local wirelines connect community houses to the network. Currently there are four houses connected to each community node (for a total of eight house, upon which we can perform various tests and measurements.

Research of PV Power Generation MPPT based on GABP Neural Network

Science.gov (United States)

Su, Yu; Lin, Xianfu

2018-05-01

Photovoltaic power generation has become the main research direction of new energy power generation. But high investment and low efficiency of photovoltaic industry arouse concern in some extent. So maximum power point tracking of photovoltaic power generation has been a popular study point. Due to slow response, oscillation at maximum power point and low precision, the algorithm based on genetic algorithm combined with BP neural network are designed detailedly in this paper. And the modeling and simulation are completed by use of MATLAB/SIMULINK. The results show that the algorithm is effective and the maximum power point can be tracked accurately and quickly.

NASCENT: an automatic protein interaction network generation tool for non-model organisms.

Science.gov (United States)

Banky, Daniel; Ordog, Rafael; Grolmusz, Vince

2009-04-24

Large quantity of reliable protein interaction data are available for model organisms in public depositories (e.g., MINT, DIP, HPRD, INTERACT). Most data correspond to experiments with the proteins of Saccharomyces cerevisiae, Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli and Mus musculus. For other important organisms the data availability is poor or non-existent. Here we present NASCENT, a completely automatic web-based tool and also a downloadable Java program, capable of modeling and generating protein interaction networks even for non-model organisms. The tool performs protein interaction network modeling through gene-name mapping, and outputs the resulting network in graphical form and also in computer-readable graph-forms, directly applicable by popular network modeling software. http://nascent.pitgroup.org.

The Global Seismographic Network (GSN): Deployment of Next Generation VBB Borehole Sensors and Improving Overall Network Noise Performance

Science.gov (United States)

Hafner, K.; Davis, P.; Wilson, D.; Sumy, D.

2017-12-01

The Global Seismographic Network (GSN) recently received delivery of the next generation Very Broadband (VBB) borehole sensors purchased through funding from the DOE. Deployment of these sensors will be underway during the end of summer and fall of 2017 and they will eventually replace the aging KS54000 sensors at approximately one-third of the GSN network stations. We will present the latest methods of deploying these sensors in the existing deep boreholes. To achieve lower noise performance at some sites, emplacement in shallow boreholes might result in lower noise performance for the existing site conditions. In some cases shallow borehole installations may be adapted to vault stations (which make up two thirds of the network), as a means of reducing tilt-induced signals on the horizontal components. The GSN is creating a prioritized list of equipment upgrades at selected stations with the ultimate goal of optimizing overall network data availability and noise performance. For an overview of the performance of the current GSN relative to selected set of metrics, we are utilizing data quality metrics and Probability Density Functions (PDFs)) generated by the IRIS Data Management Centers' (DMC) MUSTANG (Modular Utility for Statistical Knowledge Gathering) and LASSO (Latest Assessment of Seismic Station Observations) tools. We will present our metric analysis of GSN performance in 2016, and show the improvements at GSN sites resulting from recent instrumentation and infrastructure upgrades.

On the Potential of PUF for Pseudonym Generation in Vehicular Networks

NARCIS (Netherlands)

Petit, Jonathan; Bösch, C.T.; Feiri, Michael; Kargl, Frank

2012-01-01

Most proposals for security of vehicular networks foresee the generation of a comparatively large number of changing pseudonyms to prevent vehicles from being identified or tracked. Most proposals rely on communication with backend pseudonym providers to refill a vehicle’s pseudonym pool which

GalaxyGAN: Generative Adversarial Networks for recovery of galaxy features

Science.gov (United States)

Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Krishnan Santhanam, Gokula

2017-02-01

GalaxyGAN uses Generative Adversarial Networks to reliably recover features in images of galaxies. The package uses machine learning to train on higher quality data and learns to recover detailed features such as galaxy morphology by effectively building priors. This method opens up the possibility of recovering more information from existing and future imaging data.

Integrating generation and transmission networks reliability for unit commitment solution

International Nuclear Information System (INIS)

Jalilzadeh, S.; Shayeghi, H.; Hadadian, H.

2009-01-01

This paper presents a new method with integration of generation and transmission networks reliability for the solution of unit commitment (UC) problem. In fact, in order to have a more accurate assessment of system reserve requirement, in addition to unavailability of generation units, unavailability of transmission lines are also taken into account. In this way, evaluation of the required spinning reserve (SR) capacity is performed by applying reliability constraints based on loss of load probability and expected energy not supplied (EENS) indices. Calculation of the above parameters is accomplished by employing a novel procedure based on the linear programming which it also minimizes them to achieve optimum level of the SR capacity and consequently a cost-benefit reliability constrained UC schedule. In addition, a powerful solution technique called 'integer-coded genetic algorithm (ICGA)' is being used for the solution of the proposed method. Numerical results on the IEEE reliability test system show that the consideration of transmission network unavailability has an important influence on reliability indices of the UC schedules

Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

Science.gov (United States)

Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

2016-01-01

With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.

Science.gov (United States)

Schaffter, Thomas; Marbach, Daniel; Floreano, Dario

2011-08-15

Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual and supporting data. Supplementary data are available at Bioinformatics online. dario.floreano@epfl.ch.

Generation and prediction of time series by a neural network

International Nuclear Information System (INIS)

Eisenstein, E.; Kanter, I.; Kessler, D.A.; Kinzel, W.

1995-01-01

Generation and prediction of time series are analyzed for the case of a bit generator: a perceptron where in each time step the input units are shifted one bit to the right with the state of the leftmost input unit set equal to the output unit in the previous time step. The long-time dynamical behavior of the bit generator consists of cycles whose typical period scales polynomially with the size of the network and whose spatial structure is periodic with a typical finite wavelength. The generalization error on a cycle is zero for a finite training set, and global dynamical behaviors can also be learned in a finite time. Hence, a projection of a rule can be learned in a finite time

Communication network structure parameters and new knowledge generation capabilities in companies engaged in industry control system engineering projects

Directory of Open Access Journals (Sweden)

Titov Sergei

2016-01-01

Full Text Available Engineering companies engaged in business of industry control systems need to manage the processes of generation of innovations within and across their projects. Generation and diffusion of innovations materialize through the communication networks of project teams. Therefore, it is possible to hypothesize that the characteristics of communication networks play role in generation of new knowledge. With the data from 14 industry control system projects of a Russian engineering company the communication network structure characteristics were calculated and the analysis of correlation between these characteristics and knowledge generation capabilities was performed. As a result correlation between centralization of communication and the number of new technical solutions developed in projects was discovered.

Optimal power flow for distribution networks with distributed generation

Directory of Open Access Journals (Sweden)

Radosavljević Jordan

2015-01-01

Full Text Available This paper presents a genetic algorithm (GA based approach for the solution of the optimal power flow (OPF in distribution networks with distributed generation (DG units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046

Test results of sodium-water reaction testing in near prototypical LMR steam generator

International Nuclear Information System (INIS)

Boardman, C.E.; Hui, M.; Neely, H.H.

1990-01-01

An extensive test program has been performed in the United States to investigate the effects of large sodium-water reaction events in LMFBR steam generators. Tests were conducted in the Large Leak Test Rig (LLTR) located at the Energy Technology Engineering Center (ETEC). The program was divided into two phases, Series I and Series II, for the purpose of satisfying near-term and long-term needs. Series II was further subdivided into large and intermediate leak tests. This paper will emphasize the Series II intermediate leak tests and resulting conclusions for steam generator design and operation. 11 figs, 2 tabs

Report on the IAEA technical meeting on network of nuclear reaction data centres

International Nuclear Information System (INIS)

Pronyaev, V.G.; Schwerer, O.; Nichols, A.L.

2002-08-01

An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (and the biennial Data Centre Heads' Meeting) was held at the OECD Nuclear Energy Agency, Issy-les-Moulineaux (near Paris), France, from 27 to 30 May 2002. The meeting was attended by 21 participants from 12 co-operating data centres of six Member States and two international organizations. This report contains the meeting summary, conclusions and actions, status reports of the participating data centres, and working papers considered. (author)

Ground Reaction Forces Generated During Rhythmical Squats as a Dynamic Loads of the Structure

Science.gov (United States)

Pantak, Marek

2017-10-01

Dynamic forces generated by moving persons can lead to excessive vibration of the long span, slender and lightweight structure such as floors, stairs, stadium stands and footbridges. These dynamic forces are generated during walking, running, jumping and rhythmical body swaying in vertical or horizontal direction etc. In the paper the mathematical models of the Ground Reaction Forces (GRFs) generated during squats have been presented. Elaborated models was compared to the GRFs measured during laboratory tests carried out by author in wide range of frequency using force platform. Moreover, the GRFs models were evaluated during dynamic numerical analyses and dynamic field tests of the exemplary structure (steel footbridge).

Modeling of uncertainties in biochemical reactions.

Science.gov (United States)

Mišković, Ljubiša; Hatzimanikatis, Vassily

2011-02-01

Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus, we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three-step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme. Furthermore, we derive the conditions that must be satisfied by a reversible three-step enzymatic reaction operating far away from the equilibrium in order to respond to

Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

Science.gov (United States)

Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

2016-06-01

Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

Distributed generation in the Dutch LV network - self-supporting residential area

NARCIS (Netherlands)

Mes, M.; Vanalme, G.M.A.; Myrzik, J.M.A.; Bongaerts, M.; Verbong, G.P.J.; Kling, W.L.

2008-01-01

A self-supporting residential area is seen as an alternative operational approach of power supply in low voltage (LV) networks. The intention of the new approach is to exploit the advantages of distributed generation (DG) and avoid the difficulties, that come with DG when implemented in the

Summary report on [IAEA] technical meeting of the International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Dunaeva, S.; Otsuka, N.; Schwerer, O.

2009-08-01

An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at the IAEA Headquarters in Vienna from 25 to 26 May 2009. The meeting was attended by 23 participants from 13 cooperating data centres. A summary of the meeting is given in this report, along with the conclusions, actions, and status report of the participating data centres. (author)

Using Open Geographic Data to Generate Natural Language Descriptions for Hydrological Sensor Networks.

Science.gov (United States)

Molina, Martin; Sanchez-Soriano, Javier; Corcho, Oscar

2015-07-03

Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia) to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods) and their impact in the generation of sensor descriptions.

Using Open Geographic Data to Generate Natural Language Descriptions for Hydrological Sensor Networks

Directory of Open Access Journals (Sweden)

Martin Molina

2015-07-01

Full Text Available Providing descriptions of isolated sensors and sensor networks in natural language, understandable by the general public, is useful to help users find relevant sensors and analyze sensor data. In this paper, we discuss the feasibility of using geographic knowledge from public databases available on the Web (such as OpenStreetMap, Geonames, or DBpedia to automatically construct such descriptions. We present a general method that uses such information to generate sensor descriptions in natural language. The results of the evaluation of our method in a hydrologic national sensor network showed that this approach is feasible and capable of generating adequate sensor descriptions with a lower development effort compared to other approaches. In the paper we also analyze certain problems that we found in public databases (e.g., heterogeneity, non-standard use of labels, or rigid search methods and their impact in the generation of sensor descriptions.

Learning-Related Changes in Adolescents' Neural Networks during Hypothesis-Generating and Hypothesis-Understanding Training

Science.gov (United States)

Lee, Jun-Ki; Kwon, Yongju

2012-01-01

Fourteen science high school students participated in this study, which investigated neural-network plasticity associated with hypothesis-generating and hypothesis-understanding in learning. The students were divided into two groups and participated in either hypothesis-generating or hypothesis-understanding type learning programs, which were…

An Intelligent Handover Management System for Future Generation Wireless Networks

Directory of Open Access Journals (Sweden)

Kassar Meriem

2008-01-01

Full Text Available Abstract Future generation wireless networks should provide to mobile users the best connectivity to services anywhere at anytime. The most challenging problem is the seamless intersystem/vertical mobility across heterogeneous wireless networks. In order to answer it, a vertical handover management system is needed. In our paper, we propose an intelligent solution answering user requirements and ensuring service continuity. We focus on a vertical handover decision strategy based on the context-awareness concept. The given strategy chooses the appropriate time and the most suitable access network among those available to perform a handover. It uses advanced decision algorithms (for more efficiency and intelligence and it is governed by handover policies as decision rules (for more flexibility and optimization. To maintain a seamless service continuity, handover execution is based on mobile IP functionalities. We study our decision system in a case of a 3G/UMTS-WLAN scenario and we discuss all the handover decision issues in our solution.

Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks

Directory of Open Access Journals (Sweden)

J. C. Ochoa-Rivera

2002-01-01

Full Text Available A model for multivariate streamflow generation is presented, based on a multilayer feedforward neural network. The structure of the model results from two components, the neural network (NN deterministic component and a random component which is assumed to be normally distributed. It is from this second component that the model achieves the ability to incorporate effectively the uncertainty associated with hydrological processes, making it valuable as a practical tool for synthetic generation of streamflow series. The NN topology and the corresponding analytical explicit formulation of the model are described in detail. The model is calibrated with a series of monthly inflows to two reservoir sites located in the Tagus River basin (Spain, while validation is performed through estimation of a set of statistics that is relevant for water resources systems planning and management. Among others, drought and storage statistics are computed and compared for both the synthetic and historical series. The performance of the NN-based model was compared to that of a standard autoregressive AR(2 model. Results show that NN represents a promising modelling alternative for simulation purposes, with interesting potential in the context of water resources systems management and optimisation. Keywords: neural networks, perceptron multilayer, error backpropagation, hydrological scenario generation, multivariate time-series..

A Comparative Study of Multiplexing Schemes for Next Generation Optical Access Networks

Science.gov (United States)

Imtiaz, Waqas A.; Khan, Yousaf; Shah, Pir Mehar Ali; Zeeshan, M.

2014-09-01

Passive optical network (PON) is a high bandwidth, economical solution which can provide the necessary bandwidth to end-users. Wavelength division multiplexed passive optical networks (WDM PONs) and time division multiplexed passive optical networks (TDM PONs) are considered as an evolutionary step for next-generation optical access (NGOA) networks. However they fail to provide highest transmission capacity, efficient bandwidth access, and robust dispersion tolerance. Thus future PONs are considered on simpler, efficient and potentially scalable, optical code division multiplexed (OCDM) PONs. This paper compares the performance of existing PONs with OCDM PON to determine a suitable scheme for NGOA networks. Two system parameter are used in this paper: fiber length, and bit rate. Performance analysis using Optisystem shows that; for a sufficient system performance parameters i.e. bit error rate (BER) ≤ 10-9, and maximum quality factor (Q) ≥ 6, OCDMA PON efficiently performs upto 50 km with 10 Gbit/s per ONU.

An efficient forward–reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

KAUST Repository

Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro

2016-01-01

then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve

Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres

International Nuclear Information System (INIS)

Otsuka, Naohiko

2012-06-01

This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres, held at the OECD Nuclear Energy Agency (NEA) in Issy-les-Moulineaux, France from 16 to 19 April 2012. The meeting was attended by twenty-three participants representing thirteen cooperative centres from eight Member States and two International Organisations. A summary of the meeting is given in this report along with the conclusions and actions. (author)

Report on the IAEA technical meeting on the network of nuclear reaction data centres

Energy Technology Data Exchange (ETDEWEB)

Schwerer, O [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria)

2006-02-15

Results of the IAEA Technical meeting on the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 12 to 14 October 2005, are summarized in this report. The meeting was attended by 16 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, and status reports of the participating data centres. (author)

Report on the IAEA technical meeting on the network of nuclear reaction data centres

International Nuclear Information System (INIS)

Schwerer, O.

2006-02-01

Results of the IAEA Technical meeting on the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 12 to 14 October 2005, are summarized in this report. The meeting was attended by 16 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, and status reports of the participating data centres. (author)

A NETWORK-THEORETICAL APPROACH TO UNDERSTANDING INTERSTELLAR CHEMISTRY

International Nuclear Information System (INIS)

Jolley, Craig C.; Douglas, Trevor

2010-01-01

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

Impact of Distributed Generation Grid Code Requirements on Islanding Detection in LV Networks