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Sample records for network rejection method

  1. Persistent disturbance rejection via state feedback for networked control systems

    Energy Technology Data Exchange (ETDEWEB)

    Yue Dong [Institute of Information and Control Engineering Technology, Nanjing Normal University, 78 Bancang Street, Nanjing, Jiangsu 210042 (China)], E-mail: medongy@njnu.edu.cn; Lam, James [Department of Mechanical Engineering, University of Hong Kong, Pokfulam Road (Hong Kong); Wang Zidong [Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH (United Kingdom)], E-mail: Zidong.Wang@brunel.ac.uk

    2009-04-15

    The problem of persistent disturbance rejection via state feedback for networked control systems is concerned based on the Lyapunov function method. The effect of the network conditions, such as network-induced delay and data dropout, is considered in the modeling of the system. It is assumed that the state and the control signals are individually quantized by quantizers on the sensor side and the controller side. The feedback gain and the quantizer parameters that guarantee the internal stability and the disturbance rejection performance of the closed-loop system are obtained by solving some linear matrix inequalities. To illustrate the effectiveness of the proposed method, a numerical example is provided for the design of the feedback gain and the quantizer parameters.

  2. Persistent disturbance rejection via state feedback for networked control systems

    International Nuclear Information System (INIS)

    Yue Dong; Lam, James; Wang Zidong

    2009-01-01

    The problem of persistent disturbance rejection via state feedback for networked control systems is concerned based on the Lyapunov function method. The effect of the network conditions, such as network-induced delay and data dropout, is considered in the modeling of the system. It is assumed that the state and the control signals are individually quantized by quantizers on the sensor side and the controller side. The feedback gain and the quantizer parameters that guarantee the internal stability and the disturbance rejection performance of the closed-loop system are obtained by solving some linear matrix inequalities. To illustrate the effectiveness of the proposed method, a numerical example is provided for the design of the feedback gain and the quantizer parameters.

  3. Background rejection in NEXT using deep neural networks

    CERN Document Server

    Renner, J.

    2017-01-01

    We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

  4. Background rejection in NEXT using deep neural networks

    International Nuclear Information System (INIS)

    Renner, J.; Farbin, A.; Vidal, J. Muñoz; Benlloch-Rodríguez, J. M.; Botas, A.

    2017-01-01

    Here, we investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.

  5. Quasi-minimal active disturbance rejection control of MIMO perturbed linear systems based on differential neural networks and the attractive ellipsoid method.

    Science.gov (United States)

    Salgado, Iván; Mera-Hernández, Manuel; Chairez, Isaac

    2017-11-01

    This study addresses the problem of designing an output-based controller to stabilize multi-input multi-output (MIMO) systems in the presence of parametric disturbances as well as uncertainties in the state model and output noise measurements. The controller design includes a linear state transformation which separates uncertainties matched to the control input and the unmatched ones. A differential neural network (DNN) observer produces a nonlinear approximation of the matched perturbation and the unknown states simultaneously in the transformed coordinates. This study proposes the use of the Attractive Ellipsoid Method (AEM) to optimize the gains of the controller and the gain observer in the DNN structure. As a consequence, the obtained control input minimizes the convergence zone for the estimation error. Moreover, the control design uses the estimated disturbance provided by the DNN to obtain a better performance in the stabilization task in comparison with a quasi-minimal output feedback controller based on a Luenberger observer and a sliding mode controller. Numerical results pointed out the advantages obtained by the nonlinear control based on the DNN observer. The first example deals with the stabilization of an academic linear MIMO perturbed system and the second example stabilizes the trajectories of a DC-motor into a predefined operation point. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  6. The Neural Basis of Recollection Rejection: Increases in Hippocampal-Prefrontal Connectivity in the Absence of a Shared Recall-to-Reject and Target Recollection Network.

    Science.gov (United States)

    Bowman, Caitlin R; Dennis, Nancy A

    2016-08-01

    Recollection rejection or "recall-to-reject" is a mechanism that has been posited to help maintain accurate memory by preventing the occurrence of false memories. Recollection rejection occurs when the presentation of a new item during recognition triggers recall of an associated target, a mismatch in features between the new and old items is registered, and the lure is correctly rejected. Critically, this characterization of recollection rejection involves a recall signal that is conceptually similar to recollection as elicited by a target. However, previous neuroimaging studies have not evaluated the extent to which recollection rejection and target recollection rely on a common neural signal but have instead focused on recollection rejection as a postretrieval monitoring process. This study utilized a false memory paradigm in conjunction with an adapted remember-know-new response paradigm that separated "new" responses based on recollection rejection from those that were based on a lack of familiarity with the item. This procedure allowed for parallel recollection rejection and target recollection contrasts to be computed. Results revealed that, contrary to predictions from theoretical and behavioral literature, there was virtually no evidence of a common retrieval mechanism supporting recollection rejection and target recollection. Instead of the typical target recollection network, recollection rejection recruited a network of lateral prefrontal and bilateral parietal regions that is consistent with the retrieval monitoring network identified in previous neuroimaging studies of recollection rejection. However, a functional connectivity analysis revealed a component of the frontoparietal rejection network that showed increased coupling with the right hippocampus during recollection rejection responses. As such, we demonstrate a possible link between PFC monitoring network and basic retrieval mechanisms within the hippocampus that was not revealed with

  7. Spectral anomaly methods for aerial detection using KUT nuisance rejection

    International Nuclear Information System (INIS)

    Detwiler, R.S.; Pfund, D.M.; Myjak, M.J.; Kulisek, J.A.; Seifert, C.E.

    2015-01-01

    This work discusses the application and optimization of a spectral anomaly method for the real-time detection of gamma radiation sources from an aerial helicopter platform. Aerial detection presents several key challenges over ground-based detection. For one, larger and more rapid background fluctuations are typical due to higher speeds, larger field of view, and geographically induced background changes. As well, the possible large altitude or stand-off distance variations cause significant steps in background count rate as well as spectral changes due to increased gamma-ray scatter with detection at higher altitudes. The work here details the adaptation and optimization of the PNNL-developed algorithm Nuisance-Rejecting Spectral Comparison Ratios for Anomaly Detection (NSCRAD), a spectral anomaly method previously developed for ground-based applications, for an aerial platform. The algorithm has been optimized for two multi-detector systems; a NaI(Tl)-detector-based system and a CsI detector array. The optimization here details the adaptation of the spectral windows for a particular set of target sources to aerial detection and the tailoring for the specific detectors. As well, the methodology and results for background rejection methods optimized for the aerial gamma-ray detection using Potassium, Uranium and Thorium (KUT) nuisance rejection are shown. Results indicate that use of a realistic KUT nuisance rejection may eliminate metric rises due to background magnitude and spectral steps encountered in aerial detection due to altitude changes and geographically induced steps such as at land–water interfaces

  8. Design Method of Active Disturbance Rejection Variable Structure Control System

    Directory of Open Access Journals (Sweden)

    Yun-jie Wu

    2015-01-01

    Full Text Available Based on lines cluster approaching theory and inspired by the traditional exponent reaching law method, a new control method, lines cluster approaching mode control (LCAMC method, is designed to improve the parameter simplicity and structure optimization of the control system. The design guidelines and mathematical proofs are also given. To further improve the tracking performance and the inhibition of the white noise, connect the active disturbance rejection control (ADRC method with the LCAMC method and create the extended state observer based lines cluster approaching mode control (ESO-LCAMC method. Taking traditional servo control system as example, two control schemes are constructed and two kinds of comparison are carried out. Computer simulation results show that LCAMC method, having better tracking performance than the traditional sliding mode control (SMC system, makes the servo system track command signal quickly and accurately in spite of the persistent equivalent disturbances and ESO-LCAMC method further reduces the tracking error and filters the white noise added on the system states. Simulation results verify the robust property and comprehensive performance of control schemes.

  9. Designing a Pattern Recognition Neural Network with a Reject Output and Many Sets of Weights and Biases

    OpenAIRE

    Dung, Le; Mizukawa, Makoto

    2008-01-01

    Adding the reject output to the pattern recognition neural network is an approach to help the neural network can classify almost all patterns of a training data set by using many sets of weights and biases, even if the neural network is small. With a smaller number of neurons, we can implement the neural network on a hardware-based platform more easily and also reduce the response time of it. With the reject output the neural network can produce not only right or wrong results but also reject...

  10. 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.

  11. ADAPTIVE METHODS FOR STOCHASTIC DIFFERENTIAL EQUATIONS VIA NATURAL EMBEDDINGS AND REJECTION SAMPLING WITH MEMORY.

    Science.gov (United States)

    Rackauckas, Christopher; Nie, Qing

    2017-01-01

    Adaptive time-stepping with high-order embedded Runge-Kutta pairs and rejection sampling provides efficient approaches for solving differential equations. While many such methods exist for solving deterministic systems, little progress has been made for stochastic variants. One challenge in developing adaptive methods for stochastic differential equations (SDEs) is the construction of embedded schemes with direct error estimates. We present a new class of embedded stochastic Runge-Kutta (SRK) methods with strong order 1.5 which have a natural embedding of strong order 1.0 methods. This allows for the derivation of an error estimate which requires no additional function evaluations. Next we derive a general method to reject the time steps without losing information about the future Brownian path termed Rejection Sampling with Memory (RSwM). This method utilizes a stack data structure to do rejection sampling, costing only a few floating point calculations. We show numerically that the methods generate statistically-correct and tolerance-controlled solutions. Lastly, we show that this form of adaptivity can be applied to systems of equations, and demonstrate that it solves a stiff biological model 12.28x faster than common fixed timestep algorithms. Our approach only requires the solution to a bridging problem and thus lends itself to natural generalizations beyond SDEs.

  12. Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ammi, Yamina; Khaouane, Latifa; Hanini, Salah [University of Medea, Medea (Algeria)

    2015-11-15

    This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

  13. Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

    International Nuclear Information System (INIS)

    Ammi, Yamina; Khaouane, Latifa; Hanini, Salah

    2015-01-01

    This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

  14. Errors in the estimation method for the rejection of vibrations in adaptive optics systems

    Science.gov (United States)

    Kania, Dariusz

    2017-06-01

    In recent years the problem of the mechanical vibrations impact in adaptive optics (AO) systems has been renewed. These signals are damped sinusoidal signals and have deleterious effect on the system. One of software solutions to reject the vibrations is an adaptive method called AVC (Adaptive Vibration Cancellation) where the procedure has three steps: estimation of perturbation parameters, estimation of the frequency response of the plant, update the reference signal to reject/minimalize the vibration. In the first step a very important problem is the estimation method. A very accurate and fast (below 10 ms) estimation method of these three parameters has been presented in several publications in recent years. The method is based on using the spectrum interpolation and MSD time windows and it can be used to estimate multifrequency signals. In this paper the estimation method is used in the AVC method to increase the system performance. There are several parameters that affect the accuracy of obtained results, e.g. CiR - number of signal periods in a measurement window, N - number of samples in the FFT procedure, H - time window order, SNR, b - number of ADC bits, γ - damping ratio of the tested signal. Systematic errors increase when N, CiR, H decrease and when γ increases. The value for systematic error is approximately 10^-10 Hz/Hz for N = 2048 and CiR = 0.1. This paper presents equations that can used to estimate maximum systematic errors for given values of H, CiR and N before the start of the estimation process.

  15. PID Based on Attractive Ellipsoid Method for Dynamic Uncertain and External Disturbances Rejection in Mechanical Systems

    Directory of Open Access Journals (Sweden)

    Jesus Patricio Ordaz Oliver

    2015-01-01

    Full Text Available This paper presents a stability analysis for LNDS (Lagrangian nonlinear dynamical systems with dynamic uncertain using a PID controller with external disturbances rejection based on attractive ellipsoid methods, since the PID-CT (proportional integral derivative computed torque compensator has been used for the nonlinear trajectory tracking of an LNDS, when there are external perturbations and system uncertainties. The global system convergence of the trivial solution has not been proved. In this sense, we propose an approach to find the gains of the nonlinear PID-CT controller to guarantee the boundedness of the trivial solution by means of the concept of the UUB (uniform-ultimately bounded stability. In order to show the effectiveness of the methodology proposed, we applied it in a real 2-DoF robot system.

  16. Comparison of fluorescence rejection methods of baseline correction and shifted excitation Raman difference spectroscopy

    Science.gov (United States)

    Cai, Zhijian; Zou, Wenlong; Wu, Jianhong

    2017-10-01

    Raman spectroscopy has been extensively used in biochemical tests, explosive detection, food additive and environmental pollutants. However, fluorescence disturbance brings a big trouble to the applications of portable Raman spectrometer. Currently, baseline correction and shifted-excitation Raman difference spectroscopy (SERDS) methods are the most prevailing fluorescence suppressing methods. In this paper, we compared the performances of baseline correction and SERDS methods, experimentally and simulatively. Through the comparison, it demonstrates that the baseline correction can get acceptable fluorescence-removed Raman spectrum if the original Raman signal has good signal-to-noise ratio, but it cannot recover the small Raman signals out of large noise background. By using SERDS method, the Raman signals, even very weak compared to fluorescence intensity and noise level, can be clearly extracted, and the fluorescence background can be completely rejected. The Raman spectrum recovered by SERDS has good signal to noise ratio. It's proved that baseline correction is more suitable for large bench-top Raman system with better quality or signal-to-noise ratio, while the SERDS method is more suitable for noisy devices, especially the portable Raman spectrometers.

  17. A Novel Method of Robust Trajectory Linearization Control Based on Disturbance Rejection

    Directory of Open Access Journals (Sweden)

    Xingling Shao

    2014-01-01

    Full Text Available A novel method of robust trajectory linearization control for a class of nonlinear systems with uncertainties based on disturbance rejection is proposed. Firstly, on the basis of trajectory linearization control (TLC method, a feedback linearization based control law is designed to transform the original tracking error dynamics to the canonical integral-chain form. To address the issue of reducing the influence made by uncertainties, with tracking error as input, linear extended state observer (LESO is constructed to estimate the tracking error vector, as well as the uncertainties in an integrated manner. Meanwhile, the boundedness of the estimated error is investigated by theoretical analysis. In addition, decoupled controller (which has the characteristic of well-tuning and simple form based on LESO is synthesized to realize the output tracking for closed-loop system. The closed-loop stability of the system under the proposed LESO-based control structure is established. Also, simulation results are presented to illustrate the effectiveness of the control strategy.

  18. Communication Network Analysis Methods.

    Science.gov (United States)

    Farace, Richard V.; Mabee, Timothy

    This paper reviews a variety of analytic procedures that can be applied to network data, discussing the assumptions and usefulness of each procedure when applied to the complexity of human communication. Special attention is paid to the network properties measured or implied by each procedure. Factor analysis and multidimensional scaling are among…

  19. The role of indium-111 antimyosin (Fab) imaging as a noninvasive surveillance method of human heart transplant rejection

    International Nuclear Information System (INIS)

    De Nardo, D.; Scibilia, G.; Macchiarelli, A.G.

    1989-01-01

    The identification of rejection after heart transplantation in patients receiving cyclosporine immunosuppressive therapy requires the endomyocardial biopsy, an invasive method associated with a finite morbidity. To evaluate the role of indium-111 antimyosin (Fab) scintigraphy as a noninvasive surveillance method of heart transplant rejection, the Fab fragment of murine monoclonal antimyosin antibodies labeled with indium-111 was administered intravenously in 30 scintigraphic studies to 10 consecutive heart transplant recipients. Endomyocardial biopsy specimens were obtained 72 hours after each scintigraphic study. Nineteen scintigraphic studies had negative findings; no false negative finding was obtained. Eleven antimyosin scintigraphic studies had positive findings, and in these studies endomyocardial biopsy revealed mild rejection in two cases, moderate acute rejection with myocyte necrosis in two cases, myocyte necrosis as a consequence of ischemic injury in six cases, and possibly cytotoxic damage in one case. Antimyosin scintigraphy may represent a reliable screening method for the surveillance of heart transplant patients. In the presence of a negative finding from antimyosin scintigraphy, it may be possible to avoid endomyocardial biopsy. Conversely, in patients who have a positive finding from antimyosin scintigraphy, the endomyocardial biopsy is mandatory to establish the definitive diagnosis by histologic examination of the myocardium

  20. Polonium-210 assay using a background-rejecting extractive liquid-scintillation method

    International Nuclear Information System (INIS)

    Case, C.N.; McDowell, W.J.

    1981-01-01

    This paper describes a procedure which combines solvent extraction with alpha liquid scintillation spectrometry. Pulse shape discrimination electronics are used to reject beta and gamma pulses and to lower the background count to acceptable levels. Concentration of 210 Po and separation from interferring elements are accomplished using a H 3 Po 4 -HCl solution with TOPO combined with a scintillor in toluene

  1. Preventing Rejection

    Science.gov (United States)

    ... After the transplant Preventing rejection Post-transplant medications Types of immunosuppressants Switching immunosuppressants Side effects Other medications Generic and brand name drugs Post-transplant tests Infections and immunity Lifestyle changes Health concerns Back to work or ...

  2. Multiple network interface core apparatus and method

    Science.gov (United States)

    Underwood, Keith D [Albuquerque, NM; Hemmert, Karl Scott [Albuquerque, NM

    2011-04-26

    A network interface controller and network interface control method comprising providing a single integrated circuit as a network interface controller and employing a plurality of network interface cores on the single integrated circuit.

  3. Studying stellar binary systems with the Laser Interferometer Space Antenna using delayed rejection Markov chain Monte Carlo methods

    International Nuclear Information System (INIS)

    Trias, Miquel; Vecchio, Alberto; Veitch, John

    2009-01-01

    Bayesian analysis of Laser Interferometer Space Antenna (LISA) data sets based on Markov chain Monte Carlo methods has been shown to be a challenging problem, in part due to the complicated structure of the likelihood function consisting of several isolated local maxima that dramatically reduces the efficiency of the sampling techniques. Here we introduce a new fully Markovian algorithm, a delayed rejection Metropolis-Hastings Markov chain Monte Carlo method, to efficiently explore these kind of structures and we demonstrate its performance on selected LISA data sets containing a known number of stellar-mass binary signals embedded in Gaussian stationary noise.

  4. Network modelling methods for FMRI.

    Science.gov (United States)

    Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

    2011-01-15

    There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

  5. Efficiency of rejection-free methods for dynamic Monte Carlo studies of off-lattice interacting particles

    KAUST Repository

    Guerra, Marta L.; Novotny, M. A.; Watanabe, Hiroshi; Ito, Nobuyasu

    2009-01-01

    We calculate the efficiency of a rejection-free dynamic Monte Carlo method for d -dimensional off-lattice homogeneous particles interacting through a repulsive power-law potential r-p. Theoretically we find the algorithmic efficiency in the limit of low temperatures and/or high densities is asymptotically proportional to ρ (p+2) /2 T-d/2 with the particle density ρ and the temperature T. Dynamic Monte Carlo simulations are performed in one-, two-, and three-dimensional systems with different powers p, and the results agree with the theoretical predictions. © 2009 The American Physical Society.

  6. Efficiency of rejection-free methods for dynamic Monte Carlo studies of off-lattice interacting particles

    KAUST Repository

    Guerra, Marta L.

    2009-02-23

    We calculate the efficiency of a rejection-free dynamic Monte Carlo method for d -dimensional off-lattice homogeneous particles interacting through a repulsive power-law potential r-p. Theoretically we find the algorithmic efficiency in the limit of low temperatures and/or high densities is asymptotically proportional to ρ (p+2) /2 T-d/2 with the particle density ρ and the temperature T. Dynamic Monte Carlo simulations are performed in one-, two-, and three-dimensional systems with different powers p, and the results agree with the theoretical predictions. © 2009 The American Physical Society.

  7. Application of CMAC Neural Network Coupled with Active Disturbance Rejection Control Strategy on Three-motor Synchronization Control System

    Directory of Open Access Journals (Sweden)

    Hui Li

    2014-04-01

    Full Text Available Three-motor synchronous coordination system is a MI-MO nonlinear and complex control system. And it often works in poor working condition. Advanced control strategies are required to improve the control performance of the system and to achieve the decoupling between main motor speed and tension. Cerebellar Model Articulation Controller coupled with Active Disturbance Rejection Control (CMAC-ADRC control strategy is proposed. The speed of the main motor and tensions between two motors is decoupled by extended state observer (ESO in ADRC. ESO in ADRC is used to compensate internal and external disturbances of the system online. And the anti interference of the system is improved by ESO. And the same time the control model is optimized. Feedforward control is implemented by the adoption of CMAC neural network controller. And control precision of the system is improved in reason of CMAC. The overshoot of the system can be reduced without affecting the dynamic response of the system by the use of CMAC-ADRC. The simulation results show that: the CMAC- ADRC control strategy is better than the traditional PID control strategy. And CMAC-ADRC control strategy can achieve the decoupling between speed and tension. The control system using CMAC-ADRC have strong anti-interference ability and small regulate time and small overshoot. The magnitude of the system response incited by the interference using CMAC-ADRC is smaller than the system using conventional PID control 6.43 %. And the recovery time of the system with CMAC-ADRC is shorter than the system with traditional PID control 0.18 seconds. And the triangular wave tracking error of the system with CMAC-ADRC is smaller than the system with conventional PID control 0.24 rad/min. Thus the CMAC-ADRC control strategy is a good control strategy and is able to fit three-motor synchronous coordinated control.

  8. Two different modelling methods of the saturated steam turbine load rejection

    International Nuclear Information System (INIS)

    Negreanu, Gabriel-Paul; Oprea, Ion

    1999-01-01

    One of the most difficult operation regimes of a steam turbine is the load rejection. It happens usually when the main switchgear of the unit closes unexpectedly due to some external or internal causes. In this moment, the rotor balance collapses: the motor momentum is positive, the resistant momentum is zero and the rotation velocity increases rapidly. When this process occurs, the over-speed protection should activate the emergency stop valves and the control and intercept valves in order to stop the steam admission into the turbine. The paper presents two differential approaches of the fluid dynamic processes from the flow sections of the saturated steam turbine of the NPP, where the laws of mass and energy conservation are applied. In this manner, the 'power and speed versus time' diagrams can be drawn. The main parameters of such technical problem are the closure low of the valves, the large volume of internal cavities, the huge inertial momentum of the rotor and especially the moisture of the steam that evaporates when the pressure decreases and generates an extra power in the turbine. (authors)

  9. Quantitative Efficiency Evaluation Method for Transportation Networks

    Directory of Open Access Journals (Sweden)

    Jin Qin

    2014-11-01

    Full Text Available An effective evaluation of transportation network efficiency/performance is essential to the establishment of sustainable development in any transportation system. Based on a redefinition of transportation network efficiency, a quantitative efficiency evaluation method for transportation network is proposed, which could reflect the effects of network structure, traffic demands, travel choice, and travel costs on network efficiency. Furthermore, the efficiency-oriented importance measure for network components is presented, which can be used to help engineers identify the critical nodes and links in the network. The numerical examples show that, compared with existing efficiency evaluation methods, the network efficiency value calculated by the method proposed in this paper can portray the real operation situation of the transportation network as well as the effects of main factors on network efficiency. We also find that the network efficiency and the importance values of the network components both are functions of demands and network structure in the transportation network.

  10. Multiple attenuation to reflection seismic data using Radon filter and Wave Equation Multiple Rejection (WEMR) method

    Energy Technology Data Exchange (ETDEWEB)

    Erlangga, Mokhammad Puput [Geophysical Engineering, Institut Teknologi Bandung, Ganesha Street no.10 Basic Science B Buliding fl.2-3 Bandung, 40132, West Java Indonesia puput.erlangga@gmail.com (Indonesia)

    2015-04-16

    Separation between signal and noise, incoherent or coherent, is important in seismic data processing. Although we have processed the seismic data, the coherent noise is still mixing with the primary signal. Multiple reflections are a kind of coherent noise. In this research, we processed seismic data to attenuate multiple reflections in the both synthetic and real seismic data of Mentawai. There are several methods to attenuate multiple reflection, one of them is Radon filter method that discriminates between primary reflection and multiple reflection in the τ-p domain based on move out difference between primary reflection and multiple reflection. However, in case where the move out difference is too small, the Radon filter method is not enough to attenuate the multiple reflections. The Radon filter also produces the artifacts on the gathers data. Except the Radon filter method, we also use the Wave Equation Multiple Elimination (WEMR) method to attenuate the long period multiple reflection. The WEMR method can attenuate the long period multiple reflection based on wave equation inversion. Refer to the inversion of wave equation and the magnitude of the seismic wave amplitude that observed on the free surface, we get the water bottom reflectivity which is used to eliminate the multiple reflections. The WEMR method does not depend on the move out difference to attenuate the long period multiple reflection. Therefore, the WEMR method can be applied to the seismic data which has small move out difference as the Mentawai seismic data. The small move out difference on the Mentawai seismic data is caused by the restrictiveness of far offset, which is only 705 meter. We compared the real free multiple stacking data after processing with Radon filter and WEMR process. The conclusion is the WEMR method can more attenuate the long period multiple reflection than the Radon filter method on the real (Mentawai) seismic data.

  11. A Short-Circuit Method for Networks.

    Science.gov (United States)

    Ong, P. P.

    1983-01-01

    Describes a method of network analysis that allows avoidance of Kirchoff's Laws (providing the network is symmetrical) by reduction to simple series/parallel resistances. The method can be extended to symmetrical alternating current, capacitance or inductance if corresponding theorems are used. Symmetric cubic network serves as an example. (JM)

  12. Neural Networks and Their Applications in Noise - Information Storage and Retrieval Systems, and in the Rejection of Narrow-Band Interference in Direct Sequence Spread Spectrum Receivers.

    Science.gov (United States)

    Bijjani, Richard

    1990-01-01

    The introduction of neural network models has created new algorithms and application opportunities in parallel signal processing. Here, an M-ary extension of the Hopfield model is presented and is shown to have a substantially higher error correction capability, when compared to the Hopfield model. A digital image processing experiment is successfully conducted to illustrate the new model, and a holographic implementation is proposed. The use of neural networks and of linear combination filters are investigated in connection with the problem of user identification in code division multiple access systems. A multi-layer back-propagation perceptron model is then presented as a means of detecting a wideband signal in the presence of narrowband jammers and additive white Gaussian noise. The performance of the neural network is compared to that of the estimation type filter that uses a least mean squared adaptive filter, in terms of the interference rejection capability, the bit error rate and the overall robustness of the system. The nonlinear neural network filter is found to offer a faster convergence rate and an overall better performance over the LMS Widrow-Hoff filter.

  13. Cut Based Method for Comparing Complex Networks.

    Science.gov (United States)

    Liu, Qun; Dong, Zhishan; Wang, En

    2018-03-23

    Revealing the underlying similarity of various complex networks has become both a popular and interdisciplinary topic, with a plethora of relevant application domains. The essence of the similarity here is that network features of the same network type are highly similar, while the features of different kinds of networks present low similarity. In this paper, we introduce and explore a new method for comparing various complex networks based on the cut distance. We show correspondence between the cut distance and the similarity of two networks. This correspondence allows us to consider a broad range of complex networks and explicitly compare various networks with high accuracy. Various machine learning technologies such as genetic algorithms, nearest neighbor classification, and model selection are employed during the comparison process. Our cut method is shown to be suited for comparisons of undirected networks and directed networks, as well as weighted networks. In the model selection process, the results demonstrate that our approach outperforms other state-of-the-art methods with respect to accuracy.

  14. A random network based, node attraction facilitated network evolution method

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-03-01

    Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.

  15. Sampling of temporal networks: Methods and biases

    Science.gov (United States)

    Rocha, Luis E. C.; Masuda, Naoki; Holme, Petter

    2017-11-01

    Temporal networks have been increasingly used to model a diversity of systems that evolve in time; for example, human contact structures over which dynamic processes such as epidemics take place. A fundamental aspect of real-life networks is that they are sampled within temporal and spatial frames. Furthermore, one might wish to subsample networks to reduce their size for better visualization or to perform computationally intensive simulations. The sampling method may affect the network structure and thus caution is necessary to generalize results based on samples. In this paper, we study four sampling strategies applied to a variety of real-life temporal networks. We quantify the biases generated by each sampling strategy on a number of relevant statistics such as link activity, temporal paths and epidemic spread. We find that some biases are common in a variety of networks and statistics, but one strategy, uniform sampling of nodes, shows improved performance in most scenarios. Given the particularities of temporal network data and the variety of network structures, we recommend that the choice of sampling methods be problem oriented to minimize the potential biases for the specific research questions on hand. Our results help researchers to better design network data collection protocols and to understand the limitations of sampled temporal network data.

  16. Method and system for mesh network embedded devices

    Science.gov (United States)

    Wang, Ray (Inventor)

    2009-01-01

    A method and system for managing mesh network devices. A mesh network device with integrated features creates an N-way mesh network with a full mesh network topology or a partial mesh network topology.

  17. A Systematic, Automated Network Planning Method

    DEFF Research Database (Denmark)

    Holm, Jens Åge; Pedersen, Jens Myrup

    2006-01-01

    This paper describes a case study conducted to evaluate the viability of a systematic, automated network planning method. The motivation for developing the network planning method was that many data networks are planned in an adhoc manner with no assurance of quality of the solution with respect...... structures, that are ready to implement in a real world scenario, are discussed in the end of the paper. These are in the area of ensuring line independence and complexity of the design rules for the planning method....

  18. An improved sampling method of complex network

    Science.gov (United States)

    Gao, Qi; Ding, Xintong; Pan, Feng; Li, Weixing

    2014-12-01

    Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.

  19. Constructing an Intelligent Patent Network Analysis Method

    Directory of Open Access Journals (Sweden)

    Chao-Chan Wu

    2012-11-01

    Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.

  20. Complex networks principles, methods and applications

    CERN Document Server

    Latora, Vito; Russo, Giovanni

    2017-01-01

    Networks constitute the backbone of complex systems, from the human brain to computer communications, transport infrastructures to online social systems and metabolic reactions to financial markets. Characterising their structure improves our understanding of the physical, biological, economic and social phenomena that shape our world. Rigorous and thorough, this textbook presents a detailed overview of the new theory and methods of network science. Covering algorithms for graph exploration, node ranking and network generation, among the others, the book allows students to experiment with network models and real-world data sets, providing them with a deep understanding of the basics of network theory and its practical applications. Systems of growing complexity are examined in detail, challenging students to increase their level of skill. An engaging presentation of the important principles of network science makes this the perfect reference for researchers and undergraduate and graduate students in physics, ...

  1. Binary Classification Method of Social Network Users

    Directory of Open Access Journals (Sweden)

    I. A. Poryadin

    2017-01-01

    Full Text Available The subject of research is a binary classification method of social network users based on the data analysis they have placed. Relevance of the task to gain information about a person by examining the content of his/her pages in social networks is exemplified. The most common approach to its solution is a visual browsing. The order of the regional authority in our country illustrates that its using in school education is needed. The article shows restrictions on the visual browsing of pupil’s pages in social networks as a tool for the teacher and the school psychologist and justifies that a process of social network users’ data analysis should be automated. Explores publications, which describe such data acquisition, processing, and analysis methods and considers their advantages and disadvantages. The article also gives arguments to support a proposal to study the classification method of social network users. One such method is credit scoring, which is used in banks and credit institutions to assess the solvency of clients. Based on the high efficiency of the method there is a proposal for significant expansion of its using in other areas of society. The possibility to use logistic regression as the mathematical apparatus of the proposed method of binary classification has been justified. Such an approach enables taking into account the different types of data extracted from social networks. Among them: the personal user data, information about hobbies, friends, graphic and text information, behaviour characteristics. The article describes a number of existing methods of data transformation that can be applied to solve the problem. An experiment of binary gender-based classification of social network users is described. A logistic model obtained for this example includes multiple logical variables obtained by transforming the user surnames. This experiment confirms the feasibility of the proposed method. Further work is to define a system

  2. Methods for Analyzing Pipe Networks

    DEFF Research Database (Denmark)

    Nielsen, Hans Bruun

    1989-01-01

    to formulate the flow equations in terms of pipe discharges than in terms of energy heads. The behavior of some iterative methods is compared in the initial phase with large errors. It is explained why the linear theory method oscillates when the iteration gets close to the solution, and it is further...... demonstrated that this method offers good starting values for a Newton-Raphson iteration....

  3. Diagnosis method utilizing neural networks

    International Nuclear Information System (INIS)

    Watanabe, K.; Tamayama, K.

    1990-01-01

    Studies have been made on the technique of neural networks, which will be used to identify a cause of a small anomalous state in the reactor coolant system of the ATR (Advance Thermal Reactor). Three phases of analyses were carried out in this study. First, simulation for 100 seconds was made to determine how the plant parameters respond after the occurence of a transient decrease in reactivity, flow rate and temperature of feed water and increase in the steam flow rate and steam pressure, which would produce a decrease of water level in a steam drum of the ATR. Next, the simulation data was analysed utilizing an autoregressive model. From this analysis, a total of 36 coherency functions up to 0.5 Hz in each transient were computed among nine important and detectable plant parameters: neutron flux, flow rate of coolant, steam or feed water, water level in the steam drum, pressure and opening area of control valve in a steam pipe, feed water temperature and electrical power. Last, learning of neural networks composed of 96 input, 4-9 hidden and 5 output layer units was done by use of the generalized delta rule, namely a back-propagation algorithm. These convergent computations were continued as far as the difference between the desired outputs, 1 for direct cause or 0 for four other ones and actual outputs reached less than 10%. (1) Coherency functions were not governed by decreasing rate of reactivity in the range of 0.41x10 -2 dollar/s to 1.62x10 -2 dollar /s or by decreasing depth of the feed water temperature in the range of 3 deg C to 10 deg C or by a change of 10% or less in the three other causes. Change in coherency functions only depended on the type of cause. (2) The direct cause from the other four ones could be discriminated with 0.94+-0.01 of output level. A maximum of 0.06 output height was found among the other four causes. (3) Calculation load which is represented as products of learning times and numbers of the hidden units did not depend on the

  4. Calculation of portal contribution to hepatic blood flow with 99mTc-microcolloids. A noninvasive method to diagnose liver graft rejection

    International Nuclear Information System (INIS)

    Martin-Comin, J.; Mora, J.; Figueras, J.; Puchal, R.; Jaurrieta, E.; Badosa, F.; Ramos, M.

    1988-01-01

    The portal contribution (PC) to hepatic blood flow was calculated in 13 liver graft patients and 13 normal volunteers. The method is based on the quantification and normalization of the liver and spleen activity after the administration of 7 mCi (259 MBq) of 99mTc microcolloid. Forty examinations were performed in liver grafts and 13 in normal subjects. The PC was significantly higher in normal native liver (64.0 +/- 3.0%) than in functioning grafts (58.8 +/- 3.1%). In acutely rejecting patients, PC was significantly lower (52.4 +/- 2.0%) than in functioning grafts and similar to that observed in cholangitis (53.5 +/- 0.7%). The PC increases again once rejection has resolved (57.3 +/- 2.6%). During hepatitis post-transplant PC values (59.7 +/- 3.4%) were similar to those observed in functioning grafts. Overall, PC values over 55% are very unlikely to be due to rejection

  5. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  6. Reduction Method for Active Distribution Networks

    DEFF Research Database (Denmark)

    Raboni, Pietro; Chen, Zhe

    2013-01-01

    On-line security assessment is traditionally performed by Transmission System Operators at the transmission level, ignoring the effective response of distributed generators and small loads. On the other hand the required computation time and amount of real time data for including Distribution...... Networks also would be too large. In this paper an adaptive aggregation method for subsystems with power electronic interfaced generators and voltage dependant loads is proposed. With this tool may be relatively easier including distribution networks into security assessment. The method is validated...... by comparing the results obtained in PSCAD® with the detailed network model and with the reduced one. Moreover the control schemes of a wind turbine and a photovoltaic plant included in the detailed network model are described....

  7. Studying the Amount, Motivations and Perception of Consequences of Using the Social Networks Based on Quality of the Relationship between Attachment Styles with Perception of Parental Rejection and Control in Adolescents

    Directory of Open Access Journals (Sweden)

    پریسا سادات سیدموسوی

    2018-03-01

    Full Text Available Due to the importance of user behavior in social networks especially in adolescents, the purpose of this study was investigating the amount, motivations and perception of consequences of using the social networks based on the quality of the relationship between attachment styles with perception of parental rejection and control in adole-scents. The population of the research included all of the 15-18 years old adolescents of Tehran from which 146 students (77 girls and 69 boys were chosen by cluster sampling and completed the Inventory of Parent & Peer Attachment, Parental Acceptance-Rejection and Control, and Social Network Using Checklist. The results showed that the quality of the relationship with parents has a key role in prediction of the amount and the way of using the social networks and in addition could affect the consequences of using these networks. In addition, increasing of the parental control unexpectedly could not decrease the using of social networks. According to the results, it seems that although most of the adolescents are using the social networks, the relationship with parents can be critical in predicting the motives of using these networks.

  8. Development of a reject classification method, applied to the diagnotic of a nuclear reactor core: processing of thermal signals providing from out-of-reactor simulation

    International Nuclear Information System (INIS)

    Smolarz, A.

    1982-07-01

    Development of an evolution detection algorithm which aim is to extend the application field of the form recognition analysis to the diagnosis and follow-up of a complex system: study of the data from the out-of-reactor test loop with forced convection in sodium, study and description of a reject classification algorithm developed in the general point of view of evolution detection. This method is tested with theoretical data and with experimental data provided by the second test loop ISIS [fr

  9. Geometrical methods for power network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Bellucci, Stefano; Tiwari, Bhupendra Nath [Istituto Nazioneale di Fisica Nucleare, Frascati, Rome (Italy). Lab. Nazionali di Frascati; Gupta, Neeraj [Indian Institute of Technology, Kanpur (India). Dept. of Electrical Engineering

    2013-02-01

    Uses advanced geometrical methods to analyse power networks. Provides a self-contained and tutorial introduction. Includes a fully worked-out example for the IEEE 5 bus system. This book is a short introduction to power system planning and operation using advanced geometrical methods. The approach is based on well-known insights and techniques developed in theoretical physics in the context of Riemannian manifolds. The proof of principle and robustness of this approach is examined in the context of the IEEE 5 bus system. This work addresses applied mathematicians, theoretical physicists and power engineers interested in novel mathematical approaches to power network theory.

  10. Method Accelerates Training Of Some Neural Networks

    Science.gov (United States)

    Shelton, Robert O.

    1992-01-01

    Three-layer networks trained faster provided two conditions are satisfied: numbers of neurons in layers are such that majority of work done in synaptic connections between input and hidden layers, and number of neurons in input layer at least as great as number of training pairs of input and output vectors. Based on modified version of back-propagation method.

  11. Hybrid recommendation methods in complex networks.

    Science.gov (United States)

    Fiasconaro, A; Tumminello, M; Nicosia, V; Latora, V; Mantegna, R N

    2015-07-01

    We propose two recommendation methods, based on the appropriate normalization of already existing similarity measures, and on the convex combination of the recommendation scores derived from similarity between users and between objects. We validate the proposed measures on three data sets, and we compare the performance of our methods to other recommendation systems recently proposed in the literature. We show that the proposed similarity measures allow us to attain an improvement of performances of up to 20% with respect to existing nonparametric methods, and that the accuracy of a recommendation can vary widely from one specific bipartite network to another, which suggests that a careful choice of the most suitable method is highly relevant for an effective recommendation on a given system. Finally, we study how an increasing presence of random links in the network affects the recommendation scores, finding that one of the two recommendation algorithms introduced here can systematically outperform the others in noisy data sets.

  12. Preemptive scheduling with rejection

    NARCIS (Netherlands)

    Hoogeveen, H.; Skutella, M.; Woeginger, Gerhard

    2003-01-01

    We consider the problem of preemptively scheduling a set of n jobs on m (identical, uniformly related, or unrelated) parallel machines. The scheduler may reject a subset of the jobs and thereby incur job-dependent penalties for each rejected job, and he must construct a schedule for the remaining

  13. Preemptive scheduling with rejection

    NARCIS (Netherlands)

    Hoogeveen, J.A.; Skutella, M.; Woeginger, G.J.; Paterson, M.

    2000-01-01

    We consider the problem of preemptively scheduling a set of n jobs on m (identical, uniformly related, or unrelated) parallel machines. The scheduler may reject a subset of the jobs and thereby incur job-dependent penalties for each rejected job, and he must construct a schedule for the remaining

  14. Web Page Classification Method Using Neural Networks

    Science.gov (United States)

    Selamat, Ali; Omatu, Sigeru; Yanagimoto, Hidekazu; Fujinaka, Toru; Yoshioka, Michifumi

    Automatic categorization is the only viable method to deal with the scaling problem of the World Wide Web (WWW). In this paper, we propose a news web page classification method (WPCM). The WPCM uses a neural network with inputs obtained by both the principal components and class profile-based features (CPBF). Each news web page is represented by the term-weighting scheme. As the number of unique words in the collection set is big, the principal component analysis (PCA) has been used to select the most relevant features for the classification. Then the final output of the PCA is combined with the feature vectors from the class-profile which contains the most regular words in each class before feeding them to the neural networks. We have manually selected the most regular words that exist in each class and weighted them using an entropy weighting scheme. The fixed number of regular words from each class will be used as a feature vectors together with the reduced principal components from the PCA. These feature vectors are then used as the input to the neural networks for classification. The experimental evaluation demonstrates that the WPCM method provides acceptable classification accuracy with the sports news datasets.

  15. Spectral Analysis Methods of Social Networks

    Directory of Open Access Journals (Sweden)

    P. G. Klyucharev

    2017-01-01

    Full Text Available Online social networks (such as Facebook, Twitter, VKontakte, etc. being an important channel for disseminating information are often used to arrange an impact on the social consciousness for various purposes - from advertising products or services to the full-scale information war thereby making them to be a very relevant object of research. The paper reviewed the analysis methods of social networks (primarily, online, based on the spectral theory of graphs. Such methods use the spectrum of the social graph, i.e. a set of eigenvalues of its adjacency matrix, and also the eigenvectors of the adjacency matrix.Described measures of centrality (in particular, centrality based on the eigenvector and PageRank, which reflect a degree of impact one or another user of the social network has. A very popular PageRank measure uses, as a measure of centrality, the graph vertices, the final probabilities of the Markov chain, whose matrix of transition probabilities is calculated on the basis of the adjacency matrix of the social graph. The vector of final probabilities is an eigenvector of the matrix of transition probabilities.Presented a method of dividing the graph vertices into two groups. It is based on maximizing the network modularity by computing the eigenvector of the modularity matrix.Considered a method for detecting bots based on the non-randomness measure of a graph to be computed using the spectral coordinates of vertices - sets of eigenvector components of the adjacency matrix of a social graph.In general, there are a number of algorithms to analyse social networks based on the spectral theory of graphs. These algorithms show very good results, but their disadvantage is the relatively high (albeit polynomial computational complexity for large graphs.At the same time it is obvious that the practical application capacity of the spectral graph theory methods is still underestimated, and it may be used as a basis to develop new methods.The work

  16. LATE RENAL GRAFT REJECTION: PATHOLOGY AND PROGNOSIS

    Directory of Open Access Journals (Sweden)

    E.S. Stolyarevich

    2014-01-01

    Full Text Available Rejection has always been one of the most important cause of late renal graft dysfunction. Aim of the study was to analyze the prevalence of different clinico-pathological variants of rejection that cause late graft dysfunction, and evaluate their impact on long-term outcome. Materials and methods. This is a retrospective study that analyzed 294 needle core biopsy specimens from 265 renal transplant recipients with late (48,8 ± 46,1 months after transplantation allograft dysfunction caused by late acute rejection (LAR, n = 193 or chronic rejection (CR, n = 78 or both (n = 23. C4d staining was performed by immunofl uorescence (IF on frozen sections using a standard protocol. Results. Peritubular capillary C4d deposition was identifi ed in 36% samples with acute rejection and in 62% cases of chronic rejection (including 67% cases of transplant glomerulopathy, and 50% – of isolated chronic vasculopathy. 5-year graft survival for LAR vs CR vs their combination was 47, 13 and 25%, respectively. The outcome of C4d– LAR was (p < 0,01 better than of C4d+ acute rejection: at 60 months graft survival for diffuse C4d+ vs C4d− was 33% vs 53%, respectively. In cases of chronic rejection C4d+ vs C4d– it was not statistically signifi cant (34% vs 36%. Conclusion. In long-term allograft biopsy C4d positivity is more haracteristic for chronic rejection than for acute rejection. Only diffuse C4d staining affects the outcome. C4d– positivity is associated with worse allograft survival in cases of late acute rejection, but not in cases of chronic rejection

  17. Life-threatening false alarm rejection in ICU: using the rule-based and multi-channel information fusion method.

    Science.gov (United States)

    Liu, Chengyu; Zhao, Lina; Tang, Hong; Li, Qiao; Wei, Shoushui; Li, Jianqing

    2016-08-01

    False alarm (FA) rates as high as 86% have been reported in intensive care unit monitors. High FA rates decrease quality of care by slowing staff response times while increasing patient burdens and stresses. In this study, we proposed a rule-based and multi-channel information fusion method for accurately classifying the true or false alarms for five life-threatening arrhythmias: asystole (ASY), extreme bradycardia (EBR), extreme tachycardia (ETC), ventricular tachycardia (VTA) and ventricular flutter/fibrillation (VFB). The proposed method consisted of five steps: (1) signal pre-processing, (2) feature detection and validation, (3) true/false alarm determination for each channel, (4) 'real-time' true/false alarm determination and (5) 'retrospective' true/false alarm determination (if needed). Up to four signal channels, that is, two electrocardiogram signals, one arterial blood pressure and/or one photoplethysmogram signal were included in the analysis. Two events were set for the method validation: event 1 for 'real-time' and event 2 for 'retrospective' alarm classification. The results showed that 100% true positive ratio (i.e. sensitivity) on the training set were obtained for ASY, EBR, ETC and VFB types, and 94% for VTA type, accompanied by the corresponding true negative ratio (i.e. specificity) results of 93%, 81%, 78%, 85% and 50% respectively, resulting in the score values of 96.50, 90.70, 88.89, 92.31 and 64.90, as well as with a final score of 80.57 for event 1 and 79.12 for event 2. For the test set, the proposed method obtained the score of 88.73 for ASY, 77.78 for EBR, 89.92 for ETC, 67.74 for VFB and 61.04 for VTA types, with the final score of 71.68 for event 1 and 75.91 for event 2.

  18. Measurement methods on the complexity of network

    Institute of Scientific and Technical Information of China (English)

    LIN Lin; DING Gang; CHEN Guo-song

    2010-01-01

    Based on the size of network and the number of paths in the network,we proposed a model of topology complexity of a network to measure the topology complexity of the network.Based on the analyses of the effects of the number of the equipment,the types of equipment and the processing time of the node on the complexity of the network with the equipment-constrained,a complexity model of equipment-constrained network was constructed to measure the integrated complexity of the equipment-constrained network.The algorithms for the two models were also developed.An automatic generator of the random single label network was developed to test the models.The results show that the models can correctly evaluate the topology complexity and the integrated complexity of the networks.

  19. Method and tool for network vulnerability analysis

    Science.gov (United States)

    Swiler, Laura Painton [Albuquerque, NM; Phillips, Cynthia A [Albuquerque, NM

    2006-03-14

    A computer system analysis tool and method that will allow for qualitative and quantitative assessment of security attributes and vulnerabilities in systems including computer networks. The invention is based on generation of attack graphs wherein each node represents a possible attack state and each edge represents a change in state caused by a single action taken by an attacker or unwitting assistant. Edges are weighted using metrics such as attacker effort, likelihood of attack success, or time to succeed. Generation of an attack graph is accomplished by matching information about attack requirements (specified in "attack templates") to information about computer system configuration (contained in a configuration file that can be updated to reflect system changes occurring during the course of an attack) and assumed attacker capabilities (reflected in "attacker profiles"). High risk attack paths, which correspond to those considered suited to application of attack countermeasures given limited resources for applying countermeasures, are identified by finding "epsilon optimal paths."

  20. Control and estimation methods over communication networks

    CERN Document Server

    Mahmoud, Magdi S

    2014-01-01

    This book provides a rigorous framework in which to study problems in the analysis, stability and design of networked control systems. Four dominant sources of difficulty are considered: packet dropouts, communication bandwidth constraints, parametric uncertainty, and time delays. Past methods and results are reviewed from a contemporary perspective, present trends are examined, and future possibilities proposed. Emphasis is placed on robust and reliable design methods. New control strategies for improving the efficiency of sensor data processing and reducing associated time delay are presented. The coverage provided features: ·        an overall assessment of recent and current fault-tolerant control algorithms; ·        treatment of several issues arising at the junction of control and communications; ·        key concepts followed by their proofs and efficient computational methods for their implementation; and ·        simulation examples (including TrueTime simulations) to...

  1. A new hierarchical method to find community structure in networks

    Science.gov (United States)

    Saoud, Bilal; Moussaoui, Abdelouahab

    2018-04-01

    Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

  2. Social network analysis: Presenting an underused method for nursing research.

    Science.gov (United States)

    Parnell, James Michael; Robinson, Jennifer C

    2018-06-01

    This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models. © 2018 John Wiley & Sons Ltd.

  3. "Science" Rejects Postmodernism.

    Science.gov (United States)

    St. Pierre, Elizabeth Adams

    2002-01-01

    The National Research Council report, "Scientific Research in Education," claims to present an inclusive view of sciences in responding to federal attempts to legislate educational research. This article asserts that it narrowly defines science as positivism and methodology as quantitative, rejecting postmodernism and omitting other theories. Uses…

  4. SCM: A method to improve network service layout efficiency with network evolution

    Science.gov (United States)

    Zhao, Qi; Zhang, Chuanhao

    2017-01-01

    Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of “software defined network + network function virtualization” (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently. PMID:29267299

  5. SCM: A method to improve network service layout efficiency with network evolution.

    Science.gov (United States)

    Zhao, Qi; Zhang, Chuanhao; Zhao, Zheng

    2017-01-01

    Network services are an important component of the Internet, which are used to expand network functions for third-party developers. Network function virtualization (NFV) can improve the speed and flexibility of network service deployment. However, with the evolution of the network, network service layout may become inefficient. Regarding this problem, this paper proposes a service chain migration (SCM) method with the framework of "software defined network + network function virtualization" (SDN+NFV), which migrates service chains to adapt to network evolution and improves the efficiency of the network service layout. SCM is modeled as an integer linear programming problem and resolved via particle swarm optimization. An SCM prototype system is designed based on an SDN controller. Experiments demonstrate that SCM could reduce the network traffic cost and energy consumption efficiently.

  6. A flood-based information flow analysis and network minimization method for gene regulatory networks.

    Science.gov (United States)

    Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

    2013-04-24

    Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

  7. Social network extraction based on Web: 1. Related superficial methods

    Science.gov (United States)

    Khairuddin Matyuso Nasution, Mahyuddin

    2018-01-01

    Often the nature of something affects methods to resolve the related issues about it. Likewise, methods to extract social networks from the Web, but involve the structured data types differently. This paper reveals several methods of social network extraction from the same sources that is Web: the basic superficial method, the underlying superficial method, the description superficial method, and the related superficial methods. In complexity we derive the inequalities between methods and so are their computations. In this case, we find that different results from the same tools make the difference from the more complex to the simpler: Extraction of social network by involving co-occurrence is more complex than using occurrences.

  8. A new fault detection method for computer networks

    International Nuclear Information System (INIS)

    Lu, Lu; Xu, Zhengguo; Wang, Wenhai; Sun, Youxian

    2013-01-01

    Over the past few years, fault detection for computer networks has attracted extensive attentions for its importance in network management. Most existing fault detection methods are based on active probing techniques which can detect the occurrence of faults fast and precisely. But these methods suffer from the limitation of traffic overhead, especially in large scale networks. To relieve traffic overhead induced by active probing based methods, a new fault detection method, whose key is to divide the detection process into multiple stages, is proposed in this paper. During each stage, only a small region of the network is detected by using a small set of probes. Meanwhile, it also ensures that the entire network can be covered after multiple detection stages. This method can guarantee that the traffic used by probes during each detection stage is small sufficiently so that the network can operate without severe disturbance from probes. Several simulation results verify the effectiveness of the proposed method

  9. Action against Kruemmel rejected

    International Nuclear Information System (INIS)

    Anon.

    1976-01-01

    In its verdict dated September 2nd, 1976 - 10 A 211/74 -, the administrative court of Schleswig-Holstein at Schleswig has rejected with costs the action of a plaintiff resident in Hessen concerning the contestation of the 2nd partial licence for the erection of a nuclear power station at Kruemmel near Hamburg. The verdict is not subject to appeal. Furthermore, the administrative court of Schleswig-Holstein at Schleswig, in its verdict dated September 2nd, 1976 - 10 A 214/74 - has rejected with costs the actions of eight plaintiffs living in Hamburg and surroundings, concerning the contestation of the 1st, 2nd and 3rd partial licence for the erection of a nuclear power station at Kruemmel near Hamburg. An appeal against this verdict has been lodged at the higher administrative court at Lueneburg. The main gounds for the two judgments are given in full text. (orig./HP) [de

  10. A Method for Upper Bounding on Network Access Speed

    DEFF Research Database (Denmark)

    Knudsen, Thomas Phillip; Patel, A.; Pedersen, Jens Myrup

    2004-01-01

    This paper presents a method for calculating an upper bound on network access speed growth and gives guidelines for further research experiments and simulations. The method is aimed at providing a basis for simulation of long term network development and resource management.......This paper presents a method for calculating an upper bound on network access speed growth and gives guidelines for further research experiments and simulations. The method is aimed at providing a basis for simulation of long term network development and resource management....

  11. A divisive spectral method for network community detection

    International Nuclear Information System (INIS)

    Cheng, Jianjun; Li, Longjie; Yao, Yukai; Chen, Xiaoyun; Leng, Mingwei; Lu, Weiguo

    2016-01-01

    Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others. (paper: interdisciplinary statistical mechanics)

  12. Understanding Rejection between First-and-Second-Grade Elementary Students through Reasons Expressed by Rejecters.

    Science.gov (United States)

    García Bacete, Francisco J; Carrero Planes, Virginia E; Marande Perrin, Ghislaine; Musitu Ochoa, Gonzalo

    2017-01-01

    Objective: The aim of this research was to obtain the views of young children regarding their reasons for rejecting a peer. Method: To achieve this goal, we conducted a qualitative study in the context of theory building research using an analysis methodology based on Grounded Theory. The collected information was extracted through semi-structured individual interviews from a sample of 853 children aged 6 from 13 urban public schools in Spain. Results: The children provided 3,009 rejection nominations and 2,934 reasons for disliking the rejected peers. Seven reason categories emerged from the analysis. Four categories refer to behaviors of the rejected children that have a cost for individual peers or peer group such as: direct aggression, disturbance of wellbeing, problematic social and school behaviors and dominance behaviors. A further two categories refer to the identities arising from the preferences and choices of rejected and rejecter children and their peers: personal identity expressed through preferences and disliking, and social identity expressed through outgroup prejudices. The "no-behavior or no-choice" reasons were covered by one category, unfamiliarity. In addition, three context categories were found indicating the participants (interpersonal-group), the impact (low-high), and the subjectivity (subjective-objective) of the reason. Conclusion: This study provides researchers and practitioners with a comprehensive taxonomy of reasons for rejection that contributes to enrich the theoretical knowledge and improve interventions for preventing and reducing peer rejection.

  13. A novel community detection method in bipartite networks

    Science.gov (United States)

    Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

    2018-02-01

    Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

  14. Vulnerability analysis methods for road networks

    Science.gov (United States)

    Bíl, Michal; Vodák, Rostislav; Kubeček, Jan; Rebok, Tomáš; Svoboda, Tomáš

    2014-05-01

    Road networks rank among the most important lifelines of modern society. They can be damaged by either random or intentional events. Roads are also often affected by natural hazards, the impacts of which are both direct and indirect. Whereas direct impacts (e.g. roads damaged by a landslide or due to flooding) are localized in close proximity to the natural hazard occurrence, the indirect impacts can entail widespread service disabilities and considerable travel delays. The change in flows in the network may affect the population living far from the places originally impacted by the natural disaster. These effects are primarily possible due to the intrinsic nature of this system. The consequences and extent of the indirect costs also depend on the set of road links which were damaged, because the road links differ in terms of their importance. The more robust (interconnected) the road network is, the less time is usually needed to secure the serviceability of an area hit by a disaster. These kinds of networks also demonstrate a higher degree of resilience. Evaluating road network structures is therefore essential in any type of vulnerability and resilience analysis. There are a range of approaches used for evaluation of the vulnerability of a network and for identification of the weakest road links. Only few of them are, however, capable of simulating the impacts of the simultaneous closure of numerous links, which often occurs during a disaster. The primary problem is that in the case of a disaster, which usually has a large regional extent, the road network may remain disconnected. The majority of the commonly used indices use direct computation of the shortest paths or time between OD (origin - destination) pairs and therefore cannot be applied when the network breaks up into two or more components. Since extensive break-ups often occur in cases of major disasters, it is important to study the network vulnerability in these cases as well, so that appropriate

  15. Multilevel method for modeling large-scale networks.

    Energy Technology Data Exchange (ETDEWEB)

    Safro, I. M. (Mathematics and Computer Science)

    2012-02-24

    Understanding the behavior of real complex networks is of great theoretical and practical significance. It includes developing accurate artificial models whose topological properties are similar to the real networks, generating the artificial networks at different scales under special conditions, investigating a network dynamics, reconstructing missing data, predicting network response, detecting anomalies and other tasks. Network generation, reconstruction, and prediction of its future topology are central issues of this field. In this project, we address the questions related to the understanding of the network modeling, investigating its structure and properties, and generating artificial networks. Most of the modern network generation methods are based either on various random graph models (reinforced by a set of properties such as power law distribution of node degrees, graph diameter, and number of triangles) or on the principle of replicating an existing model with elements of randomization such as R-MAT generator and Kronecker product modeling. Hierarchical models operate at different levels of network hierarchy but with the same finest elements of the network. However, in many cases the methods that include randomization and replication elements on the finest relationships between network nodes and modeling that addresses the problem of preserving a set of simplified properties do not fit accurately enough the real networks. Among the unsatisfactory features are numerically inadequate results, non-stability of algorithms on real (artificial) data, that have been tested on artificial (real) data, and incorrect behavior at different scales. One reason is that randomization and replication of existing structures can create conflicts between fine and coarse scales of the real network geometry. Moreover, the randomization and satisfying of some attribute at the same time can abolish those topological attributes that have been undefined or hidden from

  16. 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.

  17. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

    Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.

  18. Modeling rejection immunity

    Directory of Open Access Journals (Sweden)

    Gaetano Andrea De

    2012-05-01

    Full Text Available Abstract Background Transplantation is often the only way to treat a number of diseases leading to organ failure. To overcome rejection towards the transplanted organ (graft, immunosuppression therapies are used, which have considerable side-effects and expose patients to opportunistic infections. The development of a model to complement the physician’s experience in specifying therapeutic regimens is therefore desirable. The present work proposes an Ordinary Differential Equations model accounting for immune cell proliferation in response to the sudden entry of graft antigens, through different activation mechanisms. The model considers the effect of a single immunosuppressive medication (e.g. cyclosporine, subject to first-order linear kinetics and acting by modifying, in a saturable concentration-dependent fashion, the proliferation coefficient. The latter has been determined experimentally. All other model parameter values have been set so as to reproduce reported state variable time-courses, and to maintain consistency with one another and with the experimentally derived proliferation coefficient. Results The proposed model substantially simplifies the chain of events potentially leading to organ rejection. It is however able to simulate quantitatively the time course of graft-related antigen and competent immunoreactive cell populations, showing the long-term alternative outcomes of rejection, tolerance or tolerance at a reduced functional tissue mass. In particular, the model shows that it may be difficult to attain tolerance at full tissue mass with acceptably low doses of a single immunosuppressant, in accord with clinical experience. Conclusions The introduced model is mathematically consistent with known physiology and can reproduce variations in immune status and allograft survival after transplantation. The model can be adapted to represent different therapeutic schemes and may offer useful indications for the optimization of

  19. Mean field methods for cortical network dynamics

    DEFF Research Database (Denmark)

    Hertz, J.; Lerchner, Alexander; Ahmadi, M.

    2004-01-01

    We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...... with the strength of the synapses in the network and with the value to which the membrane potential is reset after a spike. Generalizing the model to include conductance-based synapses gives insight into the connection between the firing statistics and the high- conductance state observed experimentally in visual...

  20. A Method for Automated Planning of FTTH Access Network Infrastructures

    DEFF Research Database (Denmark)

    Riaz, Muhammad Tahir; Pedersen, Jens Myrup; Madsen, Ole Brun

    2005-01-01

    In this paper a method for automated planning of Fiber to the Home (FTTH) access networks is proposed. We introduced a systematic approach for planning access network infrastructure. The GIS data and a set of algorithms were employed to make the planning process more automatic. The method explains...... method. The method, however, does not fully automate the planning but make the planning process significantly fast. The results and discussion are presented and conclusion is given in the end....

  1. Computer aided method of low voltage power distribution networks protection system against lightning and electromagnetic pulse generated by high altitude nuclear burst

    International Nuclear Information System (INIS)

    Laroubine, J.

    1989-01-01

    The lightning creates an electromagnetic field which produces a slow duration and high energy pulse of current on low voltage power distribution networks. On the other hand an high altitude nuclear burst generates an electromagnetic pulse which causes fast and intense interferences. We describe here the specifications of a passive filter that can reject these interferences. We used a computer aided method of simulation to create a prototype. Experimental results confirm the validity of the model used for simulation [fr

  2. Mixed Methods Analysis of Enterprise Social Networks

    DEFF Research Database (Denmark)

    Behrendt, Sebastian; Richter, Alexander; Trier, Matthias

    2014-01-01

    The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate...

  3. STEADY-STATE HEAT REJECTION RATES FOR A COAXIAL BOREHOLE HEAT EXCHANGER DURING PASSIVE AND ACTIVE COOLING DETERMINED WITH THE NOVEL STEP THERMAL RESPONSE TEST METHOD

    Directory of Open Access Journals (Sweden)

    Marija Macenić

    2018-01-01

    Full Text Available At three locations in Zagreb, classical and extended thermal response test (TRT was conducted on installed coaxial heat exchangers. With classic TR test, thermogeological properties of the ground and thermal resistance of the borehole were determined at each location. It is seen that thermal conductivity of the ground varies, due to difference in geological profile of the sites. In addition, experimental research of steady-state thermal response step test (SSTRST was carried out to determine heat rejection rates for passive and active cooling in steady state regime. Results showed that heat rejection rate is only between 8-11 W/m, which indicates that coaxial system is not suitable for passive cooling demands. Furthermore, the heat pump in passive cooling mode uses additional plate heat exchanger where there is additional temperature drop of working fluid by approximately 1,5 °C. Therefore, steady-state rejection rate for passive cooling is even lower for a real case project. Coaxial heat exchanger should be always designed for an active cooling regime with an operation of a heat pump compressor in a classical vapour compression refrigeration cycle.

  4. Dynamic baseline detection method for power data network service

    Science.gov (United States)

    Chen, Wei

    2017-08-01

    This paper proposes a dynamic baseline Traffic detection Method which is based on the historical traffic data for the Power data network. The method uses Cisco's NetFlow acquisition tool to collect the original historical traffic data from network element at fixed intervals. This method uses three dimensions information including the communication port, time, traffic (number of bytes or number of packets) t. By filtering, removing the deviation value, calculating the dynamic baseline value, comparing the actual value with the baseline value, the method can detect whether the current network traffic is abnormal.

  5. A low complexity method for the optimization of network path length in spatially embedded networks

    International Nuclear Information System (INIS)

    Chen, Guang; Yang, Xu-Hua; Xu, Xin-Li; Ming, Yong; Chen, Sheng-Yong; Wang, Wan-Liang

    2014-01-01

    The average path length of a network is an important index reflecting the network transmission efficiency. In this paper, we propose a new method of decreasing the average path length by adding edges. A new indicator is presented, incorporating traffic flow demand, to assess the decrease in the average path length when a new edge is added during the optimization process. With the help of the indicator, edges are selected and added into the network one by one. The new method has a relatively small time computational complexity in comparison with some traditional methods. In numerical simulations, the new method is applied to some synthetic spatially embedded networks. The result shows that the method can perform competitively in decreasing the average path length. Then, as an example of an application of this new method, it is applied to the road network of Hangzhou, China. (paper)

  6. An introduction to neural network methods for differential equations

    CERN Document Server

    Yadav, Neha; Kumar, Manoj

    2015-01-01

    This book introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations. The objective of this book is to provide the reader with a sound understanding of the foundations of neural networks, and a comprehensive introduction to neural network methods for solving differential equations together with recent developments in the techniques and their applications. The book comprises four major sections. Section I consists of a brief overview of differential equations and the relevant physical problems arising in science and engineering. Section II illustrates the history of neural networks starting from their beginnings in the 1940s through to the renewed...

  7. Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

    Directory of Open Access Journals (Sweden)

    Fei Gao

    2015-01-01

    Full Text Available Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is the prediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

  8. Corneal Graft Rejection: Incidence and Risk Factors

    Directory of Open Access Journals (Sweden)

    Alireza Baradaran-Rafii

    2008-12-01

    Full Text Available

    PURPOSE: To determine the incidence and risk factors of late corneal graft rejection after penetrating keratoplasty (PKP. METHODS: Records of all patients who had undergone PKP from 2002 to 2004 without immunosuppressive therapy other than systemic steroids and with at least one year of follow up were reviewed. The role of possible risk factors such as demographic factors, other host factors, donor factors, indications for PKP as well as type of rejection were evaluated. RESULTS: During the study period, 295 PKPs were performed on 286 patients (176 male, 110 female. Mean age at the time of keratoplasty was 38±20 (range, 40 days to 90 years and mean follow up period was 20±10 (range 12-43 months. Graft rejection occurred in 94 eyes (31.8% at an average of 7.3±6 months (range, 20 days to 39 months after PKP. The most common type of rejection was endothelial (20.7%. Corneal vascularization, regrafting, anterior synechiae, irritating sutures, active inflammation, additional anterior segment procedures, history of trauma, uncontrolled glaucoma, prior graft rejection, recurrence of herpetic infection and eccentric grafting increased the rate of rejection. Patient age, donor size and bilateral transplantation had no significant influence on graft rejection. CONCLUSION: Significant risk factors for corneal graft rejection include

  9. Learning Methods for Radial Basis Functions Networks

    Czech Academy of Sciences Publication Activity Database

    Neruda, Roman; Kudová, Petra

    2005-01-01

    Roč. 21, - (2005), s. 1131-1142 ISSN 0167-739X R&D Projects: GA ČR GP201/03/P163; GA ČR GA201/02/0428 Institutional research plan: CEZ:AV0Z10300504 Keywords : radial basis function networks * hybrid supervised learning * genetic algorithms * benchmarking Subject RIV: BA - General Mathematics Impact factor: 0.555, year: 2005

  10. Classification Method in Integrated Information Network Using Vector Image Comparison

    Directory of Open Access Journals (Sweden)

    Zhou Yuan

    2014-05-01

    Full Text Available Wireless Integrated Information Network (WMN consists of integrated information that can get data from its surrounding, such as image, voice. To transmit information, large resource is required which decreases the service time of the network. In this paper we present a Classification Approach based on Vector Image Comparison (VIC for WMN that improve the service time of the network. The available methods for sub-region selection and conversion are also proposed.

  11. Spectral Methods for Immunization of Large Networks

    Directory of Open Access Journals (Sweden)

    Muhammad Ahmad

    2017-11-01

    Full Text Available Given a network of nodes, minimizing the spread of a contagion using a limited budget is a well-studied problem with applications in network security, viral marketing, social networks, and public health. In real graphs, virus may infect a node which in turn infects its neighbour nodes and this may trigger an epidemic in the whole graph. The goal thus is to select the best k nodes (budget constraint that are immunized (vaccinated, screened, filtered so as the remaining graph is less prone to the epidemic. It is known that the problem is, in all practical models, computationally intractable even for moderate sized graphs. In this paper we employ ideas from spectral graph theory to define relevance and importance of nodes. Using novel graph theoretic techniques, we then design an efficient approximation algorithm to immunize the graph. Theoretical guarantees on the running time of our algorithm show that it is more efficient than any other known solution in the literature. We test the performance of our algorithm on several real world graphs. Experiments show that our algorithm scales well for large graphs and outperforms state of the art algorithms both in quality (containment of epidemic and efficiency (runtime and space complexity.

  12. Tensor network method for reversible classical computation

    Science.gov (United States)

    Yang, Zhi-Cheng; Kourtis, Stefanos; Chamon, Claudio; Mucciolo, Eduardo R.; Ruckenstein, Andrei E.

    2018-03-01

    We develop a tensor network technique that can solve universal reversible classical computational problems, formulated as vertex models on a square lattice [Nat. Commun. 8, 15303 (2017), 10.1038/ncomms15303]. By encoding the truth table of each vertex constraint in a tensor, the total number of solutions compatible with partial inputs and outputs at the boundary can be represented as the full contraction of a tensor network. We introduce an iterative compression-decimation (ICD) scheme that performs this contraction efficiently. The ICD algorithm first propagates local constraints to longer ranges via repeated contraction-decomposition sweeps over all lattice bonds, thus achieving compression on a given length scale. It then decimates the lattice via coarse-graining tensor contractions. Repeated iterations of these two steps gradually collapse the tensor network and ultimately yield the exact tensor trace for large systems, without the need for manual control of tensor dimensions. Our protocol allows us to obtain the exact number of solutions for computations where a naive enumeration would take astronomically long times.

  13. Semigroup methods for evolution equations on networks

    CERN Document Server

    Mugnolo, Delio

    2014-01-01

    This concise text is based on a series of lectures held only a few years ago and originally intended as an introduction to known results on linear hyperbolic and parabolic equations.  Yet the topic of differential equations on graphs, ramified spaces, and more general network-like objects has recently gained significant momentum and, well beyond the confines of mathematics, there is a lively interdisciplinary discourse on all aspects of so-called complex networks. Such network-like structures can be found in virtually all branches of science, engineering and the humanities, and future research thus calls for solid theoretical foundations.      This book is specifically devoted to the study of evolution equations – i.e., of time-dependent differential equations such as the heat equation, the wave equation, or the Schrödinger equation (quantum graphs) – bearing in mind that the majority of the literature in the last ten years on the subject of differential equations of graphs has been devoted to ellip...

  14. Optimization-based Method for Automated Road Network Extraction

    International Nuclear Information System (INIS)

    Xiong, D

    2001-01-01

    Automated road information extraction has significant applicability in transportation. It provides a means for creating, maintaining, and updating transportation network databases that are needed for purposes ranging from traffic management to automated vehicle navigation and guidance. This paper is to review literature on the subject of road extraction and to describe a study of an optimization-based method for automated road network extraction

  15. Diagrammatic perturbation methods in networks and sports ranking combinatorics

    International Nuclear Information System (INIS)

    Park, Juyong

    2010-01-01

    Analytic and computational tools developed in statistical physics are being increasingly applied to the study of complex networks. Here we present recent developments in the diagrammatic perturbation methods for the exponential random graph models, and apply them to the combinatoric problem of determining the ranking of nodes in directed networks that represent pairwise competitions

  16. Method of optimization onboard communication network

    Science.gov (United States)

    Platoshin, G. A.; Selvesuk, N. I.; Semenov, M. E.; Novikov, V. M.

    2018-02-01

    In this article the optimization levels of onboard communication network (OCN) are proposed. We defined the basic parameters, which are necessary for the evaluation and comparison of modern OCN, we identified also a set of initial data for possible modeling of the OCN. We also proposed a mathematical technique for implementing the OCN optimization procedure. This technique is based on the principles and ideas of binary programming. It is shown that the binary programming technique allows to obtain an inherently optimal solution for the avionics tasks. An example of the proposed approach implementation to the problem of devices assignment in OCN is considered.

  17. Network Forensics Method Based on Evidence Graph and Vulnerability Reasoning

    Directory of Open Access Journals (Sweden)

    Jingsha He

    2016-11-01

    Full Text Available As the Internet becomes larger in scale, more complex in structure and more diversified in traffic, the number of crimes that utilize computer technologies is also increasing at a phenomenal rate. To react to the increasing number of computer crimes, the field of computer and network forensics has emerged. The general purpose of network forensics is to find malicious users or activities by gathering and dissecting firm evidences about computer crimes, e.g., hacking. However, due to the large volume of Internet traffic, not all the traffic captured and analyzed is valuable for investigation or confirmation. After analyzing some existing network forensics methods to identify common shortcomings, we propose in this paper a new network forensics method that uses a combination of network vulnerability and network evidence graph. In our proposed method, we use vulnerability evidence and reasoning algorithm to reconstruct attack scenarios and then backtrack the network packets to find the original evidences. Our proposed method can reconstruct attack scenarios effectively and then identify multi-staged attacks through evidential reasoning. Results of experiments show that the evidence graph constructed using our method is more complete and credible while possessing the reasoning capability.

  18. High-resolution method for evolving complex interface networks

    Science.gov (United States)

    Pan, Shucheng; Hu, Xiangyu Y.; Adams, Nikolaus A.

    2018-04-01

    In this paper we describe a high-resolution transport formulation of the regional level-set approach for an improved prediction of the evolution of complex interface networks. The novelty of this method is twofold: (i) construction of local level sets and reconstruction of a global level set, (ii) local transport of the interface network by employing high-order spatial discretization schemes for improved representation of complex topologies. Various numerical test cases of multi-region flow problems, including triple-point advection, single vortex flow, mean curvature flow, normal driven flow, dry foam dynamics and shock-bubble interaction show that the method is accurate and suitable for a wide range of complex interface-network evolutions. Its overall computational cost is comparable to the Semi-Lagrangian regional level-set method while the prediction accuracy is significantly improved. The approach thus offers a viable alternative to previous interface-network level-set method.

  19. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  20. Systems and methods for modeling and analyzing networks

    Science.gov (United States)

    Hill, Colin C; Church, Bruce W; McDonagh, Paul D; Khalil, Iya G; Neyarapally, Thomas A; Pitluk, Zachary W

    2013-10-29

    The systems and methods described herein utilize a probabilistic modeling framework for reverse engineering an ensemble of causal models, from data and then forward simulating the ensemble of models to analyze and predict the behavior of the network. In certain embodiments, the systems and methods described herein include data-driven techniques for developing causal models for biological networks. Causal network models include computational representations of the causal relationships between independent variables such as a compound of interest and dependent variables such as measured DNA alterations, changes in mRNA, protein, and metabolites to phenotypic readouts of efficacy and toxicity.

  1. Structural reliability calculation method based on the dual neural network and direct integration method.

    Science.gov (United States)

    Li, Haibin; He, Yun; Nie, Xiaobo

    2018-01-01

    Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

  2. Protocol independent transmission method in software defined optical network

    Science.gov (United States)

    Liu, Yuze; Li, Hui; Hou, Yanfang; Qiu, Yajun; Ji, Yuefeng

    2016-10-01

    With the development of big data and cloud computing technology, the traditional software-defined network is facing new challenges (e.i., ubiquitous accessibility, higher bandwidth, more flexible management and greater security). Using a proprietary protocol or encoding format is a way to improve information security. However, the flow, which carried by proprietary protocol or code, cannot go through the traditional IP network. In addition, ultra- high-definition video transmission service once again become a hot spot. Traditionally, in the IP network, the Serial Digital Interface (SDI) signal must be compressed. This approach offers additional advantages but also bring some disadvantages such as signal degradation and high latency. To some extent, HD-SDI can also be regard as a proprietary protocol, which need transparent transmission such as optical channel. However, traditional optical networks cannot support flexible traffics . In response to aforementioned challenges for future network, one immediate solution would be to use NFV technology to abstract the network infrastructure and provide an all-optical switching topology graph for the SDN control plane. This paper proposes a new service-based software defined optical network architecture, including an infrastructure layer, a virtualization layer, a service abstract layer and an application layer. We then dwell on the corresponding service providing method in order to implement the protocol-independent transport. Finally, we experimentally evaluate that proposed service providing method can be applied to transmit the HD-SDI signal in the software-defined optical network.

  3. An algebra-based method for inferring gene regulatory networks.

    Science.gov (United States)

    Vera-Licona, Paola; Jarrah, Abdul; Garcia-Puente, Luis David; McGee, John; Laubenbacher, Reinhard

    2014-03-26

    The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the

  4. Electromagnetic field computation by network methods

    CERN Document Server

    Felsen, Leopold B; Russer, Peter

    2009-01-01

    This monograph proposes a systematic and rigorous treatment of electromagnetic field representations in complex structures. The book presents new strong models by combining important computational methods. This is the last book of the late Leopold Felsen.

  5. 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.

  6. Information loss method to measure node similarity in networks

    Science.gov (United States)

    Li, Yongli; Luo, Peng; Wu, Chong

    2014-09-01

    Similarity measurement for the network node has been paid increasing attention in the field of statistical physics. In this paper, we propose an entropy-based information loss method to measure the node similarity. The whole model is established based on this idea that less information loss is caused by seeing two more similar nodes as the same. The proposed new method has relatively low algorithm complexity, making it less time-consuming and more efficient to deal with the large scale real-world network. In order to clarify its availability and accuracy, this new approach was compared with some other selected approaches on two artificial examples and synthetic networks. Furthermore, the proposed method is also successfully applied to predict the network evolution and predict the unknown nodes' attributions in the two application examples.

  7. Momentum integral network method for thermal-hydraulic transient analysis

    International Nuclear Information System (INIS)

    Van Tuyle, G.J.

    1983-01-01

    A new momentum integral network method has been developed, and tested in the MINET computer code. The method was developed in order to facilitate the transient analysis of complex fluid flow and heat transfer networks, such as those found in the balance of plant of power generating facilities. The method employed in the MINET code is a major extension of a momentum integral method reported by Meyer. Meyer integrated the momentum equation over several linked nodes, called a segment, and used a segment average pressure, evaluated from the pressures at both ends. Nodal mass and energy conservation determined nodal flows and enthalpies, accounting for fluid compression and thermal expansion

  8. A link prediction method for heterogeneous networks based on BP neural network

    Science.gov (United States)

    Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

    2018-04-01

    Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

  9. Radionuclide diagnosis of allograft rejection

    International Nuclear Information System (INIS)

    George, E.A.

    1982-01-01

    Interaction with one or more anatomical and physiopathological characteristics of the rejecting renal allograft is suggested by those radioagents utilized specifically for the diagnosis of allograft rejection. Rejection, the most common cause of declining allograft function, is frequently mimicked clinically or masked by other immediate or long term post transplant complications. Understanding of the anatomical pathological features and kinetics of rejection and their modification by immunosuppressive maintenance and therapy are important for the proper clinical utilization of these radioagents. Furthermore, in selecting these radionuclides, one has to consider the comparative availability, preparatory and procedural simplicity, acquisition and display techniques and the possibility of timely report. The clinical utilities of radiofibrinogen, /sup 99m/Tc sulfur colloid and 67 Ga in the diagnosis of allograft rejection have been evaluated to a variable extent in the past. The potential usefulness of the recently developed preparations of 111 In labeled autologous leukocytes and platelets are presently under investigation

  10. An algebraic topological method for multimodal brain networks comparison

    Directory of Open Access Journals (Sweden)

    Tiago eSimas

    2015-07-01

    Full Text Available Understanding brain connectivity is one of the most important issues in neuroscience. Nonetheless, connectivity data can reflect either functional relationships of brain activities or anatomical connections between brain areas. Although both representations should be related, this relationship is not straightforward. We have devised a powerful method that allows different operations between networks that share the same set of nodes, by embedding them in a common metric space, enforcing transitivity to the graph topology. Here, we apply this method to construct an aggregated network from a set of functional graphs, each one from a different subject. Once this aggregated functional network is constructed, we use again our method to compare it with the structural connectivity to identify particular brain regions that differ in both modalities (anatomical and functional. Remarkably, these brain regions include functional areas that form part of the classical resting state networks. We conclude that our method -based on the comparison of the aggregated functional network- reveals some emerging features that could not be observed when the comparison is performed with the classical averaged functional network.

  11. Communication devices for network-hopping communications and methods of network-hopping communications

    Science.gov (United States)

    Buttles, John W

    2013-04-23

    Wireless communication devices include a software-defined radio coupled to processing circuitry. The system controller is configured to execute computer programming code. Storage media is coupled to the system controller and includes computer programming code configured to cause the system controller to configure and reconfigure the software-defined radio to operate on each of a plurality of communication networks according to a selected sequence. Methods for communicating with a wireless device and methods of wireless network-hopping are also disclosed.

  12. A new method to construct co-author networks

    Science.gov (United States)

    Liu, Jie; Li, Yunpeng; Ruan, Zichan; Fu, Guangyuan; Chen, Xiaowu; Sadiq, Rehan; Deng, Yong

    2015-02-01

    In this paper, we propose a new method to evaluate the importance of nodes in a given network. The proposed method is based on the PageRank algorithm. However, we have made necessary improvements to combine the importance of the node itself and that of its community status. First, we propose an improved method to better evaluate the real impact of a paper. The proposed method calibrates the real influence of a paper over time. Then we propose a scheme of evaluating the contribution of each author in a paper. We later develop a new method to combine the information of the author itself and the structure of the co-author network. We use the number of co-authorship to calculate the effective distance between two authors, and evaluate the strength of their influence to each other with the law of gravity. The strength of influence is used to build a new network of authors, which is a comprehensive topological representation of both the quality of the node and its role in network. Finally, we apply our method to the Erdos co-author community and AMiner Citation Network to identify the most influential authors.

  13. Handwritten Javanese Character Recognition Using Several Artificial Neural Network Methods

    Directory of Open Access Journals (Sweden)

    Gregorius Satia Budhi

    2015-07-01

    Full Text Available Javanese characters are traditional characters that are used to write the Javanese language. The Javanese language is a language used by many people on the island of Java, Indonesia. The use of Javanese characters is diminishing more and more because of the difficulty of studying the Javanese characters themselves. The Javanese character set consists of basic characters, numbers, complementary characters, and so on. In this research we have developed a system to recognize Javanese characters. Input for the system is a digital image containing several handwritten Javanese characters. Preprocessing and segmentation are performed on the input image to get each character. For each character, feature extraction is done using the ICZ-ZCZ method. The output from feature extraction will become input for an artificial neural network. We used several artificial neural networks, namely a bidirectional associative memory network, a counterpropagation network, an evolutionary network, a backpropagation network, and a backpropagation network combined with chi2. From the experimental results it can be seen that the combination of chi2 and backpropagation achieved better recognition accuracy than the other methods.

  14. Cell-Free DNA and Active Rejection in Kidney Allografts.

    Science.gov (United States)

    Bloom, Roy D; Bromberg, Jonathan S; Poggio, Emilio D; Bunnapradist, Suphamai; Langone, Anthony J; Sood, Puneet; Matas, Arthur J; Mehta, Shikha; Mannon, Roslyn B; Sharfuddin, Asif; Fischbach, Bernard; Narayanan, Mohanram; Jordan, Stanley C; Cohen, David; Weir, Matthew R; Hiller, David; Prasad, Preethi; Woodward, Robert N; Grskovic, Marica; Sninsky, John J; Yee, James P; Brennan, Daniel C

    2017-07-01

    Histologic analysis of the allograft biopsy specimen is the standard method used to differentiate rejection from other injury in kidney transplants. Donor-derived cell-free DNA (dd-cfDNA) is a noninvasive test of allograft injury that may enable more frequent, quantitative, and safer assessment of allograft rejection and injury status. To investigate this possibility, we prospectively collected blood specimens at scheduled intervals and at the time of clinically indicated biopsies. In 102 kidney recipients, we measured plasma levels of dd-cfDNA and correlated the levels with allograft rejection status ascertained by histology in 107 biopsy specimens. The dd-cfDNA level discriminated between biopsy specimens showing any rejection (T cell-mediated rejection or antibody-mediated rejection [ABMR]) and controls (no rejection histologically), P rejection at a cutoff of 1.0% dd-cfDNA were 61% and 84%, respectively. The AUC for discriminating ABMR from samples without ABMR was 0.87 (95% CI, 0.75 to 0.97). Positive and negative predictive values for ABMR at a cutoff of 1.0% dd-cfDNA were 44% and 96%, respectively. Median dd-cfDNA was 2.9% (ABMR), 1.2% (T cell-mediated types ≥IB), 0.2% (T cell-mediated type IA), and 0.3% in controls ( P =0.05 for T cell-mediated rejection types ≥IB versus controls). Thus, dd-cfDNA may be used to assess allograft rejection and injury; dd-cfDNA levels rejection (T cell-mediated type ≥IB or ABMR) and levels >1% indicate a probability of active rejection. Copyright © 2017 by the American Society of Nephrology.

  15. An Entropy-Based Network Anomaly Detection Method

    Directory of Open Access Journals (Sweden)

    Przemysław Bereziński

    2015-04-01

    Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

  16. Dynamic Subsidy Method for Congestion Management in Distribution Networks

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei

    2016-01-01

    Dynamic subsidy (DS) is a locational price paid by the distribution system operator (DSO) to its customers in order to shift energy consumption to designated hours and nodes. It is promising for demand side management and congestion management. This paper proposes a new DS method for congestion...... management in distribution networks, including the market mechanism, the mathematical formulation through a two-level optimization, and the method solving the optimization by tightening the constraints and linearization. Case studies were conducted with a one node system and the Bus 4 distribution network...... of the Roy Billinton Test System (RBTS) with high penetration of electric vehicles (EVs) and heat pumps (HPs). The case studies demonstrate the efficacy of the DS method for congestion management in distribution networks. Studies in this paper show that the DS method offers the customers a fair opportunity...

  17. Decomposition method for analysis of closed queuing networks

    Directory of Open Access Journals (Sweden)

    Yu. G. Nesterov

    2014-01-01

    Full Text Available This article deals with the method to estimate the average residence time in nodes of closed queuing networks with priorities and a wide range of conservative disciplines to be served. The method is based on a decomposition of entire closed queuing network into a set of simple basic queuing systems such as M|GI|m|N for each node. The unknown average residence times in the network nodes are interrelated through a system of nonlinear equations. The fact that there is a solution of this system has been proved. An iterative procedure based on Newton-Kantorovich method is proposed for finding the solution of such system. This procedure provides fast convergence to solution. Today possibilities of proposed method are limited by known analytical solutions for simple basic queuing systems of M|GI|m|N type.

  18. Network 'small-world-ness': a quantitative method for determining canonical network equivalence.

    Directory of Open Access Journals (Sweden)

    Mark D Humphries

    Full Text Available BACKGROUND: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges. This semi-quantitative definition leads to a categorical distinction ('small/not-small' rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model--the Watts-Strogatz (WS model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified. METHODOLOGY/PRINCIPAL FINDINGS: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S>1--an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process. CONCLUSIONS/SIGNIFICANCE: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing.

  19. 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)

  20. A Global Network Alignment Method Using Discrete Particle Swarm Optimization.

    Science.gov (United States)

    Huang, Jiaxiang; Gong, Maoguo; Ma, Lijia

    2016-10-19

    Molecular interactions data increase exponentially with the advance of biotechnology. This makes it possible and necessary to comparatively analyse the different data at a network level. Global network alignment is an important network comparison approach to identify conserved subnetworks and get insight into evolutionary relationship across species. Network alignment which is analogous to subgraph isomorphism is known to be an NP-hard problem. In this paper, we introduce a novel heuristic Particle-Swarm-Optimization based Network Aligner (PSONA), which optimizes a weighted global alignment model considering both protein sequence similarity and interaction conservations. The particle statuses and status updating rules are redefined in a discrete form by using permutation. A seed-and-extend strategy is employed to guide the searching for the superior alignment. The proposed initialization method "seeds" matches with high sequence similarity into the alignment, which guarantees the functional coherence of the mapping nodes. A greedy local search method is designed as the "extension" procedure to iteratively optimize the edge conservations. PSONA is compared with several state-of-art methods on ten network pairs combined by five species. The experimental results demonstrate that the proposed aligner can map the proteins with high functional coherence and can be used as a booster to effectively refine the well-studied aligners.

  1. S-curve networks and an approximate method for estimating degree distributions of complex networks

    International Nuclear Information System (INIS)

    Guo Jin-Li

    2010-01-01

    In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research. (general)

  2. S-curve networks and an approximate method for estimating degree distributions of complex networks

    Science.gov (United States)

    Guo, Jin-Li

    2010-12-01

    In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference values for optimizing the distribution of IPv4 address resource and the development of IPv6. Based on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, it proposes a finite network model with a bulk growth. The model is said to be an S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barabási-Albert method) is not suitable for the network. It develops an approximate method to predict the growth dynamics of the individual nodes, and uses this to calculate analytically the degree distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-law form. This method can overcome a shortcoming of Barabási-Albert method commonly used in current network research.

  3. A study of reactor monitoring method with neural network

    Energy Technology Data Exchange (ETDEWEB)

    Nabeshima, Kunihiko [Japan Atomic Energy Research Inst., Tokai, Ibaraki (Japan). Tokai Research Establishment

    2001-03-01

    The purpose of this study is to investigate the methodology of Nuclear Power Plant (NPP) monitoring with neural networks, which create the plant models by the learning of the past normal operation patterns. The concept of this method is to detect the symptom of small anomalies by monitoring the deviations between the process signals measured from an actual plant and corresponding output signals from the neural network model, which might not be equal if the abnormal operational patterns are presented to the input of the neural network. Auto-associative network, which has same output as inputs, can detect an kind of anomaly condition by using normal operation data only. The monitoring tests of the feedforward neural network with adaptive learning were performed using the PWR plant simulator by which many kinds of anomaly conditions can be easily simulated. The adaptively trained feedforward network could follow the actual plant dynamics and the changes of plant condition, and then find most of the anomalies much earlier than the conventional alarm system during steady state and transient operations. Then the off-line and on-line test results during one year operation at the actual NPP (PWR) showed that the neural network could detect several small anomalies which the operators or the conventional alarm system didn't noticed. Furthermore, the sensitivity analysis suggests that the plant models by neural networks are appropriate. Finally, the simulation results show that the recurrent neural network with feedback connections could successfully model the slow behavior of the reactor dynamics without adaptive learning. Therefore, the recurrent neural network with adaptive learning will be the best choice for the actual reactor monitoring system. (author)

  4. New Method for Leakage Detection by Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Attari

    2018-03-01

    Full Text Available Nowadays water loss has been turned into a global concern and on the other hand the demand for water is increasing. This problem has made the demand management and consumption pattern reform necessary. One of the most important methods for managing water consumption is to decrease the water loss. In this study by using neural networks, a new method is presented to specify the location and quantity of leakages in water distribution networks.  In this method, by producing the training data and applying it to neural network, the network is able to determine approximate location and quantity of nodal leakage with receiving the nodal pressure. Production of training data is carried out by applying assumed leakage to specific nodes in the network and calculating the new nodal pressures. The results show that by minimum use of hydraulic data taken from pressures, not only this method can determine the location of nodal leakages, but also it can specify the amount of leakage on each node with reasonable accuracy.

  5. Modified network simulation model with token method of bus access

    Directory of Open Access Journals (Sweden)

    L.V. Stribulevich

    2013-08-01

    Full Text Available Purpose. To study the characteristics of the local network with the marker method of access to the bus its modified simulation model was developed. Methodology. Defining characteristics of the network is carried out on the developed simulation model, which is based on the state diagram-layer network station with the mechanism of processing priorities, both in steady state and in the performance of control procedures: the initiation of a logical ring, the entrance and exit of the station network with a logical ring. Findings. A simulation model, on the basis of which can be obtained the dependencies of the application the maximum waiting time in the queue for different classes of access, and the reaction time usable bandwidth on the data rate, the number of network stations, the generation rate applications, the number of frames transmitted per token holding time, frame length was developed. Originality. The technique of network simulation reflecting its work in the steady condition and during the control procedures, the mechanism of priority ranking and handling was proposed. Practical value. Defining network characteristics in the real-time systems on railway transport based on the developed simulation model.

  6. Antimyosin imaging in cardiac transplant rejection

    International Nuclear Information System (INIS)

    Johnson, L.L.; Cannon, P.J.

    1991-01-01

    Fab fragments of antibodies specific for cardiac myosin have been labeled with indium-111 and injected intravenously into animals and into patients with heart transplants. The antibodies, developed by Khaw, Haber, and co-workers, localize in cardiac myocytes that have been damaged irreversibly by ischemia, myocarditis, or the rejection process. After clearance of the labeled antibody from the cardiac blood pool, planar imaging or single photon emission computed tomography is performed. Scintigrams reveal the uptake of the labeled antimyosin in areas of myocardium undergoing transplant rejection. In animal studies, the degree of antimyosin uptake appears to correlate significantly with the degree of rejection assessed at necropsy. In patients, the correlation between scans and pathologic findings from endomyocardial biopsy is not as good, possibly because of sampling error in the endomyocardial biopsy technique. The scan results at 1 year correlate with either late complications (positive) or benign course (negative). Current limitations of the method include slow blood clearance, long half-life of indium-111, and hepatic uptake. Overcoming these limitations represents a direction for current research. It is possible that from these efforts a noninvasive approach to the diagnosis and evaluation of cardiac transplantation may evolve that will decrease the number of endomyocardial biopsies required to evaluate rejection. This would be particularly useful in infants and children. 31 references

  7. Stories in Networks and Networks in Stories: A Tri-Modal Model for Mixed-Methods Social Network Research on Teachers

    Science.gov (United States)

    Baker-Doyle, Kira J.

    2015-01-01

    Social network research on teachers and schools has risen exponentially in recent years as an innovative method to reveal the role of social networks in education. However, scholars are still exploring ways to incorporate traditional quantitative methods of Social Network Analysis (SNA) with qualitative approaches to social network research. This…

  8. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  9. Utilization of Selected Data Mining Methods for Communication Network Analysis

    Directory of Open Access Journals (Sweden)

    V. Ondryhal

    2011-06-01

    Full Text Available The aim of the project was to analyze the behavior of military communication networks based on work with real data collected continuously since 2005. With regard to the nature and amount of the data, data mining methods were selected for the purpose of analyses and experiments. The quality of real data is often insufficient for an immediate analysis. The article presents the data cleaning operations which have been carried out with the aim to improve the input data sample to obtain reliable models. Gradually, by means of properly chosen SW, network models were developed to verify generally valid patterns of network behavior as a bulk service. Furthermore, unlike the commercially available communication networks simulators, the models designed allowed us to capture nonstandard models of network behavior under an increased load, verify the correct sizing of the network to the increased load, and thus test its reliability. Finally, based on previous experience, the models enabled us to predict emergency situations with a reasonable accuracy.

  10. A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks

    Directory of Open Access Journals (Sweden)

    Ke-yan Liu

    2017-05-01

    Full Text Available This work presents a method for distribution network reconfiguration with the simultaneous consideration of distributed generation (DG allocation. The uncertainties of load fluctuation before the network reconfiguration are also considered. Three optimal objectives, including minimal line loss cost, minimum Expected Energy Not Supplied, and minimum switch operation cost, are investigated. The multi-objective optimization problem is further transformed into a single-objective optimization problem by utilizing weighting factors. The proposed network reconfiguration method includes two periods. The first period is to create a feasible topology network by using binary particle swarm optimization (BPSO. Then the DG allocation problem is solved by utilizing sensitivity analysis and a Harmony Search algorithm (HSA. In the meanwhile, interval analysis is applied to deal with the uncertainties of load and devices parameters. Test cases are studied using the standard IEEE 33-bus and PG&E 69-bus systems. Different scenarios and comparisons are analyzed in the experiments. The results show the applicability of the proposed method. The performance analysis of the proposed method is also investigated. The computational results indicate that the proposed network reconfiguration algorithm is feasible.

  11. A hierarchical network modeling method for railway tunnels safety assessment

    Science.gov (United States)

    Zhou, Jin; Xu, Weixiang; Guo, Xin; Liu, Xumin

    2017-02-01

    Using network theory to model risk-related knowledge on accidents is regarded as potential very helpful in risk management. A large amount of defects detection data for railway tunnels is collected in autumn every year in China. It is extremely important to discover the regularities knowledge in database. In this paper, based on network theories and by using data mining techniques, a new method is proposed for mining risk-related regularities to support risk management in railway tunnel projects. A hierarchical network (HN) model which takes into account the tunnel structures, tunnel defects, potential failures and accidents is established. An improved Apriori algorithm is designed to rapidly and effectively mine correlations between tunnel structures and tunnel defects. Then an algorithm is presented in order to mine the risk-related regularities table (RRT) from the frequent patterns. At last, a safety assessment method is proposed by consideration of actual defects and possible risks of defects gained from the RRT. This method cannot only generate the quantitative risk results but also reveal the key defects and critical risks of defects. This paper is further development on accident causation network modeling methods which can provide guidance for specific maintenance measure.

  12. Method and device for monitoring distortion in an optical network

    NARCIS (Netherlands)

    2012-01-01

    A method and a device for monitoring of distortion in an optical network are provided, wherein at least one reference signal and at least one data signal are conveyed via an optical link and wherein a distortion of the at least one data signal is determined based on the at least one reference

  13. Improvement on the Performance of Canal Network and Method of ...

    African Journals Online (AJOL)

    This paper presents the required improvement on the performance of canal network and method of on-farm water application systems at Tunga-Kawo irrigation scheme, Wushishi, Niger state. The problems of poor delivery of water to the farmland were identified to include erosion of canal embarkment, lack of water ...

  14. Peer victimization and peer rejection during early childhood

    Science.gov (United States)

    Godleski, Stephanie A.; Kamper, Kimberly E.; Ostrov, Jamie M.; Hart, Emily J.; Blakely-McClure, Sarah J.

    2014-01-01

    Objective The development and course of the subtypes of peer victimization is a relatively understudied topic despite the association of victimization with important developmental and clinical outcomes. Moreover, understanding potential predictors, such as peer rejection and emotion regulation, in early childhood may be especially important to elucidate possible bi-directional pathways between relational and physical victimization and rejection. The current study (N = 97) was designed to explore several gaps and limitations in the peer victimization and peer rejection literature. In particular, the prospective associations between relational and physical victimization and peer rejection over the course of 3.5 months during early childhood (i.e., 3- to 5- years-old) were investigated in an integrated model. Method The study consisted of 97 (42 girls) preschool children recruited from four early childhood schools in the northeast of the US. Using observations, research assistant report and teacher report, relational and physical aggression, relational and physical victimization, peer rejection, and emotion regulation were measured in a short-term longitudinal study. Path analyses were conducted to test the overall hypothesized model. Results Peer rejection was found to predict increases in relational victimization. In addition, emotion regulation was found to predict decreases in peer rejection and physical victimization. Conclusions Implications for research and practice are discussed, including teaching coping strategies for peer rejection and emotional distress. PMID:25133659

  15. Prevalence and characteristics of foal rejection in Arabian mares.

    Science.gov (United States)

    Juarbe-Díaz, S V; Houpt, K A; Kusunose, R

    1998-09-01

    Separate surveys of Thoroughbred, Paint, and Arabian mare owners revealed a higher than expected rate of foal rejection in Arabian mares. A behavioural history form was submitted by owners of foal rejecting and nonrejecting Arabian mares, and maternal behaviour and management practices compared. Four generation pedigrees of rejecting and nonrejecting Arabian mares were also examined. Foal rejecting mares were more likely to avoid, threaten, squeal at, chase, bite, and kick their foals post partum than nonrejecting mares. Nonrejecting mares were more likely to lick, nicker and defend their foals post partum than rejecting mares. No statistically significant relationship was found between foal rejection and the type of breeding method (natural vs. artificial insemination), the presence of people at birth, the presence of nearby horses at birth, or assistance of the first nursing bout. The presence at least once of 1 of 2 related sires was statistically higher in the pedigrees of rejecting vs. nonrejecting mares. Inherited and learned or environmental factors are likely to affect the expression of foal rejection behaviour.

  16. Load management in electrical networks. Objectives, methods, prospects

    International Nuclear Information System (INIS)

    Gabioud, D.

    2008-01-01

    This illustrated article takes up the problems related to the variation of the load in electricity networks. How to handle the peak load? Different solutions in the energy demand management are discussed. Method based on the price, method based on the reduction of the load by electric utilities. Information systems are presented which gives the consumer the needed data to participate in the local load management.

  17. Quartet-based methods to reconstruct phylogenetic networks.

    Science.gov (United States)

    Yang, Jialiang; Grünewald, Stefan; Xu, Yifei; Wan, Xiu-Feng

    2014-02-20

    Phylogenetic networks are employed to visualize evolutionary relationships among a group of nucleotide sequences, genes or species when reticulate events like hybridization, recombination, reassortant and horizontal gene transfer are believed to be involved. In comparison to traditional distance-based methods, quartet-based methods consider more information in the reconstruction process and thus have the potential to be more accurate. We introduce QuartetSuite, which includes a set of new quartet-based methods, namely QuartetS, QuartetA, and QuartetM, to reconstruct phylogenetic networks from nucleotide sequences. We tested their performances and compared them with other popular methods on two simulated nucleotide sequence data sets: one generated from a tree topology and the other from a complicated evolutionary history containing three reticulate events. We further validated these methods to two real data sets: a bacterial data set consisting of seven concatenated genes of 36 bacterial species and an influenza data set related to recently emerging H7N9 low pathogenic avian influenza viruses in China. QuartetS, QuartetA, and QuartetM have the potential to accurately reconstruct evolutionary scenarios from simple branching trees to complicated networks containing many reticulate events. These methods could provide insights into the understanding of complicated biological evolutionary processes such as bacterial taxonomy and reassortant of influenza viruses.

  18. Constructing financial network based on PMFG and threshold method

    Science.gov (United States)

    Nie, Chun-Xiao; Song, Fu-Tie

    2018-04-01

    Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low-dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis.

  19. Neural network and area method interpretation of pulsed experiments

    Energy Technology Data Exchange (ETDEWEB)

    Dulla, S.; Picca, P.; Ravetto, P. [Politecnico di Torino, Dipartimento di Energetica, Corso Duca degli Abruzzi, 24 - 10129 Torino (Italy); Canepa, S. [Lab of Reactor Physics and Systems Behaviour LRS, Paul Scherrer Inst., 5232 Villigen (Switzerland)

    2012-07-01

    The determination of the subcriticality level is an important issue in accelerator-driven system technology. The area method, originally introduced by N. G. Sjoestrand, is a classical technique to interpret flux measurement for pulsed experiments in order to reconstruct the reactivity value. In recent times other methods have also been developed, to account for spatial and spectral effects, which were not included in the area method, since it is based on the point kinetic model. The artificial neural network approach can be an efficient technique to infer reactivities from pulsed experiments. In the present work, some comparisons between the two methods are carried out and discussed. (authors)

  20. A novel word spotting method based on recurrent neural networks.

    Science.gov (United States)

    Frinken, Volkmar; Fischer, Andreas; Manmatha, R; Bunke, Horst

    2012-02-01

    Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition.

  1. Neural node network and model, and method of teaching same

    Science.gov (United States)

    Parlos, Alexander G. (Inventor); Atiya, Amir F. (Inventor); Fernandez, Benito (Inventor); Tsai, Wei K. (Inventor); Chong, Kil T. (Inventor)

    1995-01-01

    The present invention is a fully connected feed forward network that includes at least one hidden layer 16. The hidden layer 16 includes nodes 20 in which the output of the node is fed back to that node as an input with a unit delay produced by a delay device 24 occurring in the feedback path 22 (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit 36 from all the other nodes within the same layer 16. The node performs a transfer function operation based on the inputs from the previous layer and the delayed outputs. The network can be implemented as analog or digital or within a general purpose processor. Two teaching methods can be used: (1) back propagation of weight calculation that includes the local feedback and the crosstalk or (2) more preferably a feed forward gradient decent which immediately follows the output computations and which also includes the local feedback and the crosstalk. Subsequent to the gradient propagation, the weights can be normalized, thereby preventing convergence to a local optimum. Education of the network can be incremental both on and off-line. An educated network is suitable for modeling and controlling dynamic nonlinear systems and time series systems and predicting the outputs as well as hidden states and parameters. The educated network can also be further educated during on-line processing.

  2. Active disturbance rejection controller for chemical reactor

    International Nuclear Information System (INIS)

    Both, Roxana; Dulf, Eva H.; Muresan, Cristina I.

    2015-01-01

    In the petrochemical industry, the synthesis of 2 ethyl-hexanol-oxo-alcohols (plasticizers alcohol) is of high importance, being achieved through hydrogenation of 2 ethyl-hexenal inside catalytic trickle bed three-phase reactors. For this type of processes the use of advanced control strategies is suitable due to their nonlinear behavior and extreme sensitivity to load changes and other disturbances. Due to the complexity of the mathematical model an approach was to use a simple linear model of the process in combination with an advanced control algorithm which takes into account the model uncertainties, the disturbances and command signal limitations like robust control. However the resulting controller is complex, involving cost effective hardware. This paper proposes a simple integer-order control scheme using a linear model of the process, based on active disturbance rejection method. By treating the model dynamics as a common disturbance and actively rejecting it, active disturbance rejection control (ADRC) can achieve the desired response. Simulation results are provided to demonstrate the effectiveness of the proposed method

  3. Active disturbance rejection controller for chemical reactor

    Energy Technology Data Exchange (ETDEWEB)

    Both, Roxana; Dulf, Eva H.; Muresan, Cristina I., E-mail: roxana.both@aut.utcluj.ro [Technical University of Cluj-Napoca, 400114 Cluj-Napoca (Romania)

    2015-03-10

    In the petrochemical industry, the synthesis of 2 ethyl-hexanol-oxo-alcohols (plasticizers alcohol) is of high importance, being achieved through hydrogenation of 2 ethyl-hexenal inside catalytic trickle bed three-phase reactors. For this type of processes the use of advanced control strategies is suitable due to their nonlinear behavior and extreme sensitivity to load changes and other disturbances. Due to the complexity of the mathematical model an approach was to use a simple linear model of the process in combination with an advanced control algorithm which takes into account the model uncertainties, the disturbances and command signal limitations like robust control. However the resulting controller is complex, involving cost effective hardware. This paper proposes a simple integer-order control scheme using a linear model of the process, based on active disturbance rejection method. By treating the model dynamics as a common disturbance and actively rejecting it, active disturbance rejection control (ADRC) can achieve the desired response. Simulation results are provided to demonstrate the effectiveness of the proposed method.

  4. Phylogenetic comparative methods on phylogenetic networks with reticulations.

    Science.gov (United States)

    Bastide, Paul; Solís-Lemus, Claudia; Kriebel, Ricardo; Sparks, K William; Ané, Cécile

    2018-04-25

    The goal of Phylogenetic Comparative Methods (PCMs) is to study the distribution of quantitative traits among related species. The observed traits are often seen as the result of a Brownian Motion (BM) along the branches of a phylogenetic tree. Reticulation events such as hybridization, gene flow or horizontal gene transfer, can substantially affect a species' traits, but are not modeled by a tree. Phylogenetic networks have been designed to represent reticulate evolution. As they become available for downstream analyses, new models of trait evolution are needed, applicable to networks. One natural extension of the BM is to use a weighted average model for the trait of a hybrid, at a reticulation point. We develop here an efficient recursive algorithm to compute the phylogenetic variance matrix of a trait on a network, in only one preorder traversal of the network. We then extend the standard PCM tools to this new framework, including phylogenetic regression with covariates (or phylogenetic ANOVA), ancestral trait reconstruction, and Pagel's λ test of phylogenetic signal. The trait of a hybrid is sometimes outside of the range of its two parents, for instance because of hybrid vigor or hybrid depression. These two phenomena are rather commonly observed in present-day hybrids. Transgressive evolution can be modeled as a shift in the trait value following a reticulation point. We develop a general framework to handle such shifts, and take advantage of the phylogenetic regression view of the problem to design statistical tests for ancestral transgressive evolution in the evolutionary history of a group of species. We study the power of these tests in several scenarios, and show that recent events have indeed the strongest impact on the trait distribution of present-day taxa. We apply those methods to a dataset of Xiphophorus fishes, to confirm and complete previous analysis in this group. All the methods developed here are available in the Julia package PhyloNetworks.

  5. New knowledge network evaluation method for design rationale management

    Science.gov (United States)

    Jing, Shikai; Zhan, Hongfei; Liu, Jihong; Wang, Kuan; Jiang, Hao; Zhou, Jingtao

    2015-01-01

    Current design rationale (DR) systems have not demonstrated the value of the approach in practice since little attention is put to the evaluation method of DR knowledge. To systematize knowledge management process for future computer-aided DR applications, a prerequisite is to provide the measure for the DR knowledge. In this paper, a new knowledge network evaluation method for DR management is presented. The method characterizes the DR knowledge value from four perspectives, namely, the design rationale structure scale, association knowledge and reasoning ability, degree of design justification support and degree of knowledge representation conciseness. The DR knowledge comprehensive value is also measured by the proposed method. To validate the proposed method, different style of DR knowledge network and the performance of the proposed measure are discussed. The evaluation method has been applied in two realistic design cases and compared with the structural measures. The research proposes the DR knowledge evaluation method which can provide object metric and selection basis for the DR knowledge reuse during the product design process. In addition, the method is proved to be more effective guidance and support for the application and management of DR knowledge.

  6. A rapid protection switching method in carrier ethernet ring networks

    Science.gov (United States)

    Yuan, Liang; Ji, Meng

    2008-11-01

    Abstract: Ethernet is the most important Local Area Network (LAN) technology since more than 90% data traffic in access layer is carried on Ethernet. From 10M to 10G, the improving Ethernet technology can be not only used in LAN, but also a good choice for MAN even WAN. MAN are always constructed in ring topology because the ring network could provide resilient path protection by using less resource (fibre or cable) than other network topologies. In layer 2 data networks, spanning tree protocol (STP) is always used to protect transmit link and preventing the formation of logic loop in networks. However, STP cannot guarantee the efficiency of service convergence when link fault happened. In fact, convergent time of networks with STP is about several minutes. Though Rapid Spanning Tree Protocol (RSTP) and Multi-Spanning Tree Protocol (MSTP) improve the STP technology, they still need a couple of seconds to achieve convergence, and can not provide sub-50ms protection switching. This paper presents a novel rapid ring protection method (RRPM) for carrier Ethernet. Unlike other link-fault detection method, it adopts distributed algorithm to detect link fault rapidly (sub-50ms). When networks restore from link fault, it can revert to the original working state. RRPM can provide single ring protection and interconnected ring protection without the formation of super loop. In normal operation, the master node blocks the secondary port for all non-RRPM Ethernet frames belonging to the given RRPM Ring, thereby avoiding a loop in the ring. When link fault happens, the node on which the failure happens moves from the "ring normal" state to the "ring fault" state. It also sends "link down" frame immediately to other nodes and blocks broken port and flushes its forwarding database. Those who receive "link down" frame will flush forwarding database and master node should unblock its secondary port. When the failure restores, the whole ring will revert to the normal state. That is

  7. BinAligner: a heuristic method to align biological networks.

    Science.gov (United States)

    Yang, Jialiang; Li, Jun; Grünewald, Stefan; Wan, Xiu-Feng

    2013-01-01

    The advances in high throughput omics technologies have made it possible to characterize molecular interactions within and across various species. Alignments and comparison of molecular networks across species will help detect orthologs and conserved functional modules and provide insights on the evolutionary relationships of the compared species. However, such analyses are not trivial due to the complexity of network and high computational cost. Here we develop a mixture of global and local algorithm, BinAligner, for network alignments. Based on the hypotheses that the similarity between two vertices across networks would be context dependent and that the information from the edges and the structures of subnetworks can be more informative than vertices alone, two scoring schema, 1-neighborhood subnetwork and graphlet, were introduced to derive the scoring matrices between networks, besides the commonly used scoring scheme from vertices. Then the alignment problem is formulated as an assignment problem, which is solved by the combinatorial optimization algorithm, such as the Hungarian method. The proposed algorithm was applied and validated in aligning the protein-protein interaction network of Kaposi's sarcoma associated herpesvirus (KSHV) and that of varicella zoster virus (VZV). Interestingly, we identified several putative functional orthologous proteins with similar functions but very low sequence similarity between the two viruses. For example, KSHV open reading frame 56 (ORF56) and VZV ORF55 are helicase-primase subunits with sequence identity 14.6%, and KSHV ORF75 and VZV ORF44 are tegument proteins with sequence identity 15.3%. These functional pairs can not be identified if one restricts the alignment into orthologous protein pairs. In addition, BinAligner identified a conserved pathway between two viruses, which consists of 7 orthologous protein pairs and these proteins are connected by conserved links. This pathway might be crucial for virus packing and

  8. Relabeling exchange method (REM) for learning in neural networks

    Science.gov (United States)

    Wu, Wen; Mammone, Richard J.

    1994-02-01

    The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.

  9. An outer approximation method for the road network design problem.

    Science.gov (United States)

    Asadi Bagloee, Saeed; Sarvi, Majid

    2018-01-01

    Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint. It results in a mixed-integer nonlinear programming (MINLP) problem which is then solved using the Outer Approximation (OA) algorithm (ii) we further relax the multi-commodity UE-TAP to a single-commodity MILP problem, that is, the multiple OD pairs are aggregated to a single OD pair. This methodology has two main advantages: (i) the method is proven to be highly efficient to solve the DNDP for a large-sized network of Winnipeg, Canada. The results suggest that within a limited number of iterations (as termination criterion), global optimum solutions are quickly reached in most of the cases; otherwise, good solutions (close to global optimum solutions) are found in early iterations. Comparative analysis of the networks of Gao and Sioux-Falls shows that for such a non-exact method the global optimum solutions are found in fewer iterations than those found in some analytically exact algorithms in the literature. (ii) Integration of the objective function among the constraints provides a commensurate capability to tackle the multi-objective (or multi-criteria) DNDP as well.

  10. The Application of Auto-Disturbance Rejection Control Optimized by Least Squares Support Vector Machines Method and Time-Frequency Representation in Voltage Source Converter-High Voltage Direct Current System.

    Science.gov (United States)

    Liu, Ying-Pei; Liang, Hai-Ping; Gao, Zhong-Ke

    2015-01-01

    In order to improve the performance of voltage source converter-high voltage direct current (VSC-HVDC) system, we propose an improved auto-disturbance rejection control (ADRC) method based on least squares support vector machines (LSSVM) in the rectifier side. Firstly, we deduce the high frequency transient mathematical model of VSC-HVDC system. Then we investigate the ADRC and LSSVM principles. We ignore the tracking differentiator in the ADRC controller aiming to improve the system dynamic response speed. On this basis, we derive the mathematical model of ADRC controller optimized by LSSVM for direct current voltage loop. Finally we carry out simulations to verify the feasibility and effectiveness of our proposed control method. In addition, we employ the time-frequency representation methods, i.e., Wigner-Ville distribution (WVD) and adaptive optimal kernel (AOK) time-frequency representation, to demonstrate our proposed method performs better than the traditional method from the perspective of energy distribution in time and frequency plane.

  11. The Application of Auto-Disturbance Rejection Control Optimized by Least Squares Support Vector Machines Method and Time-Frequency Representation in Voltage Source Converter-High Voltage Direct Current System.

    Directory of Open Access Journals (Sweden)

    Ying-Pei Liu

    Full Text Available In order to improve the performance of voltage source converter-high voltage direct current (VSC-HVDC system, we propose an improved auto-disturbance rejection control (ADRC method based on least squares support vector machines (LSSVM in the rectifier side. Firstly, we deduce the high frequency transient mathematical model of VSC-HVDC system. Then we investigate the ADRC and LSSVM principles. We ignore the tracking differentiator in the ADRC controller aiming to improve the system dynamic response speed. On this basis, we derive the mathematical model of ADRC controller optimized by LSSVM for direct current voltage loop. Finally we carry out simulations to verify the feasibility and effectiveness of our proposed control method. In addition, we employ the time-frequency representation methods, i.e., Wigner-Ville distribution (WVD and adaptive optimal kernel (AOK time-frequency representation, to demonstrate our proposed method performs better than the traditional method from the perspective of energy distribution in time and frequency plane.

  12. Network Theory and Effects of Transcranial Brain Stimulation Methods on the Brain Networks

    Directory of Open Access Journals (Sweden)

    Sema Demirci

    2014-12-01

    Full Text Available In recent years, there has been a shift from classic localizational approaches to new approaches where the brain is considered as a complex system. Therefore, there has been an increase in the number of studies involving collaborations with other areas of neurology in order to develop methods to understand the complex systems. One of the new approaches is graphic theory that has principles based on mathematics and physics. According to this theory, the functional-anatomical connections of the brain are defined as a network. Moreover, transcranial brain stimulation techniques are amongst the recent research and treatment methods that have been commonly used in recent years. Changes that occur as a result of applying brain stimulation techniques on physiological and pathological networks help better understand the normal and abnormal functions of the brain, especially when combined with techniques such as neuroimaging and electroencephalography. This review aims to provide an overview of the applications of graphic theory and related parameters, studies conducted on brain functions in neurology and neuroscience, and applications of brain stimulation systems in the changing treatment of brain network models and treatment of pathological networks defined on the basis of this theory.

  13. Acute Hepatic Allograft Rejection in Pediatric Recipients: Independent Factors

    OpenAIRE

    Dehghani, S. M.; Shahramian, I.; Afshari, M.; Bahmanyar, M.; Ataollahi, M.; Sargazi, A.

    2017-01-01

    Background: Acute cellular rejection (ACR) has a reversible effect on graft and its survival. Objective: To evaluate the relation between ACR and clinical factors in recipients of liver transplant allografts. Methods: 47 consecutive liver recipients were retrospectively studied. Their data were extracted from records and analyzed. Results: 38 (81%) of the 47 recipients experienced ACR during a 24-month follow-up. The rate of rejection was associated with none of the studied factors—recipient’...

  14. Novel method for fog monitoring using cellular networks infrastructures

    Science.gov (United States)

    David, N.; Alpert, P.; Messer, H.

    2012-08-01

    A major detrimental effect of fog is visibility limitation which can result in serious transportation accidents, traffic delays and therefore economic damage. Existing monitoring techniques including satellites, transmissometers and human observers - suffer from low spatial resolution, high cost or lack of precision when measuring near ground level. Here we show a novel technique for fog monitoring using wireless communication systems. Communication networks widely deploy commercial microwave links across the terrain at ground level. Operating at frequencies of tens of GHz they are affected by fog and are, effectively, an existing, spatially world-wide distributed sensor network that can provide crucial information about fog concentration and visibility. Fog monitoring potential is demonstrated for a heavy fog event that took place in Israel. The correlation between transmissomters and human eye observations to the visibility estimates from the nearby microwave links was found to be 0.53 and 0.61, respectively. These values indicate the high potential of the proposed method.

  15. Distance Based Method for Outlier Detection of Body Sensor Networks

    Directory of Open Access Journals (Sweden)

    Haibin Zhang

    2016-01-01

    Full Text Available We propose a distance based method for the outlier detection of body sensor networks. Firstly, we use a Kernel Density Estimation (KDE to calculate the probability of the distance to k nearest neighbors for diagnosed data. If the probability is less than a threshold, and the distance of this data to its left and right neighbors is greater than a pre-defined value, the diagnosed data is decided as an outlier. Further, we formalize a sliding window based method to improve the outlier detection performance. Finally, to estimate the KDE by training sensor readings with errors, we introduce a Hidden Markov Model (HMM based method to estimate the most probable ground truth values which have the maximum probability to produce the training data. Simulation results show that the proposed method possesses a good detection accuracy with a low false alarm rate.

  16. Development of a magnet power supply with sub-ppm ripple performance for J-PARC with a novel common-mode rejection method with an NPC inverter

    International Nuclear Information System (INIS)

    Koseki, K.; Kurimoto, Y.

    2014-01-01

    The mechanism that generates common-mode noise in inverter circuits, which are widely used in magnet power supplies, was evaluated by a circuit simulation. By following asymmetric operational sequences, pulsed voltage is applied to the parasitic capacitance of power cables that causes a common-mode current at each switching period of the semiconductor switches. Common-mode noise was also found to disturb the normal-mode excitation current by inducing higher frequency components in the applied voltage to the magnet. To eliminate the disturbing effect by the common-mode noise, a newly developed operational method that uses a neutral point clamped, NPC, inverter with reduced switching sequences was evaluated both by a circuit simulation and experimentally. The operational method for the NPC inverter could sufficiently reduce the common-mode noise. A high-power test operation performed using 16 bending magnets at the J-PARC facility achieved a ripple of less than 1 ppm in the excitation current

  17. Development of a magnet power supply with sub-ppm ripple performance for J-PARC with a novel common-mode rejection method with an NPC inverter

    Energy Technology Data Exchange (ETDEWEB)

    Koseki, K., E-mail: kunio.koseki@kek.jp; Kurimoto, Y.

    2014-05-21

    The mechanism that generates common-mode noise in inverter circuits, which are widely used in magnet power supplies, was evaluated by a circuit simulation. By following asymmetric operational sequences, pulsed voltage is applied to the parasitic capacitance of power cables that causes a common-mode current at each switching period of the semiconductor switches. Common-mode noise was also found to disturb the normal-mode excitation current by inducing higher frequency components in the applied voltage to the magnet. To eliminate the disturbing effect by the common-mode noise, a newly developed operational method that uses a neutral point clamped, NPC, inverter with reduced switching sequences was evaluated both by a circuit simulation and experimentally. The operational method for the NPC inverter could sufficiently reduce the common-mode noise. A high-power test operation performed using 16 bending magnets at the J-PARC facility achieved a ripple of less than 1 ppm in the excitation current.

  18. Efficient Pruning Method for Ensemble Self-Generating Neural Networks

    Directory of Open Access Journals (Sweden)

    Hirotaka Inoue

    2003-12-01

    Full Text Available Recently, multiple classifier systems (MCS have been used for practical applications to improve classification accuracy. Self-generating neural networks (SGNN are one of the suitable base-classifiers for MCS because of their simple setting and fast learning. However, the computation cost of the MCS increases in proportion to the number of SGNN. In this paper, we propose an efficient pruning method for the structure of the SGNN in the MCS. We compare the pruned MCS with two sampling methods. Experiments have been conducted to compare the pruned MCS with an unpruned MCS, the MCS based on C4.5, and k-nearest neighbor method. The results show that the pruned MCS can improve its classification accuracy as well as reducing the computation cost.

  19. Ted Madden's Network Methods: Applications to the Earth's Schumann Resonances

    Science.gov (United States)

    Williams, E. R.; Yu, H.

    2014-12-01

    Ted Madden made clever use of electrical circuit concepts throughout his long career in geophysical research: induced polarization, DC resistivity, magnetotellurics, Schumann resonances, the transport properties of rocks and even elasticity and the brittle failure of stressed rocks. The general methods on network analogies were presented in a terse monograph (Madden, 1972) which came to be called "The Grey Peril" by his students, named more for the challenge of deciphering the material as for the color of its cover. This talk will focus on Ted's first major use of the transmission line analogy in treating the Earth's Schumann resonances. This approach in Madden and Thompson (1965) provided a greatly simplified two-dimensional treatment of an electromagnetic problem with a notable three-dimensional structure. This skillful treatment that included the role of the Earth's magnetic field also led to predictions that the Schumann resonance energy would leak into space, predictions that have been verified nearly 50 years later in satellite observations. An extension of the network analogy by Nelson (1967) using Green's function methods provides a means to treat the inverse problem for the background Schumann resonances for the global lightning activity. The development of Madden's methods will be discussed along with concrete results based on them for the monitoring of global lightning.

  20. Complement and hyper acute rejection

    Directory of Open Access Journals (Sweden)

    Al-Rabia Mohammed

    2009-01-01

    Full Text Available Organ transplantation has been a major development in clinical medicine but its success has been marred by the immune system′s capacity to respond to "non-self" cells and tissues. A full molecular understanding of this mechanism and the myriad triggers for immune rejection is yet to be elucidated. Consequently, immunosuppressive drugs remain the mainstay of post-transplant ma-nagement; however, these interventions have side effects such as increased incidence of cancer, post-transplant lymphoproliferative disorders, susceptibility to infection if not managed appro-priately and the inconvenience to the patient of lifelong treatment. Novel therapeutic approaches based on molecular understanding of immunological processes are thus needed in this field. The notion that factors influencing successful transplants might be of use as therapeutic approaches is both scientifically and medically appealing. Recent developments in the understanding of successful transplants are expected to provide new opportunities for safer transplantation. This article reviews the present understanding of the molecular basis of rejection and the role of complement in this process as well as the possibility of generating "intelligent" therapy that better target crucial components of hyper-acute rejections.

  1. An accelerated training method for back propagation networks

    Science.gov (United States)

    Shelton, Robert O. (Inventor)

    1993-01-01

    The principal objective is to provide a training procedure for a feed forward, back propagation neural network which greatly accelerates the training process. A set of orthogonal singular vectors are determined from the input matrix such that the standard deviations of the projections of the input vectors along these singular vectors, as a set, are substantially maximized, thus providing an optimal means of presenting the input data. Novelty exists in the method of extracting from the set of input data, a set of features which can serve to represent the input data in a simplified manner, thus greatly reducing the time/expense to training the system.

  2. Time and Frequency Transfer and Dissemination Methods Using Optical Fiber Network

    National Research Council Canada - National Science Library

    Amemiya, Masaki; Imae, Michito; Fuji, Yasuhisa; Suzuyama, Tomonari; Ohshima, Shin-ichi

    2005-01-01

    ... developing. This study shows two methods using optical fiber networks to satisfy these demands. First, it is an economical remote calibration method using existing synchronous optical fiber communication networks...

  3. Efficient parsimony-based methods for phylogenetic network reconstruction.

    Science.gov (United States)

    Jin, Guohua; Nakhleh, Luay; Snir, Sagi; Tuller, Tamir

    2007-01-15

    Phylogenies--the evolutionary histories of groups of organisms-play a major role in representing relationships among biological entities. Although many biological processes can be effectively modeled as tree-like relationships, others, such as hybrid speciation and horizontal gene transfer (HGT), result in networks, rather than trees, of relationships. Hybrid speciation is a significant evolutionary mechanism in plants, fish and other groups of species. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Maximum parsimony is one of the most commonly used criteria for phylogenetic tree inference. Roughly speaking, inference based on this criterion seeks the tree that minimizes the amount of evolution. In 1990, Jotun Hein proposed using this criterion for inferring the evolution of sequences subject to recombination. Preliminary results on small synthetic datasets. Nakhleh et al. (2005) demonstrated the criterion's application to phylogenetic network reconstruction in general and HGT detection in particular. However, the naive algorithms used by the authors are inapplicable to large datasets due to their demanding computational requirements. Further, no rigorous theoretical analysis of computing the criterion was given, nor was it tested on biological data. In the present work we prove that the problem of scoring the parsimony of a phylogenetic network is NP-hard and provide an improved fixed parameter tractable algorithm for it. Further, we devise efficient heuristics for parsimony-based reconstruction of phylogenetic networks. We test our methods on both synthetic and biological data (rbcL gene in bacteria) and obtain very promising results.

  4. Experimental Evaluation of Interference Rejection Combining for 5G small cells,

    DEFF Research Database (Denmark)

    Assefa, Dereje; Berardinelli, Gilberto; Tavares, Fernando Menezes Leitão

    2015-01-01

    The Interference Rejection Combining (IRC) receiver can significantly boost the network throughput in scenarios characterized by dense uncoordinated deployment of small cells, as targeted by future 5th generation (5G) radio access technology. This paper presents an experimental study...

  5. Review of Congestion Management Methods for Distribution Networks with High Penetration of Distributed Energy Resources

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei; Liu, Zhaoxi

    2014-01-01

    This paper reviews the existing congestion management methods for distribution networks with high penetration of DERs documented in the recent research literatures. The congestion management methods for distribution networks reviewed can be grouped into two categories – market methods and direct...... control methods. The market methods consist of dynamic tariff, distribution capacity market, shadow price and flexible service market. The direct control methods are comprised of network reconfiguration, reactive power control and active power control. Based on the review of the existing methods...

  6. Experimental method to predict avalanches based on neural networks

    Directory of Open Access Journals (Sweden)

    V. V. Zhdanov

    2016-01-01

    Full Text Available The article presents results of experimental use of currently available statistical methods to classify the avalanche‑dangerous precipitations and snowfalls in the Kishi Almaty river basin. The avalanche service of Kazakhstan uses graphical methods for prediction of avalanches developed by I.V. Kondrashov and E.I. Kolesnikov. The main objective of this work was to develop a modern model that could be used directly at the avalanche stations. Classification of winter precipitations into dangerous snowfalls and non‑dangerous ones was performed by two following ways: the linear discriminant function (canonical analysis and artificial neural networks. Observational data on weather and avalanches in the gorge Kishi Almaty in the gorge Kishi Almaty were used as a training sample. Coefficients for the canonical variables were calculated by the software «Statistica» (Russian version 6.0, and then the necessary formula had been constructed. The accuracy of the above classification was 96%. Simulator by the authors L.N. Yasnitsky and F.М. Cherepanov was used to learn the neural networks. The trained neural network demonstrated 98% accuracy of the classification. Prepared statistical models are recommended to be tested at the snow‑avalanche stations. Results of the tests will be used for estimation of the model quality and its readiness for the operational work. In future, we plan to apply these models for classification of the avalanche danger by the five‑point international scale.

  7. 111-Indium-labelled platelets for diagnosis of acute kidney transplant rejection and monitoring of prostacyclin anti-rejection treatment

    International Nuclear Information System (INIS)

    Leithner, C.; Pohanka, E.; Schwarz, M.; Sinzinger, H.; Syre, G.

    1984-01-01

    33 patients were examined daily under a gamma camera after weekly injections of 111-In-labelled autologous platelets over a period of at least 4 weeks after transplantation. A group of 33 patients with long-term stable and well-functioning grafts served as controls. By means of a computerized recording technique, platelet trapping in the graft was measured and expressed as platelet-uptake index (PUI). The method worked well for the early diagnosis of acute rejection signified by an increase in PUI, accompanied by a shortening of platelet half life (t/2). 6 patients suffering from acute rejection received infusions of prostacyclin in addition to conventional high-dose methylprednisolone therapy. In 4 cases the PUI decreased again and an improvement in graft function was observed. Prostacyclin infusion treatment was applied also in 12 patients with histologically-proven chronic transplant rejection. Decreased platelet consumption by the graft and a temporary improvement in transplant function were achieved. We suggest that prostacyclin could enrich the possibilities of anti-rejection treatment by providing a tool for the suppression of platelet trapping in the graft. The platelet scan served as a useful method for the early detection of acute rejection, as well as the monitoring of prostacyclin anti-rejection treatment. (Autor)

  8. Preisach hysteresis implementation in reluctance network method, comparison with finite element method

    OpenAIRE

    Allag , Hicham; Kedous-Lebouc , Afef; Latreche , Mohamed E. H.

    2008-01-01

    International audience; In this work, an implementation of static magnetic hysteresis in the reluctance network method is presented and its effectiveness is demonstrated. This implementation is achieved by a succession of iterative steps in the form of algorithm explained and developed for simple examples. However it remains valid for any magnetic circuit. The results obtained are compared to those given by finite element method simulation and essentially the effect of relaxation is discussed...

  9. Social Causes and Consequences of Rejection Sensitivity

    Science.gov (United States)

    London, Bonita; Downey, Geraldine; Bonica, Cheryl; Paltin, Iris

    2007-01-01

    Predictions from the Rejection Sensitivity (RS) model concerning the social causes and consequences of RS were examined in a longitudinal study of 150 middle school students. Peer nominations of rejection, self-report measures of anxious and angry rejection expectations, and social anxiety, social withdrawal, and loneliness were assessed at two…

  10. 7 CFR 58.136 - Rejected milk.

    Science.gov (United States)

    2010-01-01

    ... 7 Agriculture 3 2010-01-01 2010-01-01 false Rejected milk. 58.136 Section 58.136 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Standards... Milk § 58.136 Rejected milk. A plant shall reject specific milk from a producer if the milk fails to...

  11. Peer Group Rejection and Children's Outgroup Prejudice

    Science.gov (United States)

    Nesdale, Drew; Durkin, Kevin; Maass, Anne; Kiesner, Jeff; Griffiths, Judith; Daly, Josh; McKenzie, David

    2010-01-01

    Two simulation studies examined the effect of peer group rejection on 7 and 9 year old children's outgroup prejudice. In Study 1, children (n = 88) pretended that they were accepted or rejected by their assigned group, prior to competing with a lower status outgroup. Results indicated that rejected versus accepted children showed increased…

  12. A method of network topology optimization design considering application process characteristic

    Science.gov (United States)

    Wang, Chunlin; Huang, Ning; Bai, Yanan; Zhang, Shuo

    2018-03-01

    Communication networks are designed to meet the usage requirements of users for various network applications. The current studies of network topology optimization design mainly considered network traffic, which is the result of network application operation, but not a design element of communication networks. A network application is a procedure of the usage of services by users with some demanded performance requirements, and has obvious process characteristic. In this paper, we first propose a method to optimize the design of communication network topology considering the application process characteristic. Taking the minimum network delay as objective, and the cost of network design and network connective reliability as constraints, an optimization model of network topology design is formulated, and the optimal solution of network topology design is searched by Genetic Algorithm (GA). Furthermore, we investigate the influence of network topology parameter on network delay under the background of multiple process-oriented applications, which can guide the generation of initial population and then improve the efficiency of GA. Numerical simulations show the effectiveness and validity of our proposed method. Network topology optimization design considering applications can improve the reliability of applications, and provide guidance for network builders in the early stage of network design, which is of great significance in engineering practices.

  13. Rejection index for pressure tubes

    International Nuclear Information System (INIS)

    Mitchell, A.B.; Meneley, D.

    1989-10-01

    The objective of the present study was to establish a set of criteria (or Rejection Index) which could be used to decide whether a zirconium-2 1/2 w/o niobium pressure tube in a CANDU reactor should be removed from service due to in-service degradation. A critique of key issues associated with establishing a realistic rejection index was prepared. Areas of uncertainty in available information were identified and recommendations for further analysis and laboratory testing made. A Rejection Index based on the following limits has been recommended: 1) Limits related to design intent and normal operation: any garter spring must remain within the tolerance band specified for its design location; the annulus gas system must normally be operated in a circulating mode with a procedure in place for purging to prevent accumulation of deuterium. It must remain sensitive to leaks into any part of the systems; and pressure tube dimensions and distortions must be limited to maintain the fuel channels within the original design intent; 2) Limits related to defect tolerance: adequate time margins between occurrence of a leaking crack and unstable failure must be demonstrated for all fuel channels; long lap-type flaws are unacceptable; crack-like defects of any size are unacceptable; and score marks, frat marks and other defects with contoured profiles must fall below certain depth, length and stress intensity limits; and 3) Limits related to property degradation: at operating temperature each pressure tube must be demonstrated to have a critical length in excess of a stipulated value; the maximum equivalent hydrogen level in any pressure tube should not exceed a limit which should be defined taking into account the known history of that tube; the maximum equivalent hydrogen level in any rolled joint should not exceed a limit which is presently recommended as 200 ppm equivalent hydrogen; and the maximum diametral creep strain should be limited to less than 5%

  14. Method for assessing reliability of a network considering probabilistic safety assessment

    International Nuclear Information System (INIS)

    Cepin, M.

    2005-01-01

    A method for assessment of reliability of the network is developed, which uses the features of the fault tree analysis. The method is developed in a way that the increase of the network under consideration does not require significant increase of the model. The method is applied to small examples of network consisting of a small number of nodes and a small number of their connections. The results give the network reliability. They identify equipment, which is to be carefully maintained in order that the network reliability is not reduced, and equipment, which is a candidate for redundancy, as this would improve network reliability significantly. (author)

  15. The Dissolved Oxygen Prediction Method Based on Neural Network

    Directory of Open Access Journals (Sweden)

    Zhong Xiao

    2017-01-01

    Full Text Available The dissolved oxygen (DO is oxygen dissolved in water, which is an important factor for the aquaculture. Using BP neural network method with the combination of purelin, logsig, and tansig activation functions is proposed for the prediction of aquaculture’s dissolved oxygen. The input layer, hidden layer, and output layer are introduced in detail including the weight adjustment process. The breeding data of three ponds in actual 10 consecutive days were used for experiments; these ponds were located in Beihai, Guangxi, a traditional aquaculture base in southern China. The data of the first 7 days are used for training, and the data of the latter 3 days are used for the test. Compared with the common prediction models, curve fitting (CF, autoregression (AR, grey model (GM, and support vector machines (SVM, the experimental results show that the prediction accuracy of the neural network is the highest, and all the predicted values are less than 5% of the error limit, which can meet the needs of practical applications, followed by AR, GM, SVM, and CF. The prediction model can help to improve the water quality monitoring level of aquaculture which will prevent the deterioration of water quality and the outbreak of disease.

  16. A Method to Design Synthetic Cell-Cycle Networks

    International Nuclear Information System (INIS)

    Ke-Ke, Miao

    2009-01-01

    The interactions among proteins, DNA and RNA in an organism form elaborate cell-cycle networks which govern cell growth and proliferation. Understanding the common structure of cell-cycle networks will be of great benefit to science research. Here, inspired by the importance of the cell-cycle regulatory network of yeast which has been studied intensively, we focus on small networks with 11 nodes, equivalent to that of the cell-cycle regulatory network used by Li et al. [Proc. Natl. Acad. Sci. USA 101(2004)4781] Using a Boolean model, we study the correlation between structure and function, and a possible common structure. It is found that cascade-like networks with a great number of interactions between nodes are stable. Based on these findings, we are able to construct synthetic networks that have the same functions as the cell-cycle regulatory network. (condensed matter: structure, mechanical and thermal properties)

  17. Fast Detection Method in Cooperative Cognitive Radio Networks

    Directory of Open Access Journals (Sweden)

    Zhengyi Li

    2010-01-01

    Full Text Available Cognitive Radio (CR technology improves the utilization of spectrum highly via opportunistic spectrum sharing, which requests fast detection as the spectrum utilization is dynamic. Taking into consideration the characteristic of wireless channels, we propose a fast detection scheme for a cooperative cognitive radio network, which consists of multiple CRs and a central control office. Specifically, each CR makes individual detection decision using the sequential probability ratio test combined with Neyman Pearson detection with respect to a specific observation window length. The proposed method upper bounds the detection delay. In addition, a weighted K out of N fusion rule is also proposed for the central control office to reach fast global decision based on the information collected from CRs, with more weights assigned for CRs with good channel conditions. Simulation results show that the proposed scheme can achieve fast detection while maintaining the detection accuracy.

  18. A Method for Assessing Quality of Service in Broadband Networks

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Riaz, M. Tahir; Pedersen, Jens Myrup

    2012-01-01

    Monitoring of Quality of Service (QoS) in high-speed Internet infrastructure is a challenging task. However, precise assessments must take into account the fact that the requirements for the given quality level are service-dependent. Backbone QoS monitoring and analysis requires processing of large...... taken from the description of system sockets. This paper proposes a new method for measuring the Quality of Service (QoS) level in broadband networks, based on our Volunteer-Based System for collecting the training data, Machine Learning Algorithms for generating the classification rules and application...... and provide C5.0 high-quality training data, divided into groups corresponding to different types of applications. It was found that currently existing means of collecting data (classification by ports, Deep Packet Inspection, statistical classification, public data sources) are not sufficient and they do...

  19. Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

    Directory of Open Access Journals (Sweden)

    Kang Xie

    2015-01-01

    Full Text Available According to the problems of current distributed architecture intrusion detection systems (DIDS, a new online distributed intrusion detection model based on cellular neural network (CNN was proposed, in which discrete-time CNN (DTCNN was used as weak classifier in each local node and state-controlled CNN (SCCNN was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation which allows the distributed intrusion detection to be performed better.

  20. Integrated circuit and method of arbitration in a network on an integrated circuit.

    NARCIS (Netherlands)

    2011-01-01

    The invention relates to an integrated circuit and to a method of arbitration in a network on an integrated circuit. According to the invention, a method of arbitration in a network on an integrated circuit is provided, the network comprising a router unit, the router unit comprising a first input

  1. Wireless Lan Network Security Method Wep (Wired Equivalent Privacy)

    OpenAIRE

    Ogy Charles Sario Tamawiwy; Nenny Anggraini, Skom., MT

    2008-01-01

    Scientific writing is about the WEP configuration on the WLAN network with RC4 encryption. The problem that arises is related aspects network security threats, strategic security and confidentiality of data in the network computer, the definition of encryption, definition kriptography algorithm, and algorithm functions.

  2. A joint classification method to integrate scientific and social networks

    NARCIS (Netherlands)

    Neshati, Mahmood; Asgari, Ehsaneddin; Hiemstra, Djoerd; Beigy, Hamid

    In this paper, we address the problem of scientific-social network integration to find a matching relationship between members of these networks. Utilizing several name similarity patterns and contextual properties of these networks, we design a focused crawler to find high probable matching pairs,

  3. Social Network Methods for the Educational and Psychological Sciences

    Science.gov (United States)

    Sweet, Tracy M.

    2016-01-01

    Social networks are especially applicable in educational and psychological studies involving social interactions. A social network is defined as a specific relationship among a group of individuals. Social networks arise in a variety of situations such as friendships among children, collaboration and advice seeking among teachers, and coauthorship…

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

    Science.gov (United States)

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

    2017-08-01

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

  5. Methods for extracting social network data from chatroom logs

    Science.gov (United States)

    Osesina, O. Isaac; McIntire, John P.; Havig, Paul R.; Geiselman, Eric E.; Bartley, Cecilia; Tudoreanu, M. Eduard

    2012-06-01

    Identifying social network (SN) links within computer-mediated communication platforms without explicit relations among users poses challenges to researchers. Our research aims to extract SN links in internet chat with multiple users engaging in synchronous overlapping conversations all displayed in a single stream. We approached this problem using three methods which build on previous research. Response-time analysis builds on temporal proximity of chat messages; word context usage builds on keywords analysis and direct addressing which infers links by identifying the intended message recipient from the screen name (nickname) referenced in the message [1]. Our analysis of word usage within the chat stream also provides contexts for the extracted SN links. To test the capability of our methods, we used publicly available data from Internet Relay Chat (IRC), a real-time computer-mediated communication (CMC) tool used by millions of people around the world. The extraction performances of individual methods and their hybrids were assessed relative to a ground truth (determined a priori via manual scoring).

  6. Control of beam halo-chaos using neural network self-adaptation method

    International Nuclear Information System (INIS)

    Fang Jinqing; Huang Guoxian; Luo Xiaoshu

    2004-11-01

    Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

  7. A Capacity Dimensioning Method for Broadband Distribution Networks

    DEFF Research Database (Denmark)

    Shawky, Ahmed; Pedersen, Jens Myrup; Bergheim, Hans

    2010-01-01

    This paper presents capacity dimensioning for a hypothetical distribution network in the Danish municipality of Aalborg. The number of customers in need for a better service level and the continuous increase in network traffic makes it harder for ISPs to deliver high levels of service to their cu......This paper presents capacity dimensioning for a hypothetical distribution network in the Danish municipality of Aalborg. The number of customers in need for a better service level and the continuous increase in network traffic makes it harder for ISPs to deliver high levels of service...... to their customers. This paper starts by defining three levels of services, together with traffic demands based on research of traffic distribution and generation in networks. Calculations for network dimension are then calculated. The results from the dimensioning are used to compare different network topologies...

  8. Fuzzy Entropy Method for Quantifying Supply Chain Networks Complexity

    Science.gov (United States)

    Zhang, Jihui; Xu, Junqin

    Supply chain is a special kind of complex network. Its complexity and uncertainty makes it very difficult to control and manage. Supply chains are faced with a rising complexity of products, structures, and processes. Because of the strong link between a supply chain’s complexity and its efficiency the supply chain complexity management becomes a major challenge of today’s business management. The aim of this paper is to quantify the complexity and organization level of an industrial network working towards the development of a ‘Supply Chain Network Analysis’ (SCNA). By measuring flows of goods and interaction costs between different sectors of activity within the supply chain borders, a network of flows is built and successively investigated by network analysis. The result of this study shows that our approach can provide an interesting conceptual perspective in which the modern supply network can be framed, and that network analysis can handle these issues in practice.

  9. Application of Network Analysis Method to VHTR core

    International Nuclear Information System (INIS)

    Lee, Jeong Hun; Yoon, Su Jong; Park, Goon Cherl

    2012-01-01

    A Very High Temperature Reactor (VHTR) is currently envisioned as a promising future reactor concept because of its high-efficiency and capability of generating hydrogen. Prismatic Modular Reactor (PMR) is one of the main VHTR concepts, which consists of hexagonal prismatic fuel blocks and reflector blocks made of nuclear grade graphite. However their shape could be changed by neutron damage during the reactor operation and the shape change can makes the gaps between the blocks inducing bypass flow. Most of reactor coolant flows through the coolant channel within the fuel block, but some portion of the reactor coolant bypasses to the interstitial gaps. The vertical gap and horizontal gap are called bypass gap and cross gap, respectively. CFD simulation for the full core of VHTR might be possible but it requires vast computational cost and time. Therefore, fast, flexible and reliable code is required to predict the flow distribution corresponding to the various bypass gap distribution. Consequently in this study, the flow network analysis method is applied to analyze the core flow of VHTR. The applied method was validated by comparing with SNU VHTR multiblock experiment. As a result, the calculated results show good agreements with experimental data although computational time and cost of the developed code was very small

  10. Portable Rule Extraction Method for Neural Network Decisions Reasoning

    Directory of Open Access Journals (Sweden)

    Darius PLIKYNAS

    2005-08-01

    Full Text Available Neural network (NN methods are sometimes useless in practical applications, because they are not properly tailored to the particular market's needs. We focus thereinafter specifically on financial market applications. NNs have not gained full acceptance here yet. One of the main reasons is the "Black Box" problem (lack of the NN decisions explanatory power. There are though some NN decisions rule extraction methods like decompositional, pedagogical or eclectic, but they suffer from low portability of the rule extraction technique across various neural net architectures, high level of granularity, algorithmic sophistication of the rule extraction technique etc. The authors propose to eliminate some known drawbacks using an innovative extension of the pedagogical approach. The idea is exposed by the use of a widespread MLP neural net (as a common tool in the financial problems' domain and SOM (input data space clusterization. The feedback of both nets' performance is related and targeted through the iteration cycle by achievement of the best matching between the decision space fragments and input data space clusters. Three sets of rules are generated algorithmically or by fuzzy membership functions. Empirical validation of the common financial benchmark problems is conducted with an appropriately prepared software solution.

  11. Measurement of company effectiveness using analytic network process method

    Directory of Open Access Journals (Sweden)

    Goran Janjić

    2017-07-01

    Full Text Available The sustainable development of an organisation is monitored through the organisation’s performance, which beforehand incorporates all stakeholders’ requirements in its strategy. The strategic management concept enables organisations to monitor and evaluate their effectiveness along with efficiency by monitoring of the implementation of set strategic goals. In the process of monitoring and measuring effectiveness, an organisation can use multiple-criteria decision-making methods as help. This study uses the method of analytic network process (ANP to define the weight factors of the mutual influences of all the important elements of an organisation’s strategy. The calculation of an organisation’s effectiveness is based on the weight factors and the degree of fulfilment of the goal values of the strategic map measures. New business conditions influence the changes in the importance of certain elements of an organisation’s business in relation to competitive advantage on the market, and on the market, increasing emphasis is given to non-material resources in the process of selection of the organisation’s most important measures.

  12. Measurement of company effectiveness using analytic network process method

    Science.gov (United States)

    Goran, Janjić; Zorana, Tanasić; Borut, Kosec

    2017-07-01

    The sustainable development of an organisation is monitored through the organisation's performance, which beforehand incorporates all stakeholders' requirements in its strategy. The strategic management concept enables organisations to monitor and evaluate their effectiveness along with efficiency by monitoring of the implementation of set strategic goals. In the process of monitoring and measuring effectiveness, an organisation can use multiple-criteria decision-making methods as help. This study uses the method of analytic network process (ANP) to define the weight factors of the mutual influences of all the important elements of an organisation's strategy. The calculation of an organisation's effectiveness is based on the weight factors and the degree of fulfilment of the goal values of the strategic map measures. New business conditions influence the changes in the importance of certain elements of an organisation's business in relation to competitive advantage on the market, and on the market, increasing emphasis is given to non-material resources in the process of selection of the organisation's most important measures.

  13. Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

    International Nuclear Information System (INIS)

    Zhou Gang; Ge Shengqi; Yang Li

    2010-01-01

    A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

  14. Methods for Reducing the Energy Consumption of Mobile Broadband Networks

    DEFF Research Database (Denmark)

    Micallef, Gilbert

    2010-01-01

    Up until recently, very little consideration has been given towards reducing the energy consumption of the networks supporting mobile communication. This has now become an important issue since with the predicted boost in traffic, network operators are required to upgrade and extend their networks......, increasing also their overall energy consumption. However, traffic analysis shows that during a 24 hour period, the volume of carried traffic varies continuously, with the network operating anywhere close to its full capacity for very short periods of time. The problem is that during hours of low network...... traffic the energy consumption remains high. This article proposes two major solutions for mitigating this problem. In the first case, an energy saving between 14% and 36% is observed by allowing the network to dynamically optimize its available capacity based on the traffic being carried. In the second...

  15. Stochastic model and method of zoning water networks

    OpenAIRE

    Тевяшев, Андрей Дмитриевич; Матвиенко, Ольга Ивановна

    2014-01-01

    Water consumption at different time of the day is uneven. The model of steady flow distribution in water-supply networks is calculated for maximum consumption and effectively used in the network design and reconstruction. Quasi-stationary modes, in which the parameters are random variables and vary relative to their mean values are more suitable for operational management and planning of rational network operation modes.Leaks, which sometimes exceed 50 % of the volume of water supplied, are o...

  16. Methods of Profile Cloning Detection in Online Social Networks

    Directory of Open Access Journals (Sweden)

    Zabielski Michał

    2016-01-01

    Full Text Available With the arrival of online social networks, the importance of privacy on the Internet has increased dramatically. Thus, it is important to develop mechanisms that will prevent our hidden personal data from unauthorized access and use. In this paper an attempt was made to present a concept of profile cloning detection in Online Social Networks (OSN using Graph and Networks Theory. By analysing structural similarity of network and value of attributes of user personal profile, we will be able to search for attackers which steal our identity.

  17. Role of Soluble ST2 as a Marker for Rejection after Heart Transplant

    OpenAIRE

    Lee, Ga Yeon; Choi, Jin-Oh; Ju, Eun-Seon; Lee, Yoo-Jung; Jeon, Eun-Seok

    2016-01-01

    Background and Objectives Endomyocardial biopsy is obligatory during the first year after heart transplant (HTx) for the surveillance of acute rejection. Previous attempts using cardiac biomarkers for the detection of rejection failed to show enough evidence to substitute endomyocardial biopsy. Therefore, this study sought the possibility of using soluble ST2 (sST2), a novel cardiovascular marker, as a surrogate marker for acute allograft rejection after HTx. Subjects and Methods A total of 4...

  18. A generic method for estimating system reliability using Bayesian networks

    International Nuclear Information System (INIS)

    Doguc, Ozge; Ramirez-Marquez, Jose Emmanuel

    2009-01-01

    This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples

  19. A generic method for estimating system reliability using Bayesian networks

    Energy Technology Data Exchange (ETDEWEB)

    Doguc, Ozge [Stevens Institute of Technology, Hoboken, NJ 07030 (United States); Ramirez-Marquez, Jose Emmanuel [Stevens Institute of Technology, Hoboken, NJ 07030 (United States)], E-mail: jmarquez@stevens.edu

    2009-02-15

    This study presents a holistic method for constructing a Bayesian network (BN) model for estimating system reliability. BN is a probabilistic approach that is used to model and predict the behavior of a system based on observed stochastic events. The BN model is a directed acyclic graph (DAG) where the nodes represent system components and arcs represent relationships among them. Although recent studies on using BN for estimating system reliability have been proposed, they are based on the assumption that a pre-built BN has been designed to represent the system. In these studies, the task of building the BN is typically left to a group of specialists who are BN and domain experts. The BN experts should learn about the domain before building the BN, which is generally very time consuming and may lead to incorrect deductions. As there are no existing studies to eliminate the need for a human expert in the process of system reliability estimation, this paper introduces a method that uses historical data about the system to be modeled as a BN and provides efficient techniques for automated construction of the BN model, and hence estimation of the system reliability. In this respect K2, a data mining algorithm, is used for finding associations between system components, and thus building the BN model. This algorithm uses a heuristic to provide efficient and accurate results while searching for associations. Moreover, no human intervention is necessary during the process of BN construction and reliability estimation. The paper provides a step-by-step illustration of the method and evaluation of the approach with literature case examples.

  20. Synthesis of unsymmetrical dimethylhydrazine oxalate from rejected liquid rocket propellant

    Science.gov (United States)

    Mu, Xiaogang; Yang, Jingjing; Zhang, Youzhi

    2018-02-01

    The rejected liquid propellant unsymmetrical dimethylhydrazine (UDMH) was converted to UDMH oxalate, which has commercial value. The UDMH oxalate structure and stability were investigated by the Fourier transform infrared spectroscopy, nuclear magnetic resonance spectroscopy, differential scanning calorimetry, and ultraviolet-visible spectrophotometric analysis. The results indicate that UDMH oxalate has good thermal and aqueous solution stability, a melting point of 144 °C, an initial decomposition temperature of 180 °C, and a peak wavelength of UV in aqueous solution at λ = 204 nm. This disposal method of rejected UDMH is highly efficient and environmentally safe.

  1. Kidney graft rejection studies with labeled platelets and lymphocytes

    International Nuclear Information System (INIS)

    Martin-Comin, J.

    1986-01-01

    The usefulness of In-111-labelled platelets and lymphocyte scintigraphy in acute kidney graft rejection is evaluated in 155 patients. Blood cells were labelled with 100-150 uCi of In-111-oxine and reinjected. Subsequently patients were scanned once daily from 2 hours post-reinjection up to a week. The graft/contralateral area activity ratio was calculated in all scans. It is concluded that In-111-labelled platelets scintigraphy is nowadays the method of choice for acute kidney graft rejection diagnosis, especially in patients under cyclosporine immunosuppression. (author)

  2. Statistical methods for studying the evolution of networks and behavior

    NARCIS (Netherlands)

    Schweinberger, Michael

    2007-01-01

    Studying longitudinal network and behavior data is important for understanding social processes, because human beings are interrelated, and the relationships among human beings (human networks) on one hand and human behavior on the other hand are not independent. The complex nature of longitudinal

  3. HEART ABNORMALITY CLASSIFICATIONS USING FOURIER TRANSFORMS METHOD AND NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Endah Purwanti

    2014-05-01

    Full Text Available Health problems with cardiovascular system disorder are still ranked high globally. One way to detect abnormalities in the cardiovascular system especially in the heart is through the electrocardiogram (ECG reading. However, reading ECG recording needs experience and expertise, software-based neural networks has designed to help identify any abnormalities ofthe heart through electrocardiogram digital image. This image is processed using image processing methods to obtain ordinate chart which representing the heart’s electrical potential. Feature extraction using Fourier transforms which are divided into several numbers of coefficients. As the software input, Fourier transforms coefficient have been normalized. Output of this software is divided into three classes, namely heart with atrial fibrillation, coronary heart disease and normal. Maximum accuracy rate ofthis software is 95.45%, with the distribution of the Fourier transform coefficients 1/8 and number of nodes 5, while minimum accuracy rate of this software at least 68.18% by distribution of the Fourier transform coefficients 1/32 and the number of nodes 32. Overall result accuracy rate of this software has an average of86.05% and standard deviation of7.82.

  4. CEO emotional bias and investment decision, Bayesian network method

    Directory of Open Access Journals (Sweden)

    Jarboui Anis

    2012-08-01

    Full Text Available This research examines the determinants of firms’ investment introducing a behavioral perspective that has received little attention in corporate finance literature. The following central hypothesis emerges from a set of recently developed theories: Investment decisions are influenced not only by their fundamentals but also depend on some other factors. One factor is the biasness of any CEO to their investment, biasness depends on the cognition and emotions, because some leaders use them as heuristic for the investment decision instead of fundamentals. This paper shows how CEO emotional bias (optimism, loss aversion and overconfidence affects the investment decisions. The proposed model of this paper uses Bayesian Network Method to examine this relationship. Emotional bias has been measured by means of a questionnaire comprising several items. As for the selected sample, it has been composed of some 100 Tunisian executives. Our results have revealed that the behavioral analysis of investment decision implies leader affected by behavioral biases (optimism, loss aversion, and overconfidence adjusts its investment choices based on their ability to assess alternatives (optimism and overconfidence and risk perception (loss aversion to create of shareholder value and ensure its place at the head of the management team.

  5. A novel Bayesian learning method for information aggregation in modular neural networks

    DEFF Research Database (Denmark)

    Wang, Pan; Xu, Lida; Zhou, Shang-Ming

    2010-01-01

    Modular neural network is a popular neural network model which has many successful applications. In this paper, a sequential Bayesian learning (SBL) is proposed for modular neural networks aiming at efficiently aggregating the outputs of members of the ensemble. The experimental results on eight...... benchmark problems have demonstrated that the proposed method can perform information aggregation efficiently in data modeling....

  6. Detecting Network Vulnerabilities Through Graph TheoreticalMethods

    Energy Technology Data Exchange (ETDEWEB)

    Cesarz, Patrick; Pomann, Gina-Maria; Torre, Luis de la; Villarosa, Greta; Flournoy, Tamara; Pinar, Ali; Meza Juan

    2007-09-30

    Identifying vulnerabilities in power networks is an important problem, as even a small number of vulnerable connections can cause billions of dollars in damage to a network. In this paper, we investigate a graph theoretical formulation for identifying vulnerabilities of a network. We first try to find the most critical components in a network by finding an optimal solution for each possible cutsize constraint for the relaxed version of the inhibiting bisection problem, which aims to find loosely coupled subgraphs with significant demand/supply mismatch. Then we investigate finding critical components by finding a flow assignment that minimizes the maximum among flow assignments on all edges. We also report experiments on IEEE 30, IEEE 118, and WSCC 179 benchmark power networks.

  7. Real-time method for establishing a detection map for a network of sensors

    Science.gov (United States)

    Nguyen, Hung D; Koch, Mark W; Giron, Casey; Rondeau, Daniel M; Russell, John L

    2012-09-11

    A method for establishing a detection map of a dynamically configurable sensor network. This method determines an appropriate set of locations for a plurality of sensor units of a sensor network and establishes a detection map for the network of sensors while the network is being set up; the detection map includes the effects of the local terrain and individual sensor performance. Sensor performance is characterized during the placement of the sensor units, which enables dynamic adjustment or reconfiguration of the placement of individual elements of the sensor network during network set-up to accommodate variations in local terrain and individual sensor performance. The reconfiguration of the network during initial set-up to accommodate deviations from idealized individual sensor detection zones improves the effectiveness of the sensor network in detecting activities at a detection perimeter and can provide the desired sensor coverage of an area while minimizing unintentional gaps in coverage.

  8. Multitask Learning-Based Security Event Forecast Methods for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Hui He

    2016-01-01

    Full Text Available Wireless sensor networks have strong dynamics and uncertainty, including network topological changes, node disappearance or addition, and facing various threats. First, to strengthen the detection adaptability of wireless sensor networks to various security attacks, a region similarity multitask-based security event forecast method for wireless sensor networks is proposed. This method performs topology partitioning on a large-scale sensor network and calculates the similarity degree among regional subnetworks. The trend of unknown network security events can be predicted through multitask learning of the occurrence and transmission characteristics of known network security events. Second, in case of lacking regional data, the quantitative trend of unknown regional network security events can be calculated. This study introduces a sensor network security event forecast method named Prediction Network Security Incomplete Unmarked Data (PNSIUD method to forecast missing attack data in the target region according to the known partial data in similar regions. Experimental results indicate that for an unknown security event forecast the forecast accuracy and effects of the similarity forecast algorithm are better than those of single-task learning method. At the same time, the forecast accuracy of the PNSIUD method is better than that of the traditional support vector machine method.

  9. Approximation Methods for Inference and Learning in Belief Networks: Progress and Future Directions

    National Research Council Canada - National Science Library

    Pazzan, Michael

    1997-01-01

    .... In this research project, we have investigated methods and implemented algorithms for efficiently making certain classes of inference in belief networks, and for automatically learning certain...

  10. Selection of variables for neural network analysis. Comparisons of several methods with high energy physics data

    International Nuclear Information System (INIS)

    Proriol, J.

    1994-01-01

    Five different methods are compared for selecting the most important variables with a view to classifying high energy physics events with neural networks. The different methods are: the F-test, Principal Component Analysis (PCA), a decision tree method: CART, weight evaluation, and Optimal Cell Damage (OCD). The neural networks use the variables selected with the different methods. We compare the percentages of events properly classified by each neural network. The learning set and the test set are the same for all the neural networks. (author)

  11. Research on the method of measuring space information network capacity in communication service

    Directory of Open Access Journals (Sweden)

    Zhu Shichao

    2017-02-01

    Full Text Available Because of the large scale characteristic of space information network in terms of space and time and the increasing of its complexity,existing measuring methods of information transmission capacity have been unable to measure the existing and future space information networkeffectively.In this study,we firstly established a complex model of space information network,and measured the whole space information network capacity by means of analyzing data access capability to the network and data transmission capability within the network.At last,we verified the rationality of the proposed measuring method by using STK and Matlab simulation software for collaborative simulation.

  12. A method for identifying hierarchical sub-networks / modules and weighting network links based on their similarity in sub-network / module affiliation

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-06-01

    Full Text Available Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.

  13. Approximation methods for the stability analysis of complete synchronization on duplex networks

    Science.gov (United States)

    Han, Wenchen; Yang, Junzhong

    2018-01-01

    Recently, the synchronization on multi-layer networks has drawn a lot of attention. In this work, we study the stability of the complete synchronization on duplex networks. We investigate effects of coupling function on the complete synchronization on duplex networks. We propose two approximation methods to deal with the stability of the complete synchronization on duplex networks. In the first method, we introduce a modified master stability function and, in the second method, we only take into consideration the contributions of a few most unstable transverse modes to the stability of the complete synchronization. We find that both methods work well for predicting the stability of the complete synchronization for small networks. For large networks, the second method still works pretty well.

  14. A rapid reliability estimation method for directed acyclic lifeline networks with statistically dependent components

    International Nuclear Information System (INIS)

    Kang, Won-Hee; Kliese, Alyce

    2014-01-01

    Lifeline networks, such as transportation, water supply, sewers, telecommunications, and electrical and gas networks, are essential elements for the economic and societal functions of urban areas, but their components are highly susceptible to natural or man-made hazards. In this context, it is essential to provide effective pre-disaster hazard mitigation strategies and prompt post-disaster risk management efforts based on rapid system reliability assessment. This paper proposes a rapid reliability estimation method for node-pair connectivity analysis of lifeline networks especially when the network components are statistically correlated. Recursive procedures are proposed to compound all network nodes until they become a single super node representing the connectivity between the origin and destination nodes. The proposed method is applied to numerical network examples and benchmark interconnected power and water networks in Memphis, Shelby County. The connectivity analysis results show the proposed method's reasonable accuracy and remarkable efficiency as compared to the Monte Carlo simulations

  15. Applying Statistical and Complex Network Methods to Explore the Key Signaling Molecules of Acupuncture Regulating Neuroendocrine-Immune Network

    Directory of Open Access Journals (Sweden)

    Kuo Zhang

    2018-01-01

    Full Text Available The mechanisms of acupuncture are still unclear. In order to reveal the regulatory effect of manual acupuncture (MA on the neuroendocrine-immune (NEI network and identify the key signaling molecules during MA modulating NEI network, we used a rat complete Freund’s adjuvant (CFA model to observe the analgesic and anti-inflammatory effect of MA, and, what is more, we used statistical and complex network methods to analyze the data about the expression of 55 common signaling molecules of NEI network in ST36 (Zusanli acupoint, and serum and hind foot pad tissue. The results indicate that MA had significant analgesic, anti-inflammatory effects on CFA rats; the key signaling molecules may play a key role during MA regulating NEI network, but further research is needed.

  16. Diagnosis of Rejection by Analyzing Ventricular Late Potentials in Heart Transplant Patients

    Directory of Open Access Journals (Sweden)

    Vítor Nogueira Mendes

    2016-01-01

    Full Text Available Background: Heart transplant rejection originates slow and fragmented conduction. Signal-averaged ECG (SAECG is a stratification method in the risk of rejection. Objective: To develop a risk score for rejection, using SAECG variables. Methods: We studied 28 transplant patients. First, we divided the sample into two groups based on the occurrence of acute rejection (5 with rejection and 23 without. In a second phase, we divided the sample considering the existence or not of rejection in at least one biopsy performed on the follow-up period (rejection pm1: 18 with rejection and 10 without. Results: On conventional ECG, the presence of fibrosis was the only criterion associated with acute rejection (OR = 19; 95% CI = 1.65-218.47; p = 0.02. Considering the rejection pm1, an association was found with the SAECG variables, mainly with RMS40 (OR = 0.97; 95% CI = 0.87-0.99; p = 0.03 and LAS40 (OR = 1.06; 95% IC = 1.01-1.11; p = 0.03. We formulated a risk score including those variables, and evaluated its discriminative performance in our sample. The presence of fibrosis with increasing of LAS40 and decreasing of RMS40 showed a good ability to distinguish between patients with and without rejection (AUC = 0.82; p < 0.01, assuming a cutoff point of sensitivity = 83.3% and specificity = 60%. Conclusion: The SAECG distinguished between patients with and without rejection. The usefulness of the proposed risk score must be demonstrated in larger follow-up studies.

  17. 21 CFR 1230.47 - Rejected containers.

    Science.gov (United States)

    2010-04-01

    ... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Rejected containers. 1230.47 Section 1230.47 Food... FEDERAL CAUSTIC POISON ACT Imports § 1230.47 Rejected containers. (a) In all cases where the containers... notification to the importer that the containers must be exported under customs supervision within 3 months...

  18. Net benefits: assessing the effectiveness of clinical networks in Australia through qualitative methods

    Directory of Open Access Journals (Sweden)

    Cunningham Frances C

    2012-11-01

    Full Text Available Abstract Background In the 21st century, government and industry are supplementing hierarchical, bureaucratic forms of organization with network forms, compatible with principles of devolved governance and decentralization of services. Clinical networks are employed as a key health policy approach to engage clinicians in improving patient care in Australia. With significant investment in such networks in Australia and internationally, it is important to assess their effectiveness and sustainability as implementation mechanisms. Methods In two purposively selected, musculoskeletal clinical networks, members and stakeholders were interviewed to ascertain their perceptions regarding key factors relating to network effectiveness and sustainability. We adopted a three-level approach to evaluating network effectiveness: at the community, network, and member levels, across the network lifecycle. Results Both networks studied are advisory networks displaying characteristics of the ‘enclave’ type of non-hierarchical network. They are hybrids of the mandated and natural network forms. In the short term, at member level, both networks were striving to create connectivity and collaboration of members. Over the short to medium term, at network level, both networks applied multi-disciplinary engagement in successfully developing models of care as key outputs, and disseminating information to stakeholders. In the long term, at both community and network levels, stakeholders would measure effectiveness by the broader statewide influence of the network in changing and improving practice. At community level, in the long term, stakeholders acknowledged both networks had raised the profile, and provided a ‘voice’ for musculoskeletal conditions, evidencing some progress with implementation of the network mission while pursuing additional implementation strategies. Conclusions This research sheds light on stakeholders’ perceptions of assessing clinical

  19. Method for designing networking adaptive interactive hybrid systems

    NARCIS (Netherlands)

    Kester, L. J.H.M.

    2010-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to co-ordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defence, crisis management, traffic management and public

  20. Creating networking adaptive interactive hybrid systems : A methodic approach

    NARCIS (Netherlands)

    Kester, L.J.

    2011-01-01

    Advances in network technologies enable distributed systems, operating in complex physical environments, to coordinate their activities over larger areas within shorter time intervals. Some envisioned application domains for such systems are defense, crisis management, traffic management, public

  1. MR imaging of renal transplant rejection

    International Nuclear Information System (INIS)

    Hanna, S.; Helenon, O.; Legendre, C.; Chichie, J.F.; Di Stefano, D.; Kreis, H.; Moreau, J.F.; Hopital Necker, 75 - Paris

    1991-01-01

    The results of 62 consecutive MR examinations were correlated with the subsequent clinical course and histologic results. Twenty-six cases of rejection showed a marked diminution of cortico-medullary differentiation (CMD). The renal parenchymal vascular pattern and visibility of renal sinus fat were not markedly altered in rejection and there was no difference between normal and rejected allograft shape. The ability of MR imaging to diagnose renal transplant rejection is only based on CMD, which, however, is non-specific. In 2 cases of severe rejection, T2 weighted images showed an abnormal signal intensity of the cortex due to renal infarction. Our preliminary results in 8 patients with Gd-DOTA injection showed 2 cases with necrosis seen as areas with absent contrast enhancement. This technique seems to be promising in the detection of perfusion defects. (orig.)

  2. Risk of renal allograft rejection following angiography

    International Nuclear Information System (INIS)

    Heideman, M.; Claes, G.; Nilson, A.E.

    1976-01-01

    In a retrospective study of 173 immediately functioning primary kidney transplants, correlation between angiography and renal allograft rejection was studied during the first 14 days. It was found that rejection was more frequent in kidneys undergoing angiography than in those not undergoing angiography. It was also found that in kidneys undergoing angiography an overwhelming number of the rejections started the day after angiography. These differences in rejection frequency could not be explained by differences in HLA matching or the origin of the kidneys. These findings suggest a possible connection indicating that the angiography might elicit an acute rejection episode. A possible mechanism for starting this reaction might be activation of the complement system which was found in 50 percent of the patients undergoing angiography in peripheral blood and in 100 percent when studied in vitro

  3. Image rejects/retakes--radiographic challenges.

    Science.gov (United States)

    Waaler, D; Hofmann, B

    2010-01-01

    A general held position among radiological personnel prior to digitalisation was that the problem of image rejects/retakes should more or less vanish. However, rejects/retakes still impose several challenges within radiographic imaging; they occupy unnecessary resources, expose patients to unnecessary ionizing radiation and may also indicate suboptimal quality management. The latter is the main objective of this paper, which is based on a survey of international papers published both for screen/film and digital technology. The digital revolution in imaging seems to have reduced the percentage of image rejects/retakes from 10-15 to 3-5 %. The major contribution to the decrease appears to be the dramatic reduction of incorrect exposures. At the same time, rejects/retakes due to lack of operator competence (positioning, etc.) are almost unchanged, or perhaps slightly increased (due to lack of proper technical competence, incorrect organ coding, etc.). However, the causes of rejects/retakes are in many cases defined and reported with reference to radiographers' subjective evaluations. Thus, unless radiographers share common views on image quality and acceptance criteria, objective measurements and assessments of reject/retake rates are challenging tasks. Interestingly, none of the investigated papers employs image quality parameters such as 'too much noise' as categories for rejects/retakes. Surprisingly, no reject/retake analysis seems yet to have been conducted for direct digital radiography departments. An increased percentage of rejects/retakes is related to 'digital skills' of radiographers and therefore points to areas for extended education and training. Furthermore, there is a need to investigate the inter-subjectivity of radiographers' perception of, and attitude towards, both technical and clinical image quality criteria. Finally, there may be a need to validate whether reject/retake rate analysis is such an effective quality indicator as has been asserted.

  4. Image rejects/retakes-radiographic challenges

    International Nuclear Information System (INIS)

    Waaler, D.; Hofmann, B.

    2010-01-01

    A general held position among radiological personnel prior to digitalisation was that the problem of image rejects/retakes should more or less vanish. However, rejects/retakes still impose several challenges within radiographic imaging; they occupy unnecessary resources, expose patients to unnecessary ionizing radiation and may also indicate suboptimal quality management. The latter is the main objective of this paper, which is based on a survey of international papers published both for screen/film and digital technology. The digital revolution in imaging seems to have reduced the percentage of image rejects/retakes from 10-15 to 3-5%. The major contribution to the decrease appears to be the dramatic reduction of incorrect exposures. At the same time, rejects/retakes due to lack of operator competence (positioning, etc.) are almost unchanged, or perhaps slightly increased (due to lack of proper technical competence, incorrect organ coding, etc.). However, the causes of rejects/retakes are in many cases defined and reported with reference to radiographers' subjective evaluations. Thus, unless radiographers share common views on image quality and acceptance criteria, objective measurements and assessments of reject/retake rates are challenging tasks. Interestingly, none of the investigated papers employs image quality parameters such as 'too much noise' as categories for rejects/retakes. Surprisingly, no reject/retake analysis seems yet to have been conducted for direct digital radiography departments. An increased percentage of rejects/retakes is related to 'digital skills' of radiographers and therefore points to areas for extended education and training. Furthermore, there is a need to investigate the inter subjectivity of radiographers' perception of, and attitude towards, both technical and clinical image quality criteria. Finally, there may be a need to validate whether reject/retake rate analysis is such an effective quality indicator as has been asserted

  5. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  6. Neural network based method for conversion of solar radiation data

    International Nuclear Information System (INIS)

    Celik, Ali N.; Muneer, Tariq

    2013-01-01

    Highlights: ► Generalized regression neural network is used to predict the solar radiation on tilted surfaces. ► The above network, amongst many such as multilayer perceptron, is the most successful one. ► The present neural network returns a relative mean absolute error value of 9.1%. ► The present model leads to a mean absolute error value of estimate of 14.9 Wh/m 2 . - Abstract: The receiving ends of the solar energy conversion systems that generate heat or electricity from radiation is usually tilted at an optimum angle to increase the solar incident on the surface. Solar irradiation data measured on horizontal surfaces is readily available for many locations where such solar energy conversion systems are installed. Various equations have been developed to convert solar irradiation data measured on horizontal surface to that on tilted one. These equations constitute the conventional approach. In this article, an alternative approach, generalized regression type of neural network, is used to predict the solar irradiation on tilted surfaces, using the minimum number of variables involved in the physical process, namely the global solar irradiation on horizontal surface, declination and hour angles. Artificial neural networks have been successfully used in recent years for optimization, prediction and modeling in energy systems as alternative to conventional modeling approaches. To show the merit of the presently developed neural network, the solar irradiation data predicted from the novel model was compared to that from the conventional approach (isotropic and anisotropic models), with strict reference to the irradiation data measured in the same location. The present neural network model was found to provide closer solar irradiation values to the measured than the conventional approach, with a mean absolute error value of 14.9 Wh/m 2 . The other statistical values of coefficient of determination and relative mean absolute error also indicate the

  7. Visibility Network Patterns and Methods for Studying Visual Relational Phenomena in Archeology

    Directory of Open Access Journals (Sweden)

    Tom Brughmans

    2017-08-01

    Full Text Available A review of the archeological and non-archeological use of visibility networks reveals the use of a limited range of formal techniques, in particular for representing visibility theories. This paper aims to contribute to the study of complex visual relational phenomena in landscape archeology by proposing a range of visibility network patterns and methods. We propose first- and second-order visibility graph representations of total and cumulative viewsheds, and two-mode representations of cumulative viewsheds. We present network patterns that can be used to represent aspects of visibility theories and that can be used in statistical simulation models to compare theorized networks with observed networks. We argue for the need to incorporate observed visibility network density in these simulation models, by illustrating strong differences in visibility network density in three example landscapes. The approach is illustrated through a brief case study of visibility networks of long barrows in Cranborne Chase.

  8. a Method for the Seamlines Network Automatic Selection Based on Building Vector

    Science.gov (United States)

    Li, P.; Dong, Y.; Hu, Y.; Li, X.; Tan, P.

    2018-04-01

    In order to improve the efficiency of large scale orthophoto production of city, this paper presents a method for automatic selection of seamlines network in large scale orthophoto based on the buildings' vector. Firstly, a simple model of the building is built by combining building's vector, height and DEM, and the imaging area of the building on single DOM is obtained. Then, the initial Voronoi network of the measurement area is automatically generated based on the positions of the bottom of all images. Finally, the final seamlines network is obtained by optimizing all nodes and seamlines in the network automatically based on the imaging areas of the buildings. The experimental results show that the proposed method can not only get around the building seamlines network quickly, but also remain the Voronoi network' characteristics of projection distortion minimum theory, which can solve the problem of automatic selection of orthophoto seamlines network in image mosaicking effectively.

  9. Exploring Normalization and Network Reconstruction Methods using In Silico and In Vivo Models

    Science.gov (United States)

    Abstract: Lessons learned from the recent DREAM competitions include: The search for the best network reconstruction method continues, and we need more complete datasets with ground truth from more complex organisms. It has become obvious that the network reconstruction methods t...

  10. Reject/repeat analysis and the effect prior film viewing has on a department's reject/repeat rate

    International Nuclear Information System (INIS)

    Clark, P.A.; Hogg, P.

    2003-01-01

    Purpose: Achieving cost-effectiveness within the NHS is an old initiative but one that has again been highlighted by recent government policies (The New NHS-Modern and Dependable, Stationary Office, London, 1997). It has been reiterated that it is the responsibility of individual Trusts to devise means to provide such a service. Reject/repeat analyses have long been the primary tool used to assess the cost-effectiveness of radiography departments (Quality Assurance in Diagnostic Radiology, WHO, Geneva, 1982). This research paper examines an in-house initiative (viewing patients' previous films) commonly employed in other Health Trusts in order to reduce departmental repeat/reject rates. Method: Three hundred orthopaedic patients with hip, knee and ankle prostheses were included in a reject/repeat analysis. The aim was to investigate whether or not viewing patient's previous relevant radiographs would be advantageous to the practicing radiographer. This was done through an audit cycle consisting of two audit periods each lasting for 3 months. The primary audit period recorded the baseline repeat/reject rate, with the secondary audit period recording the repeat/reject rate under an experimental condition of viewing the relevant radiographs. Results: The baseline audit revealed repeat rates of 33% in orthopaedic patients with hip, knee and ankle prostheses. The availability of prior film viewing to the radiographer reduced this repeat rate to 10.6%. Conclusion: Prior film viewing dramatically reduced the department's repeat/reject rate by 22.4%. This provides scope for significant patient dose reductions as well as reducing departmental film expenses. This is an underestimated initiative and should be used appropriately in routine clinical practice

  11. Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Ji Wei

    2010-10-01

    Full Text Available Abstract Background Microarray data discretization is a basic preprocess for many algorithms of gene regulatory network inference. Some common discretization methods in informatics are used to discretize microarray data. Selection of the discretization method is often arbitrary and no systematic comparison of different discretization has been conducted, in the context of gene regulatory network inference from time series gene expression data. Results In this study, we propose a new discretization method "bikmeans", and compare its performance with four other widely-used discretization methods using different datasets, modeling algorithms and number of intervals. Sensitivities, specificities and total accuracies were calculated and statistical analysis was carried out. Bikmeans method always gave high total accuracies. Conclusions Our results indicate that proper discretization methods can consistently improve gene regulatory network inference independent of network modeling algorithms and datasets. Our new method, bikmeans, resulted in significant better total accuracies than other methods.

  12. An ME-PC Enhanced HDMR Method for Efficient Statistical Analysis of Multiconductor Transmission Line Networks

    KAUST Repository

    Yucel, Abdulkadir C.; Bagci, Hakan; Michielssen, Eric

    2015-01-01

    An efficient method for statistically characterizing multiconductor transmission line (MTL) networks subject to a large number of manufacturing uncertainties is presented. The proposed method achieves its efficiency by leveraging a high

  13. EAP-Kerberos: A Low Latency EAP Authentication Method for Faster Handoffs in Wireless Access Networks

    Science.gov (United States)

    Zrelli, Saber; Okabe, Nobuo; Shinoda, Yoichi

    The wireless medium is a key technology for enabling ubiquitous and continuous network connectivity. It is becoming more and more important in our daily life especially with the increasing adoption of networking technologies in many fields such as medical care and transportation systems. Although most wireless technologies nowadays provide satisfying bandwidth and higher speeds, several of these technologies still lack improvements with regard to handoff performance. In this paper, we focus on wireless network technologies that rely on the Extensible Authentication Protocol for mutual authentication between the station and the access network. Such technologies include local area wireless networks (IEEE 802.11) as well as broadband wireless networks (IEEE 802.16). We present a new EAP authentication method based on a three party authentication scheme, namely Kerberos, that considerably shortens handoff delays. Compared to other methods, the proposed method has the advantage of not requiring any changes on the access points, making it readily deployable at reasonable costs.

  14. AN IMPROVEMENT ON GEOMETRY-BASED METHODS FOR GENERATION OF NETWORK PATHS FROM POINTS

    Directory of Open Access Journals (Sweden)

    Z. Akbari

    2014-10-01

    Full Text Available Determining network path is important for different purposes such as determination of road traffic, the average speed of vehicles, and other network analysis. One of the required input data is information about network path. Nevertheless, the data collected by the positioning systems often lead to the discrete points. Conversion of these points to the network path have become one of the challenges which different researchers, presents many ways for solving it. This study aims at investigating geometry-based methods to estimate the network paths from the obtained points and improve an existing point to curve method. To this end, some geometry-based methods have been studied and an improved method has been proposed by applying conditions on the best method after describing and illustrating weaknesses of them.

  15. Research on Large-Scale Road Network Partition and Route Search Method Combined with Traveler Preferences

    Directory of Open Access Journals (Sweden)

    De-Xin Yu

    2013-01-01

    Full Text Available Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.

  16. Spatial Analysis Along Networks Statistical and Computational Methods

    CERN Document Server

    Okabe, Atsuyuki

    2012-01-01

    In the real world, there are numerous and various events that occur on and alongside networks, including the occurrence of traffic accidents on highways, the location of stores alongside roads, the incidence of crime on streets and the contamination along rivers. In order to carry out analyses of those events, the researcher needs to be familiar with a range of specific techniques. Spatial Analysis Along Networks provides a practical guide to the necessary statistical techniques and their computational implementation. Each chapter illustrates a specific technique, from Stochastic Point Process

  17. Development of a general method for obtaining the geometry of microfluidic networks

    International Nuclear Information System (INIS)

    Razavi, Mohammad Sayed; Salimpour, M. R.; Shirani, Ebrahim

    2014-01-01

    In the present study, a general method for geometry of fluidic networks is developed with emphasis on pressure-driven flows in the microfluidic applications. The design method is based on general features of network's geometry such as cross-sectional area and length of channels. Also, the method is applicable to various cross-sectional shapes such as circular, rectangular, triangular, and trapezoidal cross sections. Using constructal theory, the flow resistance, energy loss and performance of the network are optimized. Also, by this method, practical design strategies for the fabrication of microfluidic networks can be improved. The design method enables rapid prediction of fluid flow in the complex network of channels and is very useful for improving proper miniaturization and integration of microfluidic networks. Minimization of flow resistance of the network of channels leads to universal constants for consecutive cross-sectional areas and lengths. For a Y-shaped network, the optimal ratios of consecutive cross-section areas (A i+1 /A i ) and lengths (L i+1 /L i ) are obtained as A i+1 /A i = 2 −2/3 and L i+1 /L i = 2 −1/3 , respectively. It is shown that energy loss in the network is proportional to the volume of network. It is also seen when the number of channels is increased both the hydraulic resistance and the volume occupied by the network are increased in a similar manner. Furthermore, the method offers that fabrication of multi-depth and multi-width microchannels should be considered as an integral part of designing procedures. Finally, numerical simulations for the fluid flow in the network have been performed and results show very good agreement with analytic results

  18. ERROR VS REJECTION CURVE FOR THE PERCEPTRON

    OpenAIRE

    PARRONDO, JMR; VAN DEN BROECK, Christian

    1993-01-01

    We calculate the generalization error epsilon for a perceptron J, trained by a teacher perceptron T, on input patterns S that form a fixed angle arccos (J.S) with the student. We show that the error is reduced from a power law to an exponentially fast decay by rejecting input patterns that lie within a given neighbourhood of the decision boundary J.S = 0. On the other hand, the error vs. rejection curve epsilon(rho), where rho is the fraction of rejected patterns, is shown to be independent ...

  19. S-curve networks and an approximate method for estimating degree distributions of complex networks

    OpenAIRE

    Guo, Jin-Li

    2010-01-01

    In the study of complex networks almost all theoretical models have the property of infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 (Internet Protocol version 4) addresses, this paper proposes a forecasting model by using S curve (Logistic curve). The growing trend of IPv4 addresses in China is forecasted. There are some reference value for optimizing the distribution of IPv4 address resource and the development of IPv6. Based o...

  20. Dimensioning Method for Conversational Video Applications in Wireless Convergent Networks

    Directory of Open Access Journals (Sweden)

    Raquel Perez Leal

    2007-12-01

    Full Text Available New convergent services are becoming possible, thanks to the expansion of IP networks based on the availability of innovative advanced coding formats such as H.264, which reduce network bandwidth requirements providing good video quality, and the rapid growth in the supply of dual-mode WiFi cellular terminals. This paper provides, first, a comprehensive subject overview as several technologies are involved, such as medium access protocol in IEEE802.11, H.264 advanced video coding standards, and conversational application characterization and recommendations. Second, the paper presents a new and simple dimensioning model of conversational video over wireless LAN. WLAN is addressed under the optimal network throughput and the perspective of video quality. The maximum number of simultaneous users resulting from throughput is limited by the collisions taking place in the shared medium with the statistical contention protocol. The video quality is conditioned by the packet loss in the contention protocol. Both approaches are analyzed within the scope of the advanced video codecs used in conversational video over IP, to conclude that conversational video dimensioning based on network throughput is not enough to ensure a satisfactory user experience, and video quality has to be taken also into account. Finally, the proposed model has been applied to a real-office scenario.

  1. Scalable power selection method for wireless mesh networks

    CSIR Research Space (South Africa)

    Olwal, TO

    2009-01-01

    Full Text Available This paper addresses the problem of a scalable dynamic power control (SDPC) for wireless mesh networks (WMNs) based on IEEE 802.11 standards. An SDPC model that accounts for architectural complexities witnessed in multiple radios and hops...

  2. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    user

    Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total ..... Genetic algorithm-based self-learning fuzzy PI controller for shunt active filter, ... Verification of global optimality of the OFC active power filters by means of ...

  3. A network traffic reduction method for cooperative positioning

    NARCIS (Netherlands)

    Das, Kallol; Wymeersch, Henk

    Cooperative positioning is suitable for applications where conventional positioning fails due to lack of connectivity with a sufficient number of reference nodes. In a dense network, as the number of cooperating devices increases, the number of packet exchanges also increases proportionally. This

  4. Assessing the safety of the road network : a simple method.

    NARCIS (Netherlands)

    Janssen, S.T.M.C.

    1993-01-01

    Research by the SWOV Institute for Road Safety Research in The Netherlands assessing the safety of the Dutch road network is presented. In The Netherlands urban and rural roads and motorways are used roughly equally, however accident rates vary greatly. These are discussed in this paper. Tables show

  5. Dimensioning Method for Conversational Video Applications in Wireless Convergent Networks

    Directory of Open Access Journals (Sweden)

    Alonso JoséI

    2008-01-01

    Full Text Available Abstract New convergent services are becoming possible, thanks to the expansion of IP networks based on the availability of innovative advanced coding formats such as H.264, which reduce network bandwidth requirements providing good video quality, and the rapid growth in the supply of dual-mode WiFi cellular terminals. This paper provides, first, a comprehensive subject overview as several technologies are involved, such as medium access protocol in IEEE802.11, H.264 advanced video coding standards, and conversational application characterization and recommendations. Second, the paper presents a new and simple dimensioning model of conversational video over wireless LAN. WLAN is addressed under the optimal network throughput and the perspective of video quality. The maximum number of simultaneous users resulting from throughput is limited by the collisions taking place in the shared medium with the statistical contention protocol. The video quality is conditioned by the packet loss in the contention protocol. Both approaches are analyzed within the scope of the advanced video codecs used in conversational video over IP, to conclude that conversational video dimensioning based on network throughput is not enough to ensure a satisfactory user experience, and video quality has to be taken also into account. Finally, the proposed model has been applied to a real-office scenario.

  6. [B-type natriuretic peptide assessment in the diagnosis of rejection after pediatric heart transplant].

    Science.gov (United States)

    Sylos, Cristina de; Azeka, Estela; Kajita, Luis; Benvenutti, Luis; Strunz, Célia Cassaro; Branco, Klébia Castello; Riso, Arlindo Almeida; Tanamati, Carla; Jatene, Marcelo; Barbero-Marcial, Miguel

    2009-03-01

    Rejection is one of the major causes of mortality following pediatric heart transplant. B-type natriuretic peptide (BNP) has been studied as a method for the diagnosis of acute rejection, especially in adult patients undergoing heart transplant. To correlate serum BNP levels with acute rejection as diagnosed by endomyocardial biopsy in patients of the pediatric heart transplant group. A total of 50 BNP samples were collected from 33 children in the postoperative period of heart transplant, and data on age, gender, skin color, blood group, immune panel, follow-up time after transplant, functional class, immunosuppressive regimen used and number of rejections were analyzed. Thirty three children with median age of 10.13 years were analyzed; of these, 54% were females and 78% were Caucasians. BNP levels were determined at a mean time from transplant of 4.25 years. Nine episodes of rejection were diagnosed in eight patients (27%) by means of endomyocardial biopsy; of these, three were grade 3A, five were grade 2, and one had humoral rejection. At the moment of biopsy, most patients were asymptomatic. The mean serum BNP level was 77.18 pg/ml, with 144.22 pg/ml in the group with rejection and 62.46 pg/ml in the group without rejection, with p = 0.02. Asymptomatic children can present acute rejection in the postoperative period of heart transplant. Serum BNP levels show a statistically significant difference in the group with rejection and thus can be an additional method in the diagnosis of cardiac rejection.

  7. Coordinator Role Mobility Method for Increasing the Life Expectancy of Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jurenoks Aleksejs

    2017-05-01

    Full Text Available The general problem of wireless sensor network nodes is the low-power batteries that significantly limit the life expectancy of a network. Nowadays the technical solutions related to energy resource management are being rapidly developed and integrated into the daily lives of people. The energy resource management systems use sensor networks for receiving and processing information during the realia time. The present paper proposes using a coordinator role mobility method for controlling the routing processes for energy balancing in nodes, which provides dynamic network reconfiguration possibilities. The method is designed to operate fully in the background and can be integrated into any exiting working system.

  8. Delay Tolerant Networking with Data Triage Method based on Emergent User Policies for Disaster Information Network System

    Directory of Open Access Journals (Sweden)

    Noriki Uchida

    2014-01-01

    Full Text Available When Disaster Information Network System is considered in local areas that were heavy damaged by the East Japan Great Earthquake in 2011, the resiliency of the network system is one of significant subjects for the restoration of the areas. DTN (Delay Tolerant Network has been focused for the effective methods for such inoperable network circumstances. However, when DTN is applied for the local areas, there are some problems such as message delivery rate and latency because there are fewer roads, cars, and pedestrians than in urban areas. In this paper, we propose the Enhanced Media Coordinate System for its architecture, and Data Triage method by emergent user policies is introduced to improve the QoS in Disaster Information Network System in local areas. In the proposed method, every message is tagged with the priority levels by data types with considering emergent user policies, and the high priority messages are firstly duplicated to transmittable nodes. Then, the experimental results by the GIS map of a Japanese coastal town and the future studies are discussed.

  9. Data Collection Method for Mobile Control Sink Node in Wireless Sensor Network Based on Compressive Sensing

    Directory of Open Access Journals (Sweden)

    Ling Yongfa

    2016-01-01

    Full Text Available The paper proposes a mobile control sink node data collection method in the wireless sensor network based on compressive sensing. This method, with regular track, selects the optimal data collection points in the monitoring area via the disc method, calcu-lates the shortest path by using the quantum genetic algorithm, and hence determines the data collection route. Simulation results show that this method has higher network throughput and better energy efficiency, capable of collecting a huge amount of data with balanced energy consumption in the network.

  10. IP2P K-means: an efficient method for data clustering on sensor networks

    Directory of Open Access Journals (Sweden)

    Peyman Mirhadi

    2013-03-01

    Full Text Available Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2 and the preliminary results show that the algorithm works effectively and relatively more precisely.

  11. A method of reconstructing the spatial measurement network by mobile measurement transmitter for shipbuilding

    International Nuclear Information System (INIS)

    Guo, Siyang; Lin, Jiarui; Yang, Linghui; Ren, Yongjie; Guo, Yin

    2017-01-01

    The workshop Measurement Position System (wMPS) is a distributed measurement system which is suitable for the large-scale metrology. However, there are some inevitable measurement problems in the shipbuilding industry, such as the restriction by obstacles and limited measurement range. To deal with these factors, this paper presents a method of reconstructing the spatial measurement network by mobile transmitter. A high-precision coordinate control network with more than six target points is established. The mobile measuring transmitter can be added into the measurement network using this coordinate control network with the spatial resection method. This method reconstructs the measurement network and broadens the measurement scope efficiently. To verify this method, two comparison experiments are designed with the laser tracker as the reference. The results demonstrate that the accuracy of point-to-point length is better than 0.4mm and the accuracy of coordinate measurement is better than 0.6mm. (paper)

  12. A multi-scale network method for two-phase flow in porous media

    Energy Technology Data Exchange (ETDEWEB)

    Khayrat, Karim, E-mail: khayratk@ifd.mavt.ethz.ch; Jenny, Patrick

    2017-08-01

    Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.

  13. A multi-scale network method for two-phase flow in porous media

    International Nuclear Information System (INIS)

    Khayrat, Karim; Jenny, Patrick

    2017-01-01

    Pore-network models of porous media are useful in the study of pore-scale flow in porous media. In order to extract macroscopic properties from flow simulations in pore-networks, it is crucial the networks are large enough to be considered representative elementary volumes. However, existing two-phase network flow solvers are limited to relatively small domains. For this purpose, a multi-scale pore-network (MSPN) method, which takes into account flow-rate effects and can simulate larger domains compared to existing methods, was developed. In our solution algorithm, a large pore network is partitioned into several smaller sub-networks. The algorithm to advance the fluid interfaces within each subnetwork consists of three steps. First, a global pressure problem on the network is solved approximately using the multiscale finite volume (MSFV) method. Next, the fluxes across the subnetworks are computed. Lastly, using fluxes as boundary conditions, a dynamic two-phase flow solver is used to advance the solution in time. Simulation results of drainage scenarios at different capillary numbers and unfavourable viscosity ratios are presented and used to validate the MSPN method against solutions obtained by an existing dynamic network flow solver.

  14. Network reliability analysis of complex systems using a non-simulation-based method

    International Nuclear Information System (INIS)

    Kim, Youngsuk; Kang, Won-Hee

    2013-01-01

    Civil infrastructures such as transportation, water supply, sewers, telecommunications, and electrical and gas networks often establish highly complex networks, due to their multiple source and distribution nodes, complex topology, and functional interdependence between network components. To understand the reliability of such complex network system under catastrophic events such as earthquakes and to provide proper emergency management actions under such situation, efficient and accurate reliability analysis methods are necessary. In this paper, a non-simulation-based network reliability analysis method is developed based on the Recursive Decomposition Algorithm (RDA) for risk assessment of generic networks whose operation is defined by the connections of multiple initial and terminal node pairs. The proposed method has two separate decomposition processes for two logical functions, intersection and union, and combinations of these processes are used for the decomposition of any general system event with multiple node pairs. The proposed method is illustrated through numerical network examples with a variety of system definitions, and is applied to a benchmark gas transmission pipe network in Memphis TN to estimate the seismic performance and functional degradation of the network under a set of earthquake scenarios.

  15. Data delivery method based on neighbor nodes' information in a mobile ad hoc network.

    Science.gov (United States)

    Kashihara, Shigeru; Hayashi, Takuma; Taenaka, Yuzo; Okuda, Takeshi; Yamaguchi, Suguru

    2014-01-01

    This paper proposes a data delivery method based on neighbor nodes' information to achieve reliable communication in a mobile ad hoc network (MANET). In a MANET, it is difficult to deliver data reliably due to instabilities in network topology and wireless network condition which result from node movement. To overcome such unstable communication, opportunistic routing and network coding schemes have lately attracted considerable attention. Although an existing method that employs such schemes, MAC-independent opportunistic routing and encoding (MORE), Chachulski et al. (2007), improves the efficiency of data delivery in an unstable wireless mesh network, it does not address node movement. To efficiently deliver data in a MANET, the method proposed in this paper thus first employs the same opportunistic routing and network coding used in MORE and also uses the location information and transmission probabilities of neighbor nodes to adapt to changeable network topology and wireless network condition. The simulation experiments showed that the proposed method can achieve efficient data delivery with low network load when the movement speed is relatively slow.

  16. Data Delivery Method Based on Neighbor Nodes’ Information in a Mobile Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Shigeru Kashihara

    2014-01-01

    Full Text Available This paper proposes a data delivery method based on neighbor nodes’ information to achieve reliable communication in a mobile ad hoc network (MANET. In a MANET, it is difficult to deliver data reliably due to instabilities in network topology and wireless network condition which result from node movement. To overcome such unstable communication, opportunistic routing and network coding schemes have lately attracted considerable attention. Although an existing method that employs such schemes, MAC-independent opportunistic routing and encoding (MORE, Chachulski et al. (2007, improves the efficiency of data delivery in an unstable wireless mesh network, it does not address node movement. To efficiently deliver data in a MANET, the method proposed in this paper thus first employs the same opportunistic routing and network coding used in MORE and also uses the location information and transmission probabilities of neighbor nodes to adapt to changeable network topology and wireless network condition. The simulation experiments showed that the proposed method can achieve efficient data delivery with low network load when the movement speed is relatively slow.

  17. 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.

  18. A hybrid network-based method for the detection of disease-related genes

    Science.gov (United States)

    Cui, Ying; Cai, Meng; Dai, Yang; Stanley, H. Eugene

    2018-02-01

    Detecting disease-related genes is crucial in disease diagnosis and drug design. The accepted view is that neighbors of a disease-causing gene in a molecular network tend to cause the same or similar diseases, and network-based methods have been recently developed to identify novel hereditary disease-genes in available biomedical networks. Despite the steady increase in the discovery of disease-associated genes, there is still a large fraction of disease genes that remains under the tip of the iceberg. In this paper we exploit the topological properties of the protein-protein interaction (PPI) network to detect disease-related genes. We compute, analyze, and compare the topological properties of disease genes with non-disease genes in PPI networks. We also design an improved random forest classifier based on these network topological features, and a cross-validation test confirms that our method performs better than previous similar studies.

  19. Methods for Inferring Health-Related Social Networks among Coworkers from Online Communication Patterns

    Science.gov (United States)

    Matthews, Luke J.; DeWan, Peter; Rula, Elizabeth Y.

    2013-01-01

    Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network. PMID

  20. Methods for inferring health-related social networks among coworkers from online communication patterns.

    Science.gov (United States)

    Matthews, Luke J; DeWan, Peter; Rula, Elizabeth Y

    2013-01-01

    Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1) the absolute number of emails exchanged, (2) logistic regression probability of an offline relationship, and (3) the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI) across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a social network.

  1. Methods for inferring health-related social networks among coworkers from online communication patterns.

    Directory of Open Access Journals (Sweden)

    Luke J Matthews

    Full Text Available Studies of social networks, mapped using self-reported contacts, have demonstrated the strong influence of social connections on the propensity for individuals to adopt or maintain healthy behaviors and on their likelihood to adopt health risks such as obesity. Social network analysis may prove useful for businesses and organizations that wish to improve the health of their populations by identifying key network positions. Health traits have been shown to correlate across friendship ties, but evaluating network effects in large coworker populations presents the challenge of obtaining sufficiently comprehensive network data. The purpose of this study was to evaluate methods for using online communication data to generate comprehensive network maps that reproduce the health-associated properties of an offline social network. In this study, we examined three techniques for inferring social relationships from email traffic data in an employee population using thresholds based on: (1 the absolute number of emails exchanged, (2 logistic regression probability of an offline relationship, and (3 the highest ranked email exchange partners. As a model of the offline social network in the same population, a network map was created using social ties reported in a survey instrument. The email networks were evaluated based on the proportion of survey ties captured, comparisons of common network metrics, and autocorrelation of body mass index (BMI across social ties. Results demonstrated that logistic regression predicted the greatest proportion of offline social ties, thresholding on number of emails exchanged produced the best match to offline network metrics, and ranked email partners demonstrated the strongest autocorrelation of BMI. Since each method had unique strengths, researchers should choose a method based on the aspects of offline behavior of interest. Ranked email partners may be particularly useful for purposes related to health traits in a

  2. Performance Evaluation of Air-Based Heat Rejection Systems

    Directory of Open Access Journals (Sweden)

    Hannes Fugmann

    2015-01-01

    Full Text Available On the basis of the Number of Transfer Units (NTU method a functional relation between electric power for fans/pumps and effectiveness in dry coolers and wet cooling towers is developed. Based on this relation, a graphical presentation method of monitoring and simulation data of heat rejection units is introduced. The functional relation allows evaluating the thermodynamic performance of differently sized heat rejection units and comparing performance among them. The method is used to evaluate monitoring data of dry coolers of different solar cooling field projects. The novelty of this approach is that performance rating is not limited by a design point or standardized operating conditions of the heat exchanger, but is realizable under flexible conditions.

  3. Thallium kinetics in rat cardiac transplant rejection

    International Nuclear Information System (INIS)

    Barak, J.H.; LaRaia, P.J.; Boucher, C.A.; Fallon, J.T.; Buckley, M.J.

    1988-01-01

    Cardiac transplant rejection is a very complex process involving both cellular and vascular injury. Recently, thallium imaging has been used to assess acute transplant rejection. It has been suggested that changes in thallium kinetics might be a sensitive indicator of transplant rejection. Accordingly, thallium kinetics were assessed in vivo in acute untreated rat heterotopic (cervical) transplant rejection. Male Lewis rats weighing 225-250 g received heterotopic heart transplants from syngeneic Lewis rats (group A; n = 13), or allogeneic Brown Norway rats (group B; n = 11). Rats were imaged serially on the 2nd and the 7th postoperative days. Serial cardiac thallium content was determined utilizing data collected every 150 sec for 2 hr. The data were fit to a monoexponential curve and the decay rate constant (/sec) derived. By day 7 all group B hearts had histological evidence of severe acute rejection, and demonstrated decreased global contraction. Group A hearts showed normal histology and contractility. However, thallium uptakes and washout of the two groups were the same. Peak thallium uptake of group B was +/- 3758 1166 counts compared with 3553 +/- 950 counts in the control group A (P = 0.6395); The 2-hr percentage of washout was 12.1 +/- 1.04 compared with 12.1 +/- 9.3 (P = 1.0000); and the decay constant was -0.00002065 +/- 0.00001799 compared with -0.00002202 +/- 0.00001508 (P = 0.8409). These data indicate that in vivo global thallium kinetics are preserved during mild-to-severe acute transplant rejection. These findings suggest that the complex cellular and extracellular processes of acute rejection limit the usefulness of thallium kinetics in the detection of acute transplant rejection

  4. Novel methods of utilizing Jitter for Network Congestion Control

    Directory of Open Access Journals (Sweden)

    Ivan

    2013-12-01

    Full Text Available This paper proposes a novel paradigm for network congestion control. Instead of perpetual conflict as in TCP, a proof-of-concept first-ever protocol enabling inter-flow communication without infrastructure support thru a side channel constructed on generic FIFO queue behaviour is presented. This enables independent flows passing thru the same bottleneck queue to communicate and achieve fair capacity sharing and a stable equilibrium state in a rapid fashion.

  5. The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

    OpenAIRE

    Ambrogioni, Luca; Güçlü, Umut; van Gerven, Marcel A. J.; Maris, Eric

    2017-01-01

    This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are determined by the outer layer of a deep neural network, trained by minimizing the negative log likelihood. This generalizes the popular quantized softmax approach, which can be seen ...

  6. Optimization of hot water transport and distribution networks by analytical method: OPTAL program

    International Nuclear Information System (INIS)

    Barreau, Alain; Caizergues, Robert; Moret-Bailly, Jean

    1977-06-01

    This report presents optimization studies of hot water transport and distribution network by minimizing operating cost. Analytical optimization is used: Lagrange's method of undetermined multipliers. Optimum diameter of each pipe is calculated for minimum network operating cost. The characteristics of the computer program used for calculations, OPTAL, are given in this report. An example of network is calculated and described: 52 branches and 27 customers. Results are discussed [fr

  7. 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...

  8. A comparative analysis on computational methods for fitting an ERGM to biological network data

    Directory of Open Access Journals (Sweden)

    Sudipta Saha

    2015-03-01

    Full Text Available Exponential random graph models (ERGM based on graph theory are useful in studying global biological network structure using its local properties. However, computational methods for fitting such models are sensitive to the type, structure and the number of the local features of a network under study. In this paper, we compared computational methods for fitting an ERGM with local features of different types and structures. Two commonly used methods, such as the Markov Chain Monte Carlo Maximum Likelihood Estimation and the Maximum Pseudo Likelihood Estimation are considered for estimating the coefficients of network attributes. We compared the estimates of observed network to our random simulated network using both methods under ERGM. The motivation was to ascertain the extent to which an observed network would deviate from a randomly simulated network if the physical numbers of attributes were approximately same. Cut-off points of some common attributes of interest for different order of nodes were determined through simulations. We implemented our method to a known regulatory network database of Escherichia coli (E. coli.

  9. Artificial neural network and classical least-squares methods for neurotransmitter mixture analysis.

    Science.gov (United States)

    Schulze, H G; Greek, L S; Gorzalka, B B; Bree, A V; Blades, M W; Turner, R F

    1995-02-01

    Identification of individual components in biological mixtures can be a difficult problem regardless of the analytical method employed. In this work, Raman spectroscopy was chosen as a prototype analytical method due to its inherent versatility and applicability to aqueous media, making it useful for the study of biological samples. Artificial neural networks (ANNs) and the classical least-squares (CLS) method were used to identify and quantify the Raman spectra of the small-molecule neurotransmitters and mixtures of such molecules. The transfer functions used by a network, as well as the architecture of a network, played an important role in the ability of the network to identify the Raman spectra of individual neurotransmitters and the Raman spectra of neurotransmitter mixtures. Specifically, networks using sigmoid and hyperbolic tangent transfer functions generalized better from the mixtures in the training data set to those in the testing data sets than networks using sine functions. Networks with connections that permit the local processing of inputs generally performed better than other networks on all the testing data sets. and better than the CLS method of curve fitting, on novel spectra of some neurotransmitters. The CLS method was found to perform well on noisy, shifted, and difference spectra.

  10. 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.

  11. An effective method to improve the robustness of small-world networks under attack

    International Nuclear Information System (INIS)

    Zhang Zheng-Zhen; Xu Wen-Jun; Lin Jia-Ru; Zeng Shang-You

    2014-01-01

    In this study, the robustness of small-world networks to three types of attack is investigated. Global efficiency is introduced as the network coefficient to measure the robustness of a small-world network. The simulation results prove that an increase in rewiring probability or average degree can enhance the robustness of the small-world network under all three types of attack. The effectiveness of simultaneously increasing both rewiring probability and average degree is also studied, and the combined increase is found to significantly improve the robustness of the small-world network. Furthermore, the combined effect of rewiring probability and average degree on network robustness is shown to be several times greater than that of rewiring probability or average degree individually. This means that small-world networks with a relatively high rewiring probability and average degree have advantages both in network communications and in good robustness to attacks. Therefore, simultaneously increasing rewiring probability and average degree is an effective method of constructing realistic networks. Consequently, the proposed method is useful to construct efficient and robust networks in a realistic scenario. (interdisciplinary physics and related areas of science and technology)

  12. Maximum entropy methods for extracting the learned features of deep neural networks.

    Science.gov (United States)

    Finnegan, Alex; Song, Jun S

    2017-10-01

    New architectures of multilayer artificial neural networks and new methods for training them are rapidly revolutionizing the application of machine learning in diverse fields, including business, social science, physical sciences, and biology. Interpreting deep neural networks, however, currently remains elusive, and a critical challenge lies in understanding which meaningful features a network is actually learning. We present a general method for interpreting deep neural networks and extracting network-learned features from input data. We describe our algorithm in the context of biological sequence analysis. Our approach, based on ideas from statistical physics, samples from the maximum entropy distribution over possible sequences, anchored at an input sequence and subject to constraints implied by the empirical function learned by a network. Using our framework, we demonstrate that local transcription factor binding motifs can be identified from a network trained on ChIP-seq data and that nucleosome positioning signals are indeed learned by a network trained on chemical cleavage nucleosome maps. Imposing a further constraint on the maximum entropy distribution also allows us to probe whether a network is learning global sequence features, such as the high GC content in nucleosome-rich regions. This work thus provides valuable mathematical tools for interpreting and extracting learned features from feed-forward neural networks.

  13. Rejection thresholds in solid chocolate-flavored compound coating.

    Science.gov (United States)

    Harwood, Meriel L; Ziegler, Gregory R; Hayes, John E

    2012-10-01

    Classical detection thresholds do not predict liking, as they focus on the presence or absence of a sensation. Recently however, Prescott and colleagues described a new method, the rejection threshold, where a series of forced choice preference tasks are used to generate a dose-response function to determine hedonically acceptable concentrations. That is, how much is too much? To date, this approach has been used exclusively in liquid foods. Here, we determined group rejection thresholds in solid chocolate-flavored compound coating for bitterness. The influences of self-identified preferences for milk or dark chocolate, as well as eating style (chewers compared to melters) on rejection thresholds were investigated. Stimuli included milk chocolate-flavored compound coating spiked with increasing amounts of sucrose octaacetate, a bitter and generally recognized as safe additive. Paired preference tests (blank compared to spike) were used to determine the proportion of the group that preferred the blank. Across pairs, spiked samples were presented in ascending concentration. We were able to quantify and compare differences between 2 self-identified market segments. The rejection threshold for the dark chocolate preferring group was significantly higher than the milk chocolate preferring group (P= 0.01). Conversely, eating style did not affect group rejection thresholds (P= 0.14), although this may reflect the amount of chocolate given to participants. Additionally, there was no association between chocolate preference and eating style (P= 0.36). Present work supports the contention that this method can be used to examine preferences within specific market segments and potentially individual differences as they relate to ingestive behavior. This work makes use of the rejection threshold method to study market segmentation, extending its use to solid foods. We believe this method has broad applicability to the sensory specialist and product developer by providing a

  14. Online solving of economic dispatch problem using neural network approach and comparing it with classical method

    International Nuclear Information System (INIS)

    Mohammadi, A.; Varahram, M.H.

    2007-01-01

    In this study, two methods for solving economic dispatch problems, namely Hopfield neural network and lambda iteration method are compared. Three sample of power system with 3, 6 and 20 units have been considered. The time required for CPU, for solving economic dispatch of these two systems has been calculated. It has been Shown that for on-line economic dispatch, Hopfield neural network is more efficient and the time required for Convergence is considerably smaller compared to classical methods. (author)

  15. METHODS OF MANAGING TRAFFIC DISTRIBUTION IN INFORMATION AND COMMUNICATION NETWORKS OF CRITICAL INFRASTRUCTURE SYSTEMS

    OpenAIRE

    Kosenko, Viktor; Persiyanova, Elena; Belotskyy, Oleksiy; Malyeyeva, Olga

    2017-01-01

    The subject matter of the article is information and communication networks (ICN) of critical infrastructure systems (CIS). The goal of the work is to create methods for managing the data flows and resources of the ICN of CIS to improve the efficiency of information processing. The following tasks were solved in the article: the data flow model of multi-level ICN structure was developed, the method of adaptive distribution of data flows was developed, the method of network resource assignment...

  16. A systemic method for evaluating the potential impacts of floods on network infrastructures

    Directory of Open Access Journals (Sweden)

    J. Eleutério

    2013-04-01

    Full Text Available Understanding network infrastructures and their operation under exceptional circumstances is fundamental for dealing with flood risks and improving the resilience of a territory. This work presents a method for evaluating potential network infrastructure dysfunctions and damage in cases of flooding. In contrast to existing approaches, this method analyses network infrastructures on an elementary scale, by considering networks as a group of elements with specific functions and individual vulnerabilities. Our analysis places assets at the centre of the evaluation process, resulting in the construction of damage-dysfunction matrices based on expert interviews. These matrices permit summarising the different vulnerabilities of network infrastructures, describing how the different components are linked to each other and how they can disrupt the operation of the network. They also identify the actions and resources needed to restore the system to operational status following damage and dysfunctions, an essential point when dealing with the question of resilience. The method promotes multi-network analyses and is illustrated by a French case study. Sixty network experts were interviewed during the analysis of the following networks: drinking water supply, waste water, public lighting, gas distribution and electricity supply.

  17. Input data preprocessing method for exchange rate forecasting via neural network

    Directory of Open Access Journals (Sweden)

    Antić Dragan S.

    2014-01-01

    Full Text Available The aim of this paper is to present a method for neural network input parameters selection and preprocessing. The purpose of this network is to forecast foreign exchange rates using artificial intelligence. Two data sets are formed for two different economic systems. Each system is represented by six categories with 70 economic parameters which are used in the analysis. Reduction of these parameters within each category was performed by using the principal component analysis method. Component interdependencies are established and relations between them are formed. Newly formed relations were used to create input vectors of a neural network. The multilayer feed forward neural network is formed and trained using batch training. Finally, simulation results are presented and it is concluded that input data preparation method is an effective way for preprocessing neural network data. [Projekat Ministarstva nauke Republike Srbije, br.TR 35005, br. III 43007 i br. III 44006

  18. Social network extraction based on Web: 3. the integrated superficial method

    Science.gov (United States)

    Nasution, M. K. M.; Sitompul, O. S.; Noah, S. A.

    2018-03-01

    The Web as a source of information has become part of the social behavior information. Although, by involving only the limitation of information disclosed by search engines in the form of: hit counts, snippets, and URL addresses of web pages, the integrated extraction method produces a social network not only trusted but enriched. Unintegrated extraction methods may produce social networks without explanation, resulting in poor supplemental information, or resulting in a social network of durmise laden, consequently unrepresentative social structures. The integrated superficial method in addition to generating the core social network, also generates an expanded network so as to reach the scope of relation clues, or number of edges computationally almost similar to n(n - 1)/2 for n social actors.

  19. Structural Decoupling and Disturbance Rejection in a Distillation Column

    DEFF Research Database (Denmark)

    Bahar, Mehrdad; Jantzen, Jan; Commault, C.

    1996-01-01

    Introduction, distillation column model, input-output decoupling, disturbance rejection, concluding remarks, references.......Introduction, distillation column model, input-output decoupling, disturbance rejection, concluding remarks, references....

  20. Fate of Manuscripts Rejected From the Red Journal

    International Nuclear Information System (INIS)

    Holliday, Emma B.; Yang, George; Jagsi, Reshma; Hoffman, Karen E.; Bennett, Katherine Egan; Grace, Calley; Zietman, Anthony L.

    2015-01-01

    Purpose: To evaluate characteristics associated with higher rates of acceptance for original manuscripts submitted for publication to the International Journal of Radiation Oncology • Biology • Physics (IJROBP) and describe the fate of rejected manuscripts. Methods and Materials: Manuscripts submitted to the IJROBP from May 1, 2010, to August 31, 2010, and May 1, 2012, to August 31, 2012, were evaluated for author demographics and acceptance status. A PubMed search was performed for each IJROBP-rejected manuscript to ascertain whether the manuscript was ultimately published elsewhere. The Impact Factor of the accepting journal and the number of citations of the published manuscript were also collected. Results: Of the 500 included manuscripts, 172 (34.4%) were accepted and 328 (65.6%) were rejected. There was no significant difference in acceptance rates according to gender or degree of the submitting author, but there were significant differences seen based on the submitting author's country, rank, and h-index. On multivariate analysis, earlier year submitted (P<.0001) and higher author h-index (P=.006) remained significantly associated with acceptance into the IJROBP. Two hundred thirty-five IJROBP-rejected manuscripts (71.7%) were ultimately published in a PubMed-listed journal as of July 2014. There were no significant differences in any submitting author characteristics. Journals accepting IJROBP-rejected manuscripts had a lower median [interquartile range] 2013 impact factor compared with the IJROBP (2.45 [1.53-3.71] vs 4.176). The IJROBP-rejected manuscripts ultimately published elsewhere had a lower median [interquartile range] number of citations (1 [0-4] vs 6 [2-11]; P<.001), which persisted on multivariate analysis. Conclusions: The acceptance rate for manuscripts submitted to the IJROBP is approximately one-third, and approximately 70% of rejected manuscripts are ultimately published in other PubMed-listed journals, but these ultimate

  1. Boundedness and convergence of online gradient method with penalty for feedforward neural networks.

    Science.gov (United States)

    Zhang, Huisheng; Wu, Wei; Liu, Fei; Yao, Mingchen

    2009-06-01

    In this brief, we consider an online gradient method with penalty for training feedforward neural networks. Specifically, the penalty is a term proportional to the norm of the weights. Its roles in the method are to control the magnitude of the weights and to improve the generalization performance of the network. By proving that the weights are automatically bounded in the network training with penalty, we simplify the conditions that are required for convergence of online gradient method in literature. A numerical example is given to support the theoretical analysis.

  2. A Biologically Inspired Energy-Efficient Duty Cycle Design Method for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jie Zhou

    2017-01-01

    Full Text Available The recent success of emerging wireless sensor networks technology has encouraged researchers to develop new energy-efficient duty cycle design algorithm in this field. The energy-efficient duty cycle design problem is a typical NP-hard combinatorial optimization problem. In this paper, we investigate an improved elite immune evolutionary algorithm (IEIEA strategy to optimize energy-efficient duty cycle design scheme and monitored area jointly to enhance the network lifetimes. Simulation results show that the network lifetime of the proposed IEIEA method increased compared to the other two methods, which means that the proposed method improves the full coverage constraints.

  3. An configuration method of patient service cloud for the home patient with multi sensor network

    International Nuclear Information System (INIS)

    Noji, Tamotsu; Arino, Masashi; Saito, Mayuko; Horii, Minoru; Ogino, Tadashi; Suto, Yasuzo; Sasaki, Hitoshi; Mansei, Kouiti

    2010-01-01

    We are advancing the research of patient service cloud in the global medical collaboration network system based on 3D electronic referral letters. In this paper it proposes one configuration method of private cloud that aims at the home care patient's health care and independence support based on voice navigation system (VONAVS). We evaluate 3D image compression rate, try image compositing Cloud's configuration by the multi sensor network, and search for the configuration method of the remote image diagnosis. The proposed configuration method expands the possibility to the global medical collaboration network system for new large areas such as a telemedicine, an emergency care, and home medical care. (author)

  4. Methods and procedures for the verification and validation of artificial neural networks

    CERN Document Server

    Taylor, Brian J

    2006-01-01

    Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning. This volume introduces some of the methods and techniques used for the verification and validation of neural networks and adaptive systems.

  5. Assessment of network inference methods: how to cope with an underdetermined problem.

    Directory of Open Access Journals (Sweden)

    Caroline Siegenthaler

    Full Text Available The inference of biological networks is an active research area in the field of systems biology. The number of network inference algorithms has grown tremendously in the last decade, underlining the importance of a fair assessment and comparison among these methods. Current assessments of the performance of an inference method typically involve the application of the algorithm to benchmark datasets and the comparison of the network predictions against the gold standard or reference networks. While the network inference problem is often deemed underdetermined, implying that the inference problem does not have a (unique solution, the consequences of such an attribute have not been rigorously taken into consideration. Here, we propose a new procedure for assessing the performance of gene regulatory network (GRN inference methods. The procedure takes into account the underdetermined nature of the inference problem, in which gene regulatory interactions that are inferable or non-inferable are determined based on causal inference. The assessment relies on a new definition of the confusion matrix, which excludes errors associated with non-inferable gene regulations. For demonstration purposes, the proposed assessment procedure is applied to the DREAM 4 In Silico Network Challenge. The results show a marked change in the ranking of participating methods when taking network inferability into account.

  6. 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)

  7. Dynamic neural network-based methods for compensation of nonlinear effects in multimode communication lines

    Science.gov (United States)

    Sidelnikov, O. S.; Redyuk, A. A.; Sygletos, S.

    2017-12-01

    We consider neural network-based schemes of digital signal processing. It is shown that the use of a dynamic neural network-based scheme of signal processing ensures an increase in the optical signal transmission quality in comparison with that provided by other methods for nonlinear distortion compensation.

  8. Ventricular function during the acute rejection of heterotopic transplanted heart: Gated blood pool studies

    International Nuclear Information System (INIS)

    Valette, H.; Bourguignon, M.H.; Desruennes, M.; Merlet, P.; Le Guludec, D.; Syrota, A.

    1991-01-01

    Twenty patients who had undergone a heterotopic heart transplant were studied prospectively to determine the relationship between rejection and ventricular dysfunction assessed from gated blood pool studies. A fully automated method for detecting ventricular edges was implemented; its success rate for the grafted left and right ventricles was 94% and 77%, respectively. The parameters, peak ejection and filling rates, were calculated pixel per pixel using a two-harmonic Fourier algorithm and then averaged over the ventricular region of interest. Peak filling and ejection rates were closely related with the severity of the rejection, while the left ventricular ejection fraction was not. Peak filling rates of both ventricles were the indices closely related to the presence of moderate rejection. Despite the low number of patients, these data suggested that gated blood pool derived indices of ventricular function are associated with ventricular dysfunction resulting from myocarditis rejection. Radionuclide ventriculography provides parametric data which are accurate and reliable for the diagnosis of rejection. (orig.)

  9. A bedside technique for the diagnosis of acute rejection in renal transplants using 111-In platelets

    International Nuclear Information System (INIS)

    Chandler, S.T.; Buckels, J.A.C.; Drolc, Z.; Hawker, R.J.; Barnes, A.D.; McCollum, C.N.

    1982-01-01

    A total of 33 patients was studied with the aim of developing a bedside method for providing early diagnosis of acute rejection using 111-In labelled platelets. Platelet deposition was detected in all patients suffering acute rejection. A significant increase in kidney/aortic arch ratio, as measured by the portable bedside system, preceded the clinical diagnosis in 70% of patients. Using this system, it appeared possible not only to diagnose acute rejection at an earlier stage but also to predict irrecoverable transplant loss even in the presence of tubular necrosis. By labelling the platelets repeatedly for at least two weeks after transplantation, the period of highest risk for acute rejection and other complications. The gamma camera should still be employed in the event of markedly increased platelet deposition to differentiate between rejection and vascular complications

  10. Smart Collection and Storage Method for Network Traffic Data

    Science.gov (United States)

    2014-09-01

    to the root of an incident or under- stand what goes on in a network may mean looking at data from weeks, months, or even years ago, as has been the...KB 1.01% 69.42 TB 694.20 TB 6,941.99 TB SuSE 6.3 .pcap 51,706 KB 1.01% 104.03 TB 1,040.27 TB 10,402.68 TB HP-UX nettl .trc0 53,391 KB 1.04% 451.13

  11. Energy-efficient algorithm for classification of states of wireless sensor network using machine learning methods

    Science.gov (United States)

    Yuldashev, M. N.; Vlasov, A. I.; Novikov, A. N.

    2018-05-01

    This paper focuses on the development of an energy-efficient algorithm for classification of states of a wireless sensor network using machine learning methods. The proposed algorithm reduces energy consumption by: 1) elimination of monitoring of parameters that do not affect the state of the sensor network, 2) reduction of communication sessions over the network (the data are transmitted only if their values can affect the state of the sensor network). The studies of the proposed algorithm have shown that at classification accuracy close to 100%, the number of communication sessions can be reduced by 80%.

  12. Peripheral blood transcriptome sequencing reveals rejection-relevant genes in long-term heart transplantation.

    Science.gov (United States)

    Chen, Yan; Zhang, Haibo; Xiao, Xue; Jia, Yixin; Wu, Weili; Liu, Licheng; Jiang, Jun; Zhu, Baoli; Meng, Xu; Chen, Weijun

    2013-10-03

    Peripheral blood-based gene expression patterns have been investigated as biomarkers to monitor the immune system and rule out rejection after heart transplantation. Recent advances in the high-throughput deep sequencing (HTS) technologies provide new leads in transcriptome analysis. By performing Solexa/Illumina's digital gene expression (DGE) profiling, we analyzed gene expression profiles of PBMCs from 6 quiescent (grade 0) and 6 rejection (grade 2R&3R) heart transplant recipients at more than 6 months after transplantation. Subsequently, quantitative real-time polymerase chain reaction (qRT-PCR) was carried out in an independent validation cohort of 47 individuals from three rejection groups (ISHLT, grade 0,1R, 2R&3R). Through DGE sequencing and qPCR validation, 10 genes were identified as informative genes for detection of cardiac transplant rejection. A further clustering analysis showed that the 10 genes were not only effective for distinguishing patients with acute cardiac allograft rejection, but also informative for discriminating patients with renal allograft rejection based on both blood and biopsy samples. Moreover, PPI network analysis revealed that the 10 genes were connected to each other within a short interaction distance. We proposed a 10-gene signature for heart transplant patients at high-risk of developing severe rejection, which was found to be effective as well in other organ transplant. Moreover, we supposed that these genes function systematically as biomarkers in long-time allograft rejection. Further validation in broad transplant population would be required before the non-invasive biomarkers can be generally utilized to predict the risk of transplant rejection. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  13. Radiation therapy treatment of acute refractory renal allograft rejection

    International Nuclear Information System (INIS)

    Godinez, J.; Thisted, R.A.; Woodle, E.S.; Thistlethwaite, J.R.; Powers, C.; Haraf, D.

    1996-01-01

    Purpose: To evaluate the impact of the use of radiotherapy to preserve the renal graft in patients with recurrent graft rejection that failed to respond to medical treatment and identify risk factors to predict the probability of graft loss. Material and Methods: Between June 1989 and December 1995, 53 renal graft recipients were treated at our institution after experiencing several episodes of rejection. Rejection was defined as an unexplained, consecutive, daily rise in serum creatinine. Each episode was confirmed with renal biopsy. Patients who experienced rejection were initially treated with solu medrol bolus and prednisone. Patients with steroid-resistant or recurrent rejection received OKT3, polyclonal antilymphocyte antibody, FK506, or mycophenolate mofetil. Those who failed to respond to medical treatment were referred for radiotherapy. Treatment consisted of a dose of 600 cGy given in 3 or 4 fractions using 6 MV photons, AP or AP/PA. All patients underwent ultrasound kidney localization; a 2 cm margin was given around the kidney. Results: Median follow-up from the date of transplant to the last follow-up was 22 months (range 1-83 months), the median time from the date of transplant to the initiation of radiotherapy was 3 months, and the median time from the initiation of radiotherapy to the last follow up was 10 months (range 0.1 to 64 months). Of these 34 men and 19 women, median age of 3), Ninety-one percent were cadaveric transplant recipients., human leukocyte antigen matching on HLA-A and HLA-B (zero antigens in 26 patients/one or two shared antigens in 27 patients), HLA-DR locus (zero antigens in 34 patients/one or two shared antigens in 19 patients), transplant panel-reactive antibodies at transplantation (median PRA-Curr of 3% and median PRA-Max of 8%), number of acute rejection episodes, interval from the date of the transplant to the first rejection (median 1 month, range 5 days to 68 months), serum creatinine levels at the time of the first

  14. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    Several different methods have been used to sense load currents and extract its ... in order to produce a reference current in shunt active power filters (SAPF), and ... technique compared to other similar methods are found quite satisfactory by ...

  15. Image rejects in general direct digital radiography

    International Nuclear Information System (INIS)

    Hofmann, Bjørn; Rosanowsky, Tine Blomberg; Jensen, Camilla; Wah, Kenneth Hong Ching

    2015-01-01

    The number of rejected images is an indicator of image quality and unnecessary imaging at a radiology department. Image reject analysis was frequent in the film era, but comparably few and small studies have been published after converting to digital radiography. One reason may be a belief that rejects have been eliminated with digitalization. To measure the extension of deleted images in direct digital radiography (DR), in order to assess the rates of rejects and unnecessary imaging and to analyze reasons for deletions, in order to improve the radiological services. All exposed images at two direct digital laboratories at a hospital in Norway were reviewed in January 2014. Type of examination, number of exposed images, and number of deleted images were registered. Each deleted image was analyzed separately and the reason for deleting the image was recorded. Out of 5417 exposed images, 596 were deleted, giving a deletion rate of 11%. A total of 51.3% were deleted due to positioning errors and 31.0% due to error in centering. The examinations with the highest percentage of deleted images were the knee, hip, and ankle, 20.6%, 18.5%, and 13.8% respectively. The reject rate is at least as high as the deletion rate and is comparable with previous film-based imaging systems. The reasons for rejection are quite different in digital systems. This falsifies the hypothesis that digitalization would eliminates rejects. A deleted image does not contribute to diagnostics, and therefore is an unnecessary image. Hence, the high rates of deleted images have implications for management, training, education, as well as for quality

  16. Image rejects in general direct digital radiography.

    Science.gov (United States)

    Hofmann, Bjørn; Rosanowsky, Tine Blomberg; Jensen, Camilla; Wah, Kenneth Hong Ching

    2015-10-01

    The number of rejected images is an indicator of image quality and unnecessary imaging at a radiology department. Image reject analysis was frequent in the film era, but comparably few and small studies have been published after converting to digital radiography. One reason may be a belief that rejects have been eliminated with digitalization. To measure the extension of deleted images in direct digital radiography (DR), in order to assess the rates of rejects and unnecessary imaging and to analyze reasons for deletions, in order to improve the radiological services. All exposed images at two direct digital laboratories at a hospital in Norway were reviewed in January 2014. Type of examination, number of exposed images, and number of deleted images were registered. Each deleted image was analyzed separately and the reason for deleting the image was recorded. Out of 5417 exposed images, 596 were deleted, giving a deletion rate of 11%. A total of 51.3% were deleted due to positioning errors and 31.0% due to error in centering. The examinations with the highest percentage of deleted images were the knee, hip, and ankle, 20.6%, 18.5%, and 13.8% respectively. The reject rate is at least as high as the deletion rate and is comparable with previous film-based imaging systems. The reasons for rejection are quite different in digital systems. This falsifies the hypothesis that digitalization would eliminates rejects. A deleted image does not contribute to diagnostics, and therefore is an unnecessary image. Hence, the high rates of deleted images have implications for management, training, education, as well as for quality.

  17. Detecting and Preventing Sybil Attacks in Wireless Sensor Networks Using Message Authentication and Passing Method.

    Science.gov (United States)

    Dhamodharan, Udaya Suriya Raj Kumar; Vayanaperumal, Rajamani

    2015-01-01

    Wireless sensor networks are highly indispensable for securing network protection. Highly critical attacks of various kinds have been documented in wireless sensor network till now by many researchers. The Sybil attack is a massive destructive attack against the sensor network where numerous genuine identities with forged identities are used for getting an illegal entry into a network. Discerning the Sybil attack, sinkhole, and wormhole attack while multicasting is a tremendous job in wireless sensor network. Basically a Sybil attack means a node which pretends its identity to other nodes. Communication to an illegal node results in data loss and becomes dangerous in the network. The existing method Random Password Comparison has only a scheme which just verifies the node identities by analyzing the neighbors. A survey was done on a Sybil attack with the objective of resolving this problem. The survey has proposed a combined CAM-PVM (compare and match-position verification method) with MAP (message authentication and passing) for detecting, eliminating, and eventually preventing the entry of Sybil nodes in the network. We propose a scheme of assuring security for wireless sensor network, to deal with attacks of these kinds in unicasting and multicasting.

  18. Detecting and Preventing Sybil Attacks in Wireless Sensor Networks Using Message Authentication and Passing Method

    Directory of Open Access Journals (Sweden)

    Udaya Suriya Raj Kumar Dhamodharan

    2015-01-01

    Full Text Available Wireless sensor networks are highly indispensable for securing network protection. Highly critical attacks of various kinds have been documented in wireless sensor network till now by many researchers. The Sybil attack is a massive destructive attack against the sensor network where numerous genuine identities with forged identities are used for getting an illegal entry into a network. Discerning the Sybil attack, sinkhole, and wormhole attack while multicasting is a tremendous job in wireless sensor network. Basically a Sybil attack means a node which pretends its identity to other nodes. Communication to an illegal node results in data loss and becomes dangerous in the network. The existing method Random Password Comparison has only a scheme which just verifies the node identities by analyzing the neighbors. A survey was done on a Sybil attack with the objective of resolving this problem. The survey has proposed a combined CAM-PVM (compare and match-position verification method with MAP (message authentication and passing for detecting, eliminating, and eventually preventing the entry of Sybil nodes in the network. We propose a scheme of assuring security for wireless sensor network, to deal with attacks of these kinds in unicasting and multicasting.

  19. A Newly Developed Method for Computing Reliability Measures in a Water Supply Network

    Directory of Open Access Journals (Sweden)

    Jacek Malinowski

    2016-01-01

    Full Text Available A reliability model of a water supply network has beens examined. Its main features are: a topology that can be decomposed by the so-called state factorization into a (relativelysmall number of derivative networks, each having a series-parallel structure (1, binary-state components (either operative or failed with given flow capacities (2, a multi-state character of the whole network and its sub-networks - a network state is defined as the maximal flow between a source (sources and a sink (sinks (3, all capacities (component, network, and sub-network have integer values (4. As the network operates, its state changes due to component failures, repairs, and replacements. A newly developed method of computing the inter-state transition intensities has been presented. It is based on the so-called state factorization and series-parallel aggregation. The analysis of these intensities shows that the failure-repair process of the considered system is an asymptotically homogenous Markov process. It is also demonstrated how certain reliability parameters useful for the network maintenance planning can be determined on the basis of the asymptotic intensities. For better understanding of the presented method, an illustrative example is given. (original abstract

  20. Nuclear power plant monitoring method by neural network and its application to actual nuclear reactor

    International Nuclear Information System (INIS)

    Nabeshima, Kunihiko; Suzuki, Katsuo; Shinohara, Yoshikuni; Tuerkcan, E.

    1995-11-01

    In this paper, the anomaly detection method for nuclear power plant monitoring and its program are described by using a neural network approach, which is based on the deviation between measured signals and output signals of neural network model. The neural network used in this study has three layered auto-associative network with 12 input/output, and backpropagation algorithm is adopted for learning. Furthermore, to obtain better dynamical model of the reactor plant, a new learning technique was developed in which the learning process of the present neural network is divided into initial and adaptive learning modes. The test results at the actual nuclear reactor shows that the neural network plant monitoring system is successfull in detecting in real-time the symptom of small anomaly over a wide power range including reactor start-up, shut-down and stationary operation. (author)

  1. The QAP weighted network analysis method and its application in international services trade

    Science.gov (United States)

    Xu, Helian; Cheng, Long

    2016-04-01

    Based on QAP (Quadratic Assignment Procedure) correlation and complex network theory, this paper puts forward a new method named QAP Weighted Network Analysis Method. The core idea of the method is to analyze influences among relations in a social or economic group by building a QAP weighted network of networks of relations. In the QAP weighted network, a node depicts a relation and an undirect edge exists between any pair of nodes if there is significant correlation between relations. As an application of the QAP weighted network, we study international services trade by using the QAP weighted network, in which nodes depict 10 kinds of services trade relations. After the analysis of international services trade by QAP weighted network, and by using distance indicators, hierarchy tree and minimum spanning tree, the conclusion shows that: Firstly, significant correlation exists in all services trade, and the development of any one service trade will stimulate the other nine. Secondly, as the economic globalization goes deeper, correlations in all services trade have been strengthened continually, and clustering effects exist in those services trade. Thirdly, transportation services trade, computer and information services trade and communication services trade have the most influence and are at the core in all services trade.

  2. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery.

    Directory of Open Access Journals (Sweden)

    Sapna Kumari

    Full Text Available BACKGROUND: Constructing coexpression networks and performing network analysis using large-scale gene expression data sets is an effective way to uncover new biological knowledge; however, the methods used for gene association in constructing these coexpression networks have not been thoroughly evaluated. Since different methods lead to structurally different coexpression networks and provide different information, selecting the optimal gene association method is critical. METHODS AND RESULTS: In this study, we compared eight gene association methods - Spearman rank correlation, Weighted Rank Correlation, Kendall, Hoeffding's D measure, Theil-Sen, Rank Theil-Sen, Distance Covariance, and Pearson - and focused on their true knowledge discovery rates in associating pathway genes and construction coordination networks of regulatory genes. We also examined the behaviors of different methods to microarray data with different properties, and whether the biological processes affect the efficiency of different methods. CONCLUSIONS: We found that the Spearman, Hoeffding and Kendall methods are effective in identifying coexpressed pathway genes, whereas the Theil-sen, Rank Theil-Sen, Spearman, and Weighted Rank methods perform well in identifying coordinated transcription factors that control the same biological processes and traits. Surprisingly, the widely used Pearson method is generally less efficient, and so is the Distance Covariance method that can find gene pairs of multiple relationships. Some analyses we did clearly show Pearson and Distance Covariance methods have distinct behaviors as compared to all other six methods. The efficiencies of different methods vary with the data properties to some degree and are largely contingent upon the biological processes, which necessitates the pre-analysis to identify the best performing method for gene association and coexpression network construction.

  3. A Belief Network Decision Support Method Applied to Aerospace Surveillance and Battle Management Projects

    National Research Council Canada - National Science Library

    Staker, R

    2003-01-01

    This report demonstrates the application of a Bayesian Belief Network decision support method for Force Level Systems Engineering to a collection of projects related to Aerospace Surveillance and Battle Management...

  4. Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

    Science.gov (United States)

    White, Forest M.; Wolf-Yadlin, Alejandro

    2016-06-01

    Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

  5. A BHR Composite Network-Based Visualization Method for Deformation Risk Level of Underground Space.

    Directory of Open Access Journals (Sweden)

    Wei Zheng

    Full Text Available This study proposes a visualization processing method for the deformation risk level of underground space. The proposed method is based on a BP-Hopfield-RGB (BHR composite network. Complex environmental factors are integrated in the BP neural network. Dynamic monitoring data are then automatically classified in the Hopfield network. The deformation risk level is combined with the RGB color space model and is displayed visually in real time, after which experiments are conducted with the use of an ultrasonic omnidirectional sensor device for structural deformation monitoring. The proposed method is also compared with some typical methods using a benchmark dataset. Results show that the BHR composite network visualizes the deformation monitoring process in real time and can dynamically indicate dangerous zones.

  6. NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS

    Directory of Open Access Journals (Sweden)

    S. Serikov

    2012-01-01

    Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.

  7. A new metric method-improved structural holes researches on software networks

    Science.gov (United States)

    Li, Bo; Zhao, Hai; Cai, Wei; Li, Dazhou; Li, Hui

    2013-03-01

    The scale software systems quickly increase with the rapid development of software technologies. Hence, how to understand, measure, manage and control software structure is a great challenge for software engineering. there are also many researches on software networks metrics: C&K, MOOD, McCabe and etc, the aim of this paper is to propose a new and better method to metric software networks. The metric method structural holes are firstly introduced to in this paper, which can not directly be applied as a result of modular characteristics on software network. Hence, structural holes is redefined in this paper and improved, calculation process and results are described in detail. The results shows that the new method can better reflect bridge role of vertexes on software network and there is a significant correlation between degree and improved structural holes. At last, a hydropower simulation system is taken as an example to show validity of the new metric method.

  8. Leadership of healthcare commissioning networks in England: a mixed-methods study on clinical commissioning groups

    Science.gov (United States)

    Zachariadis, Markos; Oborn, Eivor; Barrett, Michael; Zollinger-Read, Paul

    2013-01-01

    Objective To explore the relational challenges for general practitioner (GP) leaders setting up new network-centric commissioning organisations in the recent health policy reform in England, we use innovation network theory to identify key network leadership practices that facilitate healthcare innovation. Design Mixed-method, multisite and case study research. Setting Six clinical commissioning groups and local clusters in the East of England area, covering in total 208 GPs and 1 662 000 population. Methods Semistructured interviews with 56 lead GPs, practice managers and staff from the local health authorities (primary care trusts, PCT) as well as various healthcare professionals; 21 observations of clinical commissioning group (CCG) board and executive meetings; electronic survey of 58 CCG board members (these included GPs, practice managers, PCT employees, nurses and patient representatives) and subsequent social network analysis. Main outcome measures Collaborative relationships between CCG board members and stakeholders from their healthcare network; clarifying the role of GPs as network leaders; strengths and areas for development of CCGs. Results Drawing upon innovation network theory provides unique insights of the CCG leaders’ activities in establishing best practices and introducing new clinical pathways. In this context we identified three network leadership roles: managing knowledge flows, managing network coherence and managing network stability. Knowledge sharing and effective collaboration among GPs enable network stability and the alignment of CCG objectives with those of the wider health system (network coherence). Even though activities varied between commissioning groups, collaborative initiatives were common. However, there was significant variation among CCGs around the level of engagement with providers, patients and local authorities. Locality (sub) groups played an important role because they linked commissioning decisions with

  9. Turbofan engine diagnostics neuron network size optimization method which takes into account overlaerning effect

    Directory of Open Access Journals (Sweden)

    О.С. Якушенко

    2010-01-01

    Full Text Available  The article is devoted to the problem of gas turbine engine (GTE technical state class automatic recognition with operation parameters by neuron networks. The one of main problems for creation the neuron networks is determination of their optimal structures size (amount of layers in network and count of neurons in each layer.The method of neuron network size optimization intended for classification of GTE technical state is considered in the article. Optimization is cared out with taking into account of overlearning effect possibility when a learning network loses property of generalization and begins strictly describing educational data set. To determinate a moment when overlearning effect is appeared in learning neuron network the method  of three data sets is used. The method is based on the comparison of recognition quality parameters changes which were calculated during recognition of educational and control data sets. As the moment when network overlearning effect is appeared the moment when control data set recognition quality begins deteriorating but educational data set recognition quality continues still improving is used. To determinate this moment learning process periodically is terminated and simulation of network with education and control data sets is fulfilled. The optimization of two-, three- and four-layer networks is conducted and some results of optimization are shown. Also the extended educational set is created and shown. The set describes 16 GTE technical state classes and each class is represented with 200 points (200 possible technical state class realizations instead of 20 points using in the former articles. It was done to increase representativeness of data set.In the article the algorithm of optimization is considered and some results which were obtained with it are shown. The results of experiments were analyzed to determinate most optimal neuron network structure. This structure provides most high-quality GTE

  10. Statistical and optimization methods to expedite neural network training for transient identification

    International Nuclear Information System (INIS)

    Reifman, J.; Vitela, E.J.; Lee, J.C.

    1993-01-01

    Two complementary methods, statistical feature selection and nonlinear optimization through conjugate gradients, are used to expedite feedforward neural network training. Statistical feature selection techniques in the form of linear correlation coefficients and information-theoretic entropy are used to eliminate redundant and non-informative plant parameters to reduce the size of the network. The method of conjugate gradients is used to accelerate the network training convergence and to systematically calculate the Teaming and momentum constants at each iteration. The proposed techniques are compared with the backpropagation algorithm using the entire set of plant parameters in the training of neural networks to identify transients simulated with the Midland Nuclear Power Plant Unit 2 simulator. By using 25% of the plant parameters and the conjugate gradients, a 30-fold reduction in CPU time was obtained without degrading the diagnostic ability of the network

  11. Feature Extraction Method for High Impedance Ground Fault Localization in Radial Power Distribution Networks

    DEFF Research Database (Denmark)

    Jensen, Kåre Jean; Munk, Steen M.; Sørensen, John Aasted

    1998-01-01

    A new approach to the localization of high impedance ground faults in compensated radial power distribution networks is presented. The total size of such networks is often very large and a major part of the monitoring of these is carried out manually. The increasing complexity of industrial...... of three phase voltages and currents. The method consists of a feature extractor, based on a grid description of the feeder by impulse responses, and a neural network for ground fault localization. The emphasis of this paper is the feature extractor, and the detection of the time instance of a ground fault...... processes and communication systems lead to demands for improved monitoring of power distribution networks so that the quality of power delivery can be kept at a controlled level. The ground fault localization method for each feeder in a network is based on the centralized frequency broadband measurement...

  12. Prioritized Degree Distribution in Wireless Sensor Networks with a Network Coded Data Collection Method

    Science.gov (United States)

    Wan, Jan; Xiong, Naixue; Zhang, Wei; Zhang, Qinchao; Wan, Zheng

    2012-01-01

    The reliability of wireless sensor networks (WSNs) can be greatly affected by failures of sensor nodes due to energy exhaustion or the influence of brutal external environment conditions. Such failures seriously affect the data persistence and collection efficiency. Strategies based on network coding technology for WSNs such as LTCDS can improve the data persistence without mass redundancy. However, due to the bad intermediate performance of LTCDS, a serious ‘cliff effect’ may appear during the decoding period, and source data are hard to recover from sink nodes before sufficient encoded packets are collected. In this paper, the influence of coding degree distribution strategy on the ‘cliff effect’ is observed and the prioritized data storage and dissemination algorithm PLTD-ALPHA is presented to achieve better data persistence and recovering performance. With PLTD-ALPHA, the data in sensor network nodes present a trend that their degree distribution increases along with the degree level predefined, and the persistent data packets can be submitted to the sink node according to its degree in order. Finally, the performance of PLTD-ALPHA is evaluated and experiment results show that PLTD-ALPHA can greatly improve the data collection performance and decoding efficiency, while data persistence is not notably affected. PMID:23235451

  13. Prioritized degree distribution in wireless sensor networks with a network coded data collection method.

    Science.gov (United States)

    Wan, Jan; Xiong, Naixue; Zhang, Wei; Zhang, Qinchao; Wan, Zheng

    2012-12-12

    The reliability of wireless sensor networks (WSNs) can be greatly affected by failures of sensor nodes due to energy exhaustion or the influence of brutal external environment conditions. Such failures seriously affect the data persistence and collection efficiency. Strategies based on network coding technology for WSNs such as LTCDS can improve the data persistence without mass redundancy. However, due to the bad intermediate performance of LTCDS, a serious 'cliff effect' may appear during the decoding period, and source data are hard to recover from sink nodes before sufficient encoded packets are collected. In this paper, the influence of coding degree distribution strategy on the 'cliff effect' is observed and the prioritized data storage and dissemination algorithm PLTD-ALPHA is presented to achieve better data persistence and recovering performance. With PLTD-ALPHA, the data in sensor network nodes present a trend that their degree distribution increases along with the degree level predefined, and the persistent data packets can be submitted to the sink node according to its degree in order. Finally, the performance of PLTD-ALPHA is evaluated and experiment results show that PLTD-ALPHA can greatly improve the data collection performance and decoding efficiency, while data persistence is not notably affected.

  14. Artificial Neural Network Method at PT Buana Intan Gemilang

    Directory of Open Access Journals (Sweden)

    Shadika

    2017-01-01

    Full Text Available The textile industry is one of the industries that provide high export value by occupying the third position in Indonesia. The process of inspection on traditional textile enterprises by relying on human vision that takes an average scanning time of 19.87 seconds. Each roll of cloth should be inspected twice to avoid missed defects. This inspection process causes the buildup at the inspection station. This study proposes the automation of inspection systems using the Artificial Neural Network (ANN. The input for ANN comes from GLCM extraction. The automation system on the defect inspection resulted in a detection time of 0.56 seconds. The degree of accuracy gained in classifying the three types of defects is 88.7%. Implementing an automated inspection system results in faster processing time.

  15. System and method for time synchronization in a wireless network

    Science.gov (United States)

    Gonia, Patrick S.; Kolavennu, Soumitri N.; Mahasenan, Arun V.; Budampati, Ramakrishna S.

    2010-03-30

    A system includes multiple wireless nodes forming a cluster in a wireless network, where each wireless node is configured to communicate and exchange data wirelessly based on a clock. One of the wireless nodes is configured to operate as a cluster master. Each of the other wireless nodes is configured to (i) receive time synchronization information from a parent node, (ii) adjust its clock based on the received time synchronization information, and (iii) broadcast time synchronization information based on the time synchronization information received by that wireless node. The time synchronization information received by each of the other wireless nodes is based on time synchronization information provided by the cluster master so that the other wireless nodes substantially synchronize their clocks with the clock of the cluster master.

  16. Methods for Examining Electrophysiological Coherence in Epileptic Networks

    Directory of Open Access Journals (Sweden)

    Jasmine eSong

    2013-05-01

    Full Text Available Epilepsy may reflect a focal abnormality of cerebral tissue, but the generation of seizures typically involves propagation of abnormal activity through cerebral networks. We examined epileptiform discharges (spikes with dense array electroencephalography (dEEG in five patients to search for the possible engagement of pathological networks. Source analysis was conducted with individual electrical head models for each patient, including sensor position measurement for registration with MRI with geodesic photogrammetry; tissue segmentation and skull conductivity modeling with an atlas skull CT warped to each patient’s MRI; cortical surface extraction and tessellation into 1 square cm equivalent dipole patches; inverse source estimation with either minimum norm or cortical surface Laplacian constraints; and spectral coherence computed among equivalent dipoles aggregated within Brodmann areas with 1 Hz resolution from 1 to 70 Hz. These analyses revealed characteristic source coherence patterns in each patient during the pre-spike, spike, and post-spike intervals. For one patient with both spikes and seizure onset localized to a single temporal lobe, we observed a cluster of apparently abnormal coherences over the involved temporal lobe. For the other patients, there were apparently characteristic coherence patterns associated with the discharges, and in some cases these appeared to reflect abnormal temporal lobe synchronization, but the coherence patterns for these patients were not easily related to an unequivocal epileptogenic zone. In contrast, simple localization of the site of onset of the spike discharge, and/or the site of onset of the seizure, with noninvasive 256 dEEG was useful in predicting the characteristic site of seizure onset for those cases that were verified by intracranial EEG and/or by surgical outcome.

  17. Methods for Examining Electrophysiological Coherence in Epileptic Networks

    Science.gov (United States)

    Song, Jasmine; Tucker, Don M.; Gilbert, Tara; Hou, Jidong; Mattson, Chelsea; Luu, Phan; Holmes, Mark D.

    2013-01-01

    Epilepsy may reflect a focal abnormality of cerebral tissue, but the generation of seizures typically involves propagation of abnormal activity through cerebral networks. We examined epileptiform discharges (spikes) with dense array electroencephalography (dEEG) in five patients to search for the possible engagement of pathological networks. Source analysis was conducted with individual electrical head models for each patient, including sensor position measurement for registration with MRI with geodesic photogrammetry; tissue segmentation and skull conductivity modeling with an atlas skull warped to each patient’s MRI; cortical surface extraction and tessellation into 1 cm2 equivalent dipole patches; inverse source estimation with either minimum norm or cortical surface Laplacian constraints; and spectral coherence computed among equivalent dipoles aggregated within Brodmann areas with 1 Hz resolution from 1 to 70 Hz. These analyses revealed characteristic source coherence patterns in each patient during the pre-spike, spike, and post-spike intervals. For one patient with both spikes and seizure onset localized to a single temporal lobe, we observed a cluster of apparently abnormal coherences over the involved temporal lobe. For the other patients, there were apparently characteristic coherence patterns associated with the discharges, and in some cases these appeared to reflect abnormal temporal lobe synchronization, but the coherence patterns for these patients were not easily related to an unequivocal epileptogenic zone. In contrast, simple localization of the site of onset of the spike discharge, and/or the site of onset of the seizure, with non-invasive 256 dEEG was useful in predicting the characteristic site of seizure onset for those cases that were verified by intracranial EEG and/or by surgical outcome. PMID:23720650

  18. Comparative study of key exchange and authentication methods in application, transport and network level security mechanisms

    Science.gov (United States)

    Fathirad, Iraj; Devlin, John; Jiang, Frank

    2012-09-01

    The key-exchange and authentication are two crucial elements of any network security mechanism. IPsec, SSL/TLS, PGP and S/MIME are well-known security approaches in providing security service to network, transport and application layers; these protocols use different methods (based on their requirements) to establish keying materials and authenticates key-negotiation and participated parties. This paper studies and compares the authenticated key negotiation methods in mentioned protocols.

  19. Allorecognition pathways in transplant rejection and tolerance.

    Science.gov (United States)

    Ali, Jason M; Bolton, Eleanor M; Bradley, J Andrew; Pettigrew, Gavin J

    2013-10-27

    With the advent of cellular therapies, it has become clear that the success of future therapies in prolonging allograft survival will require an intimate understanding of the allorecognition pathways and effector mechanisms that are responsible for chronic rejection and late graft loss.Here, we consider current understanding of T-cell allorecognition pathways and discuss the most likely mechanisms by which these pathways collaborate with other effector mechanisms to cause allograft rejection. We also consider how this knowledge may inform development of future strategies to prevent allograft rejection.Although both direct and indirect pathway CD4 T cells appear active immediately after transplantation, it has emerged that indirect pathway CD4 T cells are likely to be the dominant alloreactive T-cell population late after transplantation. Their ability to provide help for generating long-lived alloantibody is likely one of the main mechanisms responsible for the progression of allograft vasculopathy and chronic rejection.Recent work has suggested that regulatory T cells may be an effective cellular therapy in transplantation. Given the above, adoptive therapy with CD4 regulatory T cells with indirect allospecificity is a rational first choice in attempting to attenuate the development and progression of chronic rejection; those with additional properties that enable inhibition of germinal center alloantibody responses hold particular appeal.

  20. Enabling Self-Propelled Condensate Flow During Phase-Change Heat Rejection Using Surface Texturing

    Data.gov (United States)

    National Aeronautics and Space Administration — A collaborative project between Oregon State University and Auburn University is proposed on the topic of heat rejection. A unique and innovative method of...

  1. Method of Creation of “Core-Gisseismic Attributes” Dependences With Use of Trainable Neural Networks

    Directory of Open Access Journals (Sweden)

    Gafurov Denis

    2016-01-01

    Full Text Available The study describes methodological techniques and results of geophysical well logging and seismic data interpretation by means of trainable neural networks. Objects of research are wells and seismic materials of Talakan field. The article also presents forecast of construction and reservoir properties of Osa horizon. The paper gives an example of creation of geological (lithological -facial model of the field based on developed methodical techniques of complex interpretation of geologicgeophysical data by trainable neural network. The constructed lithological -facial model allows specifying a geological structure of the field. The developed methodical techniques and the trained neural networks may be applied to adjacent sites for research of carbonate horizons.

  2. Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Yan, W.

    1993-11-01

    The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods

  3. Quartet-net: a quartet-based method to reconstruct phylogenetic networks.

    Science.gov (United States)

    Yang, Jialiang; Grünewald, Stefan; Wan, Xiu-Feng

    2013-05-01

    Phylogenetic networks can model reticulate evolutionary events such as hybridization, recombination, and horizontal gene transfer. However, reconstructing such networks is not trivial. Popular character-based methods are computationally inefficient, whereas distance-based methods cannot guarantee reconstruction accuracy because pairwise genetic distances only reflect partial information about a reticulate phylogeny. To balance accuracy and computational efficiency, here we introduce a quartet-based method to construct a phylogenetic network from a multiple sequence alignment. Unlike distances that only reflect the relationship between a pair of taxa, quartets contain information on the relationships among four taxa; these quartets provide adequate capacity to infer a more accurate phylogenetic network. In applications to simulated and biological data sets, we demonstrate that this novel method is robust and effective in reconstructing reticulate evolutionary events and it has the potential to infer more accurate phylogenetic distances than other conventional phylogenetic network construction methods such as Neighbor-Joining, Neighbor-Net, and Split Decomposition. This method can be used in constructing phylogenetic networks from simple evolutionary events involving a few reticulate events to complex evolutionary histories involving a large number of reticulate events. A software called "Quartet-Net" is implemented and available at http://sysbio.cvm.msstate.edu/QuartetNet/.

  4. The significance of parenchymal changes of acute cellular rejection in predicting chronic liver graft rejection

    NARCIS (Netherlands)

    Gouw, ASH; van den Heuvel, MC; van den Berg, AP; Slooff, NJH; de Jong, KP; Poppema, S

    2002-01-01

    Background. Chronic rejection (CR) in liver allografts shows a rapid onset and progressive course, leading to graft failure within the first year after transplantation. Most cases are preceded by episodes of acute cellular rejection (AR), but histological features predictive for the transition

  5. Congestion management of electric distribution networks through market based methods

    DEFF Research Database (Denmark)

    Huang, Shaojun

     EVs and HPs. Market-based congestion management methods are the focus of the thesis. They handle the potential congestion at the energy planning stage; therefore, the aggregators can optimally plan the energy consumption and have the least impact on the customers. After reviewing and identifying...... the shortcomings of the existing methods, the thesis fully studies and improves the dynamic tariff (DT) method, and proposes two  new market-based  congestion management methods,  namely the  dynamic subsidy (DS) method and the flexible demand swap method. The thesis improves the DT method from four aspects......Rapidly increasing share of intermittent renewable energy production poses a great challenge of the management and operation of the modern power systems. Deployment of a large number of flexible demands, such as electrical vehicles (EVs) and heat pumps (HPs), is believed to be a promising solution...

  6. Network-based ranking methods for prediction of novel disease associated microRNAs.

    Science.gov (United States)

    Le, Duc-Hau

    2015-10-01

    Many studies have shown roles of microRNAs on human disease and a number of computational methods have been proposed to predict such associations by ranking candidate microRNAs according to their relevance to a disease. Among them, machine learning-based methods usually have a limitation in specifying non-disease microRNAs as negative training samples. Meanwhile, network-based methods are becoming dominant since they well exploit a "disease module" principle in microRNA functional similarity networks. Of which, random walk with restart (RWR) algorithm-based method is currently state-of-the-art. The use of this algorithm was inspired from its success in predicting disease gene because the "disease module" principle also exists in protein interaction networks. Besides, many algorithms designed for webpage ranking have been successfully applied in ranking disease candidate genes because web networks share topological properties with protein interaction networks. However, these algorithms have not yet been utilized for disease microRNA prediction. We constructed microRNA functional similarity networks based on shared targets of microRNAs, and then we integrated them with a microRNA functional synergistic network, which was recently identified. After analyzing topological properties of these networks, in addition to RWR, we assessed the performance of (i) PRINCE (PRIoritizatioN and Complex Elucidation), which was proposed for disease gene prediction; (ii) PageRank with Priors (PRP) and K-Step Markov (KSM), which were used for studying web networks; and (iii) a neighborhood-based algorithm. Analyses on topological properties showed that all microRNA functional similarity networks are small-worldness and scale-free. The performance of each algorithm was assessed based on average AUC values on 35 disease phenotypes and average rankings of newly discovered disease microRNAs. As a result, the performance on the integrated network was better than that on individual ones. In

  7. Questionnaire investigation to clarify the occurrence rate and characteristics of maternal rejection behavior in Japanese black cattle (Bos taurus).

    Science.gov (United States)

    Kohari, Daisuke; Takakura, Azusa

    2017-12-01

    We conducted a questionnaire investigation among breeding farmers to clarify the actual conditions of maternal rejection in Japanese Black cattle. We asked keeping experience of maternal rejective cows and compared occurrence patterns, rejective behavior manners, birth assistance methods, colostrum feeding method for calves, parity and rearing conditions of the cows. We found that 24% of the farms had kept rejective cows and 6% of the cows in these farms indicated maternal rejections. The most common occurrence pattern was 'Occurred from the first birth (65.6%)' and behavior manner was performing no maternal grooming with aggressive behavior (75%). Almost all the farmers assisted in each parturition (P cattle was approximately 6% and many of the rejective cows continuously performed no maternal grooming with aggressive behavior. © 2017 Japanese Society of Animal Science.

  8. Preventing Allograft Rejection by Targeting Immune Metabolism

    Directory of Open Access Journals (Sweden)

    Chen-Fang Lee

    2015-10-01

    Full Text Available Upon antigen recognition and co-stimulation, T lymphocytes upregulate the metabolic machinery necessary to proliferate and sustain effector function. This metabolic reprogramming in T cells regulates T cell activation and differentiation but is not just a consequence of antigen recognition. Although such metabolic reprogramming promotes the differentiation and function of T effector cells, the differentiation of regulatory T cells employs different metabolic reprogramming. Therefore, we hypothesized that inhibition of glycolysis and glutamine metabolism might prevent graft rejection by inhibiting effector generation and function and promoting regulatory T cell generation. We devised an anti-rejection regimen involving the glycolytic inhibitor 2-deoxyglucose (2-DG, the anti-type II diabetes drug metformin, and the inhibitor of glutamine metabolism 6-diazo-5-oxo-L-norleucine (DON. Using this triple-drug regimen, we were able to prevent or delay graft rejection in fully mismatched skin and heart allograft transplantation models.

  9. Entropy-Based Clutter Rejection for Intrawall Diagnostics

    Directory of Open Access Journals (Sweden)

    Raffaele Solimene

    2012-01-01

    Full Text Available The intrawall diagnostic problem of detecting localized inhomogeneities possibly present within the wall is addressed. As well known, clutter arising from masonry structure can impair detection of embedded scatterers due to high amplitude reflections that wall front face introduces. Moreover, internal multiple reflections also can make it difficult ground penetrating radar images (radargramms interpretation. To counteract these drawbacks, a clutter rejection method, properly tailored on the wall features, is mandatory. To this end, here we employ a windowing strategy based on entropy measures of temporal traces “similarity.” Accordingly, instants of time for which radargramms exhibit entropy values greater than a prescribed threshold are “silenced.” Numerical results are presented in order to show the effectiveness of the entropy-based clutter rejection algorithm. Moreover, a comparison with the standard average trace subtraction is also included.

  10. Method and apparatus for scheduling broadcasts in social networks

    KAUST Repository

    Manzoor, Emaad Ahmed

    2016-08-25

    A method, apparatus, and computer readable medium are provided for maximizing consumption of broadcasts by a producer. An example method includes receiving selection of a total number of time slots to use for scheduling broadcasts, and receiving information regarding the producer\\'s followers. The example method further 5 includes identifying, by a processor and based on the received information, discount factors associated with the producer\\'s followers, and calculating, by the processor and based on the received information, a predicted number of competitor broadcasts during each time slot of the total number of time slots. Finally, the example method includes determining, by the processor and based on the discount factors and the predicted 10 number of competitor broadcasts during each time slot, a number of broadcasts for the producer to transmit in each time slot of the total number of time slots.

  11. Method and apparatus for scheduling broadcasts in social networks

    KAUST Repository

    Manzoor, Emaad Ahmed; Kalnis, Panos

    2016-01-01

    information regarding the producer's followers. The example method further 5 includes identifying, by a processor and based on the received information, discount factors associated with the producer's followers, and calculating, by the processor and based

  12. System, apparatus and methods to implement high-speed network analyzers

    Science.gov (United States)

    Ezick, James; Lethin, Richard; Ros-Giralt, Jordi; Szilagyi, Peter; Wohlford, David E

    2015-11-10

    Systems, apparatus and methods for the implementation of high-speed network analyzers are provided. A set of high-level specifications is used to define the behavior of the network analyzer emitted by a compiler. An optimized inline workflow to process regular expressions is presented without sacrificing the semantic capabilities of the processing engine. An optimized packet dispatcher implements a subset of the functions implemented by the network analyzer, providing a fast and slow path workflow used to accelerate specific processing units. Such dispatcher facility can also be used as a cache of policies, wherein if a policy is found, then packet manipulations associated with the policy can be quickly performed. An optimized method of generating DFA specifications for network signatures is also presented. The method accepts several optimization criteria, such as min-max allocations or optimal allocations based on the probability of occurrence of each signature input bit.

  13. The Prediction of Bandwidth On Need Computer Network Through Artificial Neural Network Method of Backpropagation

    Directory of Open Access Journals (Sweden)

    Ikhthison Mekongga

    2014-02-01

    Full Text Available The need for bandwidth has been increasing recently. This is because the development of internet infrastructure is also increasing so that we need an economic and efficient provider system. This can be achieved through good planning and a proper system. The prediction of the bandwidth consumption is one of the factors that support the planning for an efficient internet service provider system. Bandwidth consumption is predicted using ANN. ANN is an information processing system which has similar characteristics as the biologic al neural network.  ANN  is  chosen  to  predict  the  consumption  of  the  bandwidth  because  ANN  has  good  approachability  to  non-linearity.  The variable used in ANN is the historical load data. A bandwidth consumption information system was built using neural networks  with a backpropagation algorithm to make the use of bandwidth more efficient in the future both in the rental rate of the bandwidth and in the usage of the bandwidth.Keywords: Forecasting, Bandwidth, Backpropagation

  14. PbO networks composed of single crystalline nanosheets synthesized by a facile chemical precipitation method

    Energy Technology Data Exchange (ETDEWEB)

    Samberg, Joshua P. [Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Engineering Building I, Raleigh, NC 27695-7907 (United States); Kajbafvala, Amir, E-mail: amir.kajbafvala@gmail.com [Department of Materials Science and Engineering, North Carolina State University, 911 Partners Way, Engineering Building I, Raleigh, NC 27695-7907 (United States); Koolivand, Amir [Department of Chemistry, North Carolina State University, 2620 Yarbrough Drive, Raleigh, NC 27695 (United States)

    2014-03-01

    Graphical abstract: - Highlights: • Synthesis of PbO networks through a simple chemical precipitation route. • The synthesis method is rapid and low-cost. • Each network is composed of single crystalline PbO nanosheets. • A possible growth mechanism is proposed for synthesized PbO networks. - Abstract: For the field of energy storage, nanostructured lead oxide (PbO) shows immense potential for increased specific energy and deep discharge for lead acid battery technologies. In this work, PbO networks composed of single crystalline nanosheets were synthesized utilizing a simple, low cost and rapid chemical precipitation method. The PbO networks were prepared in a single reaction vessel from starting reagents of lead acetate dehydrate, ammonium hydroxide and deionized water. Lead acetate dehydrate was chosen as a reagent, as opposed to lead nitrate, to eliminate the possibility of nitrate contamination of the final product. X-ray diffraction (XRD) analysis, high resolution scanning electron microscopy (HRSEM) and high resolution transmission electron microscopy (HRTEM) analysis were used to characterize the synthesized PbO networks. The reproducible method described herein synthesized pure β-PbO (massicot) powders, with no byproducts. A possible formation mechanism for these PbO networks is proposed. The growth is found to proceed predominately in the 〈1 1 1〉 and 〈2 0 0〉 directions while being limited in the 〈0 1 1〉 direction.

  15. PbO networks composed of single crystalline nanosheets synthesized by a facile chemical precipitation method

    International Nuclear Information System (INIS)

    Samberg, Joshua P.; Kajbafvala, Amir; Koolivand, Amir

    2014-01-01

    Graphical abstract: - Highlights: • Synthesis of PbO networks through a simple chemical precipitation route. • The synthesis method is rapid and low-cost. • Each network is composed of single crystalline PbO nanosheets. • A possible growth mechanism is proposed for synthesized PbO networks. - Abstract: For the field of energy storage, nanostructured lead oxide (PbO) shows immense potential for increased specific energy and deep discharge for lead acid battery technologies. In this work, PbO networks composed of single crystalline nanosheets were synthesized utilizing a simple, low cost and rapid chemical precipitation method. The PbO networks were prepared in a single reaction vessel from starting reagents of lead acetate dehydrate, ammonium hydroxide and deionized water. Lead acetate dehydrate was chosen as a reagent, as opposed to lead nitrate, to eliminate the possibility of nitrate contamination of the final product. X-ray diffraction (XRD) analysis, high resolution scanning electron microscopy (HRSEM) and high resolution transmission electron microscopy (HRTEM) analysis were used to characterize the synthesized PbO networks. The reproducible method described herein synthesized pure β-PbO (massicot) powders, with no byproducts. A possible formation mechanism for these PbO networks is proposed. The growth is found to proceed predominately in the 〈1 1 1〉 and 〈2 0 0〉 directions while being limited in the 〈0 1 1〉 direction

  16. Application of Looped Network Analysis Method to Core of Prismatic VHTR

    International Nuclear Information System (INIS)

    Lee, Jeong-Hun; Cho, Hyoung-Kyu; Park, Goon-Cherl

    2016-01-01

    Most of reactor coolant flows through the coolant channel within the fuel block, but some portion of the reactor coolant bypasses to the interstitial gaps. The vertical gap and horizontal gap are called bypass gap and cross gap, respectively as shown in Fig. 1. CFD simulation for the full core of VHTR might be possible but it requires vast computational cost and time. Moreover, it is hard to cover whole cases corresponding to the various bypass gap distribution in the whole VHTR core. In order to solve this problem, in this study, the flow network analysis code, FastNet (Flow Analysis for Steady-state Network), was developed using the Looped Network Analysis Method. The applied method was validated by comparing with SNU VHTR multi-block experiment. A 3-demensional network modeling was conducted representing flow paths as flow resistances. Flow network analysis code, FastNet, was developed to evaluate the core bypass flow distribution by using looped network analysis method. Complex flow network could be solved simply by converting the non-linear momentum equation to the linearized equation. The FastNet code predicted the flow distribution of the SNU multi-block experiment accurately

  17. Unified pipe network method for simulation of water flow in fractured porous rock

    Science.gov (United States)

    Ren, Feng; Ma, Guowei; Wang, Yang; Li, Tuo; Zhu, Hehua

    2017-04-01

    Rock masses are often conceptualized as dual-permeability media containing fractures or fracture networks with high permeability and porous matrix that is less permeable. In order to overcome the difficulties in simulating fluid flow in a highly discontinuous dual-permeability medium, an effective unified pipe network method is developed, which discretizes the dual-permeability rock mass into a virtual pipe network system. It includes fracture pipe networks and matrix pipe networks. They are constructed separately based on equivalent flow models in a representative area or volume by taking the advantage of the orthogonality of the mesh partition. Numerical examples of fluid flow in 2-D and 3-D domain including porous media and fractured porous media are presented to demonstrate the accuracy, robustness, and effectiveness of the proposed unified pipe network method. Results show that the developed method has good performance even with highly distorted mesh. Water recharge into the fractured rock mass with complex fracture network is studied. It has been found in this case that the effect of aperture change on the water recharge rate is more significant in the early stage compared to the fracture density change.

  18. Perturbations in the Urinary Exosome in Transplant Rejection

    Energy Technology Data Exchange (ETDEWEB)

    Sigdel, Tara K.; NG, Yolanda; Lee, Sangho; Nicora, Carrie D.; Qian, Weijun; Smith, Richard D.; Camp, David G.; Sarwal, Minnie M.

    2015-01-05

    Background: Urine exosomes, vesicles exocytosed into urine by all renal epithelial cell types, occur under normal physiologic and disease states. Exosome contents may mirror disease-specific proteome perturbations in kidney injury. Analysis methodologies for the exosomal fraction of the urinary proteome were developed and for comparing the urinary exosomal fraction versus unfractionated proteome for biomarker discovery. Methods: Urine exosomes were isolated by centrifugal filtration from mid-stream, second morning void, urine samples collected from kidney transplant recipients with and without biopsy matched acute rejection. The proteomes of unfractionated whole urine (Uw) and urine exosomes (Uexo) underwent mass spectrometry-based quantitative proteomics analysis. The proteome data were analyzed for significant differential protein abundances in acute rejection (AR). Results: Identifications of 1018 and 349 proteins, Uw and Uexo fractions, respectively, demonstrated a 279 protein overlap between the two urinary compartments with 25%(70) of overlapping proteins unique to Uexoand represented membrane bound proteins (p=9.31e-7). Of 349 urine exosomal proteins identified in transplant patients 220 were not previously identified in the normal urine exosomal fraction. Uexo proteins (11), functioning in the inflammatory / stress response, were more abundant in patients with biopsy-confirmed acute rejection, 3 of which were exclusive to Uexo. Uexo AR-specific biomarkers (8) were also detected in Uw, but since they were observed at significantly lower abundances in Uw, they were not significant for AR in Uw. Conclusions: A rapid urinary exosome isolation method and quantitative measurement of enriched Uexo proteins was applied. Urine proteins specific to the exosomal fraction were detected either in unfractionated urine (at low abundances) or by Uexo fraction analysis. Perturbed proteins in the exosomal compartment of urine collected from kidney transplant patients were

  19. Human Detection System by Fusing Depth Map-Based Method and Convolutional Neural Network-Based Method

    Directory of Open Access Journals (Sweden)

    Anh Vu Le

    2017-01-01

    Full Text Available In this paper, the depth images and the colour images provided by Kinect sensors are used to enhance the accuracy of human detection. The depth-based human detection method is fast but less accurate. On the other hand, the faster region convolutional neural network-based human detection method is accurate but requires a rather complex hardware configuration. To simultaneously leverage the advantages and relieve the drawbacks of each method, one master and one client system is proposed. The final goal is to make a novel Robot Operation System (ROS-based Perception Sensor Network (PSN system, which is more accurate and ready for the real time application. The experimental results demonstrate the outperforming of the proposed method compared with other conventional methods in the challenging scenarios.

  20. Load forecasting method considering temperature effect for distribution network

    Directory of Open Access Journals (Sweden)

    Meng Xiao Fang

    2016-01-01

    Full Text Available To improve the accuracy of load forecasting, the temperature factor was introduced into the load forecasting in this paper. This paper analyzed the characteristics of power load variation, and researched the rule of the load with the temperature change. Based on the linear regression analysis, the mathematical model of load forecasting was presented with considering the temperature effect, and the steps of load forecasting were given. Used MATLAB, the temperature regression coefficient was calculated. Using the load forecasting model, the full-day load forecasting and time-sharing load forecasting were carried out. By comparing and analyzing the forecast error, the results showed that the error of time-sharing load forecasting method was small in this paper. The forecasting method is an effective method to improve the accuracy of load forecasting.

  1. Hybrid pulse pile-up rejection system as applied to Rutherford backscattering

    International Nuclear Information System (INIS)

    Boie, R.A.; Wildnauer, K.R.

    1977-01-01

    The problems of pulse on pulse pile-up and noise limited pile-up rejectors are considered in detail for Rutherford backscattering spectra. The forms of these spectra allow the distortions from pile-up and the residual pile-up after rejection to be understood via a simple model. Extended calculations allow us to predict the effects quite accurately. A new pile-up rejection system is described. The ''linear'' rejection method is implemented with peak stretchers and advantageously combined with an event counting rejector to provide a versatile high performance system

  2. Development of a multivariate tool to reject background in a WZ diboson search for the CDF experiment

    Energy Technology Data Exchange (ETDEWEB)

    Cremonesi, Matteo [Univ. of of Rome Tor Vergata (Italy)

    2015-08-27

    In the frame of the strong on-going data analysis effort of the CDF collaboration at Fermilab, a method was developed by the candidate to improve the background rejection efficiency in the search for associated pair production of electroweak W, Z bosons. The performaces of the method for vetoing the tt background in a WZ/ZZ → fνq$\\bar{q}$ diboson search are reported. The method was developed in the inclusive 2-jets sample and applied to the “tag-2 jets" region, the subsample defined by the request that the two jets carry beauty flavor. In this region the tt production is one of the largest backgrounds. The tt veto proceeds in two steps: first, a set of pre-selection cuts are applied in a candidate sample where up to two leptons are accepted in addition to a jet pair, and the ZZ component of the signal is thus preserved; next, a Neural Network is trained to indicate the probability that the event be top-pair production. To validate the the method as developed in the inclusive 2-jets sample, it is applied to veto region providing a significant rejection of this important background.

  3. A Latent Variable Clustering Method for Wireless Sensor Networks

    DEFF Research Database (Denmark)

    Vasilev, Vladislav; Iliev, Georgi; Poulkov, Vladimir

    2016-01-01

    In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work and...

  4. Bulk Electric Load Cost Calculation Methods: Iraqi Network Comparative Study

    Directory of Open Access Journals (Sweden)

    Qais M. Alias

    2016-09-01

    Full Text Available It is vital in any industry to regain the spent capitals plus running costs and a margin of profits for the industry to flourish. The electricity industry is an everyday life touching industry which follows the same finance-economic strategy. Cost allocation is a major issue in all sectors of the electric industry, viz, generation, transmission and distribution. Generation and distribution service costing’s well documented in the literature, while the transmission share is still of need for research. In this work, the cost of supplying a bulk electric load connected to the EHV system is calculated. A sample basic lump-average method is used to provide a rough costing guide. Also, two transmission pricing methods are employed, namely, the postage-stamp and the load-flow based MW-distance methods to calculate transmission share in the total cost of each individual bulk load. The three costing methods results are then analyzed and compared for the 400kV Iraqi power grid considered for a case study.

  5. Identifying influential spreaders in complex networks based on kshell hybrid method

    Science.gov (United States)

    Namtirtha, Amrita; Dutta, Animesh; Dutta, Biswanath

    2018-06-01

    Influential spreaders are the key players in maximizing or controlling the spreading in a complex network. Identifying the influential spreaders using kshell decomposition method has become very popular in the recent time. In the literature, the core nodes i.e. with the largest kshell index of a network are considered as the most influential spreaders. We have studied the kshell method and spreading dynamics of nodes using Susceptible-Infected-Recovered (SIR) epidemic model to understand the behavior of influential spreaders in terms of its topological location in the network. From the study, we have found that every node in the core area is not the most influential spreader. Even a strategically placed lower shell node can also be a most influential spreader. Moreover, the core area can also be situated at the periphery of the network. The existing indexing methods are only designed to identify the most influential spreaders from core nodes and not from lower shells. In this work, we propose a kshell hybrid method to identify highly influential spreaders not only from the core but also from lower shells. The proposed method comprises the parameters such as kshell power, node's degree, contact distance, and many levels of neighbors' influence potential. The proposed method is evaluated using nine real world network datasets. In terms of the spreading dynamics, the experimental results show the superiority of the proposed method over the other existing indexing methods such as the kshell method, the neighborhood coreness centrality, the mixed degree decomposition, etc. Furthermore, the proposed method can also be applied to large-scale networks by considering the three levels of neighbors' influence potential.

  6. Evidence reasoning method for constructing conditional probability tables in a Bayesian network of multimorbidity.

    Science.gov (United States)

    Du, Yuanwei; Guo, Yubin

    2015-01-01

    The intrinsic mechanism of multimorbidity is difficult to recognize and prediction and diagnosis are difficult to carry out accordingly. Bayesian networks can help to diagnose multimorbidity in health care, but it is difficult to obtain the conditional probability table (CPT) because of the lack of clinically statistical data. Today, expert knowledge and experience are increasingly used in training Bayesian networks in order to help predict or diagnose diseases, but the CPT in Bayesian networks is usually irrational or ineffective for ignoring realistic constraints especially in multimorbidity. In order to solve these problems, an evidence reasoning (ER) approach is employed to extract and fuse inference data from experts using a belief distribution and recursive ER algorithm, based on which evidence reasoning method for constructing conditional probability tables in Bayesian network of multimorbidity is presented step by step. A multimorbidity numerical example is used to demonstrate the method and prove its feasibility and application. Bayesian network can be determined as long as the inference assessment is inferred by each expert according to his/her knowledge or experience. Our method is more effective than existing methods for extracting expert inference data accurately and is fused effectively for constructing CPTs in a Bayesian network of multimorbidity.

  7. 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.

  8. Social network analysis using k-Path centrality method

    Science.gov (United States)

    Taniarza, Natya; Adiwijaya; Maharani, Warih

    2018-03-01

    k-Path centrality is deemed as one of the effective methods to be applied in centrality measurement in which the influential node is estimated as the node that is being passed by information path frequently. Regarding this, k-Path centrality has been employed in the analysis of this paper specifically by adapting random-algorithm approach in order to: (1) determine the influential user’s ranking in a social media Twitter; and (2) ascertain the influence of parameter α in the numeration of k-Path centrality. According to the analysis, the findings showed that the method of k-Path centrality with random-algorithm approach can be used to determine user’s ranking which influences in the dissemination of information in Twitter. Furthermore, the findings also showed that parameter α influenced the duration and the ranking results: the less the α value, the longer the duration, yet the ranking results were more stable.

  9. Lung allograft rejection in the rat. I. Accelerated rejection caused by graft lymphocytes

    International Nuclear Information System (INIS)

    Prop, J.; Nieuwenhuis, P.; Wildevuur, C.R.

    1985-01-01

    To find out to what extent rejection of lungs differs from that of other organs, functional rejection of lung allografts was studied in five combinations of inbred rat strains. Rejection could be monitored accurately by perfusion scintigraphy, and equally well by chest roentgenography. The rejection of lung grafts was found to proceed remarkably fast, when compared with heart grafts, in combinations with strong RT1-incompatibilities. This accelerated rejection pattern could be converted into rejection at a normal pace by pretreatment of the donor with 10 Gy roentgen irradiation one day before transplantation. Donor pretreatment depleted the lung graft's bronchus-associated lymphoid tissue (BALT) of lymphocytes. When grafts were depleted of all other passenger cells as well--by retransplantation from a cyclosporine-treated intermediate host--they showed an even more reduced immunogenicity, probably because of the loss of donor-type dendritic cells. These results indicate that lymphocytes from the BALT of lung grafts are capable of accelerating the rejection response

  10. Large deviations and queueing networks: Methods for rate function identification

    OpenAIRE

    Atar, Rami; Dupuis, Paul

    1999-01-01

    This paper considers the problem of rate function identification for multidimensional queueing models with feedback. A set of techniques are introduced which allow this identification when the model possesses certain structural properties. The main tools used are representation formulas for exponential integrals, weak convergence methods, and the regularity properties of associated Skorokhod Problems. Two examples are treated as special cases of the general theory: the classical Jackson netwo...

  11. Operator splitting method for simulation of dynamic flows in natural gas pipeline networks

    Science.gov (United States)

    Dyachenko, Sergey A.; Zlotnik, Anatoly; Korotkevich, Alexander O.; Chertkov, Michael

    2017-12-01

    We develop an operator splitting method to simulate flows of isothermal compressible natural gas over transmission pipelines. The method solves a system of nonlinear hyperbolic partial differential equations (PDEs) of hydrodynamic type for mass flow and pressure on a metric graph, where turbulent losses of momentum are modeled by phenomenological Darcy-Weisbach friction. Mass flow balance is maintained through the boundary conditions at the network nodes, where natural gas is injected or withdrawn from the system. Gas flow through the network is controlled by compressors boosting pressure at the inlet of the adjoint pipe. Our operator splitting numerical scheme is unconditionally stable and it is second order accurate in space and time. The scheme is explicit, and it is formulated to work with general networks with loops. We test the scheme over range of regimes and network configurations, also comparing its performance with performance of two other state of the art implicit schemes.

  12. Analysis of Heat Transfer in Power Split Device for Hybrid Electric Vehicle Using Thermal Network Method

    Directory of Open Access Journals (Sweden)

    Jixin Wang

    2014-06-01

    Full Text Available This paper presents a rational prediction of temperature field on the differential hybrid system (DHS based on the thermal network method (TNM. The whole thermal network model is built by considering both the contact thermal resistance between gasket and planet gear and the temperature effect on the physical property parameters of lubricant. The contact thermal resistance is obtained by using the concept of contact branch thermal resistance and G-W elastic model. By building an elaborate thermal network model and computing models for power losses and thermal resistances between components, the whole temperature field of DHS under typical operating condition is predicted. Results show that thermal network method can be effectively used to predict the temperature distribution and the rule of temperature variation, the surface roughness significantly affects contact thermal conduction, and the decrease in the thermal resistance of the natural convection between air and DHS housing can effectively improve the thermal environment of DHS.

  13. A Dynamic Linear Hashing Method for Redundancy Management in Train Ethernet Consist Network

    Directory of Open Access Journals (Sweden)

    Xiaobo Nie

    2016-01-01

    Full Text Available Massive transportation systems like trains are considered critical systems because they use the communication network to control essential subsystems on board. Critical system requires zero recovery time when a failure occurs in a communication network. The newly published IEC62439-3 defines the high-availability seamless redundancy protocol, which fulfills this requirement and ensures no frame loss in the presence of an error. This paper adopts these for train Ethernet consist network. The challenge is management of the circulating frames, capable of dealing with real-time processing requirements, fast switching times, high throughout, and deterministic behavior. The main contribution of this paper is the in-depth analysis it makes of network parameters imposed by the application of the protocols to train control and monitoring system (TCMS and the redundant circulating frames discarding method based on a dynamic linear hashing, using the fastest method in order to resolve all the issues that are dealt with.

  14. Modeling community succession and assembly: A novel method for network evolution

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2012-06-01

    Full Text Available The process of modeling community succession and assembly is in some sense a method for network evolution, as done by Barabasi and Albert (1999. It is also one of the methods to create a sample networkfrom the statistic network I proposed earlier. I think that the mechanism of network evolution supposed by Barabasi and Albert is most likely applicable to the natural phenomena with emergency property. For natural phenomena without emergency property, the present study indicated that a scale-free network may be produced through a new mechanism, i.e., whether the connection of a taxon x occurs, dependent on the type and property of taxon y (in particular, the degree of its direct correlation with x to be connected but not necessarily the existing number of connections of taxon y, as proposed in present study.

  15. Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

    Directory of Open Access Journals (Sweden)

    Chen Ganlang

    2017-11-01

    Full Text Available At present, in the fault diagnosis database of submarine optical fiber network, the attribute selection of large data is completed by detecting the attributes of the data, the accuracy of large data attribute selection cannot be guaranteed. In this paper, a large data attribute selection method based on support vector machines (SVM for fault diagnosis database of submarine optical fiber network is proposed. Mining large data in the database of optical fiber network fault diagnosis, and calculate its attribute weight, attribute classification is completed according to attribute weight, so as to complete attribute selection of large data. Experimental results prove that ,the proposed method can improve the accuracy of large data attribute selection in fault diagnosis database of submarine optical fiber network, and has high use value.

  16. Methodical approach to training of IT-professionals based on networking

    Directory of Open Access Journals (Sweden)

    Vyacheslav V. Zolotarev

    2017-12-01

    Full Text Available Increasing requirements to the content and form of higher education in conditions of digital economy set new tasks for professors: formation of applied competences, the involvement of students in project activities, provision of students’ online support, their individual and project work. The growing load on university professors complicates satisfaction of these requirements. The development of the professors’ network interaction makes it possible to redistribute the load for disciplines methodological provision. The article reveals possibilities of professors’ network interaction by using innovative teaching methods including gaming forms and online courses. The research scientific novelty is to implement the professors’ network interaction and experimental application of innovative teaching methods. Network interaction was carried out through the educational process of students’ preparation in following areas: information security, applied information technology, business informatics.

  17. Music genre recognition with risk and rejection

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2013-01-01

    We explore risk and rejection for music genre recognition (MGR) within the minimum risk framework of Bayesian classification. In this way, we attempt to give an MGR system knowledge that some misclassifications are worse than others, and that deferring classification to an expert may be a better...

  18. Development of enhanced sulfur rejection processes

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, R.H.; Luttrell, G.H.; Adel, G.T.; Richardson, P.E.

    1996-03-01

    Research at Virginia Tech led to the development of two complementary concepts for improving the removal of inorganic sulfur from many eastern U.S. coals. These concepts are referred to as Electrochemically Enhanced Sulfur Rejection (EESR) and Polymer Enhanced Sulfur Rejection (PESR) processes. The EESR process uses electrochemical techniques to suppress the formation of hydrophobic oxidation products believed to be responsible for the floatability of coal pyrite. The PESR process uses polymeric reagents that react with pyrite and convert floatable middlings, i.e., composite particles composed of pyrite with coal inclusions, into hydrophilic particles. These new pyritic-sulfur rejection processes do not require significant modifications to existing coal preparation facilities, thereby enhancing their adoptability by the coal industry. It is believed that these processes can be used simultaneously to maximize the rejection of both well-liberated pyrite and composite coal-pyrite particles. The project was initiated on October 1, 1992 and all technical work has been completed. This report is based on the research carried out under Tasks 2-7 described in the project proposal. These tasks include Characterization, Electrochemical Studies, In Situ Monitoring of Reagent Adsorption on Pyrite, Bench Scale Testing of the EESR Process, Bench Scale Testing of the PESR Process, and Modeling and Simulation.

  19. Ferrite grade iron oxides from ore rejects

    Indian Academy of Sciences (India)

    Iron oxyhydroxides and hydroxides were synthesized from chemically beneficiated high SiO2/Al2O3 low-grade iron ore (57.49% Fe2O3) rejects and heated to get iron oxides of 96–99.73% purity. The infrared band positions, isothermal weight loss and thermogravimetric and chemical analysis established the chemical ...

  20. Local graft irradiation in renal transplant rejection

    International Nuclear Information System (INIS)

    Kawamura, Masashi; Kataoka, Masaaki; Itoh, Hisao

    1990-01-01

    From 1977 to 1988, of 142 renal transplantations, seven recipients (4.9%) received local graft irradiation following rejective reaction refractory to antirejection medical managements. Concurrent with the administration of pulsed high dose methylprednisolone and other antirejection medical managements, the graft was irradiated with a total dose of 6.0 Gy-150 cGy per fraction every other day at the midplane of the graft using two opposing portals of 4MX Linac. The fields were defined by palpation and echography. All patients had improvements in serum creatinine on the 10th day after beginning the irradiation. Four patients with peripheral lymphocytosis during the irradiation combined with pulsed high dose methylprednisolone improved in renal functions. On the other hand, out of 3 patients with lymphcytopenic changes, in two the transplanted graft was removed due to deteriorations, and the other patient is currently suffering from chronic rejection. Local graft irradiation can be useful in maintaining a rejective graft and reversing its functions in some patients whose rejective reaction failed to respond to the antirejection medical managements. (author)

  1. Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

    Science.gov (United States)

    Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

    2014-07-01

    Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.

  2. Social networks and regional recruitment of foreign labour: Firm recruitment methods and spatial sorting in Denmark

    DEFF Research Database (Denmark)

    Schmidt, Torben Dall; Jensen, Peter Sandholt

    2012-01-01

    This paper tests the hypothesis that social networks are crucial for regional recruitment and inflows of foreign labour. New survey data on 971 firms located in Region Southern Denmark show that the predominant recruitment method of foreign labour was through networks. Danish municipal data from...... 1997–2006 furthermore reveal spatial sorting since initial shares of employees with a foreign background out of total regional employment predict foreign labour inflow rates to regional employment. Thus, social networks appear crucial for the recruitment and inflows of foreign labour, suggesting...

  3. A Novel Reliability Enhanced Handoff Method in Future Wireless Heterogeneous Networks

    Directory of Open Access Journals (Sweden)

    Wang YuPeng

    2016-01-01

    Full Text Available As the demand increases, future networks will follow the trends of network variety and service flexibility, which requires heterogeneous type of network deployment and reliable communication method. In practice, most communication failure happens due to the bad radio link quality, i.e., high-speed users suffers a lot on the problem of radio link failure, which causes the problem of communication interrupt and radio link recovery. To make the communication more reliable, especially for the high mobility users, we propose a novel communication handoff mechanism to reduce the occurrence of service interrupt. Based on computer simulation, we find that the reliability on the service is greatly improved.

  4. Enhanced water transport and salt rejection through hydrophobic zeolite pores

    Science.gov (United States)

    Humplik, Thomas; Lee, Jongho; O'Hern, Sean; Laoui, Tahar; Karnik, Rohit; Wang, Evelyn N.

    2017-12-01

    The potential of improvements to reverse osmosis (RO) desalination by incorporating porous nanostructured materials such as zeolites into the selective layer in the membrane has spurred substantial research efforts over the past decade. However, because of the lack of methods to probe transport across these materials, it is still unclear which pore size or internal surface chemistry is optimal for maximizing permeability and salt rejection. We developed a platform to measure the transport of water and salt across a single layer of zeolite crystals, elucidating the effects of internal wettability on water and salt transport through the ≈5.5 Å pores of MFI zeolites. MFI zeolites with a more hydrophobic (i.e., less attractive) internal surface chemistry facilitated an approximately order of magnitude increase in water permeability compared to more hydrophilic MFI zeolites, while simultaneously fully rejecting both potassium and chlorine ions. However, our results also demonstrated approximately two orders of magnitude lower permeability compared to molecular simulations. This decreased performance suggests that additional transport resistances (such as surface barriers, pore collapse or blockages due to contamination) may be limiting the performance of experimental nanostructured membranes. Nevertheless, the inclusion of hydrophobic sub-nanometer pores into the active layer of RO membranes should improve both the water permeability and salt rejection of future RO membranes (Fasano et al 2016 Nat. Commun. 7 12762).

  5. Exploiting Reject Option in Classification for Social Discrimination Control

    KAUST Repository

    Kamiran, Faisal

    2017-09-29

    Social discrimination is said to occur when an unfavorable decision for an individual is influenced by her membership to certain protected groups such as females and minority ethnic groups. Such discriminatory decisions often exist in historical data. Despite recent works in discrimination-aware data mining, there remains the need for robust, yet easily usable, methods for discrimination control. In this paper, we utilize reject option in classification, a general decision theoretic framework for handling instances whose labels are uncertain, for modeling and controlling discriminatory decisions. Specifically, this framework permits a formal treatment of the intuition that instances close to the decision boundary are more likely to be discriminated in a dataset. Based on this framework, we present three different solutions for discrimination-aware classification. The first solution invokes probabilistic rejection in single or multiple probabilistic classifiers while the second solution relies upon ensemble rejection in classifier ensembles. The third solution integrates one of the first two solutions with situation testing which is a procedure commonly used in the court of law. All solutions are easy to use and provide strong justifications for the decisions. We evaluate our solutions extensively on four real-world datasets and compare their performances with previously proposed discrimination-aware classifiers. The results demonstrate the superiority of our solutions in terms of both performance and flexibility of applicability. In particular, our solutions are effective at removing illegal discrimination from the predictions.

  6. Late-onset acute rejection after living donor liver transplantation

    Institute of Scientific and Technical Information of China (English)

    Nobuhisa Akamatsu; Yasuhiko Sugawara; Sumihito Tamura; Junichi Keneko; Yuichi Matsui; Kiyoshi Hasegawa; Masatoshi Makuuchi

    2006-01-01

    AIM: To investigate the incidence and risk factors of late-onset acute rejection (LAR) and to clarify the effectiveness of our immunosuppressive regime consisting of life-long administration of tacrolimus and steroids.METHODS: Adult living donor liver transplantation recipients (n = 204) who survived more than 6 mo after living donor liver transplantation were enrolled.Immunosuppression was achieved using tacrolimus and methylprednisolone. When adverse effects of tacrolimus were detected, the patient was switched to cyclosporine. Six months after transplantation,tacrolimus or cyclosporine was carefully maintained at a therapeutic level. The methylprednisolone dosage was maintained at 0.05 mg/kg per day by oral administration.Acute rejections that occurred more than 6 mo after the operation were defined as late-onset. The median followup period was 34 mo.RESULTS: LAR was observed in 15 cases (7%) and no chronic rejection was observed. The incidence of hyperlipidemia, chronic renal failure, new-onset posttransplantation diabetes, and deep fungal infection were 13%, 2%, 24%, and 17%, respectively. Conversion from tacrolimus to cyclosporine was required in 38 patients (19%). Multivariate analysis revealed that a cyclosporinebased regimen was significantly associated with LAR.CONCLUSION: Both LAR and drug-induced adverse events happen at a low incidence, supporting the safety and efficacy of the present immunosuppression regimen for living donor liver transplantation.

  7. Redox-Dependent Inflammation in Islet Transplantation Rejection

    Directory of Open Access Journals (Sweden)

    Jessie M. Barra

    2018-04-01

    Full Text Available Type 1 diabetes is an autoimmune disease that results in the progressive destruction of insulin-producing pancreatic β-cells inside the islets of Langerhans. The loss of this vital population leaves patients with a lifelong dependency on exogenous insulin and puts them at risk for life-threatening complications. One method being investigated to help restore insulin independence in these patients is islet cell transplantation. However, challenges associated with transplant rejection and islet viability have prevented long-term β-cell function. Redox signaling and the production of reactive oxygen species (ROS by recipient immune cells and transplanted islets themselves are key players in graft rejection. Therefore, dissipation of ROS generation is a viable intervention that can protect transplanted islets from immune-mediated destruction. Here, we will discuss the newly appreciated role of redox signaling and ROS synthesis during graft rejection as well as new strategies being tested for their efficacy in redox modulation during islet cell transplantation.

  8. Redox-Dependent Inflammation in Islet Transplantation Rejection

    Science.gov (United States)

    Barra, Jessie M.; Tse, Hubert M.

    2018-01-01

    Type 1 diabetes is an autoimmune disease that results in the progressive destruction of insulin-producing pancreatic β-cells inside the islets of Langerhans. The loss of this vital population leaves patients with a lifelong dependency on exogenous insulin and puts them at risk for life-threatening complications. One method being investigated to help restore insulin independence in these patients is islet cell transplantation. However, challenges associated with transplant rejection and islet viability have prevented long-term β-cell function. Redox signaling and the production of reactive oxygen species (ROS) by recipient immune cells and transplanted islets themselves are key players in graft rejection. Therefore, dissipation of ROS generation is a viable intervention that can protect transplanted islets from immune-mediated destruction. Here, we will discuss the newly appreciated role of redox signaling and ROS synthesis during graft rejection as well as new strategies being tested for their efficacy in redox modulation during islet cell transplantation. PMID:29740396

  9. Enhanced water transport and salt rejection through hydrophobic zeolite pores.

    Science.gov (United States)

    Humplik, Thomas; Lee, Jongho; O'Hern, Sean; Laoui, Tahar; Karnik, Rohit; Wang, Evelyn N

    2017-12-15

    The potential of improvements to reverse osmosis (RO) desalination by incorporating porous nanostructured materials such as zeolites into the selective layer in the membrane has spurred substantial research efforts over the past decade. However, because of the lack of methods to probe transport across these materials, it is still unclear which pore size or internal surface chemistry is optimal for maximizing permeability and salt rejection. We developed a platform to measure the transport of water and salt across a single layer of zeolite crystals, elucidating the effects of internal wettability on water and salt transport through the ≈5.5 Å pores of MFI zeolites. MFI zeolites with a more hydrophobic (i.e., less attractive) internal surface chemistry facilitated an approximately order of magnitude increase in water permeability compared to more hydrophilic MFI zeolites, while simultaneously fully rejecting both potassium and chlorine ions. However, our results also demonstrated approximately two orders of magnitude lower permeability compared to molecular simulations. This decreased performance suggests that additional transport resistances (such as surface barriers, pore collapse or blockages due to contamination) may be limiting the performance of experimental nanostructured membranes. Nevertheless, the inclusion of hydrophobic sub-nanometer pores into the active layer of RO membranes should improve both the water permeability and salt rejection of future RO membranes (Fasano et al 2016 Nat. Commun. 7 12762).

  10. Is the bitter rejection response always adaptive?

    Science.gov (United States)

    Glendinning, J I

    1994-12-01

    The bitter rejection response consists of a suite of withdrawal reflexes and negative affective responses. It is generally assumed to have evolved as a way to facilitate avoidance of foods that are poisonous because they usually taste bitter to humans. Using previously published studies, the present paper examines the relationship between bitterness and toxicity in mammals, and then assesses the ecological costs and benefits of the bitter rejection response in carnivorous, omnivorous, and herbivorous (grazing and browsing) mammals. If the bitter rejection response accurately predicts the potential toxicity of foods, then one would expect the threshold for the response to be lower for highly toxic compounds than for nontoxic compounds. The data revealed no such relationship. Bitter taste thresholds varied independently of toxicity thresholds, indicating that the bitter rejection response is just as likely to be elicited by a harmless bitter food as it is by a harmful one. Thus, it is not necessarily in an animal's best interest to have an extremely high or low bitter threshold. Based on this observation, it was hypothesized that the adaptiveness of the bitter rejection response depends upon the relative occurrence of bitter and potentially toxic compounds in an animal's diet. Animals with a relatively high occurrence of bitter and potentially toxic compounds in their diet (e.g., browsing herbivores) were predicted to have evolved a high bitter taste threshold and tolerance to dietary poisons. Such an adaptation would be necessary because a browser cannot "afford" to reject all foods that are bitter and potentially toxic without unduly restricting its dietary options. At the other extreme, animals that rarely encounter bitter and potentially toxic compounds in their diet (e.g., carnivores) were predicted to have evolved a low bitter threshold. Carnivores could "afford" to utilize such a stringent rejection mechanism because foods containing bitter and potentially

  11. Noise analysis method for monitoring the moderator temperature coefficient of pressurized water reactors: Neural network calibration

    International Nuclear Information System (INIS)

    Thomas, J.R. Jr.; Adams, J.T.

    1994-01-01

    A neural network was trained with data for the frequency response function between in-core neutron noise and core-exit thermocouple noise in a pressurized water reactor, with the moderator temperature coefficient (MTC) as target. The trained network was subsequently used to predict the MTC at other points in the same fuel cycle. Results support use of the method for operating pressurized water reactors provided noise data can be accumulated for several fuel cycles to provide a training base

  12. A Grooming Nodes Optimal Allocation Method for Multicast in WDM Networks

    Directory of Open Access Journals (Sweden)

    Chengying Wei

    2016-01-01

    Full Text Available The grooming node has the capability of grooming multicast traffic with the small granularity into established light at high cost of complexity and node architecture. In the paper, a grooming nodes optimal allocation (GNOA method is proposed to optimize the allocation of the grooming nodes constraint by the blocking probability for multicast traffic in sparse WDM networks. In the proposed GNOA method, the location of each grooming node is determined by the SCLD strategy. The improved smallest cost largest degree (SCLD strategy is designed to select the nongrooming nodes in the proposed GNOA method. The simulation results show that the proposed GNOA method can reduce the required number of grooming nodes and decrease the cost of constructing a network to guarantee a certain request blocking probability when the wavelengths per fiber and transmitter/receiver ports per node are sufficient for the optical multicast in WDM networks.

  13. Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Rui Guo

    2018-03-01

    Full Text Available Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.

  14. First-order design of geodetic networks using the simulated annealing method

    Science.gov (United States)

    Berné, J. L.; Baselga, S.

    2004-09-01

    The general problem of the optimal design for a geodetic network subject to any extrinsic factors, namely the first-order design problem, can be dealt with as a numeric optimization problem. The classic theory of this problem and the optimization methods are revised. Then the innovative use of the simulated annealing method, which has been successfully applied in other fields, is presented for this classical geodetic problem. This method, belonging to iterative heuristic techniques in operational research, uses a thermodynamical analogy to crystalline networks to offer a solution that converges probabilistically to the global optimum. Basic formulation and some examples are studied.

  15. Traffic Management by Using Admission Control Methods in Multiple Node IMS Network

    Directory of Open Access Journals (Sweden)

    Filip Chamraz

    2016-01-01

    Full Text Available The paper deals with Admission Control methods (AC as a possible solution for traffic management in IMS networks (IP Multimedia Subsystem - from the point of view of an efficient redistribution of the available network resources and keeping the parameters of Quality of Service (QoS. The paper specifically aims at the selection of the most appropriate method for the specific type of traffic and traffic management concept using AC methods on multiple nodes. The potential benefit and disadvantage of the used solution is evaluated.

  16. A New Method for Studying the Periodic System Based on a Kohonen Neural Network

    Science.gov (United States)

    Chen, David Zhekai

    2010-01-01

    A new method for studying the periodic system is described based on the combination of a Kohonen neural network and a set of chemical and physical properties. The classification results are directly shown in a two-dimensional map and easy to interpret. This is one of the major advantages of this approach over other methods reported in the…

  17. Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods

    NARCIS (Netherlands)

    Suryanarayana, Gowri; Lago Garcia, J.; Geysen, Davy; Aleksiejuk, Piotr; Johansson, Christian

    2018-01-01

    Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear

  18. BIMLR: a method for constructing rooted phylogenetic networks from rooted phylogenetic trees.

    Science.gov (United States)

    Wang, Juan; Guo, Maozu; Xing, Linlin; Che, Kai; Liu, Xiaoyan; Wang, Chunyu

    2013-09-15

    Rooted phylogenetic trees constructed from different datasets (e.g. from different genes) are often conflicting with one another, i.e. they cannot be integrated into a single phylogenetic tree. Phylogenetic networks have become an important tool in molecular evolution, and rooted phylogenetic networks are able to represent conflicting rooted phylogenetic trees. Hence, the development of appropriate methods to compute rooted phylogenetic networks from rooted phylogenetic trees has attracted considerable research interest of late. The CASS algorithm proposed by van Iersel et al. is able to construct much simpler networks than other available methods, but it is extremely slow, and the networks it constructs are dependent on the order of the input data. Here, we introduce an improved CASS algorithm, BIMLR. We show that BIMLR is faster than CASS and less dependent on the input data order. Moreover, BIMLR is able to construct much simpler networks than almost all other methods. BIMLR is available at http://nclab.hit.edu.cn/wangjuan/BIMLR/. © 2013 Elsevier B.V. All rights reserved.

  19. PAFit: A Statistical Method for Measuring Preferential Attachment in Temporal Complex Networks.

    Directory of Open Access Journals (Sweden)

    Thong Pham

    Full Text Available Preferential attachment is a stochastic process that has been proposed to explain certain topological features characteristic of complex networks from diverse domains. The systematic investigation of preferential attachment is an important area of research in network science, not only for the theoretical matter of verifying whether this hypothesized process is operative in real-world networks, but also for the practical insights that follow from knowledge of its functional form. Here we describe a maximum likelihood based estimation method for the measurement of preferential attachment in temporal complex networks. We call the method PAFit, and implement it in an R package of the same name. PAFit constitutes an advance over previous methods primarily because we based it on a nonparametric statistical framework that enables attachment kernel estimation free of any assumptions about its functional form. We show this results in PAFit outperforming the popular methods of Jeong and Newman in Monte Carlo simulations. What is more, we found that the application of PAFit to a publically available Flickr social network dataset yielded clear evidence for a deviation of the attachment kernel from the popularly assumed log-linear form. Independent of our main work, we provide a correction to a consequential error in Newman's original method which had evidently gone unnoticed since its publication over a decade ago.

  20. Germanium cryogenic detectors: Alpha surface events rejection capabilities

    International Nuclear Information System (INIS)

    Fiorucci, S.; Broniatowski, A.; Chardin, G.; Censier, B.; Lesquen, A. de; Deschamps, H.; Fesquet, M.; Jin, Y.

    2006-01-01

    Alpha surface events and multiple compton gamma interactions are the two major background components in Ge detectors for double-beta decay investigations. Two different methods have been studied to identify such type of events, using cryogenic Ge detectors developed primarily for dark matter search: (i) combined heat and ionization measurements, and (ii) pulse-shape analysis of the charge collection signals. Both methods show strong separation between electron recoil events and surface alphas. Cryogenic heat-ionization detectors therefore appear able to reject virtually all surface alpha interactions

  1. Method and system for a network mapping service

    Science.gov (United States)

    Bynum, Leo

    2017-10-17

    A method and system of publishing a map includes providing access to a plurality of map data files or mapping services between at least one publisher and at least one subscriber; defining a map in a map context comprising parameters and descriptors to substantially duplicate a map by reference to mutually accessible data or mapping services, publishing a map to a channel in a table file on server; accessing the channel by at least one subscriber, transmitting the mapping context from the server to the at least one subscriber, executing the map context by the at least one subscriber, and generating the map on a display software associated with the at least one subscriber by reconstituting the map from the references and other data in the mapping context.

  2. Protein complex detection in PPI networks based on data integration and supervised learning method.

    Science.gov (United States)

    Yu, Feng; Yang, Zhi; Hu, Xiao; Sun, Yuan; Lin, Hong; Wang, Jian

    2015-01-01

    Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection. The new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks. The experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively. The experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.

  3. Simulation of the transient processes of load rejection under different accident conditions in a hydroelectric generating set

    Science.gov (United States)

    Guo, W. C.; Yang, J. D.; Chen, J. P.; Peng, Z. Y.; Zhang, Y.; Chen, C. C.

    2016-11-01

    Load rejection test is one of the essential tests that carried out before the hydroelectric generating set is put into operation formally. The test aims at inspecting the rationality of the design of the water diversion and power generation system of hydropower station, reliability of the equipment of generating set and the dynamic characteristics of hydroturbine governing system. Proceeding from different accident conditions of hydroelectric generating set, this paper presents the transient processes of load rejection corresponding to different accident conditions, and elaborates the characteristics of different types of load rejection. Then the numerical simulation method of different types of load rejection is established. An engineering project is calculated to verify the validity of the method. Finally, based on the numerical simulation results, the relationship among the different types of load rejection and their functions on the design of hydropower station and the operation of load rejection test are pointed out. The results indicate that: The load rejection caused by the accident within the hydroelectric generating set is realized by emergency distributing valve, and it is the basis of the optimization for the closing law of guide vane and the calculation of regulation and guarantee. The load rejection caused by the accident outside the hydroelectric generating set is realized by the governor. It is the most efficient measure to inspect the dynamic characteristics of hydro-turbine governing system, and its closure rate of guide vane set in the governor depends on the optimization result in the former type load rejection.

  4. A geometrically based method for predicting stress-induced fracture aperture and flow in discrete fracture networks

    DEFF Research Database (Denmark)

    Bisdom, Kevin; Bertotti, Giovanni; Nick, Hamid

    2016-01-01

    networks, digitized from outcropping pavements. These networks cover a wide range of possible geometries and spatial distributions. The geometrically based method predicts the average hydraulic aperture and equivalent permeability of fractured porous media with error margins of less than 5%....

  5. Increased circulating follicular helper T cells with decreased programmed death-1 in chronic renal allograft rejection

    OpenAIRE

    Shi, Jian; Luo, Fengbao; Shi, Qianqian; Xu, Xianlin; He, Xiaozhou; Xia, Ying

    2015-01-01

    Background Chronic antibody-mediated rejection is a major issue that affects long-term renal allograft survival. Since follicular helper T (Tfh) cells promote the development of antigen-specific B cells in alloimmune responses, we investigated the potential roles of Tfh cells, B cells and their alloimmune-regulating molecules in the pathogenesis of chronic renal allograft rejection in this study. Methods The frequency of Tfh, B cells and the levels of their alloimmune-regulating molecules inc...

  6. Inference of directed climate networks: role of instability of causality estimation methods

    Science.gov (United States)

    Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

    2013-04-01

    Climate data are increasingly analyzed by complex network analysis methods, including graph-theoretical approaches [1]. For such analysis, links between localized nodes of climate network are typically quantified by some statistical measures of dependence (connectivity) between measured variables of interest. To obtain information on the directionality of the interactions in the networks, a wide range of methods exists. These can be broadly divided into linear and nonlinear methods, with some of the latter having the theoretical advantage of being model-free, and principally a generalization of the former [2]. However, as a trade-off, this generality comes together with lower accuracy - in particular if the system was close to linear. In an overall stationary system, this may potentially lead to higher variability in the nonlinear network estimates. Therefore, with the same control of false alarms, this may lead to lower sensitivity for detection of real changes in the network structure. These problems are discussed on the example of daily SAT and SLP data from the NCEP/NCAR reanalysis dataset. We first reduce the dimensionality of data using PCA with VARIMAX rotation to detect several dozens of components that together explain most of the data variability. We further construct directed climate networks applying a selection of most widely used methods - variants of linear Granger causality and conditional mutual information. Finally, we assess the stability of the detected directed climate networks by computing them in sliding time windows. To understand the origin of the observed instabilities and their range, we also apply the same procedure to two types of surrogate data: either with non-stationarity in network structure removed, or imposed in a controlled way. In general, the linear methods show stable results in terms of overall similarity of directed climate networks inferred. For instance, for different decades of SAT data, the Spearman correlation of edge

  7. Least-squares methods for identifying biochemical regulatory networks from noisy measurements

    Directory of Open Access Journals (Sweden)

    Heslop-Harrison Pat

    2007-01-01

    Full Text Available Abstract Background We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS. The Total Least Squares (TLS technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks. Results The superior performance of the CTLS method in identifying network interactions is demonstrated on three examples: a genetic network containing four genes, a network describing p53 activity and mdm2 messenger RNA interactions, and a recently proposed kinetic model for interleukin (IL-6 and (IL-12b messenger RNA expression as a function of ATF3 and NF-κB promoter binding. For the first example, the CTLS significantly reduces the errors in the estimation of the Jacobian for the gene network. For the second, the CTLS reduces the errors from the measurements that are corrupted by white noise and the effect of neglected kinetics. For the third, it allows the correct identification, from noisy data, of the negative regulation of (IL-6 and (IL-12b by ATF3. Conclusion The significant improvements in performance demonstrated by the CTLS method under the wide range of conditions tested here, including different levels and types of measurement noise and different numbers of data points, suggests that its application will enable

  8. Efficient and accurate Greedy Search Methods for mining functional modules in protein interaction networks.

    Science.gov (United States)

    He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei

    2012-06-25

    Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the

  9. A method for under-sampled ecological network data analysis: plant-pollination as case study

    Directory of Open Access Journals (Sweden)

    Peter B. Sorensen

    2012-01-01

    Full Text Available In this paper, we develop a method, termed the Interaction Distribution (ID method, for analysis of quantitative ecological network data. In many cases, quantitative network data sets are under-sampled, i.e. many interactions are poorly sampled or remain unobserved. Hence, the output of statistical analyses may fail to differentiate between patterns that are statistical artefacts and those which are real characteristics of ecological networks. The ID method can support assessment and inference of under-sampled ecological network data. In the current paper, we illustrate and discuss the ID method based on the properties of plant-animal pollination data sets of flower visitation frequencies. However, the ID method may be applied to other types of ecological networks. The method can supplement existing network analyses based on two definitions of the underlying probabilities for each combination of pollinator and plant species: (1, pi,j: the probability for a visit made by the i’th pollinator species to take place on the j’th plant species; (2, qi,j: the probability for a visit received by the j’th plant species to be made by the i’th pollinator. The method applies the Dirichlet distribution to estimate these two probabilities, based on a given empirical data set. The estimated mean values for pi,j and qi,j reflect the relative differences between recorded numbers of visits for different pollinator and plant species, and the estimated uncertainty of pi,j and qi,j decreases with higher numbers of recorded visits.

  10. An ME-PC Enhanced HDMR Method for Efficient Statistical Analysis of Multiconductor Transmission Line Networks

    KAUST Repository

    Yucel, Abdulkadir C.

    2015-05-05

    An efficient method for statistically characterizing multiconductor transmission line (MTL) networks subject to a large number of manufacturing uncertainties is presented. The proposed method achieves its efficiency by leveraging a high-dimensional model representation (HDMR) technique that approximates observables (quantities of interest in MTL networks, such as voltages/currents on mission-critical circuits) in terms of iteratively constructed component functions of only the most significant random variables (parameters that characterize the uncertainties in MTL networks, such as conductor locations and widths, and lumped element values). The efficiency of the proposed scheme is further increased using a multielement probabilistic collocation (ME-PC) method to compute the component functions of the HDMR. The ME-PC method makes use of generalized polynomial chaos (gPC) expansions to approximate the component functions, where the expansion coefficients are expressed in terms of integrals of the observable over the random domain. These integrals are numerically evaluated and the observable values at the quadrature/collocation points are computed using a fast deterministic simulator. The proposed method is capable of producing accurate statistical information pertinent to an observable that is rapidly varying across a high-dimensional random domain at a computational cost that is significantly lower than that of gPC or Monte Carlo methods. The applicability, efficiency, and accuracy of the method are demonstrated via statistical characterization of frequency-domain voltages in parallel wire, interconnect, and antenna corporate feed networks.

  11. Problem-Solving Methods for the Prospective Development of Urban Power Distribution Network

    Directory of Open Access Journals (Sweden)

    A. P. Karpenko

    2014-01-01

    Full Text Available This article succeeds the former A. P. K nko’ and A. I. Kuzmina’ ubl t on titled "A mathematical model of urban distribution electro-network considering its future development" (electronic scientific and technical magazine "Science and education" No. 5, 2014.The article offers a model of urban power distribution network as a set of transformer and distribution substations and cable lines. All elements of the network and new consumers are determined owing to vectors of parameters consistent with them.A problem of the urban power distribution network design, taking into account a prospective development of the city, is presented as a problem of discrete programming. It is in deciding on the optimal option to connect new consumers to the power supply network, on the number and sites to build new substations, and on the option to include them in the power supply network.Two methods, namely a reduction method for a set the nested tasks of global minimization and a decomposition method are offered to solve the problem.In reduction method the problem of prospective development of power supply network breaks into three subtasks of smaller dimension: a subtask to define the number and sites of new transformer and distribution substations, a subtask to define the option to connect new consumers to the power supply network, and a subtask to include new substations in the power supply network. The vector of the varied parameters is broken into three subvectors consistent with the subtasks. Each subtask is solved using an area of admissible vector values of the varied parameters at the fixed components of the subvectors obtained when solving the higher subtasks.In decomposition method the task is presented as a set of three, similar to reduction method, reductions of subtasks and a problem of coordination. The problem of coordination specifies a sequence of the subtasks solution, defines the moment of calculation termination. Coordination is realized by

  12. WMAXC: a weighted maximum clique method for identifying condition-specific sub-network.

    Directory of Open Access Journals (Sweden)

    Bayarbaatar Amgalan

    Full Text Available Sub-networks can expose complex patterns in an entire bio-molecular network by extracting interactions that depend on temporal or condition-specific contexts. When genes interact with each other during cellular processes, they may form differential co-expression patterns with other genes across different cell states. The identification of condition-specific sub-networks is of great importance in investigating how a living cell adapts to environmental changes. In this work, we propose the weighted MAXimum clique (WMAXC method to identify a condition-specific sub-network. WMAXC first proposes scoring functions that jointly measure condition-specific changes to both individual genes and gene-gene co-expressions. It then employs a weaker formula of a general maximum clique problem and relates the maximum scored clique of a weighted graph to the optimization of a quadratic objective function under sparsity constraints. We combine a continuous genetic algorithm and a projection procedure to obtain a single optimal sub-network that maximizes the objective function (scoring function over the standard simplex (sparsity constraints. We applied the WMAXC method to both simulated data and real data sets of ovarian and prostate cancer. Compared with previous methods, WMAXC selected a large fraction of cancer-related genes, which were enriched in cancer-related pathways. The results demonstrated that our method efficiently captured a subset of genes relevant under the investigated condition.

  13. msiDBN: A Method of Identifying Critical Proteins in Dynamic PPI Networks

    Directory of Open Access Journals (Sweden)

    Yuan Zhang

    2014-01-01

    Full Text Available Dynamics of protein-protein interactions (PPIs reveals the recondite principles of biological processes inside a cell. Shown in a wealth of study, just a small group of proteins, rather than the majority, play more essential roles at crucial points of biological processes. This present work focuses on identifying these critical proteins exhibiting dramatic structural changes in dynamic PPI networks. First, a comprehensive way of modeling the dynamic PPIs is presented which simultaneously analyzes the activity of proteins and assembles the dynamic coregulation correlation between proteins at each time point. Second, a novel method is proposed, named msiDBN, which models a common representation of multiple PPI networks using a deep belief network framework and analyzes the reconstruction errors and the variabilities across the time courses in the biological process. Experiments were implemented on data of yeast cell cycles. We evaluated our network construction method by comparing the functional representations of the derived networks with two other traditional construction methods. The ranking results of critical proteins in msiDBN were compared with the results from the baseline methods. The results of comparison showed that msiDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.

  14. Application of network methods for understanding evolutionary dynamics in discrete habitats.

    Science.gov (United States)

    Greenbaum, Gili; Fefferman, Nina H

    2017-06-01

    In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene-flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population-genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene-flow patterns. In the last decades, network theory - a branch of discrete mathematics concerned with complex interactions between discrete elements - has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population-genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology. © 2017 John Wiley & Sons Ltd.

  15. Appplication of statistical mechanical methods to the modeling of social networks

    Science.gov (United States)

    Strathman, Anthony Robert

    With the recent availability of large-scale social data sets, social networks have become open to quantitative analysis via the methods of statistical physics. We examine the statistical properties of a real large-scale social network, generated from cellular phone call-trace logs. We find this network, like many other social networks to be assortative (r = 0.31) and clustered (i.e., strongly transitive, C = 0.21). We measure fluctuation scaling to identify the presence of internal structure in the network and find that structural inhomogeneity effectively disappears at the scale of a few hundred nodes, though there is no sharp cutoff. We introduce an agent-based model of social behavior, designed to model the formation and dissolution of social ties. The model is a modified Metropolis algorithm containing agents operating under the basic sociological constraints of reciprocity, communication need and transitivity. The model introduces the concept of a social temperature. We go on to show that this simple model reproduces the global statistical network features (incl. assortativity, connected fraction, mean degree, clustering, and mean shortest path length) of the real network data and undergoes two phase transitions, one being from a "gas" to a "liquid" state and the second from a liquid to a glassy state as function of this social temperature.

  16. Neural networks and traditional time series methods: a synergistic combination in state economic forecasts.

    Science.gov (United States)

    Hansen, J V; Nelson, R D

    1997-01-01

    Ever since the initial planning for the 1997 Utah legislative session, neural-network forecasting techniques have provided valuable insights for analysts forecasting tax revenues. These revenue estimates are critically important since agency budgets, support for education, and improvements to infrastructure all depend on their accuracy. Underforecasting generates windfalls that concern taxpayers, whereas overforecasting produces budget shortfalls that cause inadequately funded commitments. The pattern finding ability of neural networks gives insightful and alternative views of the seasonal and cyclical components commonly found in economic time series data. Two applications of neural networks to revenue forecasting clearly demonstrate how these models complement traditional time series techniques. In the first, preoccupation with a potential downturn in the economy distracts analysis based on traditional time series methods so that it overlooks an emerging new phenomenon in the data. In this case, neural networks identify the new pattern that then allows modification of the time series models and finally gives more accurate forecasts. In the second application, data structure found by traditional statistical tools allows analysts to provide neural networks with important information that the networks then use to create more accurate models. In summary, for the Utah revenue outlook, the insights that result from a portfolio of forecasts that includes neural networks exceeds the understanding generated from strictly statistical forecasting techniques. In this case, the synergy clearly results in the whole of the portfolio of forecasts being more accurate than the sum of the individual parts.

  17. A DDoS Attack Detection Method Based on SVM in Software Defined Network

    Directory of Open Access Journals (Sweden)

    Jin Ye

    2018-01-01

    Full Text Available The detection of DDoS attacks is an important topic in the field of network security. The occurrence of software defined network (SDN (Zhang et al., 2018 brings up some novel methods to this topic in which some deep learning algorithm is adopted to model the attack behavior based on collecting from the SDN controller. However, the existing methods such as neural network algorithm are not practical enough to be applied. In this paper, the SDN environment by mininet and floodlight (Ning et al., 2014 simulation platform is constructed, 6-tuple characteristic values of the switch flow table is extracted, and then DDoS attack model is built by combining the SVM classification algorithms. The experiments show that average accuracy rate of our method is 95.24% with a small amount of flow collecting. Our work is of good value for the detection of DDoS attack in SDN.

  18. Method and Apparatus for Predicting Unsteady Pressure and Flow Rate Distribution in a Fluid Network

    Science.gov (United States)

    Majumdar, Alok K. (Inventor)

    2009-01-01

    A method and apparatus for analyzing steady state and transient flow in a complex fluid network, modeling phase changes, compressibility, mixture thermodynamics, external body forces such as gravity and centrifugal force and conjugate heat transfer. In some embodiments, a graphical user interface provides for the interactive development of a fluid network simulation having nodes and branches. In some embodiments, mass, energy, and specific conservation equations are solved at the nodes, and momentum conservation equations are solved in the branches. In some embodiments, contained herein are data objects for computing thermodynamic and thermophysical properties for fluids. In some embodiments, the systems of equations describing the fluid network are solved by a hybrid numerical method that is a combination of the Newton-Raphson and successive substitution methods.

  19. Neural network CT image reconstruction method for small amount of projection data

    CERN Document Server

    Ma, X F; Takeda, T

    2000-01-01

    This paper presents a new method for two-dimensional image reconstruction by using a multi-layer neural network. Though a conventionally used object function of such a neural network is composed of a sum of squared errors of the output data, we define an object function composed of a sum of squared residuals of an integral equation. By employing an appropriate numerical line integral for this integral equation, we can construct a neural network which can be used for CT image reconstruction for cases with small amount of projection data. We applied this method to some model problems and obtained satisfactory results. This method is especially useful for analyses of laboratory experiments or field observations where only a small amount of projection data is available in comparison with the well-developed medical applications.

  20. Neural network CT image reconstruction method for small amount of projection data

    International Nuclear Information System (INIS)

    Ma, X.F.; Fukuhara, M.; Takeda, T.

    2000-01-01

    This paper presents a new method for two-dimensional image reconstruction by using a multi-layer neural network. Though a conventionally used object function of such a neural network is composed of a sum of squared errors of the output data, we define an object function composed of a sum of squared residuals of an integral equation. By employing an appropriate numerical line integral for this integral equation, we can construct a neural network which can be used for CT image reconstruction for cases with small amount of projection data. We applied this method to some model problems and obtained satisfactory results. This method is especially useful for analyses of laboratory experiments or field observations where only a small amount of projection data is available in comparison with the well-developed medical applications

  1. A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Fangzhao Li

    2018-01-01

    Full Text Available Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

  2. A semi-learning algorithm for noise rejection: an fNIRS study on ADHD children

    Science.gov (United States)

    Sutoko, Stephanie; Funane, Tsukasa; Katura, Takusige; Sato, Hiroki; Kiguchi, Masashi; Maki, Atsushi; Monden, Yukifumi; Nagashima, Masako; Yamagata, Takanori; Dan, Ippeita

    2017-02-01

    In pediatrics studies, the quality of functional near infrared spectroscopy (fNIRS) signals is often reduced by motion artifacts. These artifacts likely mislead brain functionality analysis, causing false discoveries. While noise correction methods and their performance have been investigated, these methods require several parameter assumptions that apparently result in noise overfitting. In contrast, the rejection of noisy signals serves as a preferable method because it maintains the originality of the signal waveform. Here, we describe a semi-learning algorithm to detect and eliminate noisy signals. The algorithm dynamically adjusts noise detection according to the predetermined noise criteria, which are spikes, unusual activation values (averaged amplitude signals within the brain activation period), and high activation variances (among trials). Criteria were sequentially organized in the algorithm and orderly assessed signals based on each criterion. By initially setting an acceptable rejection rate, particular criteria causing excessive data rejections are neglected, whereas others with tolerable rejections practically eliminate noises. fNIRS data measured during the attention response paradigm (oddball task) in children with attention deficit/hyperactivity disorder (ADHD) were utilized to evaluate and optimize the algorithm's performance. This algorithm successfully substituted the visual noise identification done in the previous studies and consistently found significantly lower activation of the right prefrontal and parietal cortices in ADHD patients than in typical developing children. Thus, we conclude that the semi-learning algorithm confers more objective and standardized judgment for noise rejection and presents a promising alternative to visual noise rejection

  3. Comparison study on qualitative and quantitative risk assessment methods for urban natural gas pipeline network.

    Science.gov (United States)

    Han, Z Y; Weng, W G

    2011-05-15

    In this paper, a qualitative and a quantitative risk assessment methods for urban natural gas pipeline network are proposed. The qualitative method is comprised of an index system, which includes a causation index, an inherent risk index, a consequence index and their corresponding weights. The quantitative method consists of a probability assessment, a consequences analysis and a risk evaluation. The outcome of the qualitative method is a qualitative risk value, and for quantitative method the outcomes are individual risk and social risk. In comparison with previous research, the qualitative method proposed in this paper is particularly suitable for urban natural gas pipeline network, and the quantitative method takes different consequences of accidents into consideration, such as toxic gas diffusion, jet flame, fire ball combustion and UVCE. Two sample urban natural gas pipeline networks are used to demonstrate these two methods. It is indicated that both of the two methods can be applied to practical application, and the choice of the methods depends on the actual basic data of the gas pipelines and the precision requirements of risk assessment. Crown Copyright © 2011. Published by Elsevier B.V. All rights reserved.

  4. Recursion-transform method for computing resistance of the complex resistor network with three arbitrary boundaries.

    Science.gov (United States)

    Tan, Zhi-Zhong

    2015-05-01

    We develop a general recursion-transform (R-T) method for a two-dimensional resistor network with a zero resistor boundary. As applications of the R-T method, we consider a significant example to illuminate the usefulness for calculating resistance of a rectangular m×n resistor network with a null resistor and three arbitrary boundaries, a problem never solved before, since Green's function techniques and Laplacian matrix approaches are invalid in this case. Looking for the exact calculation of the resistance of a binary resistor network is important but difficult in the case of an arbitrary boundary since the boundary is like a wall or trap which affects the behavior of finite network. In this paper we obtain several general formulas of resistance between any two nodes in a nonregular m×n resistor network in both finite and infinite cases. In particular, 12 special cases are given by reducing one of the general formulas to understand its applications and meanings, and an integral identity is found when we compare the equivalent resistance of two different structures of the same problem in a resistor network.

  5. Fluvial facies reservoir productivity prediction method based on principal component analysis and artificial neural network

    Directory of Open Access Journals (Sweden)

    Pengyu Gao

    2016-03-01

    Full Text Available It is difficult to forecast the well productivity because of the complexity of vertical and horizontal developments in fluvial facies reservoir. This paper proposes a method based on Principal Component Analysis and Artificial Neural Network to predict well productivity of fluvial facies reservoir. The method summarizes the statistical reservoir factors and engineering factors that affect the well productivity, extracts information by applying the principal component analysis method and approximates arbitrary functions of the neural network to realize an accurate and efficient prediction on the fluvial facies reservoir well productivity. This method provides an effective way for forecasting the productivity of fluvial facies reservoir which is affected by multi-factors and complex mechanism. The study result shows that this method is a practical, effective, accurate and indirect productivity forecast method and is suitable for field application.

  6. Rejection Sensitivity Moderates the Impact of Rejection on Self-Concept Clarity

    Science.gov (United States)

    Ayduk, Özlem; Gyurak, Anett; Luerssen, Anna

    2014-01-01

    Self-concept clarity (SCC) refers to the extent to which self-knowledge is clearly and confidently defined, internally consistent, and temporally stable. Research shows that SCC can be undermined by failures in valued goal domains. Because preventing rejection is an important self-relevant goal for people high in rejection sensitivity (RS), it is hypothesized here that failures to attain this goal would cause them to experience diminished SCC. Study 1, an experimental study, showed that high-RS people’s SCC was undermined following rejection but not following an aversive experience unrelated to rejection. Study 2, a daily diary study of couples in relationships, used occurrence of partner conflicts to operationalize rejection. Replicating the findings in Study 1, having a conflict on any given diary day predicted a greater reduction in the SCC of high- compared to low-RS people on the following day. The implications for understanding the conditions under which rejection negatively affects the self-concept are discussed. PMID:19713567

  7. Securing ad hoc wireless sensor networks under Byzantine attacks by implementing non-cryptographic method

    Directory of Open Access Journals (Sweden)

    Shabir Ahmad Sofi

    2017-05-01

    Full Text Available Ad Hoc wireless sensor network (WSN is a collection of nodes that do not need to rely on predefined infrastructure to keep the network connected. The level of security and performance are always somehow related to each other, therefore due to limited resources in WSN, cryptographic methods for securing the network against attacks is not feasible. Byzantine attacks disrupt the communication between nodes in the network without regard to its own resource consumption. This paper discusses the performance of cluster based WSN comparing LEACH with Advanced node based clusters under byzantine attacks. This paper also proposes an algorithm for detection and isolation of the compromised nodes to mitigate the attacks by non-cryptographic means. The throughput increases after using the algorithm for isolation of the malicious nodes, 33% in case of Gray Hole attack and 62% in case of Black Hole attack.

  8. Method and system for conserving power in a telecommunications network during emergency situations

    Energy Technology Data Exchange (ETDEWEB)

    Conrad, Stephen H [Algodones, NM; O' Reilly, Gerard P [Manalapan, NJ

    2011-10-11

    Disclosed is a method and apparatus for conserving power in a telecommunications network during emergency situations. A permissible number list of emergency and/or priority numbers is stored in the telecommunications network. In the event of an emergency or power failure, input digits of a call to the telecommunications network are compared to the permissible number list. The call is processed in the telecommunications network and routed to its destination if the input digits match an entry in the permissible number list. The call is dropped without any further processing if the input digits do not match an entry in the permissible number list. Thus, power can be conserved in emergency situations by only allowing emergency and/or priority calls.

  9. Which stocks are profitable? A network method to investigate the effects of network structure on stock returns

    Science.gov (United States)

    Chen, Kun; Luo, Peng; Sun, Bianxia; Wang, Huaiqing

    2015-10-01

    According to asset pricing theory, a stock's expected returns are determined by its exposure to systematic risk. In this paper, we propose a new method for analyzing the interaction effects among industries and stocks on stock returns. We construct a complex network based on correlations of abnormal stock returns and use centrality and modularity, two popular measures in social science, to determine the effect of interconnections on industry and stock returns. Supported by previous studies, our findings indicate that a relationship exists between inter-industry closeness and industry returns and between stock centrality and stock returns. The theoretical and practical contributions of these findings are discussed.

  10. Face Recognition by Bunch Graph Method Using a Group Based Adaptive Tolerant Neural Network

    OpenAIRE

    Aradhana D.; Girish H.; Karibasappa K.; Reddy A. Chennakeshava

    2011-01-01

    This paper presents a new method for feature extraction from the facial image by using bunch graph method. These extracted geometric features of the face are used subsequently for face recognition by utilizing the group based adaptive neural network. This method is suitable, when the facial images are rotation and translation invariant. Further the technique also free from size invariance of facial image and is capable of identifying the facial images correctly when corrupted w...

  11. Method in analysis of CdZnTe γ spectrum with artificial neural network

    International Nuclear Information System (INIS)

    Ai Xianyun; Wei Yixiang; Xiao Wuyun

    2005-01-01

    The analysis of gamma-ray spectra to identify lines and their intensities usually requires expert knowledge and time consuming calculations with complex fitting functions. CdZnTe detector often exhibits asymmetric peak shape particularly at high energies making peak fitting methods and sophisticated isotope identification programs difficult to use. This paper investigates the use of the neural network to process gamma spectra measured with CdZnTe detector to verify nuclear materials. Results show that the neural network method gives advantages, in particular, when large low-energetic peak tailings are observed. (authors)

  12. A new traffic control design method for large networks with signalized intersections

    Science.gov (United States)

    Leininger, G. G.; Colony, D. C.; Seldner, K.

    1979-01-01

    The paper presents a traffic control design technique for application to large traffic networks with signalized intersections. It is shown that the design method adopts a macroscopic viewpoint to establish a new traffic modelling procedure in which vehicle platoons are subdivided into main stream queues and turning queues. Optimization of the signal splits minimizes queue lengths in the steady state condition and improves traffic flow conditions, from the viewpoint of the traveling public. Finally, an application of the design method to a traffic network with thirty-three signalized intersections is used to demonstrate the effectiveness of the proposed technique.

  13. Total lymphoid irradiation in the treatment of early or recurrent heart transplant rejection

    International Nuclear Information System (INIS)

    Salter, Susan P.; Salter, Merle M.; Kirklin, James K.; Bourge, Robert C.; Naftel, David C.

    1995-01-01

    Purpose: Recurrent acute cardiac allograft rejection is an important cause of repeat hospitalization and a major mode of mortality, particularly during the 6 months immediately following transplant. Total lymphoid irradiation (TLI) has been shown experimentally to induce a state of partial tolerance when administered prior to transplantation. Anecdotal reports of clinical experience have also suggested efficacy of TLI in treatment of recurrent cardiac rejection. The purpose of this study is to evaluate the safety and efficacy of TLI for treatment of early or recurrent heart transplant rejection. Materials and Methods: Between January 1990 and June 1992, 49 patients postallograft cardiac transplant were given courses of TLI for treatment of early or recurrent rejection after conventional therapy with Methylprednisolone, antithymocyte globulin, OKT3, and methotrexate. Two patients failed to complete their therapy and were not evaluated. Two other patients received a second TLI course, making a total of 49 courses delivered. Indications for TLI were early rejection (n = 5), recurrent rejection (n = 38), and recurrent rejection with vasculitis (n = 6). The dose goal of the TLI protocol was 8 Gy in 10 fractions given twice weekly. Three separate fields were used to encompass all major lymph node-bearing areas. The actual mean dose was 7 Gy (range 2.4-8.4 Gy), and the duration of treatment was 8 to 106 days. These variations were secondary to leukopenia or thrombocytopenia. Results: The mean posttransplant follow-up is 15 ± 1.2 months (maximum 27 months). Among patients initiating TLI within 1 month posttransplant (n = 15), the rejection frequency decreased from 1.83 episodes/patient/month pre-TLI to 0.13 episodes/patient/month post-TLI (p < 0.0001). For those who began TLI 1-3 months after transplant (n = 21), rejection decreased from 1.43 to 0.10 episodes/patient/month (p < 0.0001). When TLI was started more than 3 months posttransplant (n = 11), the pre-TLI and post

  14. Exploring genetic and non-genetic risk factors for delayed graft function, acute and subclinical rejection in renal transplant recipients

    NARCIS (Netherlands)

    Moes, Dirk Jan A. R.; Press, Rogier R.; Ackaert, Oliver; Ploeger, Bart A.; Bemelman, Frederike J.; Diack, Cheikh; Wessels, Judith A. M.; van der Straaten, Tahar; Danhof, Meindert; Sanders, Jan-Stephan F.; van der Heide, Jaap J. Homan; Guchelaar, Henk Jan; de Fijter, Johan W.

    AIMS This study aimed at identifying pharmacological factors such as pharmacogenetics and drug exposure as new predictive biomarkers for delayed graft function (DGF), acute rejection (AR) and/or subclinical rejection (SCR). METHODS Adult renal transplant recipients (n = 361) on cyclosporine-based

  15. Utility of indium-111 labelled autologous platelets in the diagnosis of renal graft rejection

    International Nuclear Information System (INIS)

    Martin-Comin, J.; Roca, M.; Grino, J.M.; Paradell, C.; Caralps, A.

    1982-01-01

    The usefulness of In-111 labelled autologous platelets in the diagnosis of renal graft rejection was studied. The method is based on imaging of the graft area at 4, 24, 48 and 72 hours after the injection of the labelled cells. The study was done in 21 renal cadaveric transplant recipients: control group: four patients without evidence of rejection. No platelet uptake was observed in any of them. Study group: in 13 patients with acute rejection and 1 with chronic rejection graft tracer uptake was seen. In the 3 others with a non-immunological sudden impairment of renal function, no activity was detected in graft area. Changes in renal platelet trapping correlated with response to antirejection therapy

  16. In-111 oxine autologous labeled platelets in the diagnosis of kidney graft rejection

    International Nuclear Information System (INIS)

    Martin-Comin, J.; Roca, M.; Grino, J.M.; Paradell, C.; Caralps, C.

    1983-01-01

    The usefulness of In-111 oxine labeled autologous platelets in the diagnosis of renal graft rejection was studied. The method is based on imaging of the graft area at 4, 24, 48, and 72 hours after the injection of the labeled cells. The study was done in 31 renal transplant recipients. The control group included four patients with normal renal function without evidence of rejection. No platelet uptake was observed in any of them. The study group included 22 patients with acute rejection which was confirmed histologically in 13. One case of chronic vascular type rejection of the graft tracer uptake was seen. There was a false-positive result due to a perirenal hematoma. In three patients with a non-immunological sudden impairment of renal function, no activity was detected in the graft area. We also evaluated the changes in platelet trapping throughout the study and they seemed to correlate with the response to the antirejection therapy

  17. Mechanisms of allograft rejection of corneal endothelium

    International Nuclear Information System (INIS)

    Tagawa, Y.; Silverstein, A.M.; Prendergast, R.A.

    1982-01-01

    The local intraocular graft-vs.-host (GVH) reaction, involving the destruction of the corneal endothelial cells of the rabbit host by sensitized donor lymphoid cells, has been used to study the mechanism of corneal allograft rejection. Pretreatment of donor cells with a specific mouse monoclonal hybridoma anti-T cell antibody and complement suppresses the destructive reaction, suggesting that a cellular-immune mechanism is primarily involved. Pretreatment of donor cells with mitomycin-C completely abolishes the local GVH reaction, indicating that the effector lymphocytes must undergo mitosis within the eye before they can engage in target cell destruction. Finally, studies of the local GVH reaction in irradiated leukopenic recipients or in preinflamed rabbit eyes suggest that host leukocytes may contribute nonspecifically to enhance the destructive process. These studies show that the local ocular GVH reaction may provide a useful model for the study of the mechanisms involved in the rejection of corneal allografts

  18. A new rejection of moral expertise.

    Science.gov (United States)

    Cowley, Christopher

    2005-01-01

    There seem to be two clearly-defined camps in the debate over the problem of moral expertise. On the one hand are the "Professionals", who reject the possibility entirely, usually because of the intractable diversity of ethical beliefs. On the other hand are the "Ethicists", who criticise the Professionals for merely stipulating science as the most appropriate paradigm for discussions of expertise. While the subject matter and methodology of good ethical thinking is certainly different from that of good clinical thinking, they argue, this is no reason for rejecting the possibility of a distinctive kind of expertise in ethics, usually based on the idea of good justification. I want to argue that both are incorrect, partly because of the reasons given by one group against the other, but more importantly because both neglect what is most distinctive about ethics: that it is personal in a very specific way, without collapsing into relativism.

  19. Neutral Pion Rejection at L2 using the CMS Endcap Preshower

    CERN Document Server

    Kyriakis, Aristotelis; Loukas, Demetrios; Mousa, Jehad; Seez, Christopher

    1999-01-01

    Applying a general Artificial Neural Network approach, we have examined the possibility of neutral pion rejection at the Level 2 Trigger stage ( L2) principally using information from the CMS Endcap Preshower. We have studied both pion/photon and pion/electron discrimination. For L2 the hope was to achieve some useful pion/electron discrimination for a high electron efficiency. For a single electron/photon efficiency of 95% the results show that no useful rejection of neutral pions against electrons/photons can be obtained using this algorithm alone, due to the presence of tracker material. If the efficiency is lowered or information from the tracker is available, the rejection can increase dramatically. This will be the case for off-line analyses.

  20. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    International Nuclear Information System (INIS)

    Nedic, Vladimir; Despotovic, Danijela; Cvetanovic, Slobodan; Despotovic, Milan; Babic, Sasa

    2014-01-01

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L eq . Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model

  1. A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation

    Directory of Open Access Journals (Sweden)

    Dongmei Huang

    2017-09-01

    Full Text Available Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

  2. A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

    Science.gov (United States)

    Huang, Dongmei; Xu, Chenyixuan; Zhao, Danfeng; Song, Wei; He, Qi

    2017-09-21

    Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

  3. Comparison of classical statistical methods and artificial neural network in traffic noise prediction

    Energy Technology Data Exchange (ETDEWEB)

    Nedic, Vladimir, E-mail: vnedic@kg.ac.rs [Faculty of Philology and Arts, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac (Serbia); Despotovic, Danijela, E-mail: ddespotovic@kg.ac.rs [Faculty of Economics, University of Kragujevac, Djure Pucara Starog 3, 34000 Kragujevac (Serbia); Cvetanovic, Slobodan, E-mail: slobodan.cvetanovic@eknfak.ni.ac.rs [Faculty of Economics, University of Niš, Trg kralja Aleksandra Ujedinitelja, 18000 Niš (Serbia); Despotovic, Milan, E-mail: mdespotovic@kg.ac.rs [Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac (Serbia); Babic, Sasa, E-mail: babicsf@yahoo.com [College of Applied Mechanical Engineering, Trstenik (Serbia)

    2014-11-15

    Traffic is the main source of noise in urban environments and significantly affects human mental and physical health and labor productivity. Therefore it is very important to model the noise produced by various vehicles. Techniques for traffic noise prediction are mainly based on regression analysis, which generally is not good enough to describe the trends of noise. In this paper the application of artificial neural networks (ANNs) for the prediction of traffic noise is presented. As input variables of the neural network, the proposed structure of the traffic flow and the average speed of the traffic flow are chosen. The output variable of the network is the equivalent noise level in the given time period L{sub eq}. Based on these parameters, the network is modeled, trained and tested through a comparative analysis of the calculated values and measured levels of traffic noise using the originally developed user friendly software package. It is shown that the artificial neural networks can be a useful tool for the prediction of noise with sufficient accuracy. In addition, the measured values were also used to calculate equivalent noise level by means of classical methods, and comparative analysis is given. The results clearly show that ANN approach is superior in traffic noise level prediction to any other statistical method. - Highlights: • We proposed an ANN model for prediction of traffic noise. • We developed originally designed user friendly software package. • The results are compared with classical statistical methods. • The results are much better predictive capabilities of ANN model.

  4. Application of 1 D Finite Element Method in Combination with Laminar Solution Method for Pipe Network Analysis

    Science.gov (United States)

    Dudar, O. I.; Dudar, E. S.

    2017-11-01

    The features of application of the 1D dimensional finite element method (FEM) in combination with the laminar solutions method (LSM) for the calculation of underground ventilating networks are considered. In this case the processes of heat and mass transfer change the properties of a fluid (binary vapour-air mix). Under the action of gravitational forces it leads to such phenomena as natural draft, local circulation, etc. The FEM relations considering the action of gravity, the mass conservation law, the dependence of vapour-air mix properties on the thermodynamic parameters are derived so that it allows one to model the mentioned phenomena. The analogy of the elastic and plastic rod deformation processes to the processes of laminar and turbulent flow in a pipe is described. Owing to this analogy, the guaranteed convergence of the elastic solutions method for the materials of plastic type means the guaranteed convergence of the LSM for any regime of a turbulent flow in a rough pipe. By means of numerical experiments the convergence rate of the FEM - LSM is investigated. This convergence rate appeared much higher than the convergence rate of the Cross - Andriyashev method. Data of other authors on the convergence rate comparison for the finite element method, the Newton method and the method of gradient are provided. These data allow one to conclude that the FEM in combination with the LSM is one of the most effective methods of calculation of hydraulic and ventilating networks. The FEM - LSM has been used for creation of the research application programme package “MineClimate” allowing to calculate the microclimate parameters in the underground ventilating networks.

  5. Limitations of a metabolic network-based reverse ecology method for inferring host-pathogen interactions.

    Science.gov (United States)

    Takemoto, Kazuhiro; Aie, Kazuki

    2017-05-25

    Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data. Our data analyses showed that host-pathogen interactions were more strongly influenced by genome size, primary network parameters (e.g., number of edges), oxygen requirement, and phylogeny than the reserve ecology-based measures. These results indicate the limitations of the reverse ecology method; however, they do not discount the importance of adopting reverse ecology approaches altogether. Rather, we highlight the need for developing more suitable methods for inferring host-pathogen interactions and conducting more careful examinations of the relationships between metabolic networks and host-pathogen interactions.

  6. Inversion of Density Interfaces Using the Pseudo-Backpropagation Neural Network Method

    Science.gov (United States)

    Chen, Xiaohong; Du, Yukun; Liu, Zhan; Zhao, Wenju; Chen, Xiaocheng

    2018-05-01

    This paper presents a new pseudo-backpropagation (BP) neural network method that can invert multi-density interfaces at one time. The new method is based on the conventional forward modeling and inverse modeling theories in addition to conventional pseudo-BP neural network arithmetic. A 3D inversion model for gravity anomalies of multi-density interfaces using the pseudo-BP neural network method is constructed after analyzing the structure and function of the artificial neural network. The corresponding iterative inverse formula of the space field is presented at the same time. Based on trials of gravity anomalies and density noise, the influence of the two kinds of noise on the inverse result is discussed and the scale of noise requested for the stability of the arithmetic is analyzed. The effects of the initial model on the reduction of the ambiguity of the result and improvement of the precision of inversion are discussed. The correctness and validity of the method were verified by the 3D model of the three interfaces. 3D inversion was performed on the observed gravity anomaly data of the Okinawa trough using the program presented herein. The Tertiary basement and Moho depth were obtained from the inversion results, which also testify the adaptability of the method. This study has made a useful attempt for the inversion of gravity density interfaces.

  7. Convergence and divergence across construction methods for human brain white matter networks: an assessment based on individual differences.

    Science.gov (United States)

    Zhong, Suyu; He, Yong; Gong, Gaolang

    2015-05-01

    Using diffusion MRI, a number of studies have investigated the properties of whole-brain white matter (WM) networks with differing network construction methods (node/edge definition). However, how the construction methods affect individual differences of WM networks and, particularly, if distinct methods can provide convergent or divergent patterns of individual differences remain largely unknown. Here, we applied 10 frequently used methods to construct whole-brain WM networks in a healthy young adult population (57 subjects), which involves two node definitions (low-resolution and high-resolution) and five edge definitions (binary, FA weighted, fiber-density weighted, length-corrected fiber-density weighted, and connectivity-probability weighted). For these WM networks, individual differences were systematically analyzed in three network aspects: (1) a spatial pattern of WM connections, (2) a spatial pattern of nodal efficiency, and (3) network global and local efficiencies. Intriguingly, we found that some of the network construction methods converged in terms of individual difference patterns, but diverged with other methods. Furthermore, the convergence/divergence between methods differed among network properties that were adopted to assess individual differences. Particularly, high-resolution WM networks with differing edge definitions showed convergent individual differences in the spatial pattern of both WM connections and nodal efficiency. For the network global and local efficiencies, low-resolution and high-resolution WM networks for most edge definitions consistently exhibited a highly convergent pattern in individual differences. Finally, the test-retest analysis revealed a decent temporal reproducibility for the patterns of between-method convergence/divergence. Together, the results of the present study demonstrated a measure-dependent effect of network construction methods on the individual difference of WM network properties. © 2015 Wiley

  8. Using 3D printed eggs to examine the egg-rejection behaviour of wild birds

    Directory of Open Access Journals (Sweden)

    Branislav Igic

    2015-05-01

    Full Text Available The coevolutionary relationships between brood parasites and their hosts are often studied by examining the egg rejection behaviour of host species using artificial eggs. However, the traditional methods for producing artificial eggs out of plasticine, plastic, wood, or plaster-of-Paris are laborious, imprecise, and prone to human error. As an alternative, 3D printing may reduce human error, enable more precise manipulation of egg size and shape, and provide a more accurate and replicable protocol for generating artificial stimuli than traditional methods. However, the usefulness of 3D printing technology for egg rejection research remains to be tested. Here, we applied 3D printing technology to the extensively studied egg rejection behaviour of American robins, Turdus migratorius. Eggs of the robin’s brood parasites, brown-headed cowbirds, Molothrus ater, vary greatly in size and shape, but it is unknown whether host egg rejection decisions differ across this gradient of natural variation. We printed artificial eggs that encompass the natural range of shapes and sizes of cowbird eggs, painted them to resemble either robin or cowbird egg colour, and used them to artificially parasitize nests of breeding wild robins. In line with previous studies, we show that robins accept mimetically coloured and reject non-mimetically coloured artificial eggs. Although we found no evidence that subtle differences in parasitic egg size or shape affect robins’ rejection decisions, 3D printing will provide an opportunity for more extensive experimentation on the potential biological or evolutionary significance of size and shape variation of foreign eggs in rejection decisions. We provide a detailed protocol for generating 3D printed eggs using either personal 3D printers or commercial printing services, and highlight additional potential future applications for this technology in the study of egg rejection.

  9. Using 3D printed eggs to examine the egg-rejection behaviour of wild birds

    Science.gov (United States)

    Nunez, Valerie; Voss, Henning U.; Croston, Rebecca; Aidala, Zachary; López, Analía V.; Van Tatenhove, Aimee; Holford, Mandë E.; Shawkey, Matthew D.; Hauber, Mark E.

    2015-01-01

    The coevolutionary relationships between brood parasites and their hosts are often studied by examining the egg rejection behaviour of host species using artificial eggs. However, the traditional methods for producing artificial eggs out of plasticine, plastic, wood, or plaster-of-Paris are laborious, imprecise, and prone to human error. As an alternative, 3D printing may reduce human error, enable more precise manipulation of egg size and shape, and provide a more accurate and replicable protocol for generating artificial stimuli than traditional methods. However, the usefulness of 3D printing technology for egg rejection research remains to be tested. Here, we applied 3D printing technology to the extensively studied egg rejection behaviour of American robins, Turdus migratorius. Eggs of the robin’s brood parasites, brown-headed cowbirds, Molothrus ater, vary greatly in size and shape, but it is unknown whether host egg rejection decisions differ across this gradient of natural variation. We printed artificial eggs that encompass the natural range of shapes and sizes of cowbird eggs, painted them to resemble either robin or cowbird egg colour, and used them to artificially parasitize nests of breeding wild robins. In line with previous studies, we show that robins accept mimetically coloured and reject non-mimetically coloured artificial eggs. Although we found no evidence that subtle differences in parasitic egg size or shape affect robins’ rejection decisions, 3D printing will provide an opportunity for more extensive experimentation on the potential biological or evolutionary significance of size and shape variation of foreign eggs in rejection decisions. We provide a detailed protocol for generating 3D printed eggs using either personal 3D printers or commercial printing services, and highlight additional potential future applications for this technology in the study of egg rejection. PMID:26038720

  10. Order Patterns Networks (orpan – a method toestimate time-evolving functional connectivity frommultivariate time series

    Directory of Open Access Journals (Sweden)

    Stefan eSchinkel

    2012-11-01

    Full Text Available Complex networks provide an excellent framework for studying the functionof the human brain activity. Yet estimating functional networks from mea-sured signals is not trivial, especially if the data is non-stationary and noisyas it is often the case with physiological recordings. In this article we proposea method that uses the local rank structure of the data to define functionallinks in terms of identical rank structures. The method yields temporal se-quences of networks which permits to trace the evolution of the functionalconnectivity during the time course of the observation. We demonstrate thepotentials of this approach with model data as well as with experimentaldata from an electrophysiological study on language processing.

  11. A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks

    Science.gov (United States)

    Sha, Chao; Qiu, Jian-mei; Li, Shu-yan; Qiang, Meng-ye; Wang, Ru-chuan

    2016-01-01

    To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods. PMID:27338401

  12. A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chao Sha

    2016-06-01

    Full Text Available To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs, a type of low-latency data gathering method with multi-Sink (LDGM for short is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods.

  13. Demand Forecasting Methods in Accommodation Establishments: A Research with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Ebru ULUCAN

    2018-05-01

    Full Text Available As it being seen in every sector, demand forecasting in tourism is been conducted with various qualitative and quantitative methods. In recent years, artificial neural network models, which have been developed as an alternative to these forecasting methods, give the nearest values in forecasting with the smallest failure percentage. This study aims to reveal that accomodation establishments can use the neural network models as an alternative while forecasting their demand. With this aim, neural network models have been tested by using the sold room values between the period of 2013-2016 of a five star hotel in Istanbul and it is found that the results acquired from the testing models are the nearest values comparing the realized figures. In the light of these results, tourism demand of the hotel for 2017 and 2018 has been forecasted.

  14. Counting hard-to-count populations: the network scale-up method for public health

    Science.gov (United States)

    Bernard, H Russell; Hallett, Tim; Iovita, Alexandrina; Johnsen, Eugene C; Lyerla, Rob; McCarty, Christopher; Mahy, Mary; Salganik, Matthew J; Saliuk, Tetiana; Scutelniciuc, Otilia; Shelley, Gene A; Sirinirund, Petchsri; Weir, Sharon

    2010-01-01

    Estimating sizes of hidden or hard-to-reach populations is an important problem in public health. For example, estimates of the sizes of populations at highest risk for HIV and AIDS are needed for designing, evaluating and allocating funding for treatment and prevention programmes. A promising approach to size estimation, relatively new to public health, is the network scale-up method (NSUM), involving two steps: estimating the personal network size of the members of a random sample of a total population and, with this information, estimating the number of members of a hidden subpopulation of the total population. We describe the method, including two approaches to estimating personal network sizes (summation and known population). We discuss the strengths and weaknesses of each approach and provide examples of international applications of the NSUM in public health. We conclude with recommendations for future research and evaluation. PMID:21106509

  15. An input feature selection method applied to fuzzy neural networks for signal esitmation

    International Nuclear Information System (INIS)

    Na, Man Gyun; Sim, Young Rok

    2001-01-01

    It is well known that the performance of a fuzzy neural networks strongly depends on the input features selected for its training. In its applications to sensor signal estimation, there are a large number of input variables related with an output. As the number of input variables increases, the training time of fuzzy neural networks required increases exponentially. Thus, it is essential to reduce the number of inputs to a fuzzy neural networks and to select the optimum number of mutually independent inputs that are able to clearly define the input-output mapping. In this work, principal component analysis (PAC), genetic algorithms (GA) and probability theory are combined to select new important input features. A proposed feature selection method is applied to the signal estimation of the steam generator water level, the hot-leg flowrate, the pressurizer water level and the pressurizer pressure sensors in pressurized water reactors and compared with other input feature selection methods

  16. Effects of composite mango ( Mangifera indica ) fruit reject meal on ...

    African Journals Online (AJOL)

    Effects of composite mango ( Mangifera indica ) fruit reject meal on growth performance, digestibility and economics of production of rabbits. ... The experiment was conducted to determine the effect of mango fruit reject ... HOW TO USE AJOL.

  17. Highly graphitized laterally interconnected SWCNT network synthesis via a sandwich-grown method

    International Nuclear Information System (INIS)

    Teng, I-Ju; Chen, Kai-Ling; Wang, Li-Chun; Kuo, Cheng-Tzu; Hsu, Hui-Lin; Jian, Sheng-Rui; Chen, Jung-Hsuan; Wang, Wei-Hsiang

    2011-01-01

    We present a sandwich-grown method for growing laterally interconnected single-walled carbon nanotube (SWCNT) networks with a high degree of graphitization by microwave plasma chemical vapour deposition (MPCVD). An Al 2 O 3 -supported Fe catalyst precursor layer deposited on an oxidized Si substrate with an upper Si cover is first pretreated in pure hydrogen, and then exposed to a gas mixture of methane/hydrogen for growth process at a lower growth temperature and a faster rate. The effects of various parameters, such as catalyst film thickness, gas flow rate, working pressure, growth time and plasma power, on the morphologies and structural characteristics of the SWCNT networks are investigated, and therefore provide the essential conditions for direct growth of laterally interconnected SWCNT networks. Analytical results demonstrate that the SWCNT-based lateral architecture comprises a mixture of graphene-sheet-wrapped catalyst particles and laterally interconnected nanotubes, isolated or branched or assembled into bundles. The results also show that the formation of the laterally interconnected SWCNT networks is related to the sandwich-like stack approach and the addition of an Al 2 O 3 layer in the MPCVD process. The successful growth of lateral SWCNT networks provides new experimental information for simply and efficiently preparing lateral SWCNTs on unpatterned substrates, and opens a pathway to create network-structured nanotube-based devices.

  18. A semi-automatic method for extracting thin line structures in images as rooted tree network

    Energy Technology Data Exchange (ETDEWEB)

    Brazzini, Jacopo [Los Alamos National Laboratory; Dillard, Scott [Los Alamos National Laboratory; Soille, Pierre [EC - JRC

    2010-01-01

    This paper addresses the problem of semi-automatic extraction of line networks in digital images - e.g., road or hydrographic networks in satellite images, blood vessels in medical images, robust. For that purpose, we improve a generic method derived from morphological and hydrological concepts and consisting in minimum cost path estimation and flow simulation. While this approach fully exploits the local contrast and shape of the network, as well as its arborescent nature, we further incorporate local directional information about the structures in the image. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the target network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given seed with this metric is combined with hydrological operators for overland flow simulation to extract the line network. The algorithm is demonstrated for the extraction of blood vessels in a retina image and of a river network in a satellite image.

  19. THE DIAGNOSIS OF LIVER ALLOGRAFT ACUTE REJECTION IN LIVER BIOPSIES

    Directory of Open Access Journals (Sweden)

    L. V. Shkalova

    2011-01-01

    Full Text Available We performed histological examination of 80 liver allograft biopsies, the diagnosis of acute rejection was proved in 34 cases. Histological changes in liver biopsies in different grades of acute rejection were estimated according to Banff classification 1995, 1997 and were compared with current literature data. The article deals with the question of morphological value of grading acute rejection on early and late, also we analyze changes in treat- ment tactics after morphological verification of liver allograft acute rejection

  20. Wireless and real-time structural damage detection: A novel decentralized method for wireless sensor networks

    Science.gov (United States)

    Avci, Onur; Abdeljaber, Osama; Kiranyaz, Serkan; Hussein, Mohammed; Inman, Daniel J.

    2018-06-01

    Being an alternative to conventional wired sensors, wireless sensor networks (WSNs) are extensively used in Structural Health Monitoring (SHM) applications. Most of the Structural Damage Detection (SDD) approaches available in the SHM literature are centralized as they require transferring data from all sensors within the network to a single processing unit to evaluate the structural condition. These methods are found predominantly feasible for wired SHM systems; however, transmission and synchronization of huge data sets in WSNs has been found to be arduous. As such, the application of centralized methods with WSNs has been a challenge for engineers. In this paper, the authors are presenting a novel application of 1D Convolutional Neural Networks (1D CNNs) on WSNs for SDD purposes. The SDD is successfully performed completely wireless and real-time under ambient conditions. As a result of this, a decentralized damage detection method suitable for wireless SHM systems is proposed. The proposed method is based on 1D CNNs and it involves training an individual 1D CNN for each wireless sensor in the network in a format where each CNN is assigned to process the locally-available data only, eliminating the need for data transmission and synchronization. The proposed damage detection method operates directly on the raw ambient vibration condition signals without any filtering or preprocessing. Moreover, the proposed approach requires minimal computational time and power since 1D CNNs merge both feature extraction and classification tasks into a single learning block. This ability is prevailingly cost-effective and evidently practical in WSNs considering the hardware systems have been occasionally reported to suffer from limited power supply in these networks. To display the capability and verify the success of the proposed method, large-scale experiments conducted on a laboratory structure equipped with a state-of-the-art WSN are reported.

  1. Are Imaging and Lesioning Convergent Methods for Assessing Functional Specialisation? Investigations Using an Artificial Neural Network

    Science.gov (United States)

    Thomas, Michael S. C.; Purser, Harry R. M.; Tomlinson, Simon; Mareschal, Denis

    2012-01-01

    This article presents an investigation of the relationship between lesioning and neuroimaging methods of assessing functional specialisation, using synthetic brain imaging (SBI) and lesioning of a connectionist network of past-tense formation. The model comprised two processing "routes": one was a direct route between layers of input and output…

  2. A Method for Evaluation of Quality of Service in Computer Networks

    DEFF Research Database (Denmark)

    Bujlow, Tomasz; Hald, Sara Ligaard; Riaz, M. Tahir

    2012-01-01

    , together with the application name taken from the description of system sockets. This paper proposes a new method for measuring the level of Quality of Service in broadband networks. It is based on our Volunteer-Based System to collect the training data, Machine Learning Algorithms to generate...

  3. A hybrid Planning Method for Transmission Network in a Deregulated Enviroment

    DEFF Research Database (Denmark)

    Xu, Zhao; Dong, Zhaoyang; Poulsen, Kit

    2006-01-01

    The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using a multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open-access scheme...

  4. A collaborative processes synchronization method with regard to system crashes and network failures

    NARCIS (Netherlands)

    Wang, Lei; Wombacher, Andreas; Ferreira Pires, Luis; van Sinderen, Marten J.; Chi, Chihung

    2014-01-01

    Processes can synchronize their states by exchanging messages. System crashes and network failures may cause message loss, so that state changes of a process may remain unnoticed by its partner processes, resulting in state inconsistency or deadlocks. In this paper we define a method to transform a

  5. Methods of Certification tests PLC-Networks in Compliance Safety Information

    Directory of Open Access Journals (Sweden)

    A. A. Balaev

    2011-12-01

    Full Text Available The aim of this research was description of the methodology of the audit plc-network to meet the requirements of information security. The technique is based on the provisions of the guidance documents and model FSTEC Russia test object methods of information on safety information.

  6. Improving the flexibility and profitability of ICT-enabled business networks: an assessment method and tool.

    NARCIS (Netherlands)

    D.J.E. Delporte-Vermeiren (Dominique)

    2003-01-01

    textabstractThis thesis deals with the development and the first empirical examination of an assessment method and decision support tool for the ex ante assessment of margin to be applied in ICT-enabled redesign of business networks. Many industries face the demand for customisation. This

  7. 47 CFR 51.329 - Notice of network changes: Methods for providing notice.

    Science.gov (United States)

    2010-10-01

    ... 47 Telecommunication 3 2010-10-01 2010-10-01 false Notice of network changes: Methods for providing notice. 51.329 Section 51.329 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED...) Filing a public notice with the Commission; or (2) Providing public notice through industry fora...

  8. Analytical maximum-likelihood method to detect patterns in real networks

    International Nuclear Information System (INIS)

    Squartini, Tiziano; Garlaschelli, Diego

    2011-01-01

    In order to detect patterns in real networks, randomized graph ensembles that preserve only part of the topology of an observed network are systematically used as fundamental null models. However, the generation of them is still problematic. Existing approaches are either computationally demanding and beyond analytic control or analytically accessible but highly approximate. Here, we propose a solution to this long-standing problem by introducing a fast method that allows one to obtain expectation values and standard deviations of any topological property analytically, for any binary, weighted, directed or undirected network. Remarkably, the time required to obtain the expectation value of any property analytically across the entire graph ensemble is as short as that required to compute the same property using the adjacency matrix of the single original network. Our method reveals that the null behavior of various correlation properties is different from what was believed previously, and is highly sensitive to the particular network considered. Moreover, our approach shows that important structural properties (such as the modularity used in community detection problems) are currently based on incorrect expressions, and provides the exact quantities that should replace them.

  9. A prediction method for the wax deposition rate based on a radial basis function neural network

    Directory of Open Access Journals (Sweden)

    Ying Xie

    2017-06-01

    Full Text Available The radial basis function neural network is a popular supervised learning tool based on machinery learning technology. Its high precision having been proven, the radial basis function neural network has been applied in many areas. The accumulation of deposited materials in the pipeline may lead to the need for increased pumping power, a decreased flow rate or even to the total blockage of the line, with losses of production and capital investment, so research on predicting the wax deposition rate is significant for the safe and economical operation of an oil pipeline. This paper adopts the radial basis function neural network to predict the wax deposition rate by considering four main influencing factors, the pipe wall temperature gradient, pipe wall wax crystal solubility coefficient, pipe wall shear stress and crude oil viscosity, by the gray correlational analysis method. MATLAB software is employed to establish the RBF neural network. Compared with the previous literature, favorable consistency exists between the predicted outcomes and the experimental results, with a relative error of 1.5%. It can be concluded that the prediction method of wax deposition rate based on the RBF neural network is feasible.

  10. 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.

  11. 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.

  12. An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

    Directory of Open Access Journals (Sweden)

    Deepa Devasenapathy

    2015-01-01

    Full Text Available The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

  13. An energy-efficient cluster-based vehicle detection on road network using intention numeration method.

    Science.gov (United States)

    Devasenapathy, Deepa; Kannan, Kathiravan

    2015-01-01

    The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.

  14. Social networking and young adults' drinking practices: innovative qualitative methods for health behavior research.

    Science.gov (United States)

    Lyons, Antonia C; Goodwin, Ian; McCreanor, Tim; Griffin, Christine

    2015-04-01

    Understandings of health behaviors can be enriched by using innovative qualitative research designs. We illustrate this with a project that used multiple qualitative methods to explore the confluence of young adults' drinking behaviors and social networking practices in Aotearoa, New Zealand. Participants were 18-25 year old males and females from diverse ethnic, class, and occupational backgrounds. In Stage 1, 34 friendship focus group discussions were video-recorded with 141 young adults who talked about their drinking and social networking practices. In Stage 2, 23 individual interviews were conducted using screen-capture software and video to record participants showing and discussing their Facebook pages. In Stage 3, a database of Web-based material regarding drinking and alcohol was developed and analyzed. In friendship group data, young adults co-constructed accounts of drinking practices and networking about drinking via Facebook as intensely social and pleasurable. However, this pleasure was less prominent in individual interviews, where there was greater explication of unpleasant or problematic experiences and practices. The pleasure derived from drinking and social networking practices was also differentiated by ethnicity, gender, and social class. Juxtaposing the Web-based data with participants' talk about their drinking and social media use showed the deep penetration of online alcohol marketing into young people's social worlds. Multiple qualitative methods, generating multimodal datasets, allowed valuable nuanced insights into young adults' drinking practices and social networking behaviors. This knowledge can usefully inform health policy, health promotion strategies, and targeted health interventions. (c) 2015 APA, all rights reserved).

  15. Graft rejection after hematopoietic cell transplantation with nonmyeloablative conditioning

    DEFF Research Database (Denmark)

    Masmas, T.N.; Petersen, S.L.; Madsen, H.O.

    2008-01-01

    over time. The storage temperature of the apheresis products was identified as a risk factor for rejection. Storage of the apheresis products at 5 degrees C diminished the risk of rejection. Low donor T cell chimerism at Day +14 significantly increased the risk of rejection. Seven patients were...

  16. Predicting outcome of acute kidney transplant rejection using

    NARCIS (Netherlands)

    Rekers, Niels Vincent

    2014-01-01

    Acute kidney transplant rejection is an important risk factors for adverse graft outcome. Once diagnosed, it remains difficult to predict the risk of graft loss and the response to anti-rejection treatment. The aim of this thesis was to identify biomarkers during acute rejection, which predict the

  17. Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

    OpenAIRE

    Chaoyang Shi; Bi Yu Chen; William H. K. Lam; Qingquan Li

    2017-01-01

    Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are f...

  18. Alternating optimization method based on nonnegative matrix factorizations for deep neural networks

    OpenAIRE

    Sakurai, Tetsuya; Imakura, Akira; Inoue, Yuto; Futamura, Yasunori

    2016-01-01

    The backpropagation algorithm for calculating gradients has been widely used in computation of weights for deep neural networks (DNNs). This method requires derivatives of objective functions and has some difficulties finding appropriate parameters such as learning rate. In this paper, we propose a novel approach for computing weight matrices of fully-connected DNNs by using two types of semi-nonnegative matrix factorizations (semi-NMFs). In this method, optimization processes are performed b...

  19. A novel maximum power point tracking method for PV systems using fuzzy cognitive networks (FCN)

    Energy Technology Data Exchange (ETDEWEB)

    Karlis, A.D. [Electrical Machines Laboratory, Department of Electrical & amp; Computer Engineering, Democritus University of Thrace, V. Sofias 12, 67100 Xanthi (Greece); Kottas, T.L.; Boutalis, Y.S. [Automatic Control Systems Laboratory, Department of Electrical & amp; Computer Engineering, Democritus University of Thrace, V. Sofias 12, 67100 Xanthi (Greece)

    2007-03-15

    Maximum power point trackers (MPPTs) play an important role in photovoltaic (PV) power systems because they maximize the power output from a PV system for a given set of conditions, and therefore maximize the array efficiency. This paper presents a novel MPPT method based on fuzzy cognitive networks (FCN). The new method gives a good maximum power operation of any PV array under different conditions such as changing insolation and temperature. The numerical results show the effectiveness of the proposed algorithm. (author)

  20. A new method of machine vision reprocessing based on cellular neural networks

    International Nuclear Information System (INIS)

    Jianhua, W.; Liping, Z.; Fenfang, Z.; Guojian, H.

    1996-01-01

    This paper proposed a method of image preprocessing in machine vision based on Cellular Neural Network (CNN). CNN is introduced to design image smoothing, image recovering, image boundary detecting and other image preprocessing problems. The proposed methods are so simple that the speed of algorithms are increased greatly to suit the needs of real-time image processing. The experimental results show a satisfactory reply

  1. Reject analysis: A comparison of radiographer and radiologist perceptions of image quality

    International Nuclear Information System (INIS)

    Mount, J.

    2016-01-01

    This study explores the potential differences in perceptions of image quality between radiographers and radiologists in a large UK hospital and the subsequent impact this has on image rejection. Image rejection, while sometimes necessary, often leads to an increased radiation dose to the patient due to the need to repeat. Moreover, this translates into increased waiting times, departmental costs, and lower patient satisfaction. Adopting a mixed methods approach, this paper first seeks to quantify the differences in radiographer and radiologist perceptions and second establish the underlying causes of such differences through a quantitative and qualitative investigation respectively. Using a standardized psychometric scale of a GP lateral knee, the study reveals significant differences in the perceptions of quality and rejection rates between radiographers and radiologists driven by a conflict in the evaluation criteria used. The study has significant implications for improving departmental performance and proposes a potential solution for reducing reject rates and image repeats. - Highlights: • Significant differences are found to exist in perceptions of image quality. • Differences in perceptions of image quality directly influence reject rates. • Radiographers judge images on technical criteria. • Radiologists judge images on diagnostic criteria. • Results suggest better communication could reduce reject rates.

  2. The Lived Experience of Psoriasis Patients from Social Stigma and Rejection: A Qualitative Study.

    Science.gov (United States)

    Ghorbanibirgani, Alireza; Fallahi-Khoshknab, Masoud; Zarea, Kourosh; Abedi, Heidarali

    2016-07-01

    Psoriasis is a common, chronic skin disease that causes challenges such as stigma and labeling from both the community and individuals due to its effects on appearance. The objective of this study was to describe and explain the social stigma and rejection experienced by patients with psoriasis. The present research is a qualitative study with a hermeneutic phenomenological approach conducted among psoriasis patients referring to the dermatology clinic and ward of Imam Khomeini hospital in Ahvaz, Iran between June and December 2014. In this study, 15 patients with psoriasis were selected by purposeful sampling, and they were asked to express their experience of stigma and rejection. The data were collected through in-depth semi-structured interviews, and Diekelmann and colleagues' method was used for data analysis. After analysis of interviews, four themes were extracted: lack of social support, unrealistic and inappropriate labeling, rejection and isolation, and feeling of absurdity and futility. These can be indicative of the patients' experience from social stigma and rejection phenomena. Patients' experiences of stigma and rejection phenomena indicated that all aspects of their lives are affected. Moreover, these findings highlight the significance of stigma and rejection concepts in providing better care to these patients.

  3. Interplay between immune responses to HLA and non-HLA self-antigens in allograft rejection.

    Science.gov (United States)

    Angaswamy, Nataraju; Tiriveedhi, Venkataswarup; Sarma, Nayan J; Subramanian, Vijay; Klein, Christina; Wellen, Jason; Shenoy, Surendra; Chapman, William C; Mohanakumar, T

    2013-11-01

    Recent studies strongly suggest an increasing role for immune responses against self-antigens (Ags) which are not encoded by the major histocompatibility complex in the immunopathogenesis of allograft rejection. Although, improved surgical techniques coupled with improved methods to detect and avoid sensitization against donor human leukocyte antigen (HLA) have improved the immediate and short term function of transplanted organs. However, acute and chronic rejection still remains a vexing problem for the long term function of the transplanted organ. Immediately following organ transplantation, several factors both immune and non immune mechanisms lead to the development of local inflammatory milieu which sets the stage for allograft rejection. Traditionally, development of antibodies (Abs) against mismatched donor HLA have been implicated in the development of Ab mediated rejection. However, recent studies from our laboratory and others have demonstrated that development of humoral and cellular immune responses against non-HLA self-Ags may contribute in the pathogenesis of allograft rejection. There are reports demonstrating that immune responses to self-Ags especially Abs to the self-Ags as well as cellular immune responses especially through IL17 has significant pro-fibrotic properties leading to chronic allograft failure. This review summarizes recent studies demonstrating the role for immune responses to self-Ags in allograft immunity leading to rejection as well as present recent evidence suggesting there is interplay between allo- and autoimmunity leading to allograft dysfunction. Copyright © 2013 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.

  4. Social Skills and Perceived Maternal Acceptance-Rejection in Relation to Depression in Infertile Women

    Directory of Open Access Journals (Sweden)

    Fariba Yazdkhasti

    2011-01-01

    Full Text Available Background: This study examines the relationship between infertile women’s social skills andtheir perception of their own mothers’ acceptance or rejection, and the role this relationship playsin predicting self-reported depression.Materials and Methods: This was a correlational study. 60 infertile women aged 25 to 35 yearsparticipated in a self-evaluation. A Social Skills Inventory, Parental Acceptance and RejectionQuestionnaire and Beck Depression Inventory were used to measure social skills, acceptancerejection and depression. Data was analyzed by SPSS software, using independent two-sample ttest, logistic regression, and ANOVA.Results: Findings showed that there are significant differences between depressed and not depressedinfertile women in their perceptions of acceptance and rejection by their mothers. Further, women'sperceptions of rejection are a more significant predictor of depression among less socially skilledinfertile women than among those who are more socially skilled. Less socially skilled women didnot show symptoms of depression when they experienced their mothers as accepting. In generalthe results of this study revealed that poorer social skills were more predictive of depression whilegood social skills moderate the effect of infertile women’s perceptions of their mothers' rejection.At the same time, the findings showed that infertile women's perceptions of acceptance moderatedthe effects of poorer social skills in predicting depression.Conclusion: Results suggest that the perception of mothers’ rejection and poor social skills are thekey factors that make infertile women prone to depression.

  5. Coincidence and coherent data analysis methods for gravitational wave bursts in a network of interferometric detectors

    International Nuclear Information System (INIS)

    Arnaud, Nicolas; Barsuglia, Matteo; Bizouard, Marie-Anne; Brisson, Violette; Cavalier, Fabien; Davier, Michel; Hello, Patrice; Kreckelbergh, Stephane; Porter, Edward K.

    2003-01-01

    Network data analysis methods are the only way to properly separate real gravitational wave (GW) transient events from detector noise. They can be divided into two generic classes: the coincidence method and the coherent analysis. The former uses lists of selected events provided by each interferometer belonging to the network and tries to correlate them in time to identify a physical signal. Instead of this binary treatment of detector outputs (signal present or absent), the latter method involves first the merging of the interferometer data and looks for a common pattern, consistent with an assumed GW waveform and a given source location in the sky. The thresholds are only applied later, to validate or not the hypothesis made. As coherent algorithms use more complete information than coincidence methods, they are expected to provide better detection performances, but at a higher computational cost. An efficient filter must yield a good compromise between a low false alarm rate (hence triggering on data at a manageable rate) and a high detection efficiency. Therefore, the comparison of the two approaches is achieved using so-called receiving operating characteristics (ROC), giving the relationship between the false alarm rate and the detection efficiency for a given method. This paper investigates this question via Monte Carlo simulations, using the network model developed in a previous article. Its main conclusions are the following. First, a three-interferometer network such as Virgo-LIGO is found to be too small to reach good detection efficiencies at low false alarm rates: larger configurations are suitable to reach a confidence level high enough to validate as true GW a detected event. In addition, an efficient network must contain interferometers with comparable sensitivities: studying the three-interferometer LIGO network shows that the 2-km interferometer with half sensitivity leads to a strong reduction of performances as compared to a network of three

  6. A new graphical method for Pinch Analysis applications: Heat exchanger network retrofit and energy integration

    International Nuclear Information System (INIS)

    Gadalla, Mamdouh A.

    2015-01-01

    Energy integration is a key solution in chemical process and crude refining industries to minimise external fuel consumption and to face the impact of growing energy crises. Typical energy integration projects can reach a reduction of heating fuels and cold utilities by up to 40% compared with original designs or existing installations. Pinch Analysis is a leading tool and regarded as an efficient method to increase energy efficiency and minimise fuel flow consumptions. It is valid for both natures of design, grassroots and retrofit situations. It can practically be applied to synthesise a HEN (heat exchanger network) or modify an existing preheat train for minimum energy consumption. Heat recovery systems or HENs are networks for exchanging heat between hot and cold process sources. All heat transferred from hot process sources into cold process sinks represent the scope for energy integration. On the other hand, energies required beyond this integrated amount are to be satisfied by external utilities. Graphical representations of Pinch Analysis, such as Composite and Grand Composite Curves are very useful for grassroots designs. Nevertheless, in retrofit situation the analysis is not adequate and besides it is graphically tedious to represent existing exchangers on such graphs. This research proposes a new graphical method for the analysis of heat recovery systems, applicable to HEN retrofit. The new graphical method is based on plotting temperatures of process hot streams versus temperatures of process cold streams. A new graph is constructed for representing existing HENs. For a given network, each existing exchanger is represented by a straight line, whose slope is proportional to the ratio of heat capacities and flows. Further, the length of each exchanger line is related to the heat flow transferred across this exchanger. This new graphical representation can easily identify exchangers across the pinch, Network Pinch, pinching matches and improper placement

  7. Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

    Science.gov (United States)

    Herskovits, Edward H.; Gerring, Joan P.

    2003-01-01

    Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

  8. A fast button surface defects detection method based on convolutional neural network

    Science.gov (United States)

    Liu, Lizhe; Cao, Danhua; Wu, Songlin; Wu, Yubin; Wei, Taoran

    2018-01-01

    Considering the complexity of the button surface texture and the variety of buttons and defects, we propose a fast visual method for button surface defect detection, based on convolutional neural network (CNN). CNN has the ability to extract the essential features by training, avoiding designing complex feature operators adapted to different kinds of buttons, textures and defects. Firstly, we obtain the normalized button region and then use HOG-SVM method to identify the front and back side of the button. Finally, a convolutional neural network is developed to recognize the defects. Aiming at detecting the subtle defects, we propose a network structure with multiple feature channels input. To deal with the defects of different scales, we take a strategy of multi-scale image block detection. The experimental results show that our method is valid for a variety of buttons and able to recognize all kinds of defects that have occurred, including dent, crack, stain, hole, wrong paint and uneven. The detection rate exceeds 96%, which is much better than traditional methods based on SVM and methods based on template match. Our method can reach the speed of 5 fps on DSP based smart camera with 600 MHz frequency.

  9. IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

    Directory of Open Access Journals (Sweden)

    A. S. Potapov

    2014-09-01

    Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

  10. A Method for Upper Bounding Long Term Growth of Network Access Speed

    Directory of Open Access Journals (Sweden)

    Thomas Phillip Knudsen

    2006-06-01

    Full Text Available The development in home Internet access speed has shown an exponential development with growth rates averaging 25% per year. For resource management in network provisioning it becomes an urgent question how long such growth can continue. This paper presents a method for calculating an upper bound to visual content driven growth, proceeding from datarate requirements for a full virtual environment. Scenarios and approaches for reducing datarate requirements are considered and discussed. The presented figures for an upper bound on network access speed are discussed and perspectives on further research presented.

  11. A New Filter Design Method for Disturbed Multilayer Hopfield Neural Networks

    Directory of Open Access Journals (Sweden)

    AHN, C. K.

    2011-05-01

    Full Text Available This paper investigates the passivity based filtering problem for multilayer Hopfield neural networks with external disturbance. A new passivity based filter design method for multilayer Hopfield neural networks is developed to ensure that the filtering error system is exponentially stable and passive from the external disturbance vector to the output error vector. The unknown gain matrix is obtained by solving a linear matrix inequality (LMI, which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed filter.

  12. A Method for Upper Bounding Long Term Growth of Network Access Speed

    DEFF Research Database (Denmark)

    Knudsen, Thomas Phillip; Pedersen, Jens Myrup; Madsen, Ole Brun

    2004-01-01

    The development in home Internet access speed has shown an exponential development with growth rates averaging 25% per year. For resource management in network provisioning it becomes an urgent question how long such growth can continue. This paper presents a method for calculating an upper bound...... to visual content driven growth, proceeding from datarate requirements for a full virtual environment. Scenarios and approaches for reducing datarate requirements are considered and discussed. The presented figures for an upper bound on network access speed are discussed and perspectives on further research...

  13. Modeling Nanoscale FinFET Performance by a Neural Network Method

    Directory of Open Access Journals (Sweden)

    Jin He

    2017-07-01

    Full Text Available This paper presents a neural network method to model nanometer FinFET performance. The principle of this method is firstly introduced and its application in modeling DC and conductance characteristics of nanoscale FinFET transistor is demonstrated in detail. It is shown that this method does not need parameter extraction routine while its prediction of the transistor performance has a small relative error within 1 % compared with measured data, thus this new method is as accurate as the physics based surface potential model.

  14. Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy

    Directory of Open Access Journals (Sweden)

    Nouri S.

    2017-03-01

    Full Text Available Background: The motions of body and tumor in some regions such as chest during radiotherapy treatments are one of the major concerns protecting normal tissues against high doses. By using real-time radiotherapy technique, it is possible to increase the accuracy of delivered dose to the tumor region by means of tracing markers on the body of patients. Objective: This study evaluates the accuracy of some artificial intelligence methods including neural network and those of combination with genetic algorithm as well as particle swarm optimization (PSO estimating tumor positions in real-time radiotherapy. Method: One hundred recorded signals of three external markers were used as input data. The signals from 3 markers thorough 10 breathing cycles of a patient treated via a cyber-knife for a lung tumor were used as data input. Then, neural network method and its combination with genetic or PSO algorithms were applied determining the tumor locations using MATLAB© software program. Results: The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. Conclusion: The internal target volume (ITV should be determined based on the applied neural network algorithm on training steps.

  15. A Power Balance Aware Wireless Charger Deployment Method for Complete Coverage in Wireless Rechargeable Sensor Networks

    Directory of Open Access Journals (Sweden)

    Tu-Liang Lin

    2016-08-01

    Full Text Available Traditional sensor nodes are usually battery powered, and the limited battery power constrains the overall lifespan of the sensors. Recently, wireless power transmission technology has been applied in wireless sensor networks (WSNs to transmit wireless power from the chargers to the sensor nodes and solve the limited battery power problem. The combination of wireless sensors and wireless chargers forms a new type of network called wireless rechargeable sensor networks (WRSNs. In this research, we focus on how to effectively deploy chargers to maximize the lifespan of a network. In WSNs, the sensor nodes near the sink consume more power than nodes far away from the sink because of frequent data forwarding. This important power unbalanced factor has not been considered, however, in previous charger deployment research. In this research, a power balance aware deployment (PBAD method is proposed to address the power unbalance in WRSNs and to design the charger deployment with maximum charging efficiency. The proposed deployment method is effectively aware of the existence of the sink node that would cause unbalanced power consumption in WRSNs. The simulation results show that the proposed PBAD algorithm performs better than other deployment methods, and fewer chargers are deployed as a result.

  16. An Efficient Mesh Generation Method for Fractured Network System Based on Dynamic Grid Deformation

    Directory of Open Access Journals (Sweden)

    Shuli Sun

    2013-01-01

    Full Text Available Meshing quality of the discrete model influences the accuracy, convergence, and efficiency of the solution for fractured network system in geological problem. However, modeling and meshing of such a fractured network system are usually tedious and difficult due to geometric complexity of the computational domain induced by existence and extension of fractures. The traditional meshing method to deal with fractures usually involves boundary recovery operation based on topological transformation, which relies on many complicated techniques and skills. This paper presents an alternative and efficient approach for meshing fractured network system. The method firstly presets points on fractures and then performs Delaunay triangulation to obtain preliminary mesh by point-by-point centroid insertion algorithm. Then the fractures are exactly recovered by local correction with revised dynamic grid deformation approach. Smoothing algorithm is finally applied to improve the quality of mesh. The proposed approach is efficient, easy to implement, and applicable to the cases of initial existing fractures and extension of fractures. The method is successfully applied to modeling of two- and three-dimensional discrete fractured network (DFN system in geological problems to demonstrate its effectiveness and high efficiency.

  17. An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

    Science.gov (United States)

    Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

    2018-04-11

    In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

  18. Improving Distributed Denial of Service (DDOS Detection using Entropy Method in Software Defined Network (SDN

    Directory of Open Access Journals (Sweden)

    Maman Abdurohman

    2017-12-01

    Full Text Available This research proposed a new method to enhance Distributed Denial of Service (DDoS detection attack on Software Defined Network (SDN environment. This research utilized the OpenFlow controller of SDN for DDoS attack detection using modified method and regarding entropy value. The new method would check whether the traffic was a normal traffic or DDoS attack by measuring the randomness of the packets. This method consisted of two steps, detecting attack and checking the entropy. The result shows that the new method can reduce false positive when there is a temporary and sudden increase in normal traffic. The new method succeeds in not detecting this as a DDoS attack. Compared to previous methods, this proposed method can enhance DDoS attack detection on SDN environment.

  19. ‘Healthy’ identities? : Revisiting rejection-identification and rejection-disidentification models among voluntary and forced immigrants

    NARCIS (Netherlands)

    Bobowik, Magdalena; Martinovic, Borja; Basabe, Nekane; Barsties, Lisa S.; Wachter, Gusta

    2017-01-01

    Rejection-identification and rejection-disidentification models propose that low-status groups identify with their in-group and disidentify with a high-status out-group in response to rejection by the latter. Our research tests these two models simultaneously among multiple groups of foreign-born

  20. Optical Calibration Process Developed for Neural-Network-Based Optical Nondestructive Evaluation Method

    Science.gov (United States)

    Decker, Arthur J.

    2004-01-01

    A completely optical calibration process has been developed at Glenn for calibrating a neural-network-based nondestructive evaluation (NDE) method. The NDE method itself detects very small changes in the characteristic patterns or vibration mode shapes of vibrating structures as discussed in many references. The mode shapes or characteristic patterns are recorded using television or electronic holography and change when a structure experiences, for example, cracking, debonds, or variations in fastener properties. An artificial neural network can be trained to be very sensitive to changes in the mode shapes, but quantifying or calibrating that sensitivity in a consistent, meaningful, and deliverable manner has been challenging. The standard calibration approach has been difficult to implement, where the response to damage of the trained neural network is compared with the responses of vibration-measurement sensors. In particular, the vibration-measurement sensors are intrusive, insufficiently sensitive, and not numerous enough. In response to these difficulties, a completely optical alternative to the standard calibration approach was proposed and tested successfully. Specifically, the vibration mode to be monitored for structural damage was intentionally contaminated with known amounts of another mode, and the response of the trained neural network was measured as a function of the peak-to-peak amplitude of the contaminating mode. The neural network calibration technique essentially uses the vibration mode shapes of the undamaged structure as standards against which the changed mode shapes are compared. The published response of the network can be made nearly independent of the contaminating mode, if enough vibration modes are used to train the net. The sensitivity of the neural network can be adjusted for the environment in which the test is to be conducted. The response of a neural network trained with measured vibration patterns for use on a vibration isolation

  1. Rejection of unfair offers can be driven by negative emotions, evidence from modified ultimatum games with anonymity.

    Science.gov (United States)

    Ma, Ning; Li, Nan; He, Xiao-Song; Sun, De-Lin; Zhang, Xiaochu; Zhang, Da-Ren

    2012-01-01

    The rejection of unfair offers can be affected by both negative emotions (e.g. anger and moral disgust) and deliberate cognitive processing of behavioral consequences (e.g. concerns of maintaining social fairness and protecting personal reputation). However, whether negative emotions are sufficient to motivate this behavior is still controversial. With modified ultimatum games, a recent study (Yamagishi T, et al. (2009) Proc Natl Acad Sci USA 106:11520-11523) found that people reject unfair offers even when this behavior increases inequity, and even when they could not communicate to the proposers. Yamagishi suggested that rejection of unfair offers could occur without people's concerning of maintaining social fairness, and could be driven by negative emotions. However, as anonymity was not sufficiently guaranteed in Yamagishi's study, the rejection rates in their experiments may have been influenced by people's concerns of protecting personal reputation (reputational concerns) in addition to negative emotions; thus, it was unclear whether the rejection was driven by negative emotions, or by reputational concerns, or both. In the present study, with specific methods to ensure anonymity, the effect of reputational concerns was successfully ruled out. We found that in a private situation in which rejection could not be driven by reputational concerns, the rejection rates of unfair offers were significantly larger than zero, and in public situations in which rejection rates could be influenced by both negative emotions and reputational concerns, rejection rates were significantly higher than that in the private situation. These results, together with Yamagishi's findings, provided more complete evidence suggesting (a) that the rejection of unfair offers can be driven by negative emotions and (b) that deliberate cognitive processing of the consequences of the behavior can increase the rejection rate, which may benefit social cooperation.

  2. Intelligent self-organization methods for wireless ad hoc sensor networks based on limited resources

    Science.gov (United States)

    Hortos, William S.

    2006-05-01

    A wireless ad hoc sensor network (WSN) is a configuration for area surveillance that affords rapid, flexible deployment in arbitrary threat environments. There is no infrastructure support and sensor nodes communicate with each other only when they are in transmission range. To a greater degree than the terminals found in mobile ad hoc networks (MANETs) for communications, sensor nodes are resource-constrained, with limited computational processing, bandwidth, memory, and power, and are typically unattended once in operation. Consequently, the level of information exchange among nodes, to support any complex adaptive algorithms to establish network connectivity and optimize throughput, not only deplete those limited resources and creates high overhead in narrowband communications, but also increase network vulnerability to eavesdropping by malicious nodes. Cooperation among nodes, critical to the mission of sensor networks, can thus be disrupted by the inappropriate choice of the method for self-organization. Recent published contributions to the self-configuration of ad hoc sensor networks, e.g., self-organizing mapping and swarm intelligence techniques, have been based on the adaptive control of the cross-layer interactions found in MANET protocols to achieve one or more performance objectives: connectivity, intrusion resistance, power control, throughput, and delay. However, few studies have examined the performance of these algorithms when implemented with the limited resources of WSNs. In this paper, self-organization algorithms for the initiation, operation and maintenance of a network topology from a collection of wireless sensor nodes are proposed that improve the performance metrics significant to WSNs. The intelligent algorithm approach emphasizes low computational complexity, energy efficiency and robust adaptation to change, allowing distributed implementation with the actual limited resources of the cooperative nodes of the network. Extensions of the

  3. Using the clustered circular layout as an informative method for visualizing protein-protein interaction networks.

    Science.gov (United States)

    Fung, David C Y; Wilkins, Marc R; Hart, David; Hong, Seok-Hee

    2010-07-01

    The force-directed layout is commonly used in computer-generated visualizations of protein-protein interaction networks. While it is good for providing a visual outline of the protein complexes and their interactions, it has two limitations when used as a visual analysis method. The first is poor reproducibility. Repeated running of the algorithm does not necessarily generate the same layout, therefore, demanding cognitive readaptation on the investigator's part. The second limitation is that it does not explicitly display complementary biological information, e.g. Gene Ontology, other than the protein names or gene symbols. Here, we present an alternative layout called the clustered circular layout. Using the human DNA replication protein-protein interaction network as a case study, we compared the two network layouts for their merits and limitations in supporting visual analysis.

  4. Application of artificial neural networks for response surface modelling in HPLC method development

    Directory of Open Access Journals (Sweden)

    Mohamed A. Korany

    2012-01-01

    Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

  5. Calculation method of water injection forward modeling and inversion process in oilfield water injection network

    Science.gov (United States)

    Liu, Long; Liu, Wei

    2018-04-01

    A forward modeling and inversion algorithm is adopted in order to determine the water injection plan in the oilfield water injection network. The main idea of the algorithm is shown as follows: firstly, the oilfield water injection network is inversely calculated. The pumping station demand flow is calculated. Then, forward modeling calculation is carried out for judging whether all water injection wells meet the requirements of injection allocation or not. If all water injection wells meet the requirements of injection allocation, calculation is stopped, otherwise the demand injection allocation flow rate of certain step size is reduced aiming at water injection wells which do not meet requirements, and next iterative operation is started. It is not necessary to list the algorithm into water injection network system algorithm, which can be realized easily. Iterative method is used, which is suitable for computer programming. Experimental result shows that the algorithm is fast and accurate.

  6. Using Compton scattering for random coincidence rejection

    International Nuclear Information System (INIS)

    Kolstein, M.; Chmeissani, M.

    2016-01-01

    The Voxel Imaging PET (VIP) project presents a new approach for the design of nuclear medicine imaging devices by using highly segmented pixel CdTe sensors. CdTe detectors can achieve an energy resolution of ≈ 1% FWHM at 511 keV and can be easily segmented into submillimeter sized voxels for optimal spatial resolution. These features help in rejecting a large part of the scattered events from the PET coincidence sample in order to obtain high quality images. Another contribution to the background are random events, i.e., hits caused by two independent gammas without a common origin. Given that 60% of 511 keV photons undergo Compton scattering in CdTe (i.e. 84% of all coincidence events have at least one Compton scattering gamma), we present a simulation study on the possibility to use the Compton scattering information of at least one of the coincident gammas within the detector to reject random coincidences. The idea uses the fact that if a gamma undergoes Compton scattering in the detector, it will cause two hits in the pixel detectors. The first hit corresponds to the Compton scattering process. The second hit shall correspond to the photoelectric absorption of the remaining energy of the gamma. With the energy deposition of the first hit, one can calculate the Compton scattering angle. By measuring the hit location of the coincident gamma, we can construct the geometric angle, under the assumption that both gammas come from the same origin. Using the difference between the Compton scattering angle and the geometric angle, random events can be rejected.

  7. Management and Nonlinear Analysis of Disinfection System of Water Distribution Networks Using Data Driven Methods

    Directory of Open Access Journals (Sweden)

    Mohammad Zounemat-Kermani

    2018-03-01

    Full Text Available Chlorination unit is widely used to supply safe drinking water and removal of pathogens from water distribution networks. Data-driven approach is one appropriate method for analyzing performance of chlorine in water supply network. In this study, multi-layer perceptron neural network (MLP with three training algorithms (gradient descent, conjugate gradient and BFGS and support vector machine (SVM with RBF kernel function were used to predict the concentration of residual chlorine in water supply networks of Ahmadabad Dafeh and Ahruiyeh villages in Kerman Province. Daily data including discharge (flow, chlorine consumption and residual chlorine were employed from the beginning of 1391 Hijri until the end of 1393 Hijri (for 3 years. To assess the performance of studied models, the criteria such as Nash-Sutcliffe efficiency (NS, root mean square error (RMSE, mean absolute percentage error (MAPE and correlation coefficient (CORR were used that in best modeling situation were 0.9484, 0.0255, 1.081, and 0.974 respectively which resulted from BFGS algorithm. The criteria indicated that MLP model with BFGS and conjugate gradient algorithms were better than all other models in 90 and 10 percent of cases respectively; while the MLP model based on gradient descent algorithm and the SVM model were better in none of the cases. According to the results of this study, proper management of chlorine concentration can be implemented by predicted values of residual chlorine in water supply network. Thus, decreased performance of perceptron network and support vector machine in water supply network of Ahruiyeh in comparison to Ahmadabad Dafeh can be inferred from improper management of chlorination.

  8. An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks

    Science.gov (United States)

    2014-01-01

    Background Motif mining has always been a hot research topic in bioinformatics. Most of current research on biological networks focuses on exact motif mining. However, due to the inevitable experimental error and noisy data, biological network data represented as the probability model could better reflect the authenticity and biological significance, therefore, it is more biological meaningful to discover probability motif in uncertain biological networks. One of the key steps in probability motif mining is frequent pattern discovery which is usually based on the possible world model having a relatively high computational complexity. Methods In this paper, we present a novel method for detecting frequent probability patterns based on circuit simulation in the uncertain biological networks. First, the partition based efficient search is applied to the non-tree like subgraph mining where the probability of occurrence in random networks is small. Then, an algorithm of probability isomorphic based on circuit simulation is proposed. The probability isomorphic combines the analysis of circuit topology structure with related physical properties of voltage in order to evaluate the probability isomorphism between probability subgraphs. The circuit simulation based probability isomorphic can avoid using traditional possible world model. Finally, based on the algorithm of probability subgraph isomorphism, two-step hierarchical clustering method is used to cluster subgraphs, and discover frequent probability patterns from the clusters. Results The experiment results on data sets of the Protein-Protein Interaction (PPI) networks and the transcriptional regulatory networks of E. coli and S. cerevisiae show that the proposed method can efficiently discover the frequent probability subgraphs. The discovered subgraphs in our study contain all probability motifs reported in the experiments published in other related papers. Conclusions The algorithm of probability graph isomorphism

  9. Perturbations in the Urinary Exosome in Transplant Rejection

    Directory of Open Access Journals (Sweden)

    Tara eSigdel

    2015-01-01

    Full Text Available Urine exosomes are small vesicles exocytosed into the urine by all renal epithelial cell types under normal physiologic and disease states. Urine exosomal proteins may mirror disease specific proteome perturbations in kidney injury. Analysis methodologies for the exosomal fraction of the urinary proteome were developed for comparing the urinary exosomal fraction versus unfractionated proteome for biomarker discovery. Urine exosomes were isolated by centrifugal filtration of urine samples collected from kidney transplant patients with and without acute rejection, which were biopsy matched. The proteomes of unfractionated whole urine (Uw and urine exosomes (Ue underwent mass spectroscopy-based quantitative proteonomics analysis. The proteome data were analyzed for significant differential protein abundances in acute rejection (AR. A total of 1018 proteins were identified in Uw and 349 proteins in Ue. 279 overlapped between the two urinary compartments and 70 proteins were unique to the Ue compartment. Of 349 exosomal proteins identified from transplant patients,220 had not been previously identified in the normal Ue fraction. 11 Ue proteins, functionally involved in an inflammatory and stress response, were more abundant in urine samples from patients with acute rejection, 3 of which are exclusive to the Ue fraction. Ue AR-specific biomarkers(8 were also detected in Uw, but since they were observed at significantly lower abundances in Uw, they were not significant for AR in Uw. A rapid urinary exosome isolation method and quantitative measurement of enriched Ue proteins was applied. Perturbed proteins in the exosomal compartment of urine collected from kidney transplant patients were specific to inflammatory responses, and were not observed in the Ue fraction from normal healthy subjects. Ue specific protein alterations in renal disease provide potential mechanistic insights and offer a unique panel of sensitive biomarkers for monitoring AR.

  10. USA: California rejects mandatory GMO labelling

    OpenAIRE

    Paull, John

    2012-01-01

    Buying organic remains the best strategy for US consumers to avoid eating GM food. The voters of California have rejected the proposal to label GMO food. The proposition was narrowly lost, 47% to 53% (4,326,770 ‘Yes’ votes vs. 4,884,961 ‘No’ votes). Proposition 47 was supported by the organic sector but opposed by a coalition of GMO companies and US multinational food companies. Californians were invited to vote into law ‘The California Right to Know Genetically Engineered Food Act’. Section ...

  11. A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2016-02-01

    Full Text Available Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

  12. Manufacturing error sensitivity analysis and optimal design method of cable-network antenna structures

    Science.gov (United States)

    Zong, Yali; Hu, Naigang; Duan, Baoyan; Yang, Guigeng; Cao, Hongjun; Xu, Wanye

    2016-03-01

    Inevitable manufacturing errors and inconsistency between assumed and actual boundary conditions can affect the shape precision and cable tensions of a cable-network antenna, and even result in failure of the structure in service. In this paper, an analytical sensitivity analysis method of the shape precision and cable tensions with respect to the parameters carrying uncertainty was studied. Based on the sensitivity analysis, an optimal design procedure was proposed to alleviate the effects of the parameters that carry uncertainty. The validity of the calculated sensitivities is examined by those computed by a finite difference method. Comparison with a traditional design method shows that the presented design procedure can remarkably reduce the influence of the uncertainties on the antenna performance. Moreover, the results suggest that especially slender front net cables, thick tension ties, relatively slender boundary cables and high tension level can improve the ability of cable-network antenna structures to resist the effects of the uncertainties on the antenna performance.

  13. Optimized star sensors laboratory calibration method using a regularization neural network.

    Science.gov (United States)

    Zhang, Chengfen; Niu, Yanxiong; Zhang, Hao; Lu, Jiazhen

    2018-02-10

    High-precision ground calibration is essential to ensure the performance of star sensors. However, the complex distortion and multi-error coupling have brought great difficulties to traditional calibration methods, especially for large field of view (FOV) star sensors. Although increasing the complexity of models is an effective way to improve the calibration accuracy, it significantly increases the demand for calibration data. In order to achieve high-precision calibration of star sensors with large FOV, a novel laboratory calibration method based on a regularization neural network is proposed. A multi-layer structure neural network is designed to represent the mapping of the star vector and the corresponding star point coordinate directly. To ensure the generalization performance of the network, regularization strategies are incorporated into the net structure and the training algorithm. Simulation and experiment results demonstrate that the proposed method can achieve high precision with less calibration data and without any other priori information. Compared with traditional methods, the calibration error of the star sensor decreased by about 30%. The proposed method can satisfy the precision requirement for large FOV star sensors.

  14. A neural network method to correct bidirectional effects in water-leaving radiance

    Science.gov (United States)

    Fan, Yongzhen; Li, Wei; Voss, Kenneth J.; Gatebe, Charles K.; Stamnes, Knut

    2017-02-01

    The standard method to convert the measured water-leaving radiances from the observation direction to the nadir direction developed by Morel and coworkers requires knowledge of the chlorophyll concentration (CHL). Also, the standard method was developed for open ocean water, which makes it unsuitable for turbid coastal waters. We introduce a neural network method to convert the water-leaving radiance (or the corresponding remote sensing reflectance) from the observation direction to the nadir direction. This method does not require any prior knowledge of the water constituents or the inherent optical properties (IOPs). This method is fast, accurate and can be easily adapted to different remote sensing instruments. Validation using NuRADS measurements in different types of water shows that this method is suitable for both open ocean and coastal waters. In open ocean or chlorophyll-dominated waters, our neural network method produces corrections similar to those of the standard method. In turbid coastal waters, especially sediment-dominated waters, a significant improvement was obtained compared to the standard method.

  15. Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods, and Applications

    Directory of Open Access Journals (Sweden)

    Mingzhu Zhao

    2013-01-01

    Full Text Available The traditional Chinese medicine (TCM, which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

  16. Method of Parallel-Hierarchical Network Self-Training and its Application for Pattern Classification and Recognition

    Directory of Open Access Journals (Sweden)

    TIMCHENKO, L.

    2012-11-01

    Full Text Available Propositions necessary for development of parallel-hierarchical (PH network training methods are discussed in this article. Unlike already known structures of the artificial neural network, where non-normalized (absolute similarity criteria are used for comparison, the suggested structure uses a normalized criterion. Based on the analysis of training rules, a conclusion is made that application of two training methods with a teacher is optimal for PH network training: error correction-based training and memory-based training. Mathematical models of training and a combined method of PH network training for recognition of static and dynamic patterns are developed.

  17. Rejection of Bromide and Bromate Ions by a Ceramic Membrane.

    Science.gov (United States)

    Moslemi, Mohammadreza; Davies, Simon H; Masten, Susan J

    2012-12-01

    Effects of pH and the addition of calcium chloride (CaCl(2)) on bromate (BrO(3) (-)) and bromide (Br(-)) rejection by a ceramic membrane were investigated. Rejection of both ions increased with pH. At pH 8, the rejection of BrO(3) (-) and Br(-) was 68% and 63%, respectively. Donnan exclusion appears to play an important role in determining rejection of BrO(3) (-) and Br(-). In the presence of CaCl(2), rejection of BrO(3) (-) and Br(-) ions was greatly reduced, confirming the importance of electrostatic interactions in determining rejection of BrO(3) (-) and Br(-). The effect of Ca(2+) is so pronounced that in most natural waters, rejection of both BrO(3) (-) and Br(-) by the membrane would be extremely small.

  18. Understanding maladaptive responses to rejection: Aggression with an audience.

    Science.gov (United States)

    DeBono, Amber; Layton, Rebekah L; Freeman, Nicholas; Muraven, Mark

    2017-01-01

    Logically, responding aggressively to rejection is maladaptive because one is unlikely to seek a relationship with an aggressor. We predict that when concealed, the illogical aggressive response to rejection is more likely, whereas when the rejected individuals' aggressive responses are perceived as public, the aggressive acts may be reduced. Participants were rejected by others (Experiment 1) or were either accepted or rejected during an online ball-tossing game (Experiment 2) and were then given an opportunity to aggress publicly or privately. Across experiments, when the opportunity to aggress was made public, rejected participants exhibited less aggressive behavior. When concerned about the perception of their public aggressive responses by others, rejected individuals' aggressive responses diminished compared with those whose actions were private. Crucially, this extended to aggression visible only to neutral others, suggesting that effects cannot solely be due to fear of retribution.

  19. Sensitivity Analysis of Dynamic Tariff Method for Congestion Management in Distribution Networks

    DEFF Research Database (Denmark)

    Huang, Shaojun; Wu, Qiuwei; Liu, Zhaoxi

    2015-01-01

    The dynamic tariff (DT) method is designed for the distribution system operator (DSO) to alleviate the congestions that might occur in a distribution network with high penetration of distribute energy resources (DERs). Sensitivity analysis of the DT method is crucial because of its decentralized...... control manner. The sensitivity analysis can obtain the changes of the optimal energy planning and thereby the line loading profiles over the infinitely small changes of parameters by differentiating the KKT conditions of the convex quadratic programming, over which the DT method is formed. Three case...

  20. Method of image segmentation using a neural network. Application to MR imaging of brain tumors

    International Nuclear Information System (INIS)

    Engler, E.; Gautherie, M.

    1992-01-01

    An original method of numerical images segmentation has been developed. This method is based on pixel clustering using a formal neural network configurated by supervised learning of pre-classified examples. The method has been applied to series of MR images of brain tumors (gliomas) with a view to proceed with a 3D-extraction of the tumor volume. This study is part of a project on cancer thermotherapy including the development of a scan-focused ultrasound system of tumor heating and a 3D-numerical thermal model