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Sample records for network rejection method

  1. Reject mechanisms for massively parallel neural network character recognition systems

    Science.gov (United States)

    Garris, Michael D.; Wilson, Charles L.

    1992-12-01

    Two reject mechanisms are compared using a massively parallel character recognition system implemented at NIST. The recognition system was designed to study the feasibility of automatically recognizing hand-printed text in a loosely constrained environment. The first method is a simple scalar threshold on the output activation of the winning neurode from the character classifier network. The second method uses an additional neural network trained on all outputs from the character classifier network to accept or reject assigned classifications. The neural network rejection method was expected to perform with greater accuracy than the scalar threshold method, but this was not supported by the test results presented. The scalar threshold method, even though arbitrary, is shown to be a viable reject mechanism for use with neural network character classifiers. Upon studying the performance of the neural network rejection method, analyses show that the two neural networks, the character classifier network and the rejection network, perform very similarly. This can be explained by the strong non-linear function of the character classifier network which effectively removes most of the correlation between character accuracy and all activations other than the winning activation. This suggests that any effective rejection network must receive information from the system which has not been filtered through the non-linear classifier.

  2. 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.

  3. Rejection

    National Research Council Canada - National Science Library

    Black, Mary

    2007-01-01

    .... [...]my long term favourite story comes from Ireland, where in the chaotic, jobless mid-1980s one friend applied for everything going and then wallpapered his bathroom with the rejection letters...

  4. 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.

  5. Method and apparatus for analog pulse pile-up rejection

    Science.gov (United States)

    De Geronimo, Gianluigi

    2013-12-31

    A method and apparatus for pulse pile-up rejection are disclosed. The apparatus comprises a delay value application constituent configured to receive a threshold-crossing time value, and provide an adjustable value according to a delay value and the threshold-crossing time value; and a comparison constituent configured to receive a peak-occurrence time value and the adjustable value, compare the peak-occurrence time value with the adjustable value, indicate pulse acceptance if the peak-occurrence time value is less than or equal to the adjustable value, and indicate pulse rejection if the peak-occurrence time value is greater than the adjustable value.

  6. 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.

  7. On the Potential of Interference Rejection Combining in B4G Networks

    DEFF Research Database (Denmark)

    Tavares, Fernando Menezes Leitão; Berardinelli, Gilberto; Mahmood, Nurul Huda

    2013-01-01

    Beyond 4th Generation (B4G) local area networks will be characterized by the dense uncoordinated deployment of small cells. This paper shows that inter-cell interference, which is a main limiting factor in such networks, can be effectively contained using Interference Rejection Combining (IRC...

  8. Normalizing Rejection.

    Science.gov (United States)

    Conn, Vicki S; Zerwic, Julie; Jefferson, Urmeka; Anderson, Cindy M; Killion, Cheryl M; Smith, Carol E; Cohen, Marlene Z; Fahrenwald, Nancy L; Herrick, Linda; Topp, Robert; Benefield, Lazelle E; Loya, Julio

    2016-02-01

    Getting turned down for grant funding or having a manuscript rejected is an uncomfortable but not unusual occurrence during the course of a nurse researcher's professional life. Rejection can evoke an emotional response akin to the grieving process that can slow or even undermine productivity. Only by "normalizing" rejection, that is, by accepting it as an integral part of the scientific process, can researchers more quickly overcome negative emotions and instead use rejection to refine and advance their scientific programs. This article provides practical advice for coming to emotional terms with rejection and delineates methods for working constructively to address reviewer comments. © The Author(s) 2015.

  9. Improving disturbance rejection of PID controllers by means of the magnitude optimum method.

    Science.gov (United States)

    Vrancić, Damir; Strmcnik, Stanko; Kocijan, Jus; de Moura Oliveira, P B

    2010-01-01

    The magnitude optimum (MO) method provides a relatively fast and non-oscillatory closed-loop tracking response for a large class of process models frequently encountered in the process and chemical industries. However, the deficiency of the method is poor disturbance rejection performance of some processes. In this paper, disturbance rejection performance of the PID controller is improved by applying the "disturbance rejection magnitude optimum" (DRMO) optimisation method, while the tracking performance has been improved by a set-point weighting and set-point filtering PID controller structure. The DRMO tuning method requires numerical optimisation for the calculation of PID controller parameters. The method was applied to two different 2-degrees-of-freedom PID controllers and has been tested on several different representatives of process models and one laboratory set-up. A comparison with some other tuning methods has shown that the proposed tuning method, with a set-point filtering PID controller, is quite efficient in improving disturbance rejection performance, while retaining tracking performance comparable with the original MO method. 2009 ISA. Published by Elsevier Ltd. All rights reserved.

  10. 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.

  11. High Capacity Downlink Transmission with MIMO Interference Subspace Rejection in Multicellular CDMA Networks

    Directory of Open Access Journals (Sweden)

    Hansen Henrik

    2004-01-01

    Full Text Available We proposed recently a new technique for multiuser detection in CDMA networks, denoted by interference subspace rejection (ISR, and evaluated its performance on the uplink. This paper extends its application to the downlink (DL. On the DL, the information about the interference is sparse, for example, spreading factor (SF and modulation of interferers may not be known, which makes the task much more challenging. We present three new ISR variants which require no prior knowledge of interfering users. The new solutions are applicable to MIMO systems and can accommodate any modulation, coding, SF, and connection type. We propose a new code allocation scheme denoted by DACCA which significantly reduces the complexity of our solution at the receiving mobile. We present estimates of user capacities and data rates attainable under practically reasonable conditions regarding interferences identified and suppressed in a multicellular interference-limited system. We show that the system capacity increases linearly with the number of antennas despite the existence of interference. Our new DL multiuser receiver consistently provides an Erlang capacity gain of at least over the single-user detector.

  12. Generation of transport lattice code KARMA library with doppler broadening rejection correction method

    Energy Technology Data Exchange (ETDEWEB)

    Park, Ho Jin; Cho, Jin Young; Park, Sang Yoon [Korea Atomic Energy Research Institute, Daejeon (Korea, Republic of); Hong, Ser Gi [Kyung Hee Univ., Yongin (Korea, Republic of); Kim, Kang Seog [Oak Ridge National Laboratory, Tennessee (United States)

    2012-10-15

    In order to solve the exact neutron transport equations, the temperature-dependent neutron cross sections including scattering kernel are needed. However, the current cross section generation systems such as NJOY do not generate the temperature dependent scatting kernels. In Monte Carlo (MC) code, the sampling of velocity of target nucleus method is used to determine the energy and direction of outgoing neutron by the approximated constant cross section model. Recently, the Doppler-broadening rejection correction (DBRC) and weight correction method are proposed for the exact sampling. In this study, the KARMA(Kernel Analyzer by Ray tracing Method for fuel Assembly) library system incorporating McCARD calculations with the DBRC method are established and the effect of improved Doppler treatment will be examined.

  13. Comparing acceptance and rejection in the classroom interaction of students who stutter and their peers: A social network analysis.

    Science.gov (United States)

    Adriaensens, Stefanie; Van Waes, Sara; Struyf, Elke

    2017-06-01

    Recent work has reported adverse effects of students' stuttering on their social and emotional functioning at school. Yet, few studies have provided an in-depth examination of classroom interaction of students who stutter (SWS). The current study uses a network perspective to compare acceptance and rejection in the classroom interaction between SWS and their peers in secondary education. The sample comprised 22 SWS and 403 non-stuttering peers (22 classes) of secondary education in Flanders (Belgium). Students' nominations regarding three acceptance and three rejection criteria were combined. Social network analysis offered procedures that considered direct and indirect interaction between all classmates. We found few significant differences: SWS and their peers were distributed similarly across positive and negative status groups. Both considered and were considered by, on average, six or seven classmates as 'a friend', who they liked and could count on, and nominated or were nominated by one or two classmates as 'no friend', somebody who they disliked and could not count on. On average, SWS and their classmates also did not differ in terms of structural position in the class group (degree, closeness and betweenness), reciprocated rejection, and clique size. However, SWS do tend to be slightly more stringent or more careful in nominating peers, which led to fewer reciprocated friendships. Our results suggest that SWS are quite accepted by peers in secondary education in Flanders. Such positive peer interaction can create a supportive and encouraging climate for SWS to deal with specific challenges. Copyright © 2017 Elsevier Inc. All rights reserved.

  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. Drawing networks of rejection - a systems biological approach to the identification of candidate genes in heart transplantation.

    Science.gov (United States)

    Cadeiras, Martin; von Bayern, Manuel; Sinha, Anshu; Shahzad, Khurram; Latif, Farhana; Lim, Wei Keat; Grenett, Hernan; Tabak, Esteban; Klingler, Tod; Califano, Andrea; Deng, Mario C

    2011-04-01

    Technological development led to an increased interest in systems biological approaches to characterize disease mechanisms and candidate genes relevant to specific diseases. We suggested that the human peripheral blood mononuclear cells (PBMC) network can be delineated by cellular reconstruction to guide identification of candidate genes. Based on 285 microarrays (7370 genes) from 98 heart transplant patients enrolled in the Cardiac Allograft Rejection Gene Expression Observational study, we used an information-theoretic, reverse-engineering algorithm called ARACNe (algorithm for the reconstruction of accurate cellular networks) and chromatin immunoprecipitation assay to reconstruct and validate a putative gene PBMC interaction network. We focused our analysis on transcription factor (TF) genes and developed a priority score to incorporate aspects of network dynamics and information from published literature to supervise gene discovery. ARACNe generated a cellular network and predicted interactions for each TF during rejection and quiescence. Genes ranked highest by priority score included those related to apoptosis, humoural and cellular immune response such as GA binding protein transcription factor (GABP), nuclear factor of κ light polypeptide gene enhancer in B-cells (NFκB), Fas (TNFRSF6)-associated via death domain (FADD) and c-AMP response element binding protein. We used the TF CREB to validate our network. ARACNe predicted 29 putative first-neighbour genes of CREB. Eleven of these (37%) were previously reported. Out of the 18 unknown predicted interactions, 14 primers were identified and 11 could be immunoprecipitated (78.6%). Overall, 75% (n= 22) inferred CREB targets were validated, a significantly higher fraction than randomly expected (P biological approaches to identify possible molecular targets and biomarkers. © 2011 The Authors Journal of Cellular and Molecular Medicine © 2011 Foundation for Cellular and Molecular Medicine/Blackwell Publishing

  16. 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.

  17. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements

    Energy Technology Data Exchange (ETDEWEB)

    Taulu, S; Simola, J [Elekta Neuromag Oy, Helsinki (Finland)

    2006-04-07

    Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.

  18. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements

    Science.gov (United States)

    Taulu, S.; Simola, J.

    2006-04-01

    Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.

  19. 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.

  20. Cogging force rejection method of linear motor based on internal model principle

    Science.gov (United States)

    Liu, Yang; Chen, Zhenyu; Yang, Tianbo

    2015-02-01

    The cogging force disturbance of linear motor is one of the main factors affecting the positioning accuracy of ultraprecision moving platform. And this drawback could not be completely overcome by improving the design of motor body, such as location modification of permanent magnet array, or optimization design of the shape of teeth-slot. So the active compensation algorithms become prevalent in cogging force rejection area. This paper proposed a control structure based on internal mode principle to attenuate the cogging force of linear motor which deteriorated the accuracy of position, and this structure could make tracking and anti-disturbing performance of close-loop designed respectively. In the first place, the cogging force was seen as the intrinsic property of linear motor and its model constituting controlled object with motor ontology model was obtained by data driven recursive identification method. Then, a control structure was designed to accommodate tracking and anti-interference ability separately by using internal model principle. Finally, the proposed method was verified in a long stroke moving platform driven by linear motor. The experiment results show that, by employing this control strategy, the positioning error caused by cogging force was decreased by 70%.

  1. A Rejection Sampling Based Method for Determining Thermal Scattering Angle and Energy

    Energy Technology Data Exchange (ETDEWEB)

    Haugen, Carl C.; Forget, Benoit; Smith, Kord S.

    2017-09-01

    Most high performance computing systems being deployed currently and envisioned for the future are based on making use of heavy parallelism across many computational nodes and many concurrent cores. These types of heavily parallel systems often have relatively little memory per core but large amounts of computing capability. This places a significant constraint on how data storage is handled in many Monte Carlo codes. This is made even more significant in fully coupled multiphysics simulations, which requires simulations of many physical phenomena be carried out concurrently on individual processing nodes, which further reduces the amount of memory available for storage of Monte Carlo data. As such, there has been a move towards on-the-fly nuclear data generation to reduce memory requirements associated with interpolation between pre-generated large nuclear data tables for a selection of system temperatures. Methods have been previously developed and implemented in MIT’s OpenMC Monte Carlo code for both the resolved resonance regime and the unresolved resonance regime, but are currently absent for the thermal energy regime. While there are many components involved in generating a thermal neutron scattering cross section on-the-fly, this work will focus on a proposed method for determining the energy and direction of a neutron after a thermal incoherent inelastic scattering event. This work proposes a rejection sampling based method using the thermal scattering kernel to determine the correct outgoing energy and angle. The goal of this project is to be able to treat the full S (a, ß) kernel for graphite, to assist in high fidelity simulations of the TREAT reactor at Idaho National Laboratory. The method is, however, sufficiently general to be applicable in other thermal scattering materials, and can be initially validated with the continuous analytic free gas model.

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

    Energy Technology Data Exchange (ETDEWEB)

    De Nardo, D.; Scibilia, G.; Macchiarelli, A.G.; Cassisi, A.; Tonelli, E.; Papalia, U.; Gallo, P.; Antolini, M.; Pitucco, G.; Reale, A. (Universita degli Studi di Roma I La Sapienza Policlinico Umberto I (Italy))

    1989-09-01

    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.

  3. Transoesophageal detection of heart graft rejection by electrical impedance: Using finite element method simulations

    Science.gov (United States)

    Giovinazzo, G.; Ribas, N.; Cinca, J.; Rosell-Ferrer, J.

    2010-04-01

    Previous studies have shown that it is possible to evaluate heart graft rejection level using a bioimpedance technique by means of an intracavitary catheter. However, this technique does not present relevant advantages compared to the gold standard for the detection of a heart rejection, which is the biopsy of the endomyocardial tissue. We propose to use a less invasive technique that consists in the use of a transoesophageal catheter and two standard ECG electrodes on the thorax. The aim of this work is to evaluate different parameters affecting the impedance measurement, including: sensitivity to electrical conductivity and permittivity of different organs in the thorax, lung edema and pleural water. From these results, we deduce the best estimator for cardiac rejection detection, and we obtain the tools to identify possible cases of false positive of heart rejection due to other factors. To achieve these objectives we have created a thoracic model and we have simulated, with a FEM program, different situations at the frequencies of 13, 30, 100, 300 and 1000 kHz. Our simulation demonstrates that the phase, at 100 and 300 kHz, has the higher sensitivity to changes in the electrical parameters of the heart muscle.

  4. Interrogation Methods and Terror Networks

    Science.gov (United States)

    Baccara, Mariagiovanna; Bar-Isaac, Heski

    We examine how the structure of terror networks varies with legal limits on interrogation and the ability of authorities to extract information from detainees. We assume that terrorist networks are designed to respond optimally to a tradeoff caused by information exchange: Diffusing information widely leads to greater internal efficiency, but it leaves the organization more vulnerable to law enforcement. The extent of this vulnerability depends on the law enforcement authority’s resources, strategy and interrogation methods. Recognizing that the structure of a terrorist network responds to the policies of law enforcement authorities allows us to begin to explore the most effective policies from the authorities’ point of view.

  5. 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.

  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. Methods for Analyzing Pipe Networks

    DEFF Research Database (Denmark)

    Nielsen, Hans Bruun

    1989-01-01

    The governing equations for a general network are first set up and then reformulated in terms of matrices. This is developed to show that the choice of model for the flow equations is essential for the behavior of the iterative method used to solve the problem. It is shown that it is better to fo...... demonstrated that this method offers good starting values for a Newton-Raphson iteration.......The governing equations for a general network are first set up and then reformulated in terms of matrices. This is developed to show that the choice of model for the flow equations is essential for the behavior of the iterative method used to solve the problem. It is shown that it is better...... 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...

  9. 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...... to consistency and long-term characteristics. The developed method gives significant improvements on these parameters. The case study was conducted as a comparison between an existing network where the traffic was known and a proposed network designed by the developed method. It turned out that the proposed...... network performed better than the existing network with regard to the performance measurements used which reflected how well the traffic was routed in the networks and the cost of establishing the networks. Challenges that need to be solved before the developed method can be used to design network...

  10. Rapid Heartbeat, But Dry Palms: Reactions of Heart Rate and Skin Conductance Levels to Social Rejection

    OpenAIRE

    Benjamin eIffland; Lisa Margareta Sansen; Claudia eCatani; Frank eNeuner

    2014-01-01

    Background: Social rejection elicits negative mood, emotional distress and neural activity in networks that are associated with physical pain. However, studies assessing physiological reactions to social rejection are rare and results of these studies were found to be ambiguous. Therefore, the present study aimed to examine and specify physiological effects of social rejection.Methods: Participants (N = 50) were assigned to either a social exclusion or inclusion condition of a virtual ball-to...

  11. Sensor Network Data Fusion Methods

    Directory of Open Access Journals (Sweden)

    Martynas Vervečka

    2011-03-01

    Full Text Available Sensor network data fusion is widely used in warfare, in areas such as automatic target recognition, battlefield surveillance, automatic vehicle control, multiple target surveillance, etc. Non-military use example are: medical equipment status monitoring, intelligent home. The paper describes sensor networks topologies, sensor network advantages against the isolated sensors, most common network topologies, their advantages and disadvantages.Article in Lithuanian

  12. 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.

  13. Nonlinear fractional order proportion-integral-derivative active disturbance rejection control method design for hypersonic vehicle attitude control

    Science.gov (United States)

    Song, Jia; Wang, Lun; Cai, Guobiao; Qi, Xiaoqiang

    2015-06-01

    Near space hypersonic vehicle model is nonlinear, multivariable and couples in the reentry process, which are challenging for the controller design. In this paper, a nonlinear fractional order proportion integral derivative (NFOPIλDμ) active disturbance rejection control (ADRC) strategy based on a natural selection particle swarm (NSPSO) algorithm is proposed for the hypersonic vehicle flight control. The NFOPIλDμ ADRC method consists of a tracking-differentiator (TD), an NFOPIλDμ controller and an extended state observer (ESO). The NFOPIλDμ controller designed by combining an FOPIλDμ method and a nonlinear states error feedback control law (NLSEF) is to overcome concussion caused by the NLSEF and conversely compensate the insufficiency for relatively simple and rough signal processing caused by the FOPIλDμ method. The TD is applied to coordinate the contradiction between rapidity and overshoot. By attributing all uncertain factors to unknown disturbances, the ESO can achieve dynamic feedback compensation for these disturbances and thus reduce their effects. Simulation results show that the NFOPIλDμ ADRC method can make the hypersonic vehicle six-degree-of-freedom nonlinear model track desired nominal signals accurately and fast, has good stability, dynamic properties and strong robustness against external environmental disturbances.

  14. Neural Network Based Active Disturbance Rejection Control of a Novel Electrohydraulic Servo System for Simultaneously Balancing and Positioning by Isoactuation Configuration

    Directory of Open Access Journals (Sweden)

    Qiang Gao

    2016-01-01

    Full Text Available To satisfy the lightweight requirements of large pipe weapons, a novel electrohydraulic servo (EHS system where the hydraulic cylinder possesses three cavities is developed and investigated in the present study. In the EHS system, the balancing cavity of the EHS is especially designed for active compensation for the unbalancing force of the system, whereas the two driving cavities are employed for positioning and disturbance rejection of the large pipe. Aiming at simultaneously balancing and positioning of the EHS system, a novel neural network based active disturbance rejection control (NNADRC strategy is developed. In the NNADRC, the radial basis function (RBF neural network is employed for online updating of parameters of the extended state observer (ESO. Thereby, the nonlinear behavior and external disturbance of the system can be accurately estimated and compensated in real time. The efficiency and superiority of the system are critically investigated by conducting numerical simulations, showing that much higher steady accuracy as well as system robustness is achieved when comparing with conventional ADRC control system. It indicates that the NNADRC is a very promising technique for achieving fast, stable, smooth, and accurate control of the novel EHS system.

  15. Orbits for the Impatient: A Bayesian Rejection-sampling Method for Quickly Fitting the Orbits of Long-period Exoplanets

    Science.gov (United States)

    Blunt, Sarah; Nielsen, Eric L.; De Rosa, Robert J.; Konopacky, Quinn M.; Ryan, Dominic; Wang, Jason J.; Pueyo, Laurent; Rameau, Julien; Marois, Christian; Marchis, Franck; Macintosh, Bruce; Graham, James R.; Duchêne, Gaspard; Schneider, Adam C.

    2017-05-01

    We describe a Bayesian rejection-sampling algorithm designed to efficiently compute posterior distributions of orbital elements for data covering short fractions of long-period exoplanet orbits. Our implementation of this method, Orbits for the Impatient (OFTI), converges up to several orders of magnitude faster than two implementations of Markov Chain Monte Carlo (MCMC) in this regime. We illustrate the efficiency of our approach by showing that OFTI calculates accurate posteriors for all existing astrometry of the exoplanet 51 Eri b up to 100 times faster than a Metropolis-Hastings MCMC. We demonstrate the accuracy of OFTI by comparing our results for several orbiting systems with those of various MCMC implementations, finding the output posteriors to be identical within shot noise. We also describe how our algorithm was used to successfully predict the location of 51 Eri b six months in the future based on less than three months of astrometry. Finally, we apply OFTI to 10 long-period exoplanets and brown dwarfs, all but one of which have been monitored over less than 3% of their orbits, producing fits to their orbits from astrometric records in the literature.

  16. 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.

  17. Constructing an Intelligent Patent Network Analysis Method

    OpenAIRE

    Chao-Chan Wu; Ching-Bang Yao

    2012-01-01

    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...

  18. 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.

  19. 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, ...

  20. 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

  1. Advanced fault diagnosis methods in molecular networks.

    Science.gov (United States)

    Habibi, Iman; Emamian, Effat S; Abdi, Ali

    2014-01-01

    Analysis of the failure of cell signaling networks is an important topic in systems biology and has applications in target discovery and drug development. In this paper, some advanced methods for fault diagnosis in signaling networks are developed and then applied to a caspase network and an SHP2 network. The goal is to understand how, and to what extent, the dysfunction of molecules in a network contributes to the failure of the entire network. Network dysfunction (failure) is defined as failure to produce the expected outputs in response to the input signals. Vulnerability level of a molecule is defined as the probability of the network failure, when the molecule is dysfunctional. In this study, a method to calculate the vulnerability level of single molecules for different combinations of input signals is developed. Furthermore, a more complex yet biologically meaningful method for calculating the multi-fault vulnerability levels is suggested, in which two or more molecules are simultaneously dysfunctional. Finally, a method is developed for fault diagnosis of networks based on a ternary logic model, which considers three activity levels for a molecule instead of the previously published binary logic model, and provides equations for the vulnerabilities of molecules in a ternary framework. Multi-fault analysis shows that the pairs of molecules with high vulnerability typically include a highly vulnerable molecule identified by the single fault analysis. The ternary fault analysis for the caspase network shows that predictions obtained using the more complex ternary model are about the same as the predictions of the simpler binary approach. This study suggests that by increasing the number of activity levels the complexity of the model grows; however, the predictive power of the ternary model does not appear to be increased proportionally.

  2. 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.

  3. NETWORK ECONOMY INNOVATIVE POTENTIAL EVALUATION METHOD

    Directory of Open Access Journals (Sweden)

    E. V. Loguinova

    2011-01-01

    Full Text Available Existing methodological approaches to assessment of the innovation potential having been analyzed, a network system innovative potential identification and characterization method is proposed that makes it possible to assess the potential’s qualitative and quantitative components and to determine their consistency with national innovative system formation and development objectives. Four stages are recommended and determined to assess the network economy innovative potential. Main structural elements of the network economy innovative potential are the resource, institutional, infrastructural and resulting factor totalities.

  4. Homotopy methods for counting reaction network equilibria

    OpenAIRE

    Craciun, Gheorghe; Helton, J. William; Williams, Ruth J

    2007-01-01

    Dynamical system models of complex biochemical reaction networks are usually high-dimensional, nonlinear, and contain many unknown parameters. In some cases the reaction network structure dictates that positive equilibria must be unique for all values of the parameters in the model. In other cases multiple equilibria exist if and only if special relationships between these parameters are satisfied. We describe methods based on homotopy invariance of degree which allow us to determine the numb...

  5. Exploration Knowledge Sharing Networks Using Social Network Analysis Methods

    Directory of Open Access Journals (Sweden)

    Győző Attila Szilágyi

    2017-10-01

    Full Text Available Knowledge sharing within organization is one of the key factor for success. The organization, where knowledge sharing takes place faster and more efficiently, is able to adapt to changes in the market environment more successfully, and as a result, it may obtain a competitive advantage. Knowledge sharing in an organization is carried out through formal and informal human communication contacts during work. This forms a multi-level complex network whose quantitative and topological characteristics largely determine how quickly and to what extent the knowledge travels within organization. The study presents how different networks of knowledge sharing in the organization can be explored by means of network analysis methods through a case study, and which role play the properties of these networks in fast and sufficient spread of knowledge in organizations. The study also demonstrates the practical applications of our research results. Namely, on the basis of knowledge sharing educational strategies can be developed in an organization, and further, competitiveness of an organization may increase due to those strategies’ application.

  6. Computer methods in electric network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Saver, P.; Hajj, I.; Pai, M.; Trick, T.

    1983-06-01

    The computational algorithms utilized in power system analysis have more than just a minor overlap with those used in electronic circuit computer aided design. This paper describes the computer methods that are common to both areas and highlights the differences in application through brief examples. Recognizing this commonality has stimulated the exchange of useful techniques in both areas and has the potential of fostering new approaches to electric network analysis through the interchange of ideas.

  7. 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

  8. Rapid Heartbeat, But Dry Palms: Reactions of Heart Rate and Skin Conductance Levels to Social Rejection

    Directory of Open Access Journals (Sweden)

    Benjamin eIffland

    2014-08-01

    Full Text Available Background: Social rejection elicits negative mood, emotional distress and neural activity in networks that are associated with physical pain. However, studies assessing physiological reactions to social rejection are rare and results of these studies were found to be ambiguous. Therefore, the present study aimed to examine and specify physiological effects of social rejection.Methods: Participants (N = 50 were assigned to either a social exclusion or inclusion condition of a virtual ball-tossing game (Cyberball. Immediate and delayed physiological (skin conductance level and heart rate reactions were recorded. In addition, subjects reported levels of affect, emotional states and fundamental needs.Results: Subjects who were socially rejected showed increased heart rates. However, social rejection had no effect on subjects’ skin conductance levels. Both conditions showed heightened arousal on this measurement. Furthermore, psychological consequences of social rejection indicated the validity of the paradigm.Conclusions: Our results reveal that social rejection evokes an immediate physiological reaction. Accelerated heart rates indicate that behavior activation rather than inhibition is associated with socially threatening events. In addition, results revealed gender-specific response patterns suggesting that sample characteristics such as differences in gender may account for ambiguous findings of physiological reactions to social rejection.

  9. Methods and applications for detecting structure in complex networks

    Science.gov (United States)

    Leicht, Elizabeth A.

    The use of networks to represent systems of interacting components is now common in many fields including the biological, physical, and social sciences. Network models are widely applicable due to their relatively simple framework of vertices and edges. Network structure, patterns of connection between vertices, impacts both the functioning of networks and processes occurring on networks. However, many aspects of network structure are still poorly understood. This dissertation presents a set of network analysis methods and applications to real-world as well as simulated networks. The methods are divided into two main types: linear algebra formulations and probabilistic mixture model techniques. Network models lend themselves to compact mathematical representation as matrices, making linear algebra techniques useful probes of network structure. We present methods for the detection of two distinct, but related, network structural forms. First, we derive a measure of vertex similarity based upon network structure. The method builds on existing ideas concerning calculation of vertex similarity, but generalizes and extends the scope to large networks. Second, we address the detection of communities or modules in a specific class of networks, directed networks. We propose a method for detecting community structure in directed networks, which is an extension of a community detection method previously only known for undirected networks. Moving away from linear algebra formulations, we propose two methods for network structure detection based on probabilistic techniques. In the first method, we use the machinery of the expectation-maximization (EM) algorithm to probe patterns of connection among vertices in static networks. The technique allows for the detection of a broad range of types of structure in networks. The second method focuses on time evolving networks. We propose an application of the EM algorithm to evolving networks that can reveal significant structural

  10. Tumor Diagnosis Using Backpropagation Neural Network Method

    Science.gov (United States)

    Ma, Lixing; Looney, Carl; Sukuta, Sydney; Bruch, Reinhard; Afanasyeva, Natalia

    1998-05-01

    For characterization of skin cancer, an artificial neural network (ANN) method has been developed to diagnose normal tissue, benign tumor and melanoma. The pattern recognition is based on a three-layer neural network fuzzy learning system. In this study, the input neuron data set is the Fourier Transform infrared (FT-IR)spectrum obtained by a new Fiberoptic Evanescent Wave Fourier Transform Infrared (FEW-FTIR) spectroscopy method in the range of 1480 to 1850 cm-1. Ten input features are extracted from the absorbency values in this region. A single hidden layer of neural nodes with sigmoids activation functions clusters the feature space into small subclasses and the output nodes are separated in different nonconvex classes to permit nonlinear discrimination of disease states. The output is classified as three classes: normal tissue, benign tumor and melanoma. The results obtained from the neural network pattern recognition are shown to be consistent with traditional medical diagnosis. Input features have also been extracted from the absorbency spectra using chemical factor analysis. These abstract features or factors are also used in the classification.

  11. A Robust Method for Inferring Network Structures.

    Science.gov (United States)

    Yang, Yang; Luo, Tingjin; Li, Zhoujun; Zhang, Xiaoming; Yu, Philip S

    2017-07-12

    Inferring the network structure from limited observable data is significant in molecular biology, communication and many other areas. It is challenging, primarily because the observable data are sparse, finite and noisy. The development of machine learning and network structure study provides a great chance to solve the problem. In this paper, we propose an iterative smoothing algorithm with structure sparsity (ISSS) method. The elastic penalty in the model is introduced for the sparse solution, identifying group features and avoiding over-fitting, and the total variation (TV) penalty in the model can effectively utilize the structure information to identify the neighborhood of the vertices. Due to the non-smoothness of the elastic and structural TV penalties, an efficient algorithm with the Nesterov's smoothing optimization technique is proposed to solve the non-smooth problem. The experimental results on both synthetic and real-world networks show that the proposed model is robust against insufficient data and high noise. In addition, we investigate many factors that play important roles in identifying the performance of ISSS.

  12. 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

  13. Comparative analysis of quantitative efficiency evaluation methods for transportation networks.

    Science.gov (United States)

    He, Yuxin; Qin, Jin; Hong, Jian

    2017-01-01

    An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess's Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified.

  14. 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."

  15. 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...

  16. A Discrete Time Queueing Approach to Model and Evaluate Slotted Ring Network Buffer using Matrix Geometric Method

    Directory of Open Access Journals (Sweden)

    Syed Asif Ali Shah

    2011-01-01

    Full Text Available Assorted analytical methods have been proposed for evaluating the performance of a slotted ring network. This paper proposes MGM (Matrix Geometric Method to analyze the station buffer of a slotted ring for DT (Discrete-Time queueing. The slotted ring is analyzed for infinite station buffer as a late arrival DT system. Utilizing the characteristics of 2-D Markov chain, various performance measures are validated with their corresponding results such as, throughput and MPAD (Mean Packet Access Delay as well as the packet rejection probability for finite station buffer. The presented results prove efficacy of the method.

  17. MBVCNN: Joint convolutional neural networks method for image recognition

    Science.gov (United States)

    Tong, Tong; Mu, Xiaodong; Zhang, Li; Yi, Zhaoxiang; Hu, Pei

    2017-05-01

    Aiming at the problem of objects in image recognition rectangle, but objects which are input into convolutional neural networks square, the object recognition model was put forward which was based on BING method to realize object estimate, used vectorization of convolutional neural networks to realize input square image in convolutional networks, therefore, built joint convolution neural networks, which achieve multiple size image input. Verified by experiments, the accuracy of multi-object image recognition was improved by 6.70% compared with single vectorization of convolutional neural networks. Therefore, image recognition method of joint convolutional neural networks can enhance the accuracy in image recognition, especially for target in rectangular shape.

  18. An improved method for network congestion control

    Science.gov (United States)

    Qiao, Xiaolin

    2013-03-01

    The rapid progress of the wireless network technology has great convenience on the people's life and work. However, because of its openness, the mobility of the terminal and the changing topology, the wireless network is more susceptible to security attacks. Authentication and key agreement is the base of the network security. The authentication and key agreement mechanism can prevent the unauthorized user from accessing the network, resist malicious network to deceive the lawful user, encrypt the session data by using the exchange key and provide the identification of the data origination. Based on characteristics of the wireless network, this paper proposed a key agreement protocol for wireless network. The authentication of protocol is based on Elliptic Curve Cryptosystems and Diffie-Hellman.

  19. Sensor Network Information Analytical Methods: Analysis of Similarities and Differences

    Directory of Open Access Journals (Sweden)

    Chen Jian

    2014-04-01

    Full Text Available In the Sensor Network information engineering literature, few references focus on the definition and design of Sensor Network information analytical methods. Among those that do are Munson, et al. and the ISO standards on functional size analysis. To avoid inconsistent vocabulary and potentially incorrect interpretation of data, Sensor Network information analytical methods must be better designed, including definitions, analysis principles, analysis rules, and base units. This paper analyzes the similarities and differences across three different views of analytical methods, and uses a process proposed for the design of Sensor Network information analytical methods to analyze two examples of such methods selected from the literature.

  20. Dynamic analysis of biochemical network using complex network method

    Directory of Open Access Journals (Sweden)

    Wang Shuqiang

    2015-01-01

    Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.

  1. Modern Community Detection Methods in Social Networks

    Directory of Open Access Journals (Sweden)

    V. O. Chesnokov

    2017-01-01

    Full Text Available Social network structure is not homogeneous. Groups of vertices which have a lot of links between them are called communities. A survey of algorithms discovering such groups is presented in the article.A popular approach to community detection is to use an graph clustering algorithm.  Methods based on inner metric optimization are common. 5 groups of algorithms are listed: based on optimization, joining vertices into clusters by some closeness measure, special subgraphs discovery, partitioning graph by deleting edges,  and based on a dynamic process or generative model.Overlapping community detection algorithms are usually just modified graph clustering algorithms. Other approaches do exist, e.g. ones based on edges clustering or constructing communities around randomly chosen vertices. Methods based on nonnegative matrix factorization are also used, but they have high computational complexity. Algorithms based on label propagation lack this disadvantage. Methods based on affiliation model are perspective. This model claims that communities define the structure of a graph.Algorithms which use node attributes are considered: ones based on latent Dirichlet allocation, initially used for text clustering, and CODICIL, where edges of node content relevance are added to the original edge set. 6 classes are listed for algorithms for graphs with node attributes: changing egdes’ weights, changing vertex distance function, building augmented graph with nodes and attributes, based on stochastic  models, partitioning attribute space and others.Overlapping community detection algorithms which effectively use node attributes are just started to appear. Methods based on partitioning attribute space,  latent Dirichlet allocation,  stochastic  models and  nonnegative matrix factorization are considered. The most effective algorithm on real datasets is CESNA. It is based on affiliation model. However, it gives results which are far from ground truth

  2. Improved security monitoring method for network bordary

    Science.gov (United States)

    Gao, Liting; Wang, Lixia; Wang, Zhenyan; Qi, Aihua

    2013-03-01

    This paper proposes a network bordary security monitoring system based on PKI. The design uses multiple safe technologies, analysis deeply the association between network data flow and system log, it can detect the intrusion activities and position invasion source accurately in time. The experiment result shows that it can reduce the rate of false alarm or missing alarm of the security incident effectively.

  3. An effective method for network module extraction from microarray data

    Directory of Open Access Journals (Sweden)

    Mahanta Priyakshi

    2012-08-01

    Full Text Available Abstract Background The development of high-throughput Microarray technologies has provided various opportunities to systematically characterize diverse types of computational biological networks. Co-expression network have become popular in the analysis of microarray data, such as for detecting functional gene modules. Results This paper presents a method to build a co-expression network (CEN and to detect network modules from the built network. We use an effective gene expression similarity measure called NMRS (Normalized mean residue similarity to construct the CEN. We have tested our method on five publicly available benchmark microarray datasets. The network modules extracted by our algorithm have been biologically validated in terms of Q value and p value. Conclusions Our results show that the technique is capable of detecting biologically significant network modules from the co-expression network. Biologist can use this technique to find groups of genes with similar functionality based on their expression information.

  4. 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....

  5. 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.

  6. Efficient Optimization Methods for Communication Network Planning and Assessment

    OpenAIRE

    Kiese, Moritz

    2010-01-01

    In this work, we develop efficient mathematical planning methods to design communication networks. First, we examine future technologies for optical backbone networks. As new, more intelligent nodes cause higher dynamics in the transport networks, fast planning methods are required. To this end, we develop a heuristic planning algorithm. The evaluation of the cost-efficiency of new, adapative transmission techniques comprises the second topic of this section. In the second part of this work, ...

  7. 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

  8. 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...

  9. DETECTING NETWORK ATTACKS IN COMPUTER NETWORKS BY USING DATA MINING METHODS

    OpenAIRE

    Platonov, V. V.; Semenov, P. O.

    2016-01-01

    The article describes an approach to the development of an intrusion detection system for computer networks. It is shown that the usage of several data mining methods and tools can improve the efficiency of protection computer networks against network at-tacks due to the combination of the benefits of signature detection and anomalies detection and the opportunity of adaptation the sys-tem for hardware and software structure of the computer network.

  10. 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.

  11. 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...

  12. An image segmentation method based on network clustering model

    Science.gov (United States)

    Jiao, Yang; Wu, Jianshe; Jiao, Licheng

    2018-01-01

    Network clustering phenomena are ubiquitous in nature and human society. In this paper, a method involving a network clustering model is proposed for mass segmentation in mammograms. First, the watershed transform is used to divide an image into regions, and features of the image are computed. Then a graph is constructed from the obtained regions and features. The network clustering model is applied to realize clustering of nodes in the graph. Compared with two classic methods, the algorithm based on the network clustering model performs more effectively in experiments.

  13. 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...

  14. 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.

  15. A new method for constructing networks from binary data

    Science.gov (United States)

    van Borkulo, Claudia D.; Borsboom, Denny; Epskamp, Sacha; Blanken, Tessa F.; Boschloo, Lynn; Schoevers, Robert A.; Waldorp, Lourens J.

    2014-08-01

    Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

  16. The research on user behavior evaluation method for network state

    Science.gov (United States)

    Zhang, Chengyuan; Xu, Haishui

    2017-08-01

    Based on the correlation between user behavior and network running state, this paper proposes a method of user behavior evaluation based on network state. Based on the analysis and evaluation methods in other fields of study, we introduce the theory and tools of data mining. Based on the network status information provided by the trusted network view, the user behavior data and the network state data are analysed. Finally, we construct the user behavior evaluation index and weight, and on this basis, we can accurately quantify the influence degree of the specific behavior of different users on the change of network running state, so as to provide the basis for user behavior control decision.

  17. 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. PMID:28421008

  18. Evolutionary method for finding communities in bipartite networks

    Science.gov (United States)

    Zhan, Weihua; Zhang, Zhongzhi; Guan, Jihong; Zhou, Shuigeng

    2011-06-01

    An important step in unveiling the relation between network structure and dynamics defined on networks is to detect communities, and numerous methods have been developed separately to identify community structure in different classes of networks, such as unipartite networks, bipartite networks, and directed networks. Here, we show that the finding of communities in such networks can be unified in a general framework—detection of community structure in bipartite networks. Moreover, we propose an evolutionary method for efficiently identifying communities in bipartite networks. To this end, we show that both unipartite and directed networks can be represented as bipartite networks, and their modularity is completely consistent with that for bipartite networks, the detection of modular structure on which can be reformulated as modularity maximization. To optimize the bipartite modularity, we develop a modified adaptive genetic algorithm (MAGA), which is shown to be especially efficient for community structure detection. The high efficiency of the MAGA is based on the following three improvements we make. First, we introduce a different measure for the informativeness of a locus instead of the standard deviation, which can exactly determine which loci mutate. This measure is the bias between the distribution of a locus over the current population and the uniform distribution of the locus, i.e., the Kullback-Leibler divergence between them. Second, we develop a reassignment technique for differentiating the informative state a locus has attained from the random state in the initial phase. Third, we present a modified mutation rule which by incorporating related operations can guarantee the convergence of the MAGA to the global optimum and can speed up the convergence process. Experimental results show that the MAGA outperforms existing methods in terms of modularity for both bipartite and unipartite networks.

  19. 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 Net...... 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.......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...

  20. 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.

  1. 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.

  2. 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...

  3. Diagrammatic perturbation methods in networks and sports ranking combinatorics

    Science.gov (United States)

    Park, Juyong

    2010-04-01

    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.

  4. Quantitative methods for ecological network analysis.

    Science.gov (United States)

    Ulanowicz, Robert E

    2004-12-01

    The analysis of networks of ecological trophic transfers is a useful complement to simulation modeling in the quest for understanding whole-ecosystem dynamics. Trophic networks can be studied in quantitative and systematic fashion at several levels. Indirect relationships between any two individual taxa in an ecosystem, which often differ in either nature or magnitude from their direct influences, can be assayed using techniques from linear algebra. The same mathematics can also be employed to ascertain where along the trophic continuum any individual taxon is operating, or to map the web of connections into a virtual linear chain that summarizes trophodynamic performance by the system. Backtracking algorithms with pruning have been written which identify pathways for the recycle of materials and energy within the system. The pattern of such cycling often reveals modes of control or types of functions exhibited by various groups of taxa. The performance of the system as a whole at processing material and energy can be quantified using information theory. In particular, the complexity of process interactions can be parsed into separate terms that distinguish organized, efficient performance from the capacity for further development and recovery from disturbance. Finally, the sensitivities of the information-theoretic system indices appear to identify the dynamical bottlenecks in ecosystem functioning.

  5. Decision support systems and methods for complex networks

    Science.gov (United States)

    Huang, Zhenyu [Richland, WA; Wong, Pak Chung [Richland, WA; Ma, Jian [Richland, WA; Mackey, Patrick S [Richland, WA; Chen, Yousu [Richland, WA; Schneider, Kevin P [Seattle, WA

    2012-02-28

    Methods and systems for automated decision support in analyzing operation data from a complex network. Embodiments of the present invention utilize these algorithms and techniques not only to characterize the past and present condition of a complex network, but also to predict future conditions to help operators anticipate deteriorating and/or problem situations. In particular, embodiments of the present invention characterize network conditions from operation data using a state estimator. Contingency scenarios can then be generated based on those network conditions. For at least a portion of all of the contingency scenarios, risk indices are determined that describe the potential impact of each of those scenarios. Contingency scenarios with risk indices are presented visually as graphical representations in the context of a visual representation of the complex network. Analysis of the historical risk indices based on the graphical representations can then provide trends that allow for prediction of future network conditions.

  6. 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.

  7. Semantic Security Methods for Software-Defined Networks

    Directory of Open Access Journals (Sweden)

    Ekaterina Ju. Antoshina

    2017-01-01

    Full Text Available Software-defined networking is a promising technology for constructing communication networks where the network management is the software that configures network devices. This contrasts with the traditional point of view where the network behaviour is updated by manual configuration uploading to devices under control. The software controller allows dynamic routing configuration inside the net depending on the quality of service. However, there must be a proof that ensures that every network flow is secure, for example, we can define security policy as follows: confidential nodes can not send data to the public segment of the network. The paper shows how this problem can be solved by using a semantic security model. We propose a method that allows us to construct semantics that captures necessary security properties the network must follow. This involves the specification that states allowed and forbidden network flows. The specification is then modeled as a decision tree that may be reduced. We use the decision tree for semantic construction that captures security requirements. The semantic can be implemented as a module of the controller software so the correctness of the control plane of the network can be ensured on-the-fly. 

  8. A model reduction method for biochemical reaction networks

    National Research Council Canada - National Science Library

    Rao, Shodhan; van der Schaft, Arjan; van Eunen, Karen; Bakker, Barbara; Jayawardhana, Bayu

    2014-01-01

    Background: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics...

  9. 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.

  10. Approximate Subgradient Methods for Lagrangian Relaxations on Networks

    Science.gov (United States)

    Mijangos, Eugenio

    Nonlinear network flow problems with linear/nonlinear side con- straints can be solved by means of Lagrangian relaxations. The dual problem is the maximization of a dual function whose value is estimated by minimizing approximately a Lagrangian function on the set defined by the network constraints. We study alternative stepsizes in the approximate subgradient methods to solve the dual problem. Some basic convergence results are put forward. Moreover, we compare the quality of the computed solutions and the efficiency of these methods.

  11. A Selection Method for Pipe Network Boosting Plans

    Science.gov (United States)

    Qiu, Weiwei; Li, Mengyao; Weng, Haoyang

    2017-12-01

    Based on the fuzzy mathematics theory, a multi-objective fuzzy comprehensive evaluation method used for selection of pipe network boosting plans was proposed by computing relative membership matrix and weight vector for indexes. The example results show that the multi-objective fuzzy comprehensive evaluation method combining the indexes and the fuzzy relationship between them is suited to realities and can provide reference for decision of pipe network boosting plan.

  12. The Method of Leader’s Overthrow in Networks

    OpenAIRE

    Belik, Ivan; Jörnsten, Kurt

    2016-01-01

    Methods for leader’s detection and overthrow in networks are useful tools for decision-making in many real-life cases, such as criminal networks with hidden patterns or money laundering networks. In the given research, we represent the algorithms that detect and overthrow the most influential node to the weaker positions following the greedy method in terms of structural modifications. We employed the concept of Shapley value from the area of cooperative games to measure a node’s leadership a...

  13. 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.

  14. 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.

  15. An improved Bayesian network method for reconstructing gene regulatory network based on candidate auto selection.

    Science.gov (United States)

    Xing, Linlin; Guo, Maozu; Liu, Xiaoyan; Wang, Chunyu; Wang, Lei; Zhang, Yin

    2017-11-17

    The reconstruction of gene regulatory network (GRN) from gene expression data can discover regulatory relationships among genes and gain deep insights into the complicated regulation mechanism of life. However, it is still a great challenge in systems biology and bioinformatics. During the past years, numerous computational approaches have been developed for this goal, and Bayesian network (BN) methods draw most of attention among these methods because of its inherent probability characteristics. However, Bayesian network methods are time consuming and cannot handle large-scale networks due to their high computational complexity, while the mutual information-based methods are highly effective but directionless and have a high false-positive rate. To solve these problems, we propose a Candidate Auto Selection algorithm (CAS) based on mutual information and breakpoint detection to restrict the search space in order to accelerate the learning process of Bayesian network. First, the proposed CAS algorithm automatically selects the neighbor candidates of each node before searching the best structure of GRN. Then based on CAS algorithm, we propose a globally optimal greedy search method (CAS + G), which focuses on finding the highest rated network structure, and a local learning method (CAS + L), which focuses on faster learning the structure with little loss of quality. Results show that the proposed CAS algorithm can effectively reduce the search space of Bayesian networks through identifying the neighbor candidates of each node. In our experiments, the CAS + G method outperforms the state-of-the-art method on simulation data for inferring GRNs, and the CAS + L method is significantly faster than the state-of-the-art method with little loss of accuracy. Hence, the CAS based methods effectively decrease the computational complexity of Bayesian network and are more suitable for GRN inference.

  16. 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.

  17. 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.

  18. Adaptation Methods in Mobile Communication Networks

    National Research Council Canada - National Science Library

    Vladimir Wieser

    2003-01-01

    Adaptation methods are the main tool for transmission rate maximization through the mobile channel and today the great attention is directed to them not only in theoretical domain but in standardization process, too...

  19. Adaptation Methods in Mobile Communication Networks

    National Research Council Canada - National Science Library

    Vladimir Wieser

    2003-01-01

      Adaptation methods are the main tool for transmission rate maximization through the mobile channel and today the great attention is directed to them not only in theoretical domain but in standardization process, too...

  20. 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

  1. Graph methods for the investigation of metabolic networks in parasitology.

    Science.gov (United States)

    Cottret, Ludovic; Jourdan, Fabien

    2010-08-01

    Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

  2. 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

  3. A Hybrid Reliable Heuristic Mapping Method Based on Survivable Virtual Networks for Network Virtualization

    Directory of Open Access Journals (Sweden)

    Qiang Zhu

    2015-01-01

    Full Text Available The reliable mapping of virtual networks is one of the hot issues in network virtualization researches. Unlike the traditional protection mechanisms based on redundancy and recovery mechanisms, we take the solution of the survivable virtual topology routing problem for reference to ensure that the rest of the mapped virtual networks keeps connected under a single node failure condition in the substrate network, which guarantees the completeness of the virtual network and continuity of services. In order to reduce the cost of the substrate network, a hybrid reliable heuristic mapping method based on survivable virtual networks (Hybrid-RHM-SVN is proposed. In Hybrid-RHM-SVN, we formulate the reliable mapping problem as an integer linear program. Firstly, we calculate the primary-cut set of the virtual network subgraph where the failed node has been removed. Then, we use the ant colony optimization algorithm to achieve the approximate optimal mapping. The links in primary-cut set should select a substrate path that does not pass through the substrate node corresponding to the virtual node that has been removed first. The simulation results show that the acceptance rate of virtual networks, the average revenue of mapping, and the recovery rate of virtual networks are increased compared with the existing reliable mapping algorithms, respectively.

  4. Image rejects in general direct digital radiography

    Science.gov (United States)

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

    2015-01-01

    Background 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. Purpose 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. Material and Methods 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. Results 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. Conclusion 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. PMID:26500784

  5. 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.

  6. 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.

  7. Combination methods for identifying influential nodes in networks

    Science.gov (United States)

    Gao, Chao; Zhong, Lu; Li, Xianghua; Zhang, Zili; Shi, Ning

    2015-11-01

    Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.

  8. Algorithmic and analytical methods in network biology

    OpenAIRE

    Koyutürk, Mehmet

    2010-01-01

    During genomic revolution, algorithmic and analytical methods for organizing, integrating, analyzing, and querying biological sequence data proved invaluable. Today, increasing availability of high-throughput data pertaining functional states of biomolecules, as well as their interactions, enables genome-scale studies of the cell from a systems perspective. The past decade witnessed significant efforts on the development of computational infrastructure for large-scale modeling and analysis of...

  9. An Efficient Synchronization Method for Wireless Networks

    Science.gov (United States)

    2013-06-01

    group-wise synchronization which is more e cient than rsync, is possible. This paper describes Dandelion , an algorithm that builds on the ideas of the...which is more efficient than rsync, is possible. This paper describes Dandelion , an algorithm that builds on the ideas of the rsync algorithm to...methods analyzed in this paper are compared using this metric. To meet this goal, this paper defines an epidemic-like algorithm called Dandelion that is

  10. Methods of graph network reconstruction in personalized medicine.

    Science.gov (United States)

    Danilov, A; Ivanov, Yu; Pryamonosov, R; Vassilevski, Yu

    2016-08-01

    The paper addresses methods for generation of individualized computational domains on the basis of medical imaging dataset. The computational domains will be used in one-dimensional (1D) and three-dimensional (3D)-1D coupled hemodynamic models. A 1D hemodynamic model employs a 1D network of a patient-specific vascular network with large number of vessels. The 1D network is the graph with nodes in the 3D space which bears additional geometric data such as length and radius of vessels. A 3D hemodynamic model requires a detailed 3D reconstruction of local parts of the vascular network. We propose algorithms which extend the automated segmentation of vascular and tubular structures, generation of centerlines, 1D network reconstruction, correction, and local adaptation. We consider two modes of centerline representation: (i) skeletal segments or sets of connected voxels and (ii) curved paths with corresponding radii. Individualized reconstruction of 1D networks depends on the mode of centerline representation. Efficiency of the proposed algorithms is demonstrated on several examples of 1D network reconstruction. The networks can be used in modeling of blood flows as well as other physiological processes in tubular structures. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

  11. 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.

  12. 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.

  13. A model reduction method for biochemical reaction networks.

    Science.gov (United States)

    Rao, Shodhan; van der Schaft, Arjan; van Eunen, Karen; Bakker, Barbara M; Jayawardhana, Bayu

    2014-05-03

    In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of complexes, defined as the left and right-hand sides of the reactions in the network. It is based on the Kron reduction of the weighted Laplacian matrix, which describes the graph structure of the complexes and reactions in the network. It does not rely on prior knowledge of the dynamic behaviour of the network and hence can be automated, as we demonstrate. The reduced network has fewer complexes, reactions, variables and parameters as compared to the original network, and yet the behaviour of a preselected set of significant metabolites in the reduced network resembles that of the original network. Moreover the reduced network largely retains the structure and kinetics of the original model. We apply our method to a yeast glycolysis model and a rat liver fatty acid beta-oxidation model. When the number of state variables in the yeast model is reduced from 12 to 7, the difference between metabolite concentrations in the reduced and the full model, averaged over time and species, is only 8%. Likewise, when the number of state variables in the rat-liver beta-oxidation model is reduced from 42 to 29, the difference between the reduced model and the full model is 7.5%. The method has improved our understanding of the dynamics of the two networks. We found that, contrary to the general disposition, the first few metabolites which were deleted from the network during our stepwise reduction approach, are not those with the shortest convergence times. It shows that our reduction approach performs differently from other approaches that are based on time-scale separation. The method can be used to facilitate fitting of the parameters or to embed a detailed model of interest in a more coarse

  14. A model reduction method for biochemical reaction networks

    NARCIS (Netherlands)

    Rao, Shodhan; van der Schaft, Arjan; van Eunen, Karen; Bakker, Barbara; Jayawardhana, Bayu

    2014-01-01

    Background: In this paper we propose a model reduction method for biochemical reaction networks governed by a variety of reversible and irreversible enzyme kinetic rate laws, including reversible Michaelis-Menten and Hill kinetics. The method proceeds by a stepwise reduction in the number of

  15. 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.

  16. Rejection-free stochastic simulation of BNGL-encoded models

    Energy Technology Data Exchange (ETDEWEB)

    Hlavacek, William S [Los Alamos National Laboratory; Monine, Michael I [Los Alamos National Laboratory; Colvin, Joshua [TRANSLATIONAL GENOM; Posner, Richard G [NORTHERN ARIZONA UNIV.; Von Hoff, Daniel D [TRANSLATIONAL GENOMICS RESEARCH INSTIT.

    2009-01-01

    Formal rules encoded using the BioNetGen language (BNGL) can be used to represent the system-level dynamics of molecular interactions. Rules allow one to compactly and implicitly specify the reaction network implied by a set of molecules and their interactions. Typically, the reaction network implied by a set of rules is large, which makes generation of the underlying rule-defined network expensive. Moreover, the cost of conventional simulation methods typically depends on network size. Together these factors have limited application of the rule-based modeling approach. To overcome this limitation, several methods have recently been developed for determining the reaction dynamics implied by rules while avoiding the expensive step of network generation. The cost of these 'network-free' simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is needed for the analysis of rule-based models of biochemical systems. Here, we present a software tool called RuleMonkey that implements a network-free stochastic simulation method for rule-based models. The method is rejection free, unlike other network-free methods that introduce null events (i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated), and the software is capable of simulating models encoded in BNGL, a general-purpose model-specification language. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant general-purpose simulator for rule-based models, as well as various problem-specific codes that implement network-free simulation methods. RuleMonkey enables the simulation of models defined by rule sets that imply large-scale reaction networks. It is faster than DYNSTOC for stiff problems, although it requires the use of more computer memory. RuleMonkey is freely available for non-commercial use as a stand

  17. The rejection-rage contingency in borderline personality disorder.

    Science.gov (United States)

    Berenson, Kathy R; Downey, Geraldine; Rafaeli, Eshkol; Coifman, Karin G; Paquin, Nina Leventhal

    2011-08-01

    Though long-standing clinical observation reflected in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.) suggests that the rage characteristic of borderline personality disorder (BPD) often appears in response to perceived rejection, the role of perceived rejection in triggering rage in BPD has never been empirically tested. Extending basic personality research on rejection sensitivity to a clinical sample, a priming-pronunciation experiment and a 21-day experience-sampling diary examined the contingent relationship between perceived rejection and rage in participants diagnosed with BPD compared with healthy controls. Despite the differences in these 2 assessment methods, the indices of rejection-contingent rage that they both produced were elevated in the BPD group and were strongly interrelated. They provide corroborating evidence that reactions to perceived rejection significantly explain the rage seen in BPD. © 2011 American Psychological Association

  18. Fractal analysis of heart graft acute rejection microscopic images.

    Science.gov (United States)

    Pijet, M; Nozynski, J; Konecka-Mrowka, D; Zakliczynski, M; Hrapkowicz, T; Zembala, M

    2014-10-01

    Endomyocardial biopsy to evaluate rejection in the transplanted heart is accepted at the "gold standard." The complexity of microscopic images suggested using digital methods for precise evaluating of acute rejection episodes with numerical representation. The aim of the present was study to characterize digitally acute rejection of the transplanted heart using complexity/fractal image analysis. Biopsy samples harvested form 40 adult recipients after orthotropic heart transplantation were collected and rejection grade was evaluated according to the International Society for Heart and Lung Transplantation (0, 1a, 1b, or 3a) at transverse and longitudinal sections. Fifteen representative digital microscope images from each grade were collected and analyzed after Sobel edge detection and binarization. Only mean fractal dimension showed a progressive and significant increase and correlation based on rejection grade using longitudinal sections. Lacunarity and number of foreground pixels showed unequivocal results. Mean fractal diameter could serve as auxiliary digital parameter for grading of acute rejection in the transplanted heart.

  19. Extended Active Disturbance Rejection Controller

    Science.gov (United States)

    Gao, Zhiqiang (Inventor); Tian, Gang (Inventor)

    2016-01-01

    Multiple designs, systems, methods and processes for controlling a system or plant using an extended active disturbance rejection control (ADRC) based controller are presented. The extended ADRC controller accepts sensor information from the plant. The sensor information is used in conjunction with an extended state observer in combination with a predictor that estimates and predicts the current state of the plant and a co-joined estimate of the system disturbances and system dynamics. The extended state observer estimates and predictions are used in conjunction with a control law that generates an input to the system based in part on the extended state observer estimates and predictions as well as a desired trajectory for the plant to follow.

  20. Decomposition method for zonal resource allocation problems in telecommunication networks

    Science.gov (United States)

    Konnov, I. V.; Kashuba, A. Yu

    2016-11-01

    We consider problems of optimal resource allocation in telecommunication networks. We first give an optimization formulation for the case where the network manager aims to distribute some homogeneous resource (bandwidth) among users of one region with quadratic charge and fee functions and present simple and efficient solution methods. Next, we consider a more general problem for a provider of a wireless communication network divided into zones (clusters) with common capacity constraints. We obtain a convex quadratic optimization problem involving capacity and balance constraints. By using the dual Lagrangian method with respect to the capacity constraint, we suggest to reduce the initial problem to a single-dimensional optimization problem, but calculation of the cost function value leads to independent solution of zonal problems, which coincide with the above single region problem. Some results of computational experiments confirm the applicability of the new methods.

  1. Exploring function prediction in protein interaction networks via clustering methods.

    Science.gov (United States)

    Trivodaliev, Kire; Bogojeska, Aleksandra; Kocarev, Ljupco

    2014-01-01

    Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.

  2. Exploring function prediction in protein interaction networks via clustering methods.

    Directory of Open Access Journals (Sweden)

    Kire Trivodaliev

    Full Text Available Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach.

  3. 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.

  4. A graph clustering method for community detection in complex networks

    Science.gov (United States)

    Zhou, HongFang; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn

    2017-03-01

    Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

  5. Gene Expression Network Reconstruction by LEP Method Using Microarray Data

    Directory of Open Access Journals (Sweden)

    Na You

    2012-01-01

    Full Text Available Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix. Due to the high dimensionality and sparsity, we utilize the LEP method to estimate it in this paper. Compared to the existing methods, the LEP reaches the highest PPV with the sensitivity controlled at the satisfactory level. A set of gene expression data from the HapMap project is analyzed for illustration.

  6. Outlier Detection Method Use for the Network Flow Anomaly Detection

    Directory of Open Access Journals (Sweden)

    Rimas Ciplinskas

    2016-06-01

    Full Text Available New and existing methods of cyber-attack detection are constantly being developed and improved because there is a great number of attacks and the demand to protect from them. In prac-tice, current methods of attack detection operates like antivirus programs, i. e. known attacks signatures are created and attacks are detected by using them. These methods have a drawback – they cannot detect new attacks. As a solution, anomaly detection methods are used. They allow to detect deviations from normal network behaviour that may show a new type of attack. This article introduces a new method that allows to detect network flow anomalies by using local outlier factor algorithm. Accom-plished research allowed to identify groups of features which showed the best results of anomaly flow detection according the highest values of precision, recall and F-measure.

  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. Reverse Engineering Cellular Networks with Information Theoretic Methods

    Directory of Open Access Journals (Sweden)

    Julio R. Banga

    2013-05-01

    Full Text Available Building mathematical models of cellular networks lies at the core of systems biology. It involves, among other tasks, the reconstruction of the structure of interactions between molecular components, which is known as network inference or reverse engineering. Information theory can help in the goal of extracting as much information as possible from the available data. A large number of methods founded on these concepts have been proposed in the literature, not only in biology journals, but in a wide range of areas. Their critical comparison is difficult due to the different focuses and the adoption of different terminologies. Here we attempt to review some of the existing information theoretic methodologies for network inference, and clarify their differences. While some of these methods have achieved notable success, many challenges remain, among which we can mention dealing with incomplete measurements, noisy data, counterintuitive behaviour emerging from nonlinear relations or feedback loops, and computational burden of dealing with large data sets.

  9. 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.

  10. Assessing Partnership Alternatives in an IT Network Employing Analytical Methods

    Directory of Open Access Journals (Sweden)

    Vahid Reza Salamat

    2016-01-01

    Full Text Available One of the main critical success factors for the companies is their ability to build and maintain an effective collaborative network. This is more critical in the IT industry where the development of sustainable competitive advantage requires an integration of various resources, platforms, and capabilities provided by various actors. Employing such a collaborative network will dramatically change the operations management and promote flexibility and agility. Despite its importance, there is a lack of an analytical tool on collaborative network building process. In this paper, we propose an optimization model employing AHP and multiobjective programming for collaborative network building process based on two interorganizational relationships’ theories, namely, (i transaction cost theory and (ii resource-based view, which are representative of short-term and long-term considerations. The five different methods were employed to solve the formulation and their performances were compared. The model is implemented in an IT company who was in process of developing a large-scale enterprise resource planning (ERP system. The results show that the collaborative network formed through this selection process was more efficient in terms of cost, time, and development speed. The framework offers novel theoretical underpinning and analytical solutions and can be used as an effective tool in selecting network alternatives.

  11. Quantitative Method for Network Security Situation Based on Attack Prediction

    Directory of Open Access Journals (Sweden)

    Hao Hu

    2017-01-01

    Full Text Available Multistep attack prediction and security situation awareness are two big challenges for network administrators because future is generally unknown. In recent years, many investigations have been made. However, they are not sufficient. To improve the comprehensiveness of prediction, in this paper, we quantitatively convert attack threat into security situation. Actually, two algorithms are proposed, namely, attack prediction algorithm using dynamic Bayesian attack graph and security situation quantification algorithm based on attack prediction. The first algorithm aims to provide more abundant information of future attack behaviors by simulating incremental network penetration. Through timely evaluating the attack capacity of intruder and defense strategies of defender, the likely attack goal, path, and probability and time-cost are predicted dynamically along with the ongoing security events. Furthermore, in combination with the common vulnerability scoring system (CVSS metric and network assets information, the second algorithm quantifies the concealed attack threat into the surfaced security risk from two levels: host and network. Examples show that our method is feasible and flexible for the attack-defense adversarial network environment, which benefits the administrator to infer the security situation in advance and prerepair the critical compromised hosts to maintain normal network communication.

  12. 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.

  13. Acute rejection and humoral sensitization in lung transplant recipients.

    Science.gov (United States)

    Martinu, Tereza; Chen, Dong-Feng; Palmer, Scott M

    2009-01-15

    Despite the recent introduction of many improved immunosuppressive agents for use in transplantation, acute rejection affects up to 55% of lung transplant recipients within the first year after transplant. Acute lung allograft rejection is defined as perivascular or peribronchiolar mononuclear inflammation. Although histopathologic signs of rejection often resolve with treatment, the frequency and severity of acute rejections represent the most important risk factor for the subsequent development of bronchiolitis obliterans syndrome (BOS), a condition of progressive airflow obstruction that limits survival to only 50% at 5 years after lung transplantation. Recent evidence demonstrates that peribronchiolar mononuclear inflammation (also known as lymphocytic bronchiolitis) or even a single episode of minimal perivascular inflammation significantly increase the risk for BOS. We comprehensively review the clinical presentation, diagnosis, histopathologic features, and mechanisms of acute cellular lung rejection. In addition, we consider emerging evidence that humoral rejection occurs in lung transplantation, characterized by local complement activation or the presence of antibody to donor human leukocyte antigens (HLA). We discuss in detail methods for HLA antibody detection as well as the clinical relevance, the mechanisms, and the pathologic hallmarks of humoral injury. Treatment options for cellular rejection include high-dose methylprednisolone, antithymocyte globulin, or alemtuzumab. Treatment options for humoral rejection include intravenous immunoglobulin, plasmapheresis, or rituximab. A greater mechanistic understanding of cellular and humoral forms of rejection and their role in the pathogenesis of BOS is critical in developing therapies that extend long-term survival after lung transplantation.

  14. An efficient neural network based method for medical image segmentation.

    Science.gov (United States)

    Torbati, Nima; Ayatollahi, Ahmad; Kermani, Ali

    2014-01-01

    The aim of this research is to propose a new neural network based method for medical image segmentation. Firstly, a modified self-organizing map (SOM) network, named moving average SOM (MA-SOM), is utilized to segment medical images. After the initial segmentation stage, a merging process is designed to connect the objects of a joint cluster together. A two-dimensional (2D) discrete wavelet transform (DWT) is used to build the input feature space of the network. The experimental results show that MA-SOM is robust to noise and it determines the input image pattern properly. The segmentation results of breast ultrasound images (BUS) demonstrate that there is a significant correlation between the tumor region selected by a physician and the tumor region segmented by our proposed method. In addition, the proposed method segments X-ray computerized tomography (CT) and magnetic resonance (MR) head images much better than the incremental supervised neural network (ISNN) and SOM-based methods. © 2013 Published by Elsevier Ltd.

  15. 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.

  16. 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.

  17. Dynamic Subsidy Method for Congestion Management in Distribution Networks

    OpenAIRE

    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...

  18. 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...... obtain by running simulations of a time dynamic sensor network. The performance of the proposed method outperforms the existing clustering methods, such as the Girvan-Newmans algorithm, the Kargers algorithm and the Spectral Clustering method, in terms of packet acceptance probability and delay....

  19. A Network Centrality Method for the Rating Problem

    Science.gov (United States)

    2015-01-01

    We propose a new method for aggregating the information of multiple users rating multiple items. Our approach is based on the network relations induced between items by the rating activity of the users. Our method correlates better than the simple average with respect to the original rankings of the users, and besides, it is computationally more efficient than other methods proposed in the literature. Moreover, our method is able to discount the information that would be obtained adding to the system additional users with a systematically biased rating activity. PMID:25830502

  20. Private Information and Insurance Rejections.

    Science.gov (United States)

    Hendren, Nathaniel

    2013-09-01

    Across a wide set of non-group insurance markets, applicants are rejected based on observable, often high-risk, characteristics. This paper argues that private information, held by the potential applicant pool, explains rejections. I formulate this argument by developing and testing a model in which agents may have private information about their risk. I first derive a new no-trade result that theoretically explains how private information could cause rejections. I then develop a new empirical methodology to test whether this no-trade condition can explain rejections. The methodology uses subjective probability elicitations as noisy measures of agents beliefs. I apply this approach to three non-group markets: long-term care, disability, and life insurance. Consistent with the predictions of the theory, in all three settings I find significant amounts of private information held by those who would be rejected; I find generally more private information for those who would be rejected relative to those who can purchase insurance; and I show it is enough private information to explain a complete absence of trade for those who would be rejected. The results suggest private information prevents the existence of large segments of these three major insurance markets.

  1. S-curve networks and a new method for estimating degree distributions of complex networks

    CERN Document Server

    Guo, Jin-Li

    2010-01-01

    In the study of complex networks almost all theoretical models are infinite growth, but the size of actual networks is finite. According to statistics from the China Internet IPv4 addresses, we propose 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 on the laws of IPv4 growth, that is, the bulk growth and the finitely growing limit, we propose a finite network model with the bulk growth. The model is called S-curve network. Analysis demonstrates that the analytic method based on uniform distributions (i.e., Barab\\'asi-Albert method) is not suitable for the network. We develop a new method to predict the growth dynamics of the individual nodes, and use this to calculate analytically the connectivity distribution and the scaling exponents. The analytical result agrees with the simulation well, obeying an approximately power-...

  2. 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.

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

    Science.gov (United States)

    Parlos, A.G.; Atiya, A.F.; Fernandez, B.; Tsai, W.K.; Chong, K.T.

    1995-12-26

    The present invention is a fully connected feed forward network that includes at least one hidden layer. The hidden layer includes nodes 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 occurring in the feedback path (local feedback). Each node within each layer also receives a delayed output (crosstalk) produced by a delay unit from all the other nodes within the same layer. 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. 21 figs.

  4. Waste heat rejection from geothermal power stations

    Energy Technology Data Exchange (ETDEWEB)

    Robertson, R.C.

    1978-12-01

    This study of waste heat rejection from geothermal power stations is concerned only with the heat rejected from the power cycle. The heat contained in reinjected or otherwise discharged geothermal fluids is not included with the waste heat considered here. The heat contained in the underflow from the flashtanks in such systems is not considered as part of the heat rejected from the power cycle. By following this definition of the waste heat to be rejected, various methods of waste heat dissipation are discussed without regard for the particular arrangement to obtain heat from the geothermal source. Recent conceptual design studies made for 50-MW(e) geothermal power stations at Heber and Niland, California, are of particular interst. The former uses a flashed-steam system and the latter a binary cycle that uses isopentane. In last-quarter 1976 dollars, the total estimated capital costs were about $750/kW and production costs about 50 mills/kWhr. If wet/dry towers were used to conserve 50% of the water evaporation at Heber, production costs would be about 65 mills/kWhr.

  5. 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...... 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...

  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. 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

  8. Noniterative convex optimization methods for network component analysis.

    Science.gov (United States)

    Jacklin, Neil; Ding, Zhi; Chen, Wei; Chang, Chunqi

    2012-01-01

    This work studies the reconstruction of gene regulatory networks by the means of network component analysis (NCA). We will expound a family of convex optimization-based methods for estimating the transcription factor control strengths and the transcription factor activities (TFAs). The approach taken in this work is to decompose the problem into a network connectivity strength estimation phase and a transcription factor activity estimation phase. In the control strength estimation phase, we formulate a new subspace-based method incorporating a choice of multiple error metrics. For the source estimation phase we propose a total least squares (TLS) formulation that generalizes many existing methods. Both estimation procedures are noniterative and yield the optimal estimates according to various proposed error metrics. We test the performance of the proposed algorithms on simulated data and experimental gene expression data for the yeast Saccharomyces cerevisiae and demonstrate that the proposed algorithms have superior effectiveness in comparison with both Bayesian Decomposition (BD) and our previous FastNCA approach, while the computational complexity is still orders of magnitude less than BD.

  9. Social Cognition of Rejected Status Students in Late Elementary School: An Examination of Low, Medium, and High Social Prominence Subtypes

    Science.gov (United States)

    Hall, Cristin M.

    2011-01-01

    The present study examined the social network perceptions of fifth grade rejected students (N = 723). Rejected students were separated into low-, medium-, and high-social prominence subtypes. Cluster analysis was also used to create analytically-derived subtypes of rejected students including the following characteristics: social network…

  10. Fractional active disturbance rejection control.

    Science.gov (United States)

    Li, Dazi; Ding, Pan; Gao, Zhiqiang

    2016-05-01

    A fractional active disturbance rejection control (FADRC) scheme is proposed to improve the performance of commensurate linear fractional order systems (FOS) and the robust analysis shows that the controller is also applicable to incommensurate linear FOS control. In FADRC, the traditional extended states observer (ESO) is generalized to a fractional order extended states observer (FESO) by using the fractional calculus, and the tracking differentiator plus nonlinear state error feedback are replaced by a fractional proportional-derivative controller. To simplify controller tuning, the linear bandwidth-parameterization method has been adopted. The impacts of the observer bandwidth ωo and controller bandwidth ωc on system performance are then analyzed. Finally, the FADRC stability and frequency-domain characteristics for linear single-input single-output FOS are analyzed. Simulation results by FADRC and ADRC on typical FOS are compared to demonstrate the superiority and effectiveness of the proposed scheme. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  11. 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.

  12. Tumor diagnosis using the backpropagation neural network method

    Science.gov (United States)

    Ma, Lixing; Sukuta, Sydney; Bruch, Reinhard F.; Afanasyeva, Natalia I.; Looney, Carl G.

    1998-04-01

    For characterization of skin cancer, an artificial neural network method has been developed to diagnose normal tissue, benign tumor and melanoma. The pattern recognition is based on a three-layer neural network fuzzy learning system. In this study, the input neuron data set is the Fourier transform IR spectrum obtained by a new fiberoptic evanescent wave Fourier transform IR spectroscopy method in the range of 1480 to 1850 cm-1. Ten input features are extracted from the absorbency values in this region. A single hidden layer of neural nodes with sigmoids activation functions clusters the feature space into small subclasses and the output nodes are separated in different nonconvex classes to permit nonlinear discrimination of disease states. The output is classified as three classes: normal tissue, benign tumor and melanoma. The results obtained from the neural network pattern recognition are shown to be consistent with traditional medical diagnosis. Input features have also been extracted from the absorbency spectra using chemical factor analysis. These abstract features or factors are also used in the classification.

  13. A Method for Designing Assembly Tolerance Networks of Mechanical Assemblies

    Directory of Open Access Journals (Sweden)

    Yi Zhang

    2012-01-01

    Full Text Available When designing mechanical assemblies, assembly tolerance design is an important issue which must be seriously considered by designers. Assembly tolerances reflect functional requirements of assembling, which can be used to control assembling qualities and production costs. This paper proposes a new method for designing assembly tolerance networks of mechanical assemblies. The method establishes the assembly structure tree model of an assembly based on its product structure tree model. On this basis, assembly information model and assembly relation model are set up based on polychromatic sets (PS theory. According to the two models, the systems of location relation equations and interference relation equations are established. Then, using methods of topologically related surfaces (TTRS theory and variational geometric constraints (VGC theory, three VGC reasoning matrices are constructed. According to corresponding relations between VGCs and assembly tolerance types, the reasoning matrices of tolerance types are also established by using contour matrices of PS. Finally, an exemplary product is used to construct its assembly tolerance networks and meanwhile to verify the feasibility and effectiveness of the proposed method.

  14. 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.

  15. Using analytic network process for evaluating mobile text entry methods.

    Science.gov (United States)

    Ocampo, Lanndon A; Seva, Rosemary R

    2016-01-01

    This paper highlights a preference evaluation methodology for text entry methods in a touch keyboard smartphone using analytic network process (ANP). Evaluation of text entry methods in literature mainly considers speed and accuracy. This study presents an alternative means for selecting text entry method that considers user preference. A case study was carried out with a group of experts who were asked to develop a selection decision model of five text entry methods. The decision problem is flexible enough to reflect interdependencies of decision elements that are necessary in describing real-life conditions. Results showed that QWERTY method is more preferred than other text entry methods while arrangement of keys is the most preferred criterion in characterizing a sound method. Sensitivity analysis using simulation of normally distributed random numbers under fairly large perturbation reported the foregoing results reliable enough to reflect robust judgment. The main contribution of this paper is the introduction of a multi-criteria decision approach in the preference evaluation of text entry methods. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

  16. A survey of spectrum prediction methods in cognitive radio networks

    Science.gov (United States)

    Wu, Jianwei; Li, Yanling

    2017-04-01

    Spectrum prediction technology is an effective way to solve the problems of processing latency, spectrum access, spectrum collision and energy consumption in cognitive radio networks. Spectral prediction technology is divided into three categories according to its nature, namely, spectral prediction method based on regression analysis, spectrum prediction method based on Markov model and spectrum prediction method based on machine learning. By analyzing and comparing the three kinds of prediction models, the author hopes to provide some reference for the later researchers. In this paper, the development situation, practical application and existent problems of three kinds of forecasting models are analyzed and summarized. On this basis, this paper discusses the development trend of the next step.

  17. 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.

  18. A network centrality method for the rating problem

    CERN Document Server

    Li, Yongli; Wu, Chong

    2014-01-01

    We propose a new method for aggregating the information of multiple reviewers rating multiple products. Our approach is based on the network relations induced between products by the rating activity of the reviewers. We show that our method is algorithmically implementable even for large numbers of both products and consumers, as is the case for many online sites. Moreover, comparing it with the simple average, which is mostly used in practice, and with other methods previously proposed in the literature, it performs very well under various dimension, proving itself to be an optimal trade--off between computational efficiency, accordance with the reviewers original orderings, and robustness with respect to the inclusion of systematically biased reports.

  19. 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.

  20. 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)

    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. PMID:26098556

  1. 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.

  2. Effect of Donor and Recipient Factors on Corneal Graft Rejection

    Science.gov (United States)

    Stulting, R. Doyle; Sugar, Alan; Beck, Roy; Belin, Michael; Dontchev, Mariya; Feder, Robert S.; Gal, Robin L.; Holland, Edward J.; Kollman, Craig; Mannis, Mark J.; Price, Francis; Stark, Walter; Verdier, David D.

    2014-01-01

    Purpose To assess the relationship between donor and recipient factors and corneal allograft rejection in eyes that underwent penetrating keratoplasty (PK) in the Cornea Donor Study. Methods 1090 subjects undergoing corneal transplantation for a moderate risk condition (principally Fuchs’ dystrophy or pseudophakic corneal edema) were followed for up to 5 years. Associations of baseline recipient and donor factors with the occurrence of a probable or definite rejection event were assessed in univariate and multivariate proportional hazards models. Results Eyes with pseudophakic or aphakic corneal edema (N=369) were more likely to experience a rejection event than eyes with Fuchs’ dystrophy (N=676) (34% ± 6% versus 22% ± 4%; hazard ratio = 1.56; 95% confidence interval 1.21 to 2.03). Among eyes with Fuchs’dystrophy, a higher probability of a rejection event was observed in phakic post-transplant eyes compared with eyes that underwent cataract extraction with or without intraocular lens implantation during PK (29% vs. 19%; hazard ratio = 0.54; 95% confidence interval 0.36 to 0.82). Female recipients had a higher probability of a rejection event than males (29% vs. 21%; hazard ratio=1.42; 95% confidence interval 1.08 to 1.87), after controlling for the effect of preoperative diagnosis and lens status. Donor age and donor recipient ABO compatibility were not associated with rejection. Conclusions There was a substantially higher graft rejection rate in eyes with pseudophakic or aphakic corneal edema compared with eyes with Fuchs’ dystrophy. Female recipients were more likely to have a rejection event than males. Graft rejection was not associated with donor age. PMID:22488114

  3. Shift-excitation Raman difference spectroscopy-difference deconvolution method for the luminescence background rejection from Raman spectra of solid samples.

    Science.gov (United States)

    Osticioli, Iacopo; Zoppi, Angela; Castellucci, Emilio Mario

    2007-08-01

    The feasibility of the shift-excitation Raman difference spectroscopy-difference deconvolution (SERDS-DDM) method for fluorescence suppression from Raman spectra of solid samples is discussed. For SERDS measurements a tunable diode laser source with an emission band centered at 684 nm is coupled to a conventional micro-Raman apparatus and a monochromator device is used for checking the excitation frequency stability. The shifted Raman spectra are then mathematically treated and a deconvolution procedure is used to reconstruct the Raman spectrum devoid of fluorescence. Two different cases are presented. In the first one, fluorescence is intrinsic to the sample and the Raman spectrum of cinnabar pigment is finally reconstructed. In the second, the presence of an external luminescence background in the spectrum of a pure sulfur crystal is considered. The SERDS-DDM reconstructed spectra are compared with spectra obtained via multi-point baseline subtraction and a significant improvement in the detection of weak bands is demonstrated. Practical insights for the application of this method are presented as well.

  4. Method for stitching microbial images using a neural network

    Science.gov (United States)

    Semenishchev, E. A.; Voronin, V. V.; Marchuk, V. I.; Tolstova, I. V.

    2017-05-01

    Currently an analog microscope has a wide distribution in the following fields: medicine, animal husbandry, monitoring technological objects, oceanography, agriculture and others. Automatic method is preferred because it will greatly reduce the work involved. Stepper motors are used to move the microscope slide and allow to adjust the focus in semi-automatic or automatic mode view with transfer images of microbiological objects from the eyepiece of the microscope to the computer screen. Scene analysis allows to locate regions with pronounced abnormalities for focusing specialist attention. This paper considers the method for stitching microbial images, obtained of semi-automatic microscope. The method allows to keep the boundaries of objects located in the area of capturing optical systems. Objects searching are based on the analysis of the data located in the area of the camera view. We propose to use a neural network for the boundaries searching. The stitching image boundary is held of the analysis borders of the objects. To auto focus, we use the criterion of the minimum thickness of the line boundaries of object. Analysis produced the object located in the focal axis of the camera. We use method of recovery of objects borders and projective transform for the boundary of objects which are based on shifted relative to the focal axis. Several examples considered in this paper show the effectiveness of the proposed approach on several test images.

  5. An entropy method for floodplain monitoring network design

    Science.gov (United States)

    Ridolfi, E.; Yan, K.; Alfonso, L.; Di Baldassarre, G.; Napolitano, F.; Russo, F.; Bates, Paul D.

    2012-09-01

    In recent years an increasing number of flood-related fatalities has highlighted the necessity of improving flood risk management to reduce human and economic losses. In this framework, monitoring of flood-prone areas is a key factor for building a resilient environment. In this paper a method for designing a floodplain monitoring network is presented. A redundant network of cheap wireless sensors (GridStix) measuring water depth is considered over a reach of the River Dee (UK), with sensors placed both in the channel and in the floodplain. Through a Three Objective Optimization Problem (TOOP) the best layouts of sensors are evaluated, minimizing their redundancy, maximizing their joint information content and maximizing the accuracy of the observations. A simple raster-based inundation model (LISFLOOD-FP) is used to generate a synthetic GridStix data set of water stages. The Digital Elevation Model (DEM) that is used for hydraulic model building is the globally and freely available SRTM DEM.

  6. 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.

  7. 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.

  8. 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...

  9. 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…

  10. New Neural Network Methods for Forecasting Regional Employment

    NARCIS (Netherlands)

    Patuelli, R.; Reggiani, A; Nijkamp, P.; Blien, U.

    2006-01-01

    In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. Neural networks are modern statistical tools based on learning algorithms that are able to process large amounts of data. Neural networks are enjoying

  11. 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.

  12. 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).

  13. 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.

  14. 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…

  15. 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…

  16. 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...

  17. 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.

  18. 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.

  19. 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.

  20. System and method for generating a relationship network

    Science.gov (United States)

    Franks, Kasian [Kensington, CA; Myers, Cornelia A [St. Louis, MO; Podowski, Raf M [Pleasant Hill, CA

    2011-07-26

    A computer-implemented system and process for generating a relationship network is disclosed. The system provides a set of data items to be related and generates variable length data vectors to represent the relationships between the terms within each data item. The system can be used to generate a relationship network for documents, images, or any other type of file. This relationship network can then be queried to discover the relationships between terms within the set of data items.

  1. 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.

  2. 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.

  3. 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....

  4. Examining the Emergence of Large-Scale Structures in Collaboration Networks: Methods in Sociological Analysis

    Science.gov (United States)

    Ghosh, Jaideep; Kshitij, Avinash

    2017-01-01

    This article introduces a number of methods that can be useful for examining the emergence of large-scale structures in collaboration networks. The study contributes to sociological research by investigating how clusters of research collaborators evolve and sometimes percolate in a collaboration network. Typically, we find that in our networks,…

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

    African Journals Online (AJOL)

    user

    Consequently, many structures based on artificial neural network (ANN) have been developed in the literature, The most significant ... Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion. 1. ..... and pure shunt active fitters, IEEE 38th Conf on Industry Applications, Vol. 2, pp.

  6. Epileptic neuronal networks: methods of identification and clinical relevance

    NARCIS (Netherlands)

    Stefan, H.; Lopes da Silva, F.H.

    2013-01-01

    The main objective of this paper is to examine evidence for the concept that epileptic activity should be envisaged in terms of functional connectivity and dynamics of neuronal networks. Basic concepts regarding structure and dynamics of neuronal networks are briefly described. Particular attention

  7. 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

  8. Epileptic neuronal networks: methods of identification and clinical relevance.

    Science.gov (United States)

    Stefan, Hermann; Lopes da Silva, Fernando H

    2013-01-01

    The main objective of this paper is to examine evidence for the concept that epileptic activity should be envisaged in terms of functional connectivity and dynamics of neuronal networks. Basic concepts regarding structure and dynamics of neuronal networks are briefly described. Particular attention is given to approaches that are derived, or related, to the concept of causality, as formulated by Granger. Linear and non-linear methodologies aiming at characterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed. The relevance of functional dynamical analysis of neuronal networks with respect to clinical queries in focal cortical dysplasias, temporal lobe epilepsies, and "generalized" epilepsies is emphasized. In the light of the concepts of epileptic neuronal networks, and recent experimental findings, the dichotomic classification in focal and generalized epilepsy is re-evaluated. It is proposed that so-called "generalized epilepsies," such as absence seizures, are actually fast spreading epilepsies, the onset of which can be tracked down to particular neuronal networks using appropriate network analysis. Finally new approaches to delineate epileptogenic networks are discussed.

  9. The double queue method: a numerical method for integrate-and-fire neuron networks.

    Science.gov (United States)

    Lee, G; Farhat, N H

    2001-01-01

    Numerical methods for initial-value problems based on finite-differencing of differential equations (FDM) are not well suited for the simulation of an integrate-and-fire neuron network (IFNN) due to the discontinuities implied by the firing condition of the neurons. The Double Queue Method (DQM) is an event-queue based numerical method designed for the simulation of an IFNN that can deal with such discontinuities properly. In the DQM, the states of individual neurons at the next predicted discontinuous points are determined by an analytic solution, meaning an optimal performance in both accuracy and speed. A comparison study with the FDM demonstrates the superiority of the DQM, and provides some examples where the FDM gives inaccurate results that can possibly lead to a false conclusion about the dynamics of an IFNN.

  10. A parietal memory network revealed by multiple MRI methods.

    Science.gov (United States)

    Gilmore, Adrian W; Nelson, Steven M; McDermott, Kathleen B

    2015-09-01

    The manner by which the human brain learns and recognizes stimuli is a matter of ongoing investigation. Through examination of meta-analyses of task-based functional MRI and resting state functional connectivity MRI, we identified a novel network strongly related to learning and memory. Activity within this network at encoding predicts subsequent item memory, and at retrieval differs for recognized and unrecognized items. The direction of activity flips as a function of recent history: from deactivation for novel stimuli to activation for stimuli that are familiar due to recent exposure. We term this network the 'parietal memory network' (PMN) to reflect its broad involvement in human memory processing. We provide a preliminary framework for understanding the key functional properties of the network. Copyright © 2015 Elsevier Ltd. All rights reserved.

  11. 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.

  12. 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.

  13. 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...

  14. Epileptic neuronal networks: methods of identification andclinical relevance.

    Directory of Open Access Journals (Sweden)

    Hermann eStefan

    2013-03-01

    Full Text Available The main objective of this paper is to examine evidence for the concept that epileptic activityshould be envisaged in terms of functional connectivity and dynamics of neuronal networks,Basic concepts regarding structure and dynamics of neuronal networks are briefly described.Particular attention is given to approaches that are derived, or related, to the concept ofcausality, as formulated by Granger. Linear and non linear methodologies aiming atcharacterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed. The relevance of functional dynamicalanalysis of neuronal networks with respect to clinical queries in focal cortical dysplasias,temporal lobe epilepsies and "generalized epilepsies is emphasized. In the light of theconcepts of epileptic neuronal networks, and recent experimental findings, the dichotomicclassification in focal and generalized epilepsy is re-evaluated. It is proposed that so-called"generalized epilepsies", such as absence seizures, are actually fast spreading epilepsies, theonset of which can be tracked down to particular neuronal networks using appropriatenetwork analysis. Finally new approaches to delineate epileptogenic networks are discussed.

  15. Implicit methods for qualitative modeling of gene regulatory networks.

    Science.gov (United States)

    Garg, Abhishek; Mohanram, Kartik; De Micheli, Giovanni; Xenarios, Ioannis

    2012-01-01

    Advancements in high-throughput technologies to measure increasingly complex biological phenomena at the genomic level are rapidly changing the face of biological research from the single-gene single-protein experimental approach to studying the behavior of a gene in the context of the entire genome (and proteome). This shift in research methodologies has resulted in a new field of network biology that deals with modeling cellular behavior in terms of network structures such as signaling pathways and gene regulatory networks. In these networks, different biological entities such as genes, proteins, and metabolites interact with each other, giving rise to a dynamical system. Even though there exists a mature field of dynamical systems theory to model such network structures, some technical challenges are unique to biology such as the inability to measure precise kinetic information on gene-gene or gene-protein interactions and the need to model increasingly large networks comprising thousands of nodes. These challenges have renewed interest in developing new computational techniques for modeling complex biological systems. This chapter presents a modeling framework based on Boolean algebra and finite-state machines that are reminiscent of the approach used for digital circuit synthesis and simulation in the field of very-large-scale integration (VLSI). The proposed formalism enables a common mathematical framework to develop computational techniques for modeling different aspects of the regulatory networks such as steady-state behavior, stochasticity, and gene perturbation experiments.

  16. 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.

  17. Fast, moment-based estimation methods for delay network tomography

    Energy Technology Data Exchange (ETDEWEB)

    Lawrence, Earl Christophre [Los Alamos National Laboratory; Michailidis, George [U OF MICHIGAN; Nair, Vijayan N [U OF MICHIGAN

    2008-01-01

    Consider the delay network tomography problem where the goal is to estimate distributions of delays at the link-level using data on end-to-end delays. These measurements are obtained using probes that are injected at nodes located on the periphery of the network and sent to other nodes also located on the periphery. Much of the previous literature deals with discrete delay distributions by discretizing the data into small bins. This paper considers more general models with a focus on computationally efficient estimation. The moment-based schemes presented here are designed to function well for larger networks and for applications like monitoring that require speedy solutions.

  18. Active disturbance rejection control for fractional-order system.

    Science.gov (United States)

    Li, Mingda; Li, Donghai; Wang, Jing; Zhao, Chunzhe

    2013-05-01

    Fractional-order proportional-integral (PI) and proportional-integral-derivative (PID) controllers are the most commonly used controllers in fractional-order systems. However, this paper proposes a simple integer-order control scheme for fractional-order system based on active disturbance rejection method. By treating the fractional-order dynamics as a common disturbance and actively rejecting it, active disturbance rejection control (ADRC) can achieve the desired response. External disturbance, sensor noise, and parameter disturbance are also estimated using extended state observer. The ADRC stability of rational-order model is analyzed. Simulation results on three typical fractional-order systems are provided to demonstrate the effectiveness of the proposed method. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.

    Science.gov (United States)

    Tasaki, Shinya; Sauerwine, Ben; Hoff, Bruce; Toyoshiba, Hiroyoshi; Gaiteri, Chris; Chaibub Neto, Elias

    2015-04-01

    Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis-Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data. Copyright © 2015 by the Genetics Society of America.

  20. Investigating the Effects of Imputation Methods for Modelling Gene Networks Using a Dynamic Bayesian Network from Gene Expression Data

    Science.gov (United States)

    CHAI, Lian En; LAW, Chow Kuan; MOHAMAD, Mohd Saberi; CHONG, Chuii Khim; CHOON, Yee Wen; DERIS, Safaai; ILLIAS, Rosli Md

    2014-01-01

    Background: Gene expression data often contain missing expression values. Therefore, several imputation methods have been applied to solve the missing values, which include k-nearest neighbour (kNN), local least squares (LLS), and Bayesian principal component analysis (BPCA). However, the effects of these imputation methods on the modelling of gene regulatory networks from gene expression data have rarely been investigated and analysed using a dynamic Bayesian network (DBN). Methods: In the present study, we separately imputed datasets of the Escherichia coli S.O.S. DNA repair pathway and the Saccharomyces cerevisiae cell cycle pathway with kNN, LLS, and BPCA, and subsequently used these to generate gene regulatory networks (GRNs) using a discrete DBN. We made comparisons on the basis of previous studies in order to select the gene network with the least error. Results: We found that BPCA and LLS performed better on larger networks (based on the S. cerevisiae dataset), whereas kNN performed better on smaller networks (based on the E. coli dataset). Conclusion: The results suggest that the performance of each imputation method is dependent on the size of the dataset, and this subsequently affects the modelling of the resultant GRNs using a DBN. In addition, on the basis of these results, a DBN has the capacity to discover potential edges, as well as display interactions, between genes. PMID:24876803

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

    Science.gov (United States)

    2012-01-01

    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 network effectiveness at

  2. 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

  3. 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

  4. 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

  5. Stillbirth Collaborative Research Network: design, methods and recruitment experience.

    Science.gov (United States)

    Parker, Corette B; Hogue, Carol J R; Koch, Matthew A; Willinger, Marian; Reddy, Uma M; Thorsten, Vanessa R; Dudley, Donald J; Silver, Robert M; Coustan, Donald; Saade, George R; Conway, Deborah; Varner, Michael W; Stoll, Barbara; Pinar, Halit; Bukowski, Radek; Carpenter, Marshall; Goldenberg, Robert

    2011-09-01

    The Stillbirth Collaborative Research Network (SCRN) has conducted a multisite, population-based, case-control study, with prospective enrollment of stillbirths and livebirths at the time of delivery. This paper describes the general design, methods and recruitment experience. The SCRN attempted to enroll all stillbirths and a representative sample of livebirths occurring to residents of pre-defined geographical catchment areas delivering at 59 hospitals associated with five clinical sites. Livebirths <32 weeks gestation and women of African descent were oversampled. The recruitment hospitals were chosen to ensure access to at least 90% of all stillbirths and livebirths to residents of the catchment areas. Participants underwent a standardised protocol including maternal interview, medical record abstraction, placental pathology, biospecimen testing and, in stillbirths, post-mortem examination. Recruitment began in March 2006 and was completed in September 2008 with 663 women with a stillbirth and 1932 women with a livebirth enrolled, representing 69% and 63%, respectively, of the women identified. Additional surveillance for stillbirths continued until June 2009 and a follow-up of the case-control study participants was completed in December 2009. Among consenting women, there were high consent rates for the various study components. For the women with stillbirths, 95% agreed to a maternal interview, chart abstraction and a placental pathological examination; 91% of the women with a livebirth agreed to all of these components. Additionally, 84% of the women with stillbirths agreed to a fetal post-mortem examination. This comprehensive study is poised to systematically study a wide range of potential causes of, and risk factors for, stillbirths and to better understand the scope and incidence of the problem. © 2011 Blackwell Publishing Ltd.

  6. Spectral methods for network community detection and graph partitioning

    OpenAIRE

    Newman, M.E.J.

    2013-01-01

    We consider three distinct and well studied problems concerning network structure: community detection by modularity maximization, community detection by statistical inference, and normalized-cut graph partitioning. Each of these problems can be tackled using spectral algorithms that make use of the eigenvectors of matrix representations of the network. We show that with certain choices of the free parameters appearing in these spectral algorithms the algorithms for all three problems are, in...

  7. Electrical network method for the thermal or structural characterization of a conducting material sample or structure

    Science.gov (United States)

    Ortiz, Marco G.

    1993-01-01

    A method for modeling a conducting material sample or structure system, as an electrical network of resistances in which each resistance of the network is representative of a specific physical region of the system. The method encompasses measuring a resistance between two external leads and using this measurement in a series of equations describing the network to solve for the network resistances for a specified region and temperature. A calibration system is then developed using the calculated resistances at specified temperatures. This allows for the translation of the calculated resistances to a region temperature. The method can also be used to detect and quantify structural defects in the system.

  8. Towards a logic-based method to infer provenance-aware molecular networks

    OpenAIRE

    Aslaoui-Errafi, Zahira; Cohen-Boulakia, Sarah; Froidevaux, Christine; Gloaguen, Pauline; Poupon, Anne; Rougny, Adrien; Yahiaoui, Meriem

    2012-01-01

    International audience; Providing techniques to automatically infer molecular networks is particularly important to understand complex relationships between biological objects. We present a logic-based method to infer such networks and show how it allows inferring signalling networks from the design of a knowledge base. Provenance of inferred data has been carefully collected, allowing quality evaluation. More precisely, our method (i) takes into account various kinds of biological experiment...

  9. Capturing complexity: Mixing methods in the analysis of a European tobacco control policy network.

    Science.gov (United States)

    Weishaar, Heide; Amos, Amanda; Collin, Jeff

    Social network analysis (SNA), a method which can be used to explore networks in various contexts, has received increasing attention. Drawing on the development of European smoke-free policy, this paper explores how a mixed method approach to SNA can be utilised to investigate a complex policy network. Textual data from public documents, consultation submissions and websites were extracted, converted and analysed using plagiarism detection software and quantitative network analysis, and qualitative data from public documents and 35 interviews were thematically analysed. While the quantitative analysis enabled understanding of the network's structure and components, the qualitative analysis provided in-depth information about specific actors' positions, relationships and interactions. The paper establishes that SNA is suited to empirically testing and analysing networks in EU policymaking. It contributes to methodological debates about the antagonism between qualitative and quantitative approaches and demonstrates that qualitative and quantitative network analysis can offer a powerful tool for policy analysis.

  10. An intelligent scheduling method based on improved particle swarm optimization algorithm for drainage pipe network

    Science.gov (United States)

    Luo, Yaqi; Zeng, Bi

    2017-08-01

    This paper researches the drainage routing problem in drainage pipe network, and propose an intelligent scheduling method. The method relates to the design of improved particle swarm optimization algorithm, the establishment of the corresponding model from the pipe network, and the process by using the algorithm based on improved particle swarm optimization to find the optimum drainage route in the current environment.

  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. Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.

    Science.gov (United States)

    Liu, Jianxiao; Tian, Zonglin

    2017-01-01

    Mining the genes related to maize carotenoid components is important to improve the carotenoid content and the quality of maize. On the basis of using the entropy estimation method with Gaussian kernel probability density estimator, we use the three-phase dependency analysis (TPDA) Bayesian network structure learning method to construct the network of maize gene and carotenoid components traits. In the case of using two discretization methods and setting different discretization values, we compare the learning effect and efficiency of 10 kinds of Bayesian network structure learning methods. The method is verified and analyzed on the maize dataset of global germplasm collection with 527 elite inbred lines. The result confirmed the effectiveness of the TPDA method, which outperforms significantly another 9 kinds of Bayesian network learning methods. It is an efficient method of mining genes for maize carotenoid components traits. The parameters obtained by experiments will help carry out practical gene mining effectively in the future.

  13. Indicators of rejection in groups of children

    Directory of Open Access Journals (Sweden)

    Vera Regina Miranda Gomes da Silva

    2001-12-01

    Full Text Available The issue of rejection is motivating countless researchers due to the close relationship between peer rejection and difficulties in future adjustment. The study of this issue can contribute towards the detection of factors that trigger rejection, consequently making possible preventive interventions. This text is intended to provide a summary of the research undertaken by the author as part of her Master's Degree, the aim of which was to detect indicators of rejection in the final year of a private primary school in Curitiba (n=52. In this study, “indicators of rejection” are understood to be criteria that lead children to exclude each other from their recreation and working activities, within the context of the school. The study used one instrument designed to collect data from the children (“voting for the least popular” and “tag” and another aimed at obtaining data from the teachers (“teacher's card”. The analysis and cross-referencing of the data obtained indicated that children tend to reject their peers based on their inadequate behaviour (bossiness, disruptive behaviour, and this perception was reinforced by the data obtained from the teachers. On the other hand, children tend to chose other children who frequently demonstrate sociable behaviour to be their peers. Keywords: Rejection, rejected children, peer rejection, recognition of rejection.

  14. 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.

  15. 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.

  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. Diffusion-based outlier rejection for underwater navigation

    DEFF Research Database (Denmark)

    Vike, Steinar; Jouffroy, Jerome

    This paper addresses the issue of rejecting spurious acoustic position measurements for the estimation of trajectories in underwater vehicle navigation. The method relies on the diffusion-based observer approach (Jouffroy and Opderbecke, 2004; 2005), which allows one to consider and process entire...

  18. Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study

    Science.gov (United States)

    2014-01-01

    Background Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers’ careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. Methods A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Results Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Conclusions Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world. PMID:24460819

  19. Rapid heartbeat, but dry palms: reactions of heart rate and skin conductance levels to social rejection.

    Science.gov (United States)

    Iffland, Benjamin; Sansen, Lisa M; Catani, Claudia; Neuner, Frank

    2014-01-01

    Social rejection elicits negative mood, emotional distress, and neural activity in networks that are associated with physical pain. However, studies assessing physiological reactions to social rejection are rare and results of these studies were found to be ambiguous. Therefore, the present study aimed to examine and specify physiological effects of social rejection. Participants (n = 50) were assigned to either a social exclusion or inclusion condition of a virtual ball-tossing game (Cyberball). Immediate and delayed physiological [skin conductance level (SCL) and heart rate] reactions were recorded. In addition, subjects reported levels of affect, emotional states, and fundamental needs. Subjects who were socially rejected showed increased heart rates. However, social rejection had no effect on subjects' SCLs. Both conditions showed heightened arousal on this measurement. Furthermore, psychological consequences of social rejection indicated the validity of the paradigm. Our results reveal that social rejection evokes an immediate physiological reaction. Accelerated heart rates indicate that behavior activation rather than inhibition is associated with socially threatening events. In addition, results revealed gender-specific response patterns suggesting that sample characteristics such as differences in gender may account for ambiguous findings of physiological reactions to social rejection.

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

    National Research Council Canada - National Science Library

    Ke-yan Liu; Wanxing Sheng; Yongmei Liu; Xiaoli Meng

    2017-01-01

    .... 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...

  1. A Multilayer Improved RBM Network Based Image Compression Method in Wireless Sensor Networks

    National Research Council Canada - National Science Library

    Cheng, Chunling; Wang, Shu; Chen, Xingguo; Yang, Yanying

    2016-01-01

    The processing capacity and power of nodes in a Wireless Sensor Network (WSN) are limited. And most image compression algorithms in WSN are subject to random image content changes or have low image qualities after the images are decoded...

  2. 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.

  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. 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...

  5. DISCRETE VOLUME-ELEMENT METHOD FOR NETWORK WATER- QUALITY MODELS

    Science.gov (United States)

    An explicit dynamic water-quality modeling algorithm is developed for tracking dissolved substances in water-distribution networks. The algorithm is based on a mass-balance relation within pipes that considers both advective transport and reaction kinetics. Complete mixing of m...

  6. 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.

  7. 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.

  8. A Method for Group Extraction in Complex Social Networks

    Science.gov (United States)

    Bródka, Piotr; Musial, Katarzyna; Kazienko, Przemysław

    The extraction of social groups from social networks existing among employees in the company, its customers or users of various computer systems became one of the research areas of growing importance. Once we have discovered the groups, we can utilise them, in different kinds of recommender systems or in the analysis of the team structure and communication within a given population.

  9. Motif-Synchronization: A new method for analysis of dynamic brain networks with EEG

    Science.gov (United States)

    Rosário, R. S.; Cardoso, P. T.; Muñoz, M. A.; Montoya, P.; Miranda, J. G. V.

    2015-12-01

    The major aim of this work was to propose a new association method known as Motif-Synchronization. This method was developed to provide information about the synchronization degree and direction between two nodes of a network by counting the number of occurrences of some patterns between any two time series. The second objective of this work was to present a new methodology for the analysis of dynamic brain networks, by combining the Time-Varying Graph (TVG) method with a directional association method. We further applied the new algorithms to a set of human electroencephalogram (EEG) signals to perform a dynamic analysis of the brain functional networks (BFN).

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

    Science.gov (United States)

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

    2017-07-01

    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.

  11. 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.

  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. Plane deformation monitoring network and computational method of the NSRL storage ring

    Energy Technology Data Exchange (ETDEWEB)

    Xiaoye, He; Guicheng, Wang; Shengkuan, Lu [National Synchrotron Radiation Lab., USTC, Hefei, P.R. (China); Xingzhou, Wang [Wuhan Technical University of Surveying and Mapping, Wuhan, P.R. (China)

    1999-07-01

    The NSRL (National Synchrotron Radiation Laboratory, China) accelerator consists of 3 major parts: the 800 MeV electron storage ring, the transport line and the 200 MeV electron linac. The storage ring contains 12 dipoles, 32 quadrupoles, 14 sextupoles, some kickers and septums, etc. During the installation of the storage ring, an alignment network was established, which is called the Construction Control Network (CCN). This network is a trilateration network. Dipoles were chosen as the primary reference for the alignment and installation. All other components were easily aligned from the 2 adjacent dipoles by means of optical instrumentation and other techniques. Differing from CCN, the purpose of Deformation Monitoring Network (DMN) is to monitor the displacement of the components in the storage ring, DMN requires high precision and being able to repeat positions. This article presents both networks and the method used to calculate the plane deformation monitoring network.

  14. Not all rejections are alike : Competence and warmth as a fundamental distinction in social rejection

    NARCIS (Netherlands)

    Celik, P.; Lammers, J.; van Beest, I.; Bekker, M.H.J.; Vonk, R.

    2013-01-01

    Social rejection can lead to a variety of emotions. Two studies show that specific emotional reactions to social rejection can be understood by relying on the fundamental distinction between competence and warmth. Rejection that is perceived to be due to incompetence leads to anger, whereas

  15. 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

  16. 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

  17. 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.

  18. 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...

  19. A scanning method for detecting clustering pattern of both attribute and structure in social networks

    Science.gov (United States)

    Wang, Tai-Chi; Phoa, Frederick Kin Hing

    2016-03-01

    Community/cluster is one of the most important features in social networks. Many cluster detection methods were proposed to identify such an important pattern, but few were able to identify the statistical significance of the clusters by considering the likelihood of network structure and its attributes. Based on the definition of clustering, we propose a scanning method, originated from analyzing spatial data, for identifying clusters in social networks. Since the properties of network data are more complicated than those of spatial data, we verify our method's feasibility via simulation studies. The results show that the detection powers are affected by cluster sizes and connection probabilities. According to our simulation results, the detection accuracy of structure clusters and both structure and attribute clusters detected by our proposed method is better than that of other methods in most of our simulation cases. In addition, we apply our proposed method to some empirical data to identify statistically significant clusters.

  20. 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.

  1. 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.

  2. Network biology methods integrating biological data for translational science

    OpenAIRE

    Bebek, Gurkan; Koyutürk, Mehmet; Price, Nathan D.; Chance, Mark R.

    2012-01-01

    The explosion of biomedical data, both on the genomic and proteomic side as well as clinical data, will require complex integration and analysis to provide new molecular variables to better understand the molecular basis of phenotype. Currently, much data exist in silos and is not analyzed in frameworks where all data are brought to bear in the development of biomarkers and novel functional targets. This is beginning to change. Network biology approaches, which emphasize the interactions betw...

  3. 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.

  4. 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.

  5. Efficient method for AC transmission network expansion planning

    Energy Technology Data Exchange (ETDEWEB)

    Rahmani, M. [Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman (Iran); Faculdade de Engenharia de Ilha Solteira, UNESP - Univ Estadual Paulista, Departamento de Engenharia Eletrica, Ilha Solteira, SP (Brazil); Rashidinejad, M. [Electrical Engineering Department, Shahid Bahonar University of Kerman, Kerman (Iran); Carreno, E.M. [Centro de Engenharia, Universidade Estadual do Oeste de Parana, UNIOESTE, Foz do Iguacu - PR (Brazil); Romero, R. [Faculdade de Engenharia de Ilha Solteira, UNESP - Univ Estadual Paulista, Departamento de Engenharia Eletrica, Ilha Solteira, SP (Brazil)

    2010-09-15

    A combinatorial mathematical model in tandem with a metaheuristic technique for solving transmission network expansion planning (TNEP) using an AC model associated with reactive power planning (RPP) is presented in this paper. AC-TNEP is handled through a prior DC model while additional lines as well as VAr-plants are used as reinforcements to cope with real network requirements. The solution of the reinforcement stage can be obtained by assuming all reactive demands are supplied locally to achieve a solution for AC-TNEP and by neglecting the local reactive sources, a reactive power planning (RPP) will be managed to find the minimum required reactive power sources. Binary GA as well as a real genetic algorithm (RGA) are employed as metaheuristic optimization techniques for solving this combinatorial TNEP as well as the RPP problem. High quality results related with lower investment costs through case studies on test systems show the usefulness of the proposal when working directly with the AC model in transmission network expansion planning, instead of relaxed models. (author)

  6. An Evolutionary Perspective on Mate Rejection.

    Science.gov (United States)

    Kelly, Ashleigh J; Dubbs, Shelli L; Barlow, Fiona Kate

    2016-01-01

    We argue that mate rejection and ex-partner relationships are important, multifaceted topics that have been underresearched in social and evolutionary psychology. Mate rejection and relationship dissolution are ubiquitous and form integral parts of the human experience. Both also carry with them potential risks and benefits to our fitness and survival. Hence, we expect that mate rejection would have given rise to evolved behavioral and psychological adaptations. Herein, we outline some of the many unanswered questions in evolutionary psychology on these topics, at each step presenting novel hypotheses about how men and women should behave when rejecting a mate or potential mate or in response to rejection. We intend these hypotheses and suggestions for future research to be used as a basis for enriching our understanding of human mating from an evolutionary perspective.

  7. METHOD OF MAXIMAL INFORMATIVE ZONE FOR VIRTUAL REFERENCE STATION DEVELOPMENT IN KINEMATIC SYSTEMS OF GPS NETWORKS

    Directory of Open Access Journals (Sweden)

    R. A. Eminov,

    2013-03-01

    Full Text Available The existing actual material on experimental assessment of positioning error in VRS GPS networks is analyzed where the mobile receiver is provided with virtual reference station. The method of highly informative zone is suggested for removal of initial uncertainty in reference station selection with the aim to develop minimal GPS network consisting of three reference stations. Methodical recommendations and directions are given for the suggested method application.

  8. X-ray film reject rate analysis at eight selected government hospitals ...

    African Journals Online (AJOL)

    admin

    Objective: The purpose of this research was to identify the main causes of film faults as well as the pattern and magnitude of film rejection. Methods: Using a prospective cross-sectional hospital based approach; eight public hospitals were selected in Addis. Ababa through .... clinical radiology service. In this respect, reject ...

  9. Disclosing Sexual Assault Within Social Networks: A Mixed-Method Investigation.

    Science.gov (United States)

    Dworkin, Emily R; Pittenger, Samantha L; Allen, Nicole E

    2016-03-01

    Most survivors of sexual assault disclose their experiences within their social networks, and these disclosure decisions can have important implications for their entry into formal systems and well-being, but no research has directly examined these networks as a strategy to understand disclosure decisions. Using a mixed-method approach that combined survey data, social network analysis, and interview data, we investigate whom, among potential informal responders in the social networks of college students who have experienced sexual assault, survivors contact regarding their assault, and how survivors narrate the role of networks in their decisions about whom to contact. Quantitative results suggest that characteristics of survivors, their social networks, and members of these networks are associated with disclosure decisions. Using data from social network analysis, we identified that survivors tended to disclose to a smaller proportion of their network when many network members had relationships with each other or when the network had more subgroups. Our qualitative analysis helps to contextualize these findings. © Society for Community Research and Action 2016.

  10. Networking among young global health researchers through an intensive training approach: a mixed methods exploratory study.

    Science.gov (United States)

    Lenters, Lindsey M; Cole, Donald C; Godoy-Ruiz, Paula

    2014-01-25

    Networks are increasingly regarded as essential in health research aimed at influencing practice and policies. Less research has focused on the role networking can play in researchers' careers and its broader impacts on capacity strengthening in health research. We used the Canadian Coalition for Global Health Research (CCGHR) annual Summer Institute for New Global Health Researchers (SIs) as an opportunity to explore networking among new global health researchers. A mixed-methods exploratory study was conducted among SI alumni and facilitators who had participated in at least one SI between 2004 and 2010. Alumni and facilitators completed an online short questionnaire, and a subset participated in an in-depth interview. Thematic analysis of the qualitative data was triangulated with quantitative results and CCGHR reports on SIs. Synthesis occurred through the development of a process model relevant to networking through the SIs. Through networking at the SIs, participants experienced decreased isolation and strengthened working relationships. Participants accessed new knowledge, opportunities, and resources through networking during the SI. Post-SI, participants reported ongoing contact and collaboration, although most participants desired more opportunities for interaction. They made suggestions for structural supports to networking among new global health researchers. Networking at the SI contributed positively to opportunities for individuals, and contributed to the formation of a network of global health researchers. Intentional inclusion of networking in health research capacity strengthening initiatives, with supportive resources and infrastructure could create dynamic, sustainable networks accessible to global health researchers around the world.

  11. The Daily Relation between Parental Rejection and Emotional Eating in Youngsters: A Diary Study.

    Science.gov (United States)

    Vandewalle, Julie; Mabbe, Elien; Debeuf, Taaike; Braet, Caroline; Moens, Ellen

    2017-01-01

    KEY POINTS  Cross-sectional survey studies have demonstrated significant associations between parental rejection and peer rejection on the one hand and disturbed eating in youngsters, like emotional eating, on the other hand. In this study, we wanted to expand our knowledge on these relationships by investigating the daily fluctuations in these variables. Youngsters completed a 7-day diary to assess daily parental rejection, peer rejection and emotional eating. Using multilevel analyses, our results showed that daily variations in parental rejection were related to daily variations in emotional eating of the youngsters. This highlights the importance of addressing the parent-child relationship in interventions for emotional eating in youngsters. Background: This study investigated the daily relation between parental rejection and peer rejection on the one hand and emotional eating in youngsters on the other hand. Methods: Participants (N = 55) between the ages of 11 and 15 years completed a 7-day diary. A multilevel design was used to examine day-to-day within-person relationships between parental and peer rejection (measured by CHS) and emotional eating (measured by DEBQ-C) of youngsters. Results: The results showed that daily variations in parental rejection were related to daily variations in emotional eating of the youngsters. Daily peer rejection was only marginally significantly related to the emotional eating of the youngsters. Conclusions: These results indicate that especially parental rejection, and to a lesser extent peer rejection, are associated with the emotional eating of youngsters. The findings highlight the importance of addressing the parent-child relationship in interventions for emotional eating in youngsters.

  12. The Daily Relation between Parental Rejection and Emotional Eating in Youngsters: A Diary Study

    Directory of Open Access Journals (Sweden)

    Julie Vandewalle

    2017-05-01

    Full Text Available KEY POINTS Cross-sectional survey studies have demonstrated significant associations between parental rejection and peer rejection on the one hand and disturbed eating in youngsters, like emotional eating, on the other hand. In this study, we wanted to expand our knowledge on these relationships by investigating the daily fluctuations in these variables. Youngsters completed a 7-day diary to assess daily parental rejection, peer rejection and emotional eating. Using multilevel analyses, our results showed that daily variations in parental rejection were related to daily variations in emotional eating of the youngsters. This highlights the importance of addressing the parent-child relationship in interventions for emotional eating in youngsters.Background: This study investigated the daily relation between parental rejection and peer rejection on the one hand and emotional eating in youngsters on the other hand.Methods: Participants (N = 55 between the ages of 11 and 15 years completed a 7-day diary. A multilevel design was used to examine day-to-day within-person relationships between parental and peer rejection (measured by CHS and emotional eating (measured by DEBQ-C of youngsters.Results: The results showed that daily variations in parental rejection were related to daily variations in emotional eating of the youngsters. Daily peer rejection was only marginally significantly related to the emotional eating of the youngsters.Conclusions: These results indicate that especially parental rejection, and to a lesser extent peer rejection, are associated with the emotional eating of youngsters. The findings highlight the importance of addressing the parent-child relationship in interventions for emotional eating in youngsters.

  13. 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.

  14. 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.

  15. Friends in the classroom : a comparison between two methods for the assessment of students' friendship networks

    NARCIS (Netherlands)

    Pijl, Sip Jan; Koster, Marloes; Hannink, Anne; Stratingh, Anna

    2011-01-01

    One of the methods used most often to assess students' friendships and friendship networks is the reciprocal nomination method. However, an often heard complaint is that this technique produces rather negative outcomes. This study compares the reciprocal nomination method with another method to

  16. 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

    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...

  17. Communities and beyond: Mesoscopic analysis of a large social network with complementary methods

    Science.gov (United States)

    Tibély, Gergely; Kovanen, Lauri; Karsai, Márton; Kaski, Kimmo; Kertész, János; Saramäki, Jari

    2011-05-01

    Community detection methods have so far been tested mostly on small empirical networks and on synthetic benchmarks. Much less is known about their performance on large real-world networks, which nonetheless are a significant target for application. We analyze the performance of three state-of-the-art community detection methods by using them to identify communities in a large social network constructed from mobile phone call records. We find that all methods detect communities that are meaningful in some respects but fall short in others, and that there often is a hierarchical relationship between communities detected by different methods. Our results suggest that community detection methods could be useful in studying the general mesoscale structure of networks, as opposed to only trying to identify dense structures.

  18. 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.

  19. 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.

  20. 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.

  1. Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies

    Directory of Open Access Journals (Sweden)

    Maciej Szmit

    2012-01-01

    Full Text Available The traditional Holt-Winters method is used, among others, in behavioural analysis of network traffic for development of adaptive models for various types of traffic in sample computer networks. This paper is devoted to the application of extended versions of these models for development of predicted templates and intruder detection.

  2. Usage of Modified Holt-Winters Method in the Anomaly Detection of Network Traffic: Case Studies

    OpenAIRE

    Maciej Szmit; Anna Szmit

    2012-01-01

    The traditional Holt-Winters method is used, among others, in behavioural analysis of network traffic for development of adaptive models for various types of traffic in sample computer networks. This paper is devoted to the application of extended versions of these models for development of predicted templates and intruder detection.

  3. 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.

  4. 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

  5. Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks

    Directory of Open Access Journals (Sweden)

    Joshi Anagha

    2009-05-01

    Full Text Available Abstract Background A myriad of methods to reverse-engineer transcriptional regulatory networks have been developed in recent years. Direct methods directly reconstruct a network of pairwise regulatory interactions while module-based methods predict a set of regulators for modules of coexpressed genes treated as a single unit. To date, there has been no systematic comparison of the relative strengths and weaknesses of both types of methods. Results We have compared a recently developed module-based algorithm, LeMoNe (Learning Module Networks, to a mutual information based direct algorithm, CLR (Context Likelihood of Relatedness, using benchmark expression data and databases of known transcriptional regulatory interactions for Escherichia coli and Saccharomyces cerevisiae. A global comparison using recall versus precision curves hides the topologically distinct nature of the inferred networks and is not informative about the specific subtasks for which each method is most suited. Analysis of the degree distributions and a regulator specific comparison show that CLR is 'regulator-centric', making true predictions for a higher number of regulators, while LeMoNe is 'target-centric', recovering a higher number of known targets for fewer regulators, with limited overlap in the predicted interactions between both methods. Detailed biological examples in E. coli and S. cerevisiae are used to illustrate these differences and to prove that each method is able to infer parts of the network where the other fails. Biological validation of the inferred networks cautions against over-interpreting recall and precision values computed using incomplete reference networks. Conclusion Our results indicate that module-based and direct methods retrieve largely distinct parts of the underlying transcriptional regulatory networks. The choice of algorithm should therefore be based on the particular biological problem of interest and not on global metrics which cannot be

  6. 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.

  7. 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.

  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. 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...

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

    Science.gov (United States)

    Kumari, Sapna; Nie, Jeff; Chen, Huann-Sheng; Ma, Hao; Stewart, Ron; Li, Xiang; Lu, Meng-Zhu; Taylor, William M; Wei, Hairong

    2012-01-01

    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. 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. 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.

  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

    2012-12-12

    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.

  13. 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.

  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. Scalable Optimization Methods for Distribution Networks With High PV Integration

    Energy Technology Data Exchange (ETDEWEB)

    Guggilam, Swaroop S.; Dall' Anese, Emiliano; Chen, Yu Christine; Dhople, Sairaj V.; Giannakis, Georgios B.

    2016-07-01

    This paper proposes a suite of algorithms to determine the active- and reactive-power setpoints for photovoltaic (PV) inverters in distribution networks. The objective is to optimize the operation of the distribution feeder according to a variety of performance objectives and ensure voltage regulation. In general, these algorithms take a form of the widely studied ac optimal power flow (OPF) problem. For the envisioned application domain, nonlinear power-flow constraints render pertinent OPF problems nonconvex and computationally intensive for large systems. To address these concerns, we formulate a quadratic constrained quadratic program (QCQP) by leveraging a linear approximation of the algebraic power-flow equations. Furthermore, simplification from QCQP to a linearly constrained quadratic program is provided under certain conditions. The merits of the proposed approach are demonstrated with simulation results that utilize realistic PV-generation and load-profile data for illustrative distribution-system test feeders.

  16. Methods of information theory and algorithmic complexity for network biology.

    Science.gov (United States)

    Zenil, Hector; Kiani, Narsis A; Tegnér, Jesper

    2016-03-01

    We survey and introduce concepts and tools located at the intersection of information theory and network biology. We show that Shannon's information entropy, compressibility and algorithmic complexity quantify different local and global aspects of synthetic and biological data. We show examples such as the emergence of giant components in Erdös-Rényi random graphs, and the recovery of topological properties from numerical kinetic properties simulating gene expression data. We provide exact theoretical calculations, numerical approximations and error estimations of entropy, algorithmic probability and Kolmogorov complexity for different types of graphs, characterizing their variant and invariant properties. We introduce formal definitions of complexity for both labeled and unlabeled graphs and prove that the Kolmogorov complexity of a labeled graph is a good approximation of its unlabeled Kolmogorov complexity and thus a robust definition of graph complexity. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data

    OpenAIRE

    Taner Tunç

    2012-01-01

    Logistic regression (LR) is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure. Another technique used for classification is feedforward artificial neural networks. Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function. In this study, a hybrid approach of model-based logistic regression technique and data-based artif...

  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. StegHash: New Method for Information Hiding in Open Social Networks

    OpenAIRE

    Szczypiorski, Krzysztof

    2016-01-01

    In this paper a new method for information hiding in open social networks is introduced. The method, called StegHash, is based on the use of hashtags in various open social networks to connect multimedia files (like images, movies, songs) with embedded hidden messages. The evaluation of the system was performed on two social media services (Twitter and Instagram) with a simple environment as a proof of concept. The experiments proved that the initial idea was correct, thus the proposed system...

  20. Nucleic Acid Drugs for Prevention of Cardiac Rejection

    Directory of Open Access Journals (Sweden)

    Jun-ichi Suzuki

    2009-01-01

    Full Text Available Heart transplantation has been broadly performed in humans. However, occurrence of acute and chronic rejection has not yet been resolved. Several inflammatory factors, such as cytokines and adhesion molecules, enhance the rejection. The graft arterial disease (GAD, which is a type of chronic rejection, is characterized by intimal thickening comprised of proliferative smooth muscle cells. Specific treatments that target the attenuation of acute rejection and GAD formation have not been well studied in cardiac transplantation. Recent progress in the nucleic acid drugs, such as antisense oligodeoxynucleotides (ODNs to regulate the transcription of disease-related genes, has important roles in therapeutic applications. Transfection of cis-element double-stranded DNA, named as “decoy,” has been also reported to be a useful nucleic acid drug. This decoy strategy has been not only a useful method for the experimental studies of gene regulation but also a novel clinical strategy. In this paper, we reviewed the experimental results of NF-κB, E2F, AP-1, and STAT-1 decoy and other ODNs using the experimental heart transplant models.

  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. Contextualized Network Analysis: Theory and Methods for Networks with Node Covariates

    Science.gov (United States)

    Binkiewicz, Norbert M.

    Biological and social systems consist of myriad interacting units. The interactions can be intuitively represented in the form of a graph or network. Measurements of these graphs can reveal the underlying structure of these interactions, which provides insight into the systems that generated the graphs. Moreover, in applications such as neuroconnectomics, social networks, and genomics, graph data is accompanied by contextualizing measures on each node. We leverage these node covariates to help uncover latent communities, using a modification of spectral clustering. Statistical guarantees are provided under a joint mixture model called the node contextualized stochastic blockmodel, including a bound on the mis-clustering rate. For most simulated conditions, covariate assisted spectral clustering yields superior results relative to both regularized spectral clustering without node covariates and an adaptation of canonical correlation analysis. We apply covariate assisted spectral clustering to large brain graphs derived from diffusion MRI, using the node locations or neurological regions as covariates. In both cases, covariate assisted spectral clustering yields clusters that are easier to interpret neurologically. A low rank update algorithm is developed to reduce the computational cost of determining the tuning parameter for covariate assisted spectral clustering. As simulations demonstrate, the low rank update algorithm increases the speed of covariate assisted spectral clustering up to ten-fold, while practically matching the clustering performance of the standard algorithm. Graphs with node attributes are sometimes accompanied by ground truth labels that align closely with the latent communities in the graph. We consider the example of a mouse retina neuron network accompanied by the neuron spatial location and neuronal cell types. In this example, the neuronal cell type is considered a ground truth label. Current approaches for defining neuronal cell type vary

  3. 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.

  4. A new method of UV-patternable hydrophobization of micro- and nanofluidic networks

    NARCIS (Netherlands)

    Arayanarakool, Rerngchai; Shui, Lingling; van den Berg, Albert; Eijkel, Jan C.T.

    2011-01-01

    This work reports a new method to hydrophobize glass-based micro- and nanofluidic networks. Conventional methods of hydrophobizing glass surfaces often create particulate debris causing clogging especially in shallow nanochannels or require skilful handling. Our novel method employs oxygen plasma,

  5. T cell immunohistochemistry refines lung transplant acute rejection diagnosis and grading.

    Science.gov (United States)

    Cheng, Lin; Guo, Haizhou; Qiao, Xinwei; Liu, Quan; Nie, Jun; Li, Jinsong; Wang, Jianjun; Jiang, Ke

    2013-10-14

    Lung transplant volume has been increasing. However, inaccurate and uncertain diagnosis for lung transplant rejection hurdles long-term outcome due to, in part, interobserver variability in rejection grading. Therefore, a more reliable method to facilitate diagnosing and grading rejection is warranted. Rat lung grafts were harvested on day 3, 7, 14 and 28 post transplant for histological and immunohistochemical assessment. No immunosuppressive treatment was administered. We explored the value of interstitial T lymphocytes quantification by immunohistochemistry and compared the role of T cell immunohistochemistry with H&E staining in diagnosing and grading lung transplant rejection. Typical acute rejection from grade A1 to A4 was found. Rejection severity was heterogeneously distributed in one-third transplanted lungs (14/40): lesions in apex and center were more augmented than in the base and periphery of the grafts, respectively. Immunohistochemistry showed profound difference in T lymphocyte infiltration among grade A1 to A4 rejections. The coincidence rate of H&E and immunohistochemistry was 77.5%. The amount of interstitial T lymphocyte infiltration increased gradually with the upgrading of rejection. The statistical analysis demonstrated that the difference in the amount of interstitial T lymphocytes between grade A2 and A3 was not obvious. However, T lymphocytes in lung tissue of grade A4 were significantly more abundant than in other grades. Rejection severity was heterogeneously distributed within lung grafts. Immunohistochemistry improves the sensitivity and specificity of rejection diagnosis, and interstitial T lymphocyte quantitation has potential value in diagnosing and monitoring lung allograft rejection. The virtual slides for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1536075282108217.

  6. A Method of DTM Construction Based on Quadrangular Irregular Networks and Related Error Analysis.

    Science.gov (United States)

    Kang, Mengjun; Wang, Mingjun; Du, Qingyun

    2015-01-01

    A new method of DTM construction based on quadrangular irregular networks (QINs) that considers all the original data points and has a topological matrix is presented. A numerical test and a real-world example are used to comparatively analyse the accuracy of QINs against classical interpolation methods and other DTM representation methods, including SPLINE, KRIGING and triangulated irregular networks (TINs). The numerical test finds that the QIN method is the second-most accurate of the four methods. In the real-world example, DTMs are constructed using QINs and the three classical interpolation methods. The results indicate that the QIN method is the most accurate method tested. The difference in accuracy rank seems to be caused by the locations of the data points sampled. Although the QIN method has drawbacks, it is an alternative method for DTM construction.

  7. A Method of DTM Construction Based on Quadrangular Irregular Networks and Related Error Analysis.

    Directory of Open Access Journals (Sweden)

    Mengjun Kang

    Full Text Available A new method of DTM construction based on quadrangular irregular networks (QINs that considers all the original data points and has a topological matrix is presented. A numerical test and a real-world example are used to comparatively analyse the accuracy of QINs against classical interpolation methods and other DTM representation methods, including SPLINE, KRIGING and triangulated irregular networks (TINs. The numerical test finds that the QIN method is the second-most accurate of the four methods. In the real-world example, DTMs are constructed using QINs and the three classical interpolation methods. The results indicate that the QIN method is the most accurate method tested. The difference in accuracy rank seems to be caused by the locations of the data points sampled. Although the QIN method has drawbacks, it is an alternative method for DTM construction.

  8. Comparisons of topological properties in autism for the brain network construction methods

    Science.gov (United States)

    Lee, Min-Hee; Kim, Dong Youn; Lee, Sang Hyeon; Kim, Jin Uk; Chung, Moo K.

    2015-03-01

    Structural brain networks can be constructed from the white matter fiber tractography of diffusion tensor imaging (DTI), and the structural characteristics of the brain can be analyzed from its networks. When brain networks are constructed by the parcellation method, their network structures change according to the parcellation scale selection and arbitrary thresholding. To overcome these issues, we modified the Ɛ -neighbor construction method proposed by Chung et al. (2011). The purpose of this study was to construct brain networks for 14 control subjects and 16 subjects with autism using both the parcellation and the Ɛ-neighbor construction method and to compare their topological properties between two methods. As the number of nodes increased, connectedness decreased in the parcellation method. However in the Ɛ-neighbor construction method, connectedness remained at a high level even with the rising number of nodes. In addition, statistical analysis for the parcellation method showed significant difference only in the path length. However, statistical analysis for the Ɛ-neighbor construction method showed significant difference with the path length, the degree and the density.

  9. 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.

  10. 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

    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. Mixed-method, multisite and case study research. Six clinical commissioning groups and local clusters in the East of England area, covering in total 208 GPs and 1 662 000 population. 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. 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. 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 patient needs and brought the leaders closer to frontline stakeholders

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  12. 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.

  13. 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.

  14. 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

  15. Fate of Manuscripts Rejected From the Red Journal

    Energy Technology Data Exchange (ETDEWEB)

    Holliday, Emma B., E-mail: emmaholliday@gmail.com [Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Yang, George [The University of South Florida Morsani College of Medicine, Tampa, Florida (United States); Jagsi, Reshma [Department of Radiation Oncology, The University of Michigan, Ann Arbor, Michigan (United States); Hoffman, Karen E. [Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States); Bennett, Katherine Egan; Grace, Calley [Scientific Publications, American Society for Radiation Oncology, Fairfax, Virginia (United States); Zietman, Anthony L. [Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts (United States)

    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

  16. Involvement of the Fas system in liver allograft rejection.

    Science.gov (United States)

    Rivero, M; Crespo, J; Mayorga, M; Fábrega, E; Casafont, F; Pons-Romero, F

    2002-06-01

    Recent studies suggest that apoptosis is an important mechanism of cell death in the rejection of liver allografts and that this process is mediated via Fas. The aim of this study was to analyze the expression of the Fas system during the liver allograft rejection and its evolution after treatment. We evaluated 14 patients with liver allograft rejection before and after treatment. Fas immunostaining was performed by the labeled streptavidin-biotin peroxidase method using a 200-fold dilution of a monoclonal antibody. Assessment of apoptosis was determined by the terminal deoxynucleotidyltransferase-mediated deoxyuridine triphosphate nick end labeling (TUNEL) technique on deparaffined liver samples. Serum levels of soluble Fas antigen (sFas) were detected by an enzyme immunoassay procedure. Twelve liver transplant patients without allograft rejection were analyzed as a control group. The number of hepatocytes expressing Fas antigen, the percentage of apoptotic hepatocytes, and the sFas levels were higher in patients with liver allograft rejection than in controls (27.9+/-23.1% vs 1.4+/-1.2%, p < 0.001; 2.2+/-0.9% vs 1.0+/-0.1%, p = 0.02; 24.2+/-39.6 vs 2.8+/-4.0 IU/ml, p = 0.03, respectively). There was a correlation between the levels of sFas, AST (r = 0.86, p < 0.001), ALT (r = 0.78, p = 0.02), and gamma-globulin levels (r = 0.86, p < 0.001). After the rejection treatment we found a significant decrease in the Fas antigen expression (18.6+/-13.3%, p < 0.05), TUNEL index (0.2+/-0.4, p < 0.05), and levels of sFas (9.9+/-30.25 IU/ml, p = 0.005). 1) The demonstration of hepatocytes with Fas antigen expression and the labeling of the nuclei by the TUNEL assay suggest that apoptosis mediated by the Fas system plays a role in the pathogenesis of liver allograft rejection. 2) The Fas expression and the sFas levels decreased in patients with treatment response.

  17. 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....

  18. 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.

  19. Echo state network prediction method and its application in flue gas turbine condition prediction

    Science.gov (United States)

    Wang, Shaohong; Chen, Tao; Xu, Xiaoli

    2010-12-01

    On the background of the complex production process of fluid catalytic cracking energy recovery system in large-scale petrochemical refineries, this paper introduced an improved echo state network (ESN) model prediction method which is used to address the condition trend prediction problem of the key power equipment--flue gas turbine. Singular value decomposition method was used to obtain the ESN output weight. Through selecting the appropriate parameters and discarding small singular value, this method overcame the defective solution problem in the prediction by using the linear regression algorithm, improved the prediction performance of echo state network, and gave the network prediction process. In order to solve the problem of noise contained in production data, the translation-invariant wavelet transform analysis method is combined to denoise the noisy time series before prediction. Condition trend prediction results show the effectiveness of the proposed method.

  20. Dynamic positioning system based on active disturbance rejection technology

    Science.gov (United States)

    Lei, Zhengling; Guo, Chen; Fan, Yunsheng

    2015-08-01

    A dynamically positioned vessel, by the International Maritime Organization (IMO) and the certifying class societies (DNV, ABS, LR, etc.), is defined as a vessel that maintains its position and heading (fixed location or pre-determined track) exclusively by means of active thrusters. The development of control technology promotes the upgrading of dynamic positioning (DP) systems. Today there are two different DP systems solutions available on the market: DP system based on PID regulator and that based on model-based control. Both systems have limited disturbance rejection capability due to their design principle. In this paper, a new DP system solution is proposed based on Active Disturbance Rejection Control (ADRC) technology. This technology is composed of Tracking-Differentiator (TD), Extended State Observer (ESO) and Nonlinear Feedback Combination. On one hand, both TD and ESO can act as filters and can be used in place of conventional filters; on the other hand, the total disturbance of the system can be estimated and compensated by ESO, which therefore enhances the system's disturbance rejection capability. This technology's advantages over other methods lie in two aspects: 1) This method itself can not only achieve control objectives but also filter noisy measurements without other specialized filters; 2) This method offers a new useful approach to suppress the ocean disturbance. The simulation results demonstrate the effectiveness of the proposed method.

  1. Prediction of epitopes using neural network based methods

    DEFF Research Database (Denmark)

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2011-01-01

    In this paper, we describe the methodologies behind three different aspects of the NetMHC family for prediction of MHC class I binding, mainly to HLAs. We have updated the prediction servers, NetMHC-3.2, NetMHCpan-2.2, and a new consensus method, NetMHCcons, which, in their previous versions, hav...

  2. Mixed methods analysis of urban environmental stewardship networks

    Science.gov (United States)

    James J.T. Connolly; Erika S. Svendsen; Dana R. Fisher; Lindsay K. Campbell

    2015-01-01

    While mixed methods approaches to research have been accepted practice within the social sciences for several decades (Tashakkori and Teddlie 2003), the rising demand for cross-disciplinary analyses of socio-environmental processes has necessitated a renewed examination of this approach within environmental studies. Urban environmental stewardship is one area where it...

  3. 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.

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

    African Journals Online (AJOL)

    The latter combines both the strategies for extracting the reference currents and controlling DC link voltage which ensure suitable transit of powers to supply the inverter. To investigate the performance of this identification method, the study has been accomplished using simulation with MATLAB Simulink Power System ...

  5. 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.

  6. A systematic molecular circuit design method for gene networks under biochemical time delays and molecular noises

    Directory of Open Access Journals (Sweden)

    Chang Yu-Te

    2008-11-01

    Full Text Available Abstract Background Gene networks in nanoscale are of nonlinear stochastic process. Time delays are common and substantial in these biochemical processes due to gene transcription, translation, posttranslation protein modification and diffusion. Molecular noises in gene networks come from intrinsic fluctuations, transmitted noise from upstream genes, and the global noise affecting all genes. Knowledge of molecular noise filtering and biochemical process delay compensation in gene networks is crucial to understand the signal processing in gene networks and the design of noise-tolerant and delay-robust gene circuits for synthetic biology. Results A nonlinear stochastic dynamic model with multiple time delays is proposed for describing a gene network under process delays, intrinsic molecular fluctuations, and extrinsic molecular noises. Then, the stochastic biochemical processing scheme of gene regulatory networks for attenuating these molecular noises and compensating process delays is investigated from the nonlinear signal processing perspective. In order to improve the robust stability for delay toleration and noise filtering, a robust gene circuit for nonlinear stochastic time-delay gene networks is engineered based on the nonlinear robust H∞ stochastic filtering scheme. Further, in order to avoid solving these complicated noise-tolerant and delay-robust design problems, based on Takagi-Sugeno (T-S fuzzy time-delay model and linear matrix inequalities (LMIs technique, a systematic gene circuit design method is proposed to simplify the design procedure. Conclusion The proposed gene circuit design method has much potential for application to systems biology, synthetic biology and drug design when a gene regulatory network has to be designed for improving its robust stability and filtering ability of disease-perturbed gene network or when a synthetic gene network needs to perform robustly under process delays and molecular noises.

  7. Comparing reports of peer rejection: associations with rejection sensitivity, victimization, aggression, and friendship.

    Science.gov (United States)

    Zimmer-Gembeck, Melanie J; Nesdale, Drew; McGregor, Leanne; Mastro, Shawna; Goodwin, Belinda; Downey, Geraldine

    2013-12-01

    Perceiving that one is rejected is an important correlate of emotional maladjustment. Yet, self-perceptions can substantially differ from classmate-reports of who is rejected. In this study, discrepancies between self- and classmate-reports of rejection were identified in 359 Australian adolescents (age 10-12 years). As expected, adolescents who overestimated rejection reported more rejection sensitivity and felt more victimized by their peers, but were not seen by peers as more victimized. Adolescents who underestimated rejection identified themselves as high in overt aggression, and their peers identified them as high in overt and relational aggression and low in prosocial behavior. Yet, underestimators' feelings of friendship satisfaction did not seem to suffer and they reported low rejection sensitivity. Results suggest that interventions to promote adolescent health should explicitly recognize the different needs of those who do and do not seem to perceive their high rejection, as well as adolescents who overestimate their rejection. Copyright © 2013 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  8. Method of derivation and differentiation of mouse embryonic stem cells generating synchronous neuronal networks.

    Science.gov (United States)

    Gazina, Elena V; Morrisroe, Emma; Mendis, Gunarathna D C; Michalska, Anna E; Chen, Joseph; Nefzger, Christian M; Rollo, Benjamin N; Reid, Christopher A; Pera, Martin F; Petrou, Steven

    2018-01-01

    Stem cells-derived neuronal cultures hold great promise for in vitro disease modelling and drug screening. However, currently stem cells-derived neuronal cultures do not recapitulate the functional properties of primary neurons, such as network properties. Cultured primary murine neurons develop networks which are synchronised over large fractions of the culture, whereas neurons derived from mouse embryonic stem cells (ESCs) display only partly synchronised network activity and human pluripotent stem cells-derived neurons have mostly asynchronous network properties. Therefore, strategies to improve correspondence of derived neuronal cultures with primary neurons need to be developed to validate the use of stem cell-derived neuronal cultures as in vitro models. By combining serum-free derivation of ESCs from mouse blastocysts with neuronal differentiation of ESCs in morphogen-free adherent culture we generated neuronal networks with properties recapitulating those of mature primary cortical cultures. After 35days of differentiation ESC-derived neurons developed network activity very similar to that of mature primary cortical neurons. Importantly, ESC plating density was critical for network development. Compared to the previously published methods this protocol generated more synchronous neuronal networks, with high similarity to the networks formed in mature primary cortical culture. We have demonstrated that ESC-derived neuronal networks recapitulating key properties of mature primary cortical networks can be generated by optimising both stem cell derivation and differentiation. This validates the approach of using ESC-derived neuronal cultures for disease modelling and in vitro drug screening. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. A Multi-level Fuzzy Evaluation Method for Smart Distribution Network Based on Entropy Weight

    Science.gov (United States)

    Li, Jianfang; Song, Xiaohui; Gao, Fei; Zhang, Yu

    2017-05-01

    Smart distribution network is considered as the future trend of distribution network. In order to comprehensive evaluate smart distribution construction level and give guidance to the practice of smart distribution construction, a multi-level fuzzy evaluation method based on entropy weight is proposed. Firstly, focus on both the conventional characteristics of distribution network and new characteristics of smart distribution network such as self-healing and interaction, a multi-level evaluation index system which contains power supply capability, power quality, economy, reliability and interaction is established. Then, a combination weighting method based on Delphi method and entropy weight method is put forward, which take into account not only the importance of the evaluation index in the experts’ subjective view, but also the objective and different information from the index values. Thirdly, a multi-level evaluation method based on fuzzy theory is put forward. Lastly, an example is conducted based on the statistical data of some cites’ distribution network and the evaluation method is proved effective and rational.

  10. Network gateway security method for enterprise Grid: a literature review

    Science.gov (United States)

    Sujarwo, A.; Tan, J.

    2017-03-01

    The computational Grid has brought big computational resources closer to scientists. It enables people to do a large computational job anytime and anywhere without any physical border anymore. However, the massive and spread of computer participants either as user or computational provider arise problems in security. The challenge is on how the security system, especially the one which filters data in the gateway could works in flexibility depends on the registered Grid participants. This paper surveys what people have done to approach this challenge, in order to find the better and new method for enterprise Grid. The findings of this paper is the dynamically controlled enterprise firewall to secure the Grid resources from unwanted connections with a new firewall controlling method and components.

  11. Pruning method for a cluster-based neural network

    Science.gov (United States)

    Ranney, Kenneth I.; Khatri, Hiralal; Nguyen, Lam H.; Sichina, Jeffrey

    2000-08-01

    Many radar automatic target detection (ATD) algorithms operate on a set of data statistics or features rather than on the raw radar sensor data. These features are selected based on their ability to separate target data samples from background clutter samples. The ATD algorithms often operate on the features through a set of parameters that must be determined from a set of training data that are statistically similar to the data set to be encountered in practice. The designer usually attempts to minimize the number of features used by the algorithm -- a process commonly referred to as pruning. This not only reduces the computational demands of the algorithm, but it also prevents overspecialization to the samples from the training data set. Thus, the algorithm will perform better on a set of test data samples it has not encountered during training. The Optimal Brain Surgeon (OBS) and Divergence Method provide two different approaches to pruning. We apply the two methods to a set of radar data features to determine a new, reduced set of features. We then evaluate the resulting feature sets and discuss the differences between the two methods.

  12. 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.

  13. A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data

    Directory of Open Access Journals (Sweden)

    Taner Tunç

    2012-01-01

    Full Text Available Logistic regression (LR is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure. Another technique used for classification is feedforward artificial neural networks. Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function. In this study, a hybrid approach of model-based logistic regression technique and data-based artificial neural network was proposed for classification purposes. The proposed approach was applied to lung cancer data, and obtained results were compared. It was seen that the proposed hybrid approach was superior to logistic regression and feedforward artificial neural networks with respect to many criteria.

  14. A computational method based on CVSS for quantifying the vulnerabilities in computer network

    Directory of Open Access Journals (Sweden)

    Shahriyar Mohammadi

    2014-10-01

    Full Text Available Network vulnerability taxonomy has become increasingly important in the area of information and data exchange not only for its potential use in identification of vulnerabilities but also in their assessment and prioritization. Computer networks play an important role in information and communication infrastructure. However, they are constantly exposed to a variety of vulnerability risks. In their attempts to create secure information exchange systems, scientists have concentrated on understanding the nature and typology of these vulnerabilities. Their efforts aimed at establishing secure networks have led to the development of a variety of methods and techniques for quantifying vulnerability. The objective of the present paper is developing a method based on the second edition of common vulnerability scoring system (CVSS for the quantification of Computer Network vulnerabilities. It is expected that the proposed model will help in the identification and effective management of vulnerabilities by their quantification.

  15. Operator Splitting Method for Simulation of Dynamic Flows in Natural Gas Pipeline Networks

    CERN Document Server

    Dyachenko, Sergey A; Chertkov, Michael

    2016-01-01

    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.

  16. 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.

  17. 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.

  18. 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.

  19. Simulation of Foam Divot Weight on External Tank Utilizing Least Squares and Neural Network Methods

    Science.gov (United States)

    Chamis, Christos C.; Coroneos, Rula M.

    2007-01-01

    Simulation of divot weight in the insulating foam, associated with the external tank of the U.S. space shuttle, has been evaluated using least squares and neural network concepts. The simulation required models based on fundamental considerations that can be used to predict under what conditions voids form, the size of the voids, and subsequent divot ejection mechanisms. The quadratic neural networks were found to be satisfactory for the simulation of foam divot weight in various tests associated with the external tank. Both linear least squares method and the nonlinear neural network predicted identical results.

  20. 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...

  1. Comparison of artificial neural network analysis with other multimarker methods for detecting genetic association

    OpenAIRE

    Curtis David

    2007-01-01

    Abstract Background Debate remains as to the optimal method for utilising genotype data obtained from multiple markers in case-control association studies. I and colleagues have previously described a method of association analysis using artificial neural networks (ANNs), whose performance compared favourably to single-marker methods. Here, the perfomance of ANN analysis is compared with other multi-marker methods, comprising different haplotype-based analyses and locus-based analyses. Result...

  2. Thermal Analysis of AC Contactor Using Thermal Network Finite Difference Analysis Method

    Science.gov (United States)

    Niu, Chunping; Chen, Degui; Li, Xingwen; Geng, Yingsan

    To predict the thermal behavior of switchgear quickly, the Thermal Network Finite Difference Analysis method (TNFDA) is adopted in thermal analysis of AC contactor in the paper. The thermal network model is built with nodes, thermal resistors and heat generators, and it is solved using finite difference method (FDM). The main circuit and the control system are connected by thermal resistors network, which solves the problem of multi-sources interaction in the application of TNFDA. The temperature of conducting wires is calculated according to the heat transfer process and the fundamental equations of thermal conduction. It provides a method to solve the problem of boundary conditions in applying the TNFDA. The comparison between the results of TNFDA and measurements shows the feasibility and practicability of the method.

  3. Study on Negative Resistance Mechanism and Elimination Method of Network Simplification

    Science.gov (United States)

    Zhu, Lin; Fu, Dong; Sheng, Qiliang; Wang, Bei; Hu, Xinge

    2017-05-01

    The key of the further development of power system dynamic equivalence study is that correct understanding of negative resistance mechanism in network simplification and how to get rid of negative resistance. This paper analyzes the network simplification of power system, and it thoroughly studies the mechanism of equivalent branches with negative resistance. This paper also leads the definition of the constant impedance load and studies the property and the size of constant impedance load to further explain the mechanism of negative resistance production. This paper proposes a network transformation method which is based on power flow calculation results. This method transforms series branches which include negative resistance into PI-type branches. Finally the validity of this method is verified in sample cases from the China Southern Power Grid and IEEE 39 bus system. This method also solves the problem that some simulation software can’t model the branches with negative resistance.

  4. 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.

  5. A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions.

    Science.gov (United States)

    Samal, Satya Swarup; Grigoriev, Dima; Fröhlich, Holger; Weber, Andreas; Radulescu, Ovidiu

    2015-12-01

    Model reduction of biochemical networks relies on the knowledge of slow and fast variables. We provide a geometric method, based on the Newton polytope, to identify slow variables of a biochemical network with polynomial rate functions. The gist of the method is the notion of tropical equilibration that provides approximate descriptions of slow invariant manifolds. Compared to extant numerical algorithms such as the intrinsic low-dimensional manifold method, our approach is symbolic and utilizes orders of magnitude instead of precise values of the model parameters. Application of this method to a large collection of biochemical network models supports the idea that the number of dynamical variables in minimal models of cell physiology can be small, in spite of the large number of molecular regulatory actors.

  6. The research of elevator health diagnosis method based on Bayesian network

    Science.gov (United States)

    Liu, Chang; Zhang, Xinzheng; Liu, Xindong; Chen, Can

    2017-08-01

    Elevator, as a complex mechanical system, is hard to determine the factors that affect components’ status. In accordance with this special characteristic, the Elevator Fault Diagnosis Model is proposed based on Bayesian Network in this paper. The method uses different samples of the elevator and adopts Monte Carlo inference mechanism for Bayesian Network Model structure and parameter learning. Eventually, an elevator fault diagnosis model based on Bayesian network is established, which accords with the theory of elevator operation. In this paper, we use different kinds of fault data samples to test the method. Experimental results demonstrate the higher accuracy of our method. This paper provides a good assistant method by means of Fault prediction and Health diagnosis of elevator system at present.

  7. Bayesian Computation Methods for Inferring Regulatory Network Models Using Biomedical Data.

    Science.gov (United States)

    Tian, Tianhai

    2016-01-01

    The rapid advancement of high-throughput technologies provides huge amounts of information for gene expression and protein activity in the genome-wide scale. The availability of genomics, transcriptomics, proteomics, and metabolomics dataset gives an unprecedented opportunity to study detailed molecular regulations that is very important to precision medicine. However, it is still a significant challenge to design effective and efficient method to infer the network structure and dynamic property of regulatory networks. In recent years a number of computing methods have been designed to explore the regulatory mechanisms as well as estimate unknown model parameters. Among them, the Bayesian inference method can combine both prior knowledge and experimental data to generate updated information regarding the regulatory mechanisms. This chapter gives a brief review for Bayesian statistical methods that are used to infer the network structure and estimate model parameters based on experimental data.

  8. Load management in electrical networks. Objectives, methods, prospects; Gestion de la charge du reseau electrique. Objectifs, methodes, perspectives

    Energy Technology Data Exchange (ETDEWEB)

    Gabioud, D.

    2008-07-01

    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.

  9. The fate of triaged and rejected manuscripts.

    Science.gov (United States)

    Zoccali, Carmine; Amodeo, Daniela; Argiles, Angel; Arici, Mustafa; D'arrigo, Graziella; Evenepoel, Pieter; Fliser, Danilo; Fox, Jonathan; Gesualdo, Loreto; Jadoul, Michel; Ketteler, Markus; Malyszko, Jolanta; Massy, Ziad; Mayer, Gert; Ortiz, Alberto; Sever, Mehmet; Vanholder, Raymond; Vinck, Caroline; Wanner, Christopher; Więcek, Andrzej

    2015-12-01

    In 2011, Nephrology Dialysis and Transplantation (NDT) established a more restrictive selection process for manuscripts submitted to the journal, reducing the acceptance rate from 25% (2008-2009) to currently about 12-15%. To achieve this goal, we decided to score the priority of manuscripts submitted to NDT and to reject more papers at triage than in the past. This new scoring system allows a rapid decision for the authors without external review. However, the risk of such a restrictive policy may be that the journal might fail to capture important studies that are eventually published in higher-ranked journals. To look into this problem, we analysed random samples of papers (∼10%) rejected by NDT in 2012. Of the papers rejected at triage and those rejected after regular peer review, 59 and 61%, respectively, were accepted in other journals. A detailed analysis of these papers showed that only 4 out of 104 and 7 out of 93 of the triaged and rejected papers, respectively, were published in journals with an impact factor higher than that of NDT. Furthermore, for all these papers, independent assessors confirmed the evaluation made by the original reviewers. The number of citations of these papers was similar to that typically obtained by publications in the corresponding journals. Even though the analyses seem reassuring, previous observations made by leading journals warn that the risk of 'big misses', resulting from selective editorial policies, remains a real possibility. We will therefore continue to maintain a high degree of alertness and will periodically track the history of manuscripts rejected by NDT, particularly papers that are rejected at triage by our journal. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

  10. A Cut Cell Method for Simulating Spatial Models of Biochemical Reaction Networks in Arbitrary Geometries.

    Science.gov (United States)

    Strychalski, Wanda; Adalsteinsson, David; Elston, Timothy C

    2010-01-01

    Cells use signaling networks consisting of multiple interacting proteins to respond to changes in their environment. In many situations, such as chemotaxis, spatial and temporal information must be transmitted through the network. Recent computational studies have emphasized the importance of cellular geometry in signal transduction, but have been limited in their ability to accurately represent complex cell morphologies. We present a finite volume method that addresses this problem. Our method uses Cartesian cut cells and is second order in space and time. We use our method to simulate several models of signaling systems in realistic cell morphologies obtained from live cell images and examine the effects of geometry on signal transduction.

  11. Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.

    Science.gov (United States)

    Yuan, Junjia; Meng, Hao; Zhu, Qidan; Zhou, Jiajia

    2016-01-01

    The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method.

  12. The Usage Of Artificial Neural Networks Method In The Diagnosis Of Rheumatoid Arthritis

    OpenAIRE

    Tok, Kadir; Saritas, Ismail

    2016-01-01

    In this study, artificial neural networks (ANN) method is used for the diagnosis of rheumatoid arthritis in order to support medical diagnostics. For the diagnosis of rheumatoid arthritis, backpropagation algorithm was examined in Matlab R2015b environment in artificial neural networks. With the system, the data in a data set, which are received from the patients with rheumatoid arthritis and from the people who are not suffering from rheumatoid arthritis, are classified successfully. Also, A...

  13. 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…

  14. A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization.

    Science.gov (United States)

    Li, Wei; Wang, Miao; Li, Yapeng; Huang, Yue; Chen, Xi

    2016-01-01

    The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the "intrinsic edges" to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV) method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases.

  15. A Novel Brain Network Construction Method for Exploring Age-Related Functional Reorganization

    Directory of Open Access Journals (Sweden)

    Wei Li

    2016-01-01

    Full Text Available The human brain undergoes complex reorganization and changes during aging. Using graph theory, scientists can find differences in topological properties of functional brain networks between young and elderly adults. However, these differences are sometimes significant and sometimes not. Several studies have even identified disparate differences in topological properties during normal aging or in age-related diseases. One possible reason for this issue is that existing brain network construction methods cannot fully extract the “intrinsic edges” to prevent useful signals from being buried into noises. This paper proposes a new subnetwork voting (SNV method with sliding window to construct functional brain networks for young and elderly adults. Differences in the topological properties of brain networks constructed from the classic and SNV methods were consistent. Statistical analysis showed that the SNV method can identify much more statistically significant differences between groups than the classic method. Moreover, support vector machine was utilized to classify young and elderly adults; its accuracy, based on the SNV method, reached 89.3%, significantly higher than that with classic method. Therefore, the SNV method can improve consistency within a group and highlight differences between groups, which can be valuable for the exploration and auxiliary diagnosis of aging and age-related diseases.

  16. A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling

    Science.gov (United States)

    Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo

    1996-01-01

    The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.

  17. Multi-tree Coding Method (MCM) for drainage networks supporting high-efficient search

    Science.gov (United States)

    Wang, Hao; Fu, Xudong; Wang, Guangqian

    2013-03-01

    River coding method for drainage networks plays an very important role in the physical simulation of river basins. In this study we developed a new river coding method named Multi-tree Coding Method (MCM), which has the following features: (1) it is established on a topological pattern reflecting the dendriform structure of drainage networks; (2) the multi-tree code can be effectively managed by the database to perform convenient topological search toward drainage networks using Structured Query Language (SQL); (3) the multi-tree code does not exhibit digital overflow problems in the computer, thus any resolution and scale drainage networks can easily be coded; and (4) it supports high-efficient search process. A river reach can be directly positioned in a drainage network under MCM, without the complex search process from all river reaches. This feature has great potential to improve the computational performance of basin models. We demonstrate here the efficiency and practicality of MCM by testing it in the Yalu Tsangpo river basin, Tibet. A drainage network with 140,745 digital reaches was extracted from the digital elevation model (DEM), and the multi-tree codes of all river reaches were obtained.

  18. A symmetry-based method to infer structural brain networks from probabilistic tractography data

    Directory of Open Access Journals (Sweden)

    Kamal Shadi

    2016-11-01

    Full Text Available Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs from probabilistic tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA, does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold. Instead, we exploit a basic limitation of the tractography process: the observed streamlines from a source to a target do not provide any information about the polarity of the underlying white matter, and so if there are some fibers connecting two voxels (or two ROIs X and Y, tractography should be able in principle to follow this connection in both directions, from X to Y and from Y to X. We leverage this limitation to formulate the network inference process as an optimization problem that minimizes the (appropriately normalized asymmetry of the observed network. We evaluate the proposed method using both the FiberCup dataset and based on a noise model that randomly corrupts the observed connectivity of synthetic networks. As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are associated with various neuropsychiatric conditions including depression, anxiety and addiction.

  19. An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks.

    Science.gov (United States)

    Ching, Wai-Ki; Zhang, Shuqin; Ng, Michael K; Akutsu, Tatsuya

    2007-06-15

    Probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. The steady-state probability distribution of a PBN gives important information about the captured genetic network. The computation of the steady-state probability distribution usually includes construction of the transition probability matrix and computation of the steady-state probability distribution. The size of the transition probability matrix is 2(n)-by-2(n) where n is the number of genes in the genetic network. Therefore, the computational costs of these two steps are very expensive and it is essential to develop a fast approximation method. In this article, we propose an approximation method for computing the steady-state probability distribution of a PBN based on neglecting some Boolean networks (BNs) with very small probabilities during the construction of the transition probability matrix. An error analysis of this approximation method is given and theoretical result on the distribution of BNs in a PBN with at most two Boolean functions for one gene is also presented. These give a foundation and support for the approximation method. Numerical experiments based on a genetic network are given to demonstrate the efficiency of the proposed method.

  20. Method and system for a network mapping service

    Energy Technology Data Exchange (ETDEWEB)

    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.

  1. A Topology Potential-Based Method for Identifying Essential Proteins from PPI Networks.

    Science.gov (United States)

    Li, Min; Lu, Yu; Wang, Jianxin; Wu, Fang-Xiang; Pan, Yi

    2015-01-01

    Essential proteins are indispensable for cellular life. It is of great significance to identify essential proteins that can help us understand the minimal requirements for cellular life and is also very important for drug design. However, identification of essential proteins based on experimental approaches are typically time-consuming and expensive. With the development of high-throughput technology in the post-genomic era, more and more protein-protein interaction data can be obtained, which make it possible to study essential proteins from the network level. There have been a series of computational approaches proposed for predicting essential proteins based on network topologies. Most of these topology based essential protein discovery methods were to use network centralities. In this paper, we investigate the essential proteins' topological characters from a completely new perspective. To our knowledge it is the first time that topology potential is used to identify essential proteins from a protein-protein interaction (PPI) network. The basic idea is that each protein in the network can be viewed as a material particle which creates a potential field around itself and the interaction of all proteins forms a topological field over the network. By defining and computing the value of each protein's topology potential, we can obtain a more precise ranking which reflects the importance of proteins from the PPI network. The experimental results show that topology potential-based methods TP and TP-NC outperform traditional topology measures: degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), subgraph centrality (SC), eigenvector centrality (EC), information centrality (IC), and network centrality (NC) for predicting essential proteins. In addition, these centrality measures are improved on their performance for identifying essential proteins in biological network when controlled by topology potential.

  2. Prognostic factors for the evolution and reversibility of chronic rejection in pediatric liver transplantation

    Directory of Open Access Journals (Sweden)

    Ana Cristina Aoun Tannuri

    2016-04-01

    Full Text Available OBJECTIVE: Chronic rejection remains a major cause of graft failure with indication for re-transplantation. The incidence of chronic rejection remains high in the pediatric population. Although several risk factors have been implicated in adults, the prognostic factors for the evolution and reversibility of chronic rejection in pediatric liver transplantation are not known. Hence, the current study aimed to determine the factors involved in the progression or reversibility of pediatric chronic rejection by evaluating a series of chronic rejection cases following liver transplantation. METHODS: Chronic rejection cases were identified by performing liver biopsies on patients based on clinical suspicion. Treatment included maintaining high levels of tacrolimus and the introduction of mofetil mycophenolate. The children were divided into 2 groups: those with favorable outcomes and those with adverse outcomes. Multivariate analysis was performed to identify potential risk factors in these groups. RESULTS: Among 537 children subjected to liver transplantation, chronic rejection occurred in 29 patients (5.4%. In 10 patients (10/29, 34.5%, remission of chronic rejection was achieved with immunosuppression (favorable outcomes group. In the remaining 19 patients (19/29, 65.5%, rejection could not be controlled (adverse outcomes group and resulted in re-transplantation (7 patients, 24.1% or death (12 patients, 41.4%. Statistical analysis showed that the presence of ductopenia was associated with worse outcomes (risk ratio=2.08, p=0.01. CONCLUSION: The presence of ductopenia is associated with poor prognosis in pediatric patients with chronic graft rejection.

  3. 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

  4. 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

  5. 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.

  6. 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.

  7. A Representation Method for Complex Road Networks in Virtual Geographic Environments

    Directory of Open Access Journals (Sweden)

    Peibei Zheng

    2017-11-01

    Full Text Available Road networks are important for modelling the urban geographic environment. It is necessary to determine the spatial relationships of road intersections when using maps to help researchers conduct virtual urban geographic experiments (because a road intersection might occur as a connected cross or as an unconnected bridge overpass. Based on the concept of using different map layers to organize the render order of each road segment, three methods (manual, semi-automatic and mask-based automatic are available to help map designers arrange the rendering order. However, significant efforts are still needed, and rendering efficiency remains problematic with these methods. This paper considers the Discrete, Crossing, Overpass, Underpass, Conjunction, Up-overlap and Down-overlap spatial relationships of road intersections. An automatic method is proposed to represent these spatial relationships when drawing road networks on a map. The data-layer organization method (reflecting road grade and elevation-level information and the symbol-layer decomposition method (reflecting road covering order in the vertical direction are designed to determine the rendering order of each road element when rendering a map. In addition, an “auxiliary-drawing-action” (for drawing road segments belonging to different grades and elevations is proposed to adjust the rendering sequences automatically. Two experiments are conducted to demonstrate the feasibility and efficiency of the method, and the results demonstrate that it can effectively handle spatial relationships of road networks in map representations. Using the proposed method, the difficulty of rendering complex road networks can be reduced.

  8. 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

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Low CD4/CD8 Ratio in Bronchus-Associated Lymphoid Tissue Is Associated with Lung Allograft Rejection.

    Science.gov (United States)

    Shenoy, K V; Solomides, C; Cordova, F; Rogers, T J; Ciccolella, D; Criner, G J

    2012-01-01

    Background. Bronchus-associated lymphoid tissue (BALT) has been associated with lung allograft rejection in rat transplant models. In human transplant recipients, BALT has not been linked to clinically significant rejection. We hypothesize that the immunohistochemical composition of BALT varies with the presence of acute lung allograft rejection. Methods. We retrospectively examined 40 human lung allograft recipients transplanted from 3/1/1999 to 6/1/2008. Patients were grouped by frequency and severity of acute rejection based on International Society of Heart Lung Transplant (ISHLT) criteria. Transbronchial biopsies were reviewed for BALT by a blinded pathologist. BALT if present was immunohistochemically stained to determine T-and B-cell subpopulations. Results. BALT presence was associated with an increased frequency of acute rejection episodes in the first year after transplantation. Patients with a lower CD4/CD8 ratio had an increased rejection rate; however, BALT size or densities of T-cell and B-cell subpopulations did not correlate with rejection rate. Conclusion. The presence of BALT is associated with an increased frequency of rejection one year after transplant. The lower the CD4/CD8 ratio, the more acute rejection episodes occur in the first year after transplantation. The immunohistochemical composition of BALT may predict patients prone to frequent episodes of acute cellular rejection.

  16. Low CD4/CD8 Ratio in Bronchus-Associated Lymphoid Tissue Is Associated with Lung Allograft Rejection

    Directory of Open Access Journals (Sweden)

    K. V. Shenoy

    2012-01-01

    Full Text Available Background. Bronchus-associated lymphoid tissue (BALT has been associated with lung allograft rejection in rat transplant models. In human transplant recipients, BALT has not been linked to clinically significant rejection. We hypothesize that the immunohistochemical composition of BALT varies with the presence of acute lung allograft rejection. Methods. We retrospectively examined 40 human lung allograft recipients transplanted from 3/1/1999 to 6/1/2008. Patients were grouped by frequency and severity of acute rejection based on International Society of Heart Lung Transplant (ISHLT criteria. Transbronchial biopsies were reviewed for BALT by a blinded pathologist. BALT if present was immunohistochemically stained to determine T-and B-cell subpopulations. Results. BALT presence was associated with an increased frequency of acute rejection episodes in the first year after transplantation. Patients with a lower CD4/CD8 ratio had an increased rejection rate; however, BALT size or densities of T-cell and B-cell subpopulations did not correlate with rejection rate. Conclusion. The presence of BALT is associated with an increased frequency of rejection one year after transplant. The lower the CD4/CD8 ratio, the more acute rejection episodes occur in the first year after transplantation. The immunohistochemical composition of BALT may predict patients prone to frequent episodes of acute cellular rejection.

  17. 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.

  18. Peer Rejection as a Social Antecedent to Rejection Sensitivity in Youth: The Role of Relational Valuation.

    Science.gov (United States)

    Wang, Jennifer; McDonald, Kristina L; Rubin, Kenneth H; Laursen, Brett

    2012-11-01

    Although much is known about the consequences of rejection sensitivity (RS), less is known about its social antecedents, particularly during development. Despite research demonstrating the role of peer rejection in the development and maintenance of problematic social schema like RS, little is known about why some youth are more susceptible to these negative consequences than others. We examined how relational valuation might moderate the effects of peer rejection on RS in a sample of 294 youth (138 boys) who made the transition from middle to high school. Results from path analysis revealed that 8th grade peer rejection was most highly associated with 9th grade RS for youth who held high regard for social relationships. Findings demonstrate the importance of examining cognitive moderators in the links between negative social experiences and problematic social schema, and highlight the need to move beyond simple main effects models for understanding the heterogeneity of rejection.

  19. Measuring geographic access to health care: raster and network-based methods

    Directory of Open Access Journals (Sweden)

    Delamater Paul L

    2012-05-01

    Full Text Available Abstract Background Inequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted. Methods We examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan’s Certificate of Need (CON Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment. Results In both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan. Conclusions Differences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the

  20. 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.

  1. A Linkage Matching Method for Road Networks Considering the Similarity of Upper and Lower Spatial Relation

    Directory of Open Access Journals (Sweden)

    LIU Chuang

    2016-11-01

    Full Text Available Existing road network matching methods mostly use the characteristics of the road's own nodes and arcs to carry on the matching process, while less attention is focused on the importance of the road neighborhood elements in the road network matching, thus affecting further improvement of the matching efficiency and accuracy. In response to these problems, a linkage matching method for road network considering the similarity of upper and lower spatial relation is proposed. The linkage matching imitates the human thinking process of searching for target objects by the signal features and spatial correlation when reading maps, regarding matching as a reasoning process of goal feature searching and information association transmitting. Firstly, classify the complex road network by using Stroke technology. Secondly, establish the road network linkage matching model based on road skeleton relation tree. Finally, select the high-level road in the classifying results of the source data as the reference road to start matching, calculate the road between the upper and lower levels of the spatial relationship similarity, and through a step-by-step iteration, make the matching information transmit in the road network linkage matching model thus to obtain the final matching results. Experiment shows that the mentioned algorithm can narrow the search range of the data to be matched, effectively improving the match efficiency and accuracy, especially applicable to the data with large non systematic geometric location deviation.

  2. 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

  3. A New Training Method for Analyzable Structured Neural Network and Application of Daily Peak Load Forecasting

    Science.gov (United States)

    Iizaka, Tatsuya; Matsui, Tetsuro; Fukuyama, Yoshikazu

    This paper presents a daily peak load forecasting method using an analyzable structured neural network (ASNN) in order to explain forecasting reasons. In this paper, we propose a new training method for ASNN in order to explain forecasting reason more properly than the conventional training method. ASNN consists of two types of hidden units. One type of hidden units has connecting weights between the hidden units and only one group of related input units. Another one has connecting weights between the hidden units and all input units. The former type of hidden units allows to explain forecasting reasons. The latter type of hidden units ensures the forecasting performance. The proposed training method make the former type of hidden units train only independent relations between the input factors and output, and make the latter type of hidden units train only complicated interactions between input factors. The effectiveness of the proposed neural network is shown using actual daily peak load. ASNN trained by the proposed method can explain forecasting reasons more properly than ASNN trained by the conventional method. Moreover, the proposed neural network can forecast daily peak load more accurately than conventional neural network trained by the back propagation algorithm.

  4. A vascular image registration method based on network structure and circuit simulation.

    Science.gov (United States)

    Chen, Li; Lian, Yuxi; Guo, Yi; Wang, Yuanyuan; Hatsukami, Thomas S; Pimentel, Kristi; Balu, Niranjan; Yuan, Chun

    2017-05-02

    Image registration is an important research topic in the field of image processing. Applying image registration to vascular image allows multiple images to be strengthened and fused, which has practical value in disease detection, clinical assisted therapy, etc. However, it is hard to register vascular structures with high noise and large difference in an efficient and effective method. Different from common image registration methods based on area or features, which were sensitive to distortion and uncertainty in vascular structure, we proposed a novel registration method based on network structure and circuit simulation. Vessel images were transformed to graph networks and segmented to branches to reduce the calculation complexity. Weighted graph networks were then converted to circuits, in which node voltages of the circuit reflecting the vessel structures were used for node registration. The experiments in the two-dimensional and three-dimensional simulation and clinical image sets showed the success of our proposed method in registration. The proposed vascular image registration method based on network structure and circuit simulation is stable, fault tolerant and efficient, which is a useful complement to the current mainstream image registration methods.

  5. Network Slicing in Industry 4.0 Applications: Abstraction Methods and End-to-End Analysis

    DEFF Research Database (Denmark)

    Nielsen, Jimmy Jessen; Popovski, Petar; Kalør, Anders Ellersgaard

    2018-01-01

    Industry 4.0 refers to the fourth industrial revolution, and introduces modern communication and computation technologies such as 5G, cloud computing and Internet of Things to industrial manufacturing systems. As a result, many devices, machines and applications will rely on connectivity, while...... having different requirements from the network, ranging from high reliability and low latency to high data rates. Furthermore, these industrial networks will be highly heterogeneous as they will feature a number of diverse communication technologies. In this article, we propose network slicing...... as a mechanism to handle the diverse set of requirements to the network. We present methods for slicing deterministic and packet-switched industrial communication protocols at an abstraction level which is decoupled from the specific implementation of the underlying technologies, and hence simplifies the slicing...

  6. The Global Oscillation Network Group site survey. 1: Data collection and analysis methods

    Science.gov (United States)

    Hill, Frank; Fischer, George; Grier, Jennifer; Leibacher, John W.; Jones, Harrison B.; Jones, Patricia P.; Kupke, Renate; Stebbins, Robin T.

    1994-01-01

    The Global Oscillation Network Group (GONG) Project is planning to place a set of instruments around the world to observe solar oscillations as continuously as possible for at least three years. The Project has now chosen the sites that will comprise the network. This paper describes the methods of data collection and analysis that were used to make this decision. Solar irradiance data were collected with a one-minute cadence at fifteen sites around the world and analyzed to produce statistics of cloud cover, atmospheric extinction, and transparency power spectra at the individual sites. Nearly 200 reasonable six-site networks were assembled from the individual stations, and a set of statistical measures of the performance of the networks was analyzed using a principal component analysis. An accompanying paper presents the results of the survey.

  7. 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.

  8. 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.

  9. An Efficient Steady-State Analysis Method for Large Boolean Networks with High Maximum Node Connectivity.

    Science.gov (United States)

    Hong, Changki; Hwang, Jeewon; Cho, Kwang-Hyun; Shin, Insik

    2015-01-01

    Boolean networks have been widely used to model biological processes lacking detailed kinetic information. Despite their simplicity, Boolean network dynamics can still capture some important features of biological systems such as stable cell phenotypes represented by steady states. For small models, steady states can be determined through exhaustive enumeration of all state transitions. As the number of nodes increases, however, the state space grows exponentially thus making it difficult to find steady states. Over the last several decades, many studies have addressed how to handle such a state space explosion. Recently, increasing attention has been paid to a satisfiability solving algorithm due to its potential scalability to handle large networks. Meanwhile, there still lies a problem in the case of large models with high maximum node connectivity where the satisfiability solving algorithm is known to be computationally intractable. To address the problem, this paper presents a new partitioning-based method that breaks down a given network into smaller subnetworks. Steady states of each subnetworks are identified by independently applying the satisfiability solving algorithm. Then, they are combined to construct the steady states of the overall network. To efficiently apply the satisfiability solving algorithm to each subnetwork, it is crucial to find the best partition of the network. In this paper, we propose a method that divides each subnetwork to be smallest in size and lowest in maximum node connectivity. This minimizes the total cost of finding all steady states in entire subnetworks. The proposed algorithm is compared with others for steady states identification through a number of simulations on both published small models and randomly generated large models with differing maximum node connectivities. The simulation results show that our method can scale up to several hundreds of nodes even for Boolean networks with high maximum node connectivity. The

  10. Research on Evaluation Method Based on Modified Buckley Decision Making and Bayesian Network

    Directory of Open Access Journals (Sweden)

    Neng-pu Yang

    2015-01-01

    Full Text Available This work presents a novel evaluation method, which can be applied in the field of risk assessment, project management, cause analysis, and so forth. Two core technologies are used in the method, namely, modified Buckley Decision Making and Bayesian Network. Based on the modified Buckley Decision Making, the fuzzy probabilities of element factors are calibrated. By the forward and backward calculation of Bayesian Network, the structure importance, probability importance, and criticality importance of each factor are calculated and discussed. A numerical example of risk evaluation for dangerous goods transport process is given to verify the method. The results indicate that the method can efficiently identify the weakest element factor. In addition, the method can improve the reliability and objectivity for evaluation.

  11. A Least Square Method Based Model for Identifying Protein Complexes in Protein-Protein Interaction Network

    Directory of Open Access Journals (Sweden)

    Qiguo Dai

    2014-01-01

    Full Text Available Protein complex formed by a group of physical interacting proteins plays a crucial role in cell activities. Great effort has been made to computationally identify protein complexes from protein-protein interaction (PPI network. However, the accuracy of the prediction is still far from being satisfactory, because the topological structures of protein complexes in the PPI network are too complicated. This paper proposes a novel optimization framework to detect complexes from PPI network, named PLSMC. The method is on the basis of the fact that if two proteins are in a common complex, they are likely to be interacting. PLSMC employs this relation to determine complexes by a penalized least squares method. PLSMC is applied to several public yeast PPI networks, and compared with several state-of-the-art methods. The results indicate that PLSMC outperforms other methods. In particular, complexes predicted by PLSMC can match known complexes with a higher accuracy than other methods. Furthermore, the predicted complexes have high functional homogeneity.

  12. 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.

  13. Generalized method of moments for estimating parameters of stochastic reaction networks.

    Science.gov (United States)

    Lück, Alexander; Wolf, Verena

    2016-10-21

    Discrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks. We propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies. The generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.

  14. A «Typical American Philosopher» in the Spanish Academic Pedagogy of the Franco Dictatorship (1939–1976. Dewey and Active Schooling Methods: Rejection, Pragmatic uses and «Orthodoxification»

    Directory of Open Access Journals (Sweden)

    Carlos Martínez Valle

    2016-07-01

    Full Text Available The article analyses the evolution of the uses of Dewey’s name and ideas by the educational establishment of the Franco dictatorship (1939–1975. Although classics, during that period Dewey’s works no longer fashionable. Indeed, there were radical differences between Dewey’s pedagogical ideas and the Spanish school of educational theory, which was based upon ideas derived from natural law. This prevented the real understanding and acceptance of his way of thinking, and he was even used to bolster arguments contrary to his own. This process was reinforced by the academic practices adopted for reading and reproducing knowledge, in which his words were de-contextualized, simplified and adapted from one manual to the next, until Dewey’s message had been entirely overturned. Although his thought was attacked for his impiety, and considered foreign to the Spanish reality, the Catholic «revolution» and the need for new educational practices designed to indoctrinate pupils into the principles of the regime promoted the rehabilitation of activism for the social sciences and school catechesis. Dewey was also used to further functional differentiation within academia, authorizing the creation of Social Pedagogy as a research field. Nevertheless, the essentialist anthropology and teleological conception of education in Spanish schooling led it to reject Dewey’s ideas of experience and democracy.

  15. 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

  16. 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.

  17. [Study on diagnostic methods of breathing disorders based on fuzzy logic inference and the neural network].

    Science.gov (United States)

    Chen, Min; Yin, Xuezhi

    2011-07-01

    This paper descries a new non-invasive method for diagnosis of breathing disorders based on adaptive-network-based fuzzy inference system (ANFIS). In this method, PetCO2, SpO2 and HR are chosen as inputs, and the breathing condition is selected as output ofANFIS. The inputs and output are then classified into fuzzy subsets by experts' knowledge. After, the fuzzy IF-THEN rules are built up according to the corresponding membership functions by set up of fuzzy subsets. The neural network was finally established and the membership functions and fuzzy rules were optimized by training. The results of experiment shows that ANFIS is more effective than BP Network regarding the diagnosis of breathing disorders.

  18. 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.

  19. 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.

  20. 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).

  1. A Self-Driven and Adaptive Adjusting Teaching Learning Method for Optimizing Optical Multicast Network Throughput

    Science.gov (United States)

    Liu, Huanlin; Xu, Yifan; Chen, Yong; Zhang, Mingjia

    2016-09-01

    With the development of one point to multiple point applications, network resources become scarcer and wavelength channels become more crowded in optical networks. To improve the bandwidth utilization, the multicast routing algorithm based on network coding can greatly increase the resource utilization, but it is most difficult to maximize the network throughput owing to ignoring the differences between the multicast receiving nodes. For making full use of the destination nodes' receives ability to maximize optical multicast's network throughput, a new optical multicast routing algorithm based on teaching-learning-based optimization (MR-iTLBO) is proposed in the paper. In order to increase the diversity of learning, a self-driven learning method is adopted in MR-iTLBO algorithm, and the mutation operator of genetic algorithm is introduced to prevent the algorithm into a local optimum. For increasing learner's learning efficiency, an adaptive learning factor is designed to adjust the learning process. Moreover, the reconfiguration scheme based on probability vector is devised to expand its global search capability in MR-iTLBO algorithm. The simulation results show that performance in terms of network throughput and convergence rate has been improved significantly with respect to the TLBO and the variant TLBO.

  2. 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.

  3. Ultrastructural basis of acute renal allograft rejection

    NARCIS (Netherlands)

    V.D. Vuzevski (Vojislav)

    1976-01-01

    textabstractAn attempt was made: I. to demonstrate the evolution and the time of onset of the ultrastructural morphological changes in the renal parenchyma and blood vessels, as well as the ultrastructural feature of the interstitial cellular infiltration in acute rejection of kidney allografts; 2.

  4. Ferrite grade iron oxides from ore rejects

    Indian Academy of Sciences (India)

    Unknown

    Abstract. 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 ...

  5. 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 ...

  6. 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.

  7. Accept & Reject Statement-Based Uncertainty Models

    NARCIS (Netherlands)

    E. Quaeghebeur (Erik); G. de Cooman; F. Hermans (Felienne)

    2015-01-01

    textabstractWe develop a framework for modelling and reasoning with uncertainty based on accept and reject statements about gambles. It generalises the frameworks found in the literature based on statements of acceptability, desirability, or favourability and clarifies their relative position. Next

  8. Rejection Pathways in Heart Transplant Recipients

    NARCIS (Netherlands)

    N.M. van Besouw (Nicole)

    1999-01-01

    textabstractSince the beginning of this century experimental heart transplantations in animal studies were performed.' These studies were started in Rotterdam in the seventies to compare heterotopic and orthotopic heart transplantations, and to study the process of chronic rejection. The history of

  9. 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...

  10. Detection of EEG-resting state independent networks by eLORETA-ICA method.

    Science.gov (United States)

    Aoki, Yasunori; Ishii, Ryouhei; Pascual-Marqui, Roberto D; Canuet, Leonides; Ikeda, Shunichiro; Hata, Masahiro; Imajo, Kaoru; Matsuzaki, Haruyasu; Musha, Toshimitsu; Asada, Takashi; Iwase, Masao; Takeda, Masatoshi

    2015-01-01

    Recent functional magnetic resonance imaging (fMRI) studies have shown that functional networks can be extracted even from resting state data, the so called "Resting State independent Networks" (RS-independent-Ns) by applying independent component analysis (ICA). However, compared to fMRI, electroencephalography (EEG) and magnetoencephalography (MEG) have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RS-independent-Ns and their interactions in all frequency bands. We applied exact low resolution brain electromagnetic tomography-ICA (eLORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RS-independent-Ns in alpha, beta and gamma frequency bands. Next, taking into account previous neuroimaging findings, five RS-independent-Ns were identified: (1) the visual network in alpha frequency band, (2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) in alpha and beta frequency bands and left posterior dorsal visual pathway (DVP) in alpha frequency band, (3) self-referential processing network, characterized by a negative correlation between the medial prefrontal cortex (mPFC) in beta frequency band and right temporoparietal junction (TPJ) in alpha frequency band, (4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus in alpha frequency band; and (5) sensorimotor network in beta and gamma frequency bands. We selected eLORETA-ICA which has many advantages over the other network visualization methods and overall findings indicate that eLORETA-ICA with EEG data can identify five RS-independent-Ns in their intrinsic frequency bands, and correct

  11. 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…

  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. A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks

    Directory of Open Access Journals (Sweden)

    Ruchi Sachan

    2016-01-01

    Full Text Available The exponential growth in data traffic due to the modernization of smart devices has resulted in the need for a high-capacity wireless network in the future. To successfully deploy 5G network, it must be capable of handling the growth in the data traffic. The increasing amount of traffic volume puts excessive stress on the important factors of the resource allocation methods such as scalability and throughput. In this paper, we define a network planning as an optimization problem with the decision variables such as transmission power and transmitter (BS location in 5G networks. The decision variables lent themselves to interesting implementation using several heuristic approaches, such as differential evolution (DE algorithm and Real-coded Genetic Algorithm (RGA. The key contribution of this paper is that we modified RGA-based method to find the optimal configuration of BSs not only by just offering an optimal coverage of underutilized BSs but also by optimizing the amounts of power consumption. A comparison is also carried out to evaluate the performance of the conventional approach of DE and standard RGA with our modified RGA approach. The experimental results showed that our modified RGA can find the optimal configuration of 5G/LTE network planning problems, which is better performed than DE and standard RGA.

  14. 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.

  15. 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.

  16. 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).

  17. Corneal Graft Rejection Ten Years after Penetrating Keratoplasty in the Cornea Donor Study

    Science.gov (United States)

    Dunn, Steven P.; Gal, Robin L.; Kollman, Craig; Raghinaru, Dan; Dontchev, Mariya; Blanton, Christopher L.; Holland, Edward J; Lass, Jonathan H.; Kenyon, Kenneth R.; Mannis, Mark J; Mian, Shahzad I.; Rapuano, Christopher J.; Stark, Walter J.; Beck, Roy W.

    2015-01-01

    Purpose To assess the effect of donor and recipient factors on corneal allograft rejection and evaluate whether a rejection event was associated with graft failure. Methods 1,090 subjects undergoing penetrating keratoplasty for a moderate risk condition (principally Fuchs’ dystrophy or pseudophakic corneal edema) were followed for up to 12 years. Associations of baseline recipient and donor factors with the occurrence of a rejection event were assessed in univariate and multivariate proportional hazards models. Results Among 651 eyes with a surviving graft at 5 years, the 10-year graft failure (± 99% CI) rates were 12% ± 4% among eyes with no rejection events in the first 5 years, 17% ± 12% in eyes with at least one probable, but no definite rejection event, and 22% ± 20% in eyes with at least one definite rejection event. The only baseline factor significantly associated with a higher risk of definite graft rejection was a preoperative history of glaucoma, particularly when prior glaucoma surgery had been performed and glaucoma medications were being used at time of transplant (10-year incidence 35% ± 23% compared with 14% ± 4% in eyes with no history of glaucoma/intraocular pressure treatment, p=0.008). Conclusion Those patients who experienced a definite rejection event frequently went on to graft failure raising important questions as to how we might change acute and long-term corneal graft management. Multivariate analysis indicated that the prior use of glaucoma medications and glaucoma filtering surgery was a significant risk factor related to a definite rejection event. PMID:25119961

  18. A new computational method to split large biochemical networks into coherent subnets

    Directory of Open Access Journals (Sweden)

    Verwoerd Wynand S

    2011-02-01

    Full Text Available Abstract Background Compared to more general networks, biochemical networks have some special features: while generally sparse, there are a small number of highly connected metabolite nodes; and metabolite nodes can also be divided into two classes: internal nodes with associated mass balance constraints and external ones without. Based on these features, reclassifying selected internal nodes (separators to external ones can be used to divide a large complex metabolic network into simpler subnetworks. Selection of separators based on node connectivity is commonly used but affords little detailed control and tends to produce excessive fragmentation. The method proposed here (Netsplitter allows the user to control separator selection. It combines local connection degree partitioning with global connectivity derived from random walks on the network, to produce a more even distribution of subnetwork sizes. Partitioning is performed progressively and the interactive visual matrix presentation used allows the user considerable control over the process, while incorporating special strategies to maintain the network integrity and minimise the information loss due to partitioning. Results Partitioning of a genome scale network of 1348 metabolites and 1468 reactions for Arabidopsis thaliana encapsulates 66% of the network into 10 medium sized subnets. Applied to the flavonoid subnetwork extracted in this way, it is shown that Netsplitter separates this naturally into four subnets with recognisable functionality, namely synthesis of lignin precursors, flavonoids, coumarin and benzenoids. A quantitative quality measure called efficacy is constructed and shows that the new method gives improved partitioning for several metabolic networks, including bacterial, plant and mammal species. Conclusions For the examples studied the Netsplitter method is a considerable improvement on the performance of connection degree partitioning, giving a better balance of

  19. Prototyping of chemical composition of complex crystals using method of neural networks

    Science.gov (United States)

    Blagin, A. V.; Nefedov, V. V.; Nefedova, N. A.

    2017-10-01

    The traditional methods of study of multi-component crystal heterojunction structures by means of X-ray diffractometry and electroscopy are expensive and complex. The present paper discusses the information technology based on the use of artificial neural networks for identification of the chemical composition of complex semiconductor structures. The obtained results allow supposing the successful use of this method for many multi-component systems.

  20. 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

  1. Opinion Impact Models and Opinion Consensus Methods in Ad Hoc Tactical Social Networks

    OpenAIRE

    Demin Li; Jie Zhou; Jingjuan Zhu; Jiacun Wang

    2013-01-01

    Ad hoc social networks are special social networks, such as ad hoc tactical social networks, ad hoc firefighter social networks, and ad hoc vehicular social networks. The social networks possess both the properties of ad hoc network and social network. One of the challenge problems in ad hoc social networks is opinion impact and consensus, and the opinion impact plays a key role for information fusion and decision support in ad hoc social networks. In this paper, consider the impact of physic...

  2. 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.

  3. 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.

  4. 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.

  5. Coincidence and coherent data analysis methods for gravitational wave bursts in a network of interferometric detectors

    Science.gov (United States)

    Arnaud, Nicolas; Barsuglia, Matteo; Bizouard, Marie-Anne; Brisson, Violette; Cavalier, Fabien; Davier, Michel; Hello, Patrice; Kreckelbergh, Stephane; Porter, Edward K.

    2003-11-01

    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 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...

  7. An Efficient Neural Network Based Modeling Method for Automotive EMC Simulation

    Science.gov (United States)

    Frank, Florian; Weigel, Robert

    2011-09-01

    This paper presents a newly developed methodology for VHDL-AMS model integration into SPICE-based EMC simulations. To this end the VHDL-AMS model, which is available in a compiled version only, is characterized under typical loading conditions, and afterwards a neural network based technique is applied to convert characteristic voltage and current data into an equivalent circuit in SPICE syntax. After the explanation of the whole method and the presentation of a newly developed switched state space dynamic neural network model, the entire analysis process is demonstrated using a typical application from automotive industry.

  8. An investigation of the impact of using different methods for network meta-analysis: a protocol for an empirical evaluation.

    Science.gov (United States)

    Karahalios, Amalia Emily; Salanti, Georgia; Turner, Simon L; Herbison, G Peter; White, Ian R; Veroniki, Areti Angeliki; Nikolakopoulou, Adriani; Mckenzie, Joanne E

    2017-06-24

    Network meta-analysis, a method to synthesise evidence from multiple treatments, has increased in popularity in the past decade. Two broad approaches are available to synthesise data across networks, namely, arm- and contrast-synthesis models, with a range of models that can be fitted within each. There has been recent debate about the validity of the arm-synthesis models, but to date, there has been limited empirical evaluation comparing results using the methods applied to a large number of networks. We aim to address this gap through the re-analysis of a large cohort of published networks of interventions using a range of network meta-analysis methods. We will include a subset of networks from a database of network meta-analyses of randomised trials that have been identified and curated from the published literature. The subset of networks will include those where the primary outcome is binary, the number of events and participants are reported for each direct comparison, and there is no evidence of inconsistency in the network. We will re-analyse the networks using three contrast-synthesis methods and two arm-synthesis methods. We will compare the estimated treatment effects, their standard errors, treatment hierarchy based on the surface under the cumulative ranking (SUCRA) curve, the SUCRA value, and the between-trial heterogeneity variance across the network meta-analysis methods. We will investigate whether differences in the results are affected by network characteristics and baseline risk. The results of this study will inform whether, in practice, the choice of network meta-analysis method matters, and if it does, in what situations differences in the results between methods might arise. The results from this research might also inform future simulation studies.

  9. Connectivity-consistent mapping method for 2-D discrete fracture networks

    Science.gov (United States)

    Roubinet, Delphine; de Dreuzy, Jean-Raynald; Davy, Philippe

    2010-07-01

    We present a new flow computation method in 2-D discrete fracture networks (DFN) intermediary between the classical DFN flow simulation method and the projection onto continuous grids. The method divides the simulation complexity by solving for flows successively at a local mesh scale and at the global domain scale. At the local mesh scale, flows are determined by classical DFN flow simulations and approximated by an equivalent hydraulic matrix (EHM) relating heads and flow rates discretized on the mesh borders. Assembling the equivalent hydraulic matrices provides for a domain-scale discretization of the flow equation. The equivalent hydraulic matrices transfer the connectivity and flow structure complexities from the local mesh scale to the domain scale. Compared to existing geometrical mapping or equivalent tensor methods, the EHM method broadens the simulation range of flow to all types of 2-D fracture networks both below and above the representative elementary volume (REV). Additional computation linked to the derivation of the local mesh-scale equivalent hydraulic matrices increases the accuracy and reliability of the method. Compared to DFN methods, the EHM method first provides a simpler domain-scale alternative permeability model. Second, it enhances the simulation capacities to larger fracture networks where flow discretization on the DFN structure yields system sizes too large to be solved using the most advanced multigrid and multifrontal methods. We show that the EHM method continuously moves from the DFN method to the tensor representation as a function of the local mesh-scale discretization. The balance between accuracy and model simplification can be optimally controlled by adjusting the domain-scale and local mesh-scale discretizations.

  10. [Flow cytometry controlled induction therapy with ATG and noninvasive monitoring of rejection--a modern management concept after heart transplantation].

    Science.gov (United States)

    Wagner, F M; Tugtekin, S M; Matschke, K; Platzbecker, U; Gulielmos, V; Schüler, S

    1998-01-01

    We introduce our concept of non-invasive transplant monitoring. The introduction of individualized immunosuppression by means of flow cytometry leads to a lower incidence of acute graft rejection and preserves immuncompetence. With the simultaneous use of echocardiography and intramyocardial electrogram (IMEG) acute graft rejections can be safely identified without using any invasive method.

  11. Factors Related to Rejection of Care and Behaviors Directed towards Others: A Longitudinal Study in Nursing Home Residents with Dementia

    NARCIS (Netherlands)

    Galindo Garre, F.; Volicer, L.; van der Steen, J.T.

    2015-01-01

    Aims: The aim of this study was to analyze factors related to rejection of care and behaviors directed towards others in nursing home residents with dementia. Methods: The relationship of lack of understanding, depression, psychosis and pain with rejection of care and behaviors directed towards

  12. Factors Related to Rejection of Care and Behaviors Directed towards Others: A Longitudinal Study in Nursing Home Residents with Dementia

    NARCIS (Netherlands)

    Galindo-Garre, F.; Volicer, L.; Steen, J.T. van der

    2015-01-01

    AIMS: The aim of this study was to analyze factors related to rejection of care and behaviors directed towards others in nursing home residents with dementia. METHODS: The relationship of lack of understanding, depression, psychosis and pain with rejection of care and behaviors directed towards

  13. 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.

  14. 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.

  15. Comparative study of computational methods to detect the correlated reaction sets in biochemical networks.

    Science.gov (United States)

    Xi, Yanping; Chen, Yi-Ping Phoebe; Qian, Chen; Wang, Fei

    2011-03-01

    Correlated reaction sets (Co-Sets) are mathematically defined modules in biochemical reaction networks which facilitate the study of biological processes by decomposing complex reaction networks into conceptually simple units. According to the degree of association, Co-Sets can be classified into three types: perfect, partial and directional. Five approaches have been developed to calculate Co-Sets, including network-based pathway analysis, Monte Carlo sampling, linear optimization, enzyme subsets and hard-coupled reaction sets. However, differences in design and implementation of these methods lead to discrepancies in the resulted Co-Sets as well as in their use in biotechnology which need careful interpretation. In this paper, we provide a comparative study of the methods for Co-Sets computing in detail from four aspects: (i) sensitivity, (ii) completeness and soundness, (iii) flexibility and (iv) scalability. By applying them to Escherichia coli core metabolic network, the differences and relationships among these methods are clearly articulated which may be useful for potential users.

  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. To Accept or Reject? The Impact of Adolescent Rejection Sensitivity on Early Adult Romantic Relationships

    Science.gov (United States)

    Hafen, Christopher A.; Spilker, Ann; Chango, Joanna; Marston, Emily S.; Allen, Joseph P.

    2013-01-01

    Successfully navigating entry into romantic relationships is a key task in adolescence, which sensitivity to rejection can make difficult to accomplish. This study uses multi-informant data from a community sample of 180 adolescents assessed repeatedly from age 16 to 22. Individuals with elevated levels of rejection sensitivity at age 16 were less likely to have a romantic partner at age 22, reported more anxiety and avoidance when they did have relationships, and were observed to be more negative in their interactions with romantic partners. In addition, females whose rejection sensitivity increased during late adolescence were more likely to adopt a submissive pattern within adult romantic relationships, further suggesting a pattern in which rejection sensitivity forecasts difficulties. PMID:24729668

  18. Serum exosomal protein profiling for the non-invasive detection of cardiac allograft rejection.

    Science.gov (United States)

    Kennel, Peter J; Saha, Amit; Maldonado, Dawn A; Givens, Raymond; Brunjes, Danielle L; Castillero, Estibaliz; Zhang, Xiaokan; Ji, Ruiping; Yahi, Alexandre; George, Isaac; Mancini, Donna M; Koller, Antonius; Fine, Barry; Zorn, Emmanuel; Colombo, Paolo C; Tatonetti, Nicholas; Chen, Emily I; Schulze, P Christian

    2017-07-19

    Exosomes are cell-derived circulating vesicles that play an important role in cell-cell communication. Exosomes are actively assembled and carry messenger RNAs, microRNAs and proteins. The "gold standard" for cardiac allograft surveillance is endomyocardial biopsy (EMB), an invasive technique with a distinct complication profile. The development of novel, non-invasive methods for the early diagnosis of allograft rejection is warranted. We hypothesized that the exosomal proteome is altered in acute rejection, allowing for a distinction between non-rejection and rejection episodes. Serum samples were collected from heart transplant (HTx) recipients with no rejection, acute cellular rejection (ACR) and antibody-mediated rejection (AMR). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of serum exosome was performed using a mass spectrometer (Orbitrap Fusion Tribrid). Principal component analysis (PCA) revealed a clustering of 3 groups: (1) control and heart failure (HF); (2) HTx without rejection; and (3) ACR and AMR. A total of 45 proteins were identified that could distinguish between groups (q < 0.05). Comparison of serum exosomal proteins from control, HF and non-rejection HTx revealed 17 differentially expressed proteins in at least 1 group (q < 0.05). Finally, comparisons of non-rejection HTx, ACR and AMR serum exosomes revealed 15 differentially expressed proteins in at least 1 group (q < 0.05). Of these 15 proteins, 8 proteins are known to play a role in the immune response. Of note, the majority of proteins identified were associated with complement activation, adaptive immunity such as immunoglobulin components and coagulation. Characterizing of circulating exosomal proteome in different cardiac disease states reveals unique protein expression patterns indicative of the respective pathologies. Our data suggest that HTx and allograft rejection alter the circulating exosomal protein content. Exosomal protein analysis could be a novel approach

  19. A novel active disturbance rejection based tracking design for laser system with quadrant photodetector

    Science.gov (United States)

    Manojlović, Stojadin M.; Barbarić, Žarko P.; Mitrović, Srđan T.

    2015-06-01

    A new tracking design for laser systems with different arrangements of a quadrant photodetector, based on the principle of active disturbance rejection control is suggested. The detailed models of quadrant photodetector with standard add-subtract, difference-over-sum and diagonal-difference-over-sum algorithms for displacement signals are included in the control loop. Target moving, non-linearity of a photodetector, parameter perturbations and exterior disturbances are treated as a total disturbance. Active disturbance rejection controllers with linear extended state observers for total disturbance estimation and rejection are designed. Proposed methods are analysed in frequency domain to quantify their stability characteristics and disturbance rejection performances. It is shown through simulations, that tracking errors are effectively compensated, providing the laser spot positioning in the area near the centre of quadrant photodetector where the mentioned algorithms have the highest sensitivity, which provides tracking of the manoeuvring targets with high accuracy.

  20. Sampled-data active disturbance rejection output feedback control for systems with mismatched uncertainties

    Directory of Open Access Journals (Sweden)

    Jun You

    2016-12-01

    Full Text Available This article investigates the sampled-data disturbance rejection control problem for a class of non-integral-chain systems with mismatched uncertainties. Aiming to reject the adverse effects caused by general mismatched uncertainties via digital control strategy, a new generalized discrete-time extended state observer is first proposed to estimate the lumped disturbances in the sampling point. A disturbance rejection control law is then constructed in a sampled-data form, which will lead to easier implementation in practices. By carefully selecting the control gains and a sampling period sufficiently small to restrain the state growth under a zero-order-holder input, the bounded-input bounded-output stability of the hybrid closed-loop system and the disturbance rejection ability are delicately proved even the controller is dormant within two neighbor sampling points. Numerical simulation results demonstrate the feasibility and efficacy of the proposed method.

  1. Relay-Assisted Partial Packet Recovery with IDMA Method in CDMA Wireless Network

    CERN Document Server

    Luo, Zhifeng; Wong, Albert Kai-sun; Qiu, Shuisheng

    2010-01-01

    Automatic Repeat Request (ARQ) is an effective technique for reliable transmission of packets in wireless networks. In ARQ, however, only a few erroneous bits in a packet will cause the entire packet to be discarded at the receiver. In this case, it's wasteful to retransmit the correct bit in the received packet. The partial packet recovery only retransmits the unreliable decoded bits in order to increase the throughput of network. In addition, the cooperative transmission based on Interleave-division multiple-access (IDMA) can obtain diversity gains with multiple relays with different locations for multiple sources simultaneously. By exploring the diversity from the channel between relay and destination, we propose a relay-assisted partial packet recovery in CDMA wireless network to improve the performance of throughput. In the proposed scheme, asynchronous IDMA iterative chip-by-chip multiuser detection is utilized as a method of multiple partial recovery, which can be a complementarity in a current CDMA ne...

  2. 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.

  3. 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.

  4. Dataset for Testing Contamination Source Identification Methods for Water Distribution Networks

    Science.gov (United States)

    This dataset includes the results of a simulation study using the source inversion techniques available in the Water Security Toolkit. The data was created to test the different techniques for accuracy, specificity, false positive rate, and false negative rate. The tests examined different parameters including measurement error, modeling error, injection characteristics, time horizon, network size, and sensor placement. The water distribution system network models that were used in the study are also included in the dataset. This dataset is associated with the following publication:Seth, A., K. Klise, J. Siirola, T. Haxton , and C. Laird. Testing Contamination Source Identification Methods for Water Distribution Networks. Journal of Environmental Division, Proceedings of American Society of Civil Engineers. American Society of Civil Engineers (ASCE), Reston, VA, USA, ., (2016).

  5. Global Consistency Management Methods Based on Escrow Approaches in Mobile ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    Takahiro Hara

    2010-01-01

    Full Text Available In a mobile ad hoc network, consistency management of data operations on replicas is a crucial issue for system performance. In our previous work, we classified several primitive consistency levels according to the requirements from applications and provided protocols to realize them. In this paper, we assume special types of applications in which the instances of each data item can be partitioned and propose two consistency management protocols which are combinations of an escrow method and our previously proposed protocols. We also report simulation results to investigate the characteristics of these protocols in a mobile ad hoc network. From the simulation results, we confirm that the protocols proposed in this paper drastically improve data availability and reduce the traffic for data operations while maintaining the global consistency in the entire network.

  6. Image retrieval method based on metric learning for convolutional neural network

    Science.gov (United States)

    Wang, Jieyuan; Qian, Ying; Ye, Qingqing; Wang, Biao

    2017-09-01

    At present, the research of content-based image retrieval (CBIR) focuses on learning effective feature for the representations of origin images and similarity measures. The retrieval accuracy and efficiency are crucial to a CBIR. With the rise of deep learning, convolutional network is applied in the domain of image retrieval and achieved remarkable results, but the image visual feature extraction of convolutional neural network exist high dimension problems, this problem makes the image retrieval and speed ineffective. This paper uses the metric learning for the image visual features extracted from the convolutional neural network, decreased the feature redundancy, improved the retrieval performance. The work in this paper is also a necessary part for further implementation of feature hashing to the approximate-nearest-neighbor (ANN) retrieval method.

  7. Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method

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    Lun Li

    2014-01-01

    Full Text Available Associating microRNAs (miRNAs with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification.

  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. Methodical Principles for Determination of Optimum Breaking Places of Distributive Electrical Networks with Due Account of Supply Network of 110 kV and Higher

    Directory of Open Access Journals (Sweden)

    M. I. Fursanov

    2008-01-01

    Full Text Available A specified model and algorithm for optimization of slit points in a distributive 10 (6 kV electrical network with due account of supply network of 110 kV and higher have been developed in the paper. In order to determine loss values in supply network a special mathematical model of the closed network has been constructed and the model permits to execute the given operation with minimum computing expenses. The paper proposes and analyzes methods for registration of task limitations: damage due to insufficient supply of electric power, possible network overloads for permissible currents, power supply provision for the 1st category consumers, prohibition against switching in public network and switching-on of sectional apparatus being switched-off according to normal scheme.

  10. Modeling the rejection probability in plant imports.

    Science.gov (United States)

    Surkov, I V; van der Werf, W; van Kooten, O; Lansink, A G J M Oude

    2008-06-01

    Phytosanitary inspection of imported plants and flowers is a major means for preventing pest invasions through international trade, but in a majority of countries availability of resources prevents inspection of all imports. Prediction of the likelihood of pest infestation in imported shipments could help maximize the efficiency of inspection by targeting inspection on shipments with the highest likelihood of infestation. This paper applies a multinomial logistic (MNL) regression model to data on import inspections of ornamental plant commodities in the Netherlands from 1998 to 2001 to investigate whether it is possible to predict the probability that a shipment will be (i) accepted for import, (ii) rejected for import because of detected pests, or (iii) rejected due to other reasons. Four models were estimated: (i) an all-species model, including all plant imports (136,251 shipments) in the data set, (ii) a four-species model, including records on the four ornamental commodities that accounted for 28.9% of inspected and 49.5% of rejected shipments, and two models for single commodities with large import volumes and percentages of rejections, (iii) Dianthus (16.9% of inspected and 46.3% of rejected shipments), and (iv) Chrysanthemum (6.9 and 8.6%, respectively). All models were highly significant (P < 0.001). The models for Dianthus and Chrysanthemum and for the set of four ornamental commodities showed a better fit to data than the model for all ornamental commodities. Variables that characterized the imported shipment's region of origin, the shipment's size, the company that imported the shipment, and season and year of import, were significant in most of the estimated models. The combined results of this study suggest that the MNL model can be a useful tool for modeling the probability of rejecting imported commodities even with a small set of explanatory variables. The MNL model can be helpful in better targeting of resources for import inspection. The

  11. Lung cancer risk prediction method based on feature selection and artificial neural network.

    Science.gov (United States)

    Xie, Nan-Nan; Hu, Liang; Li, Tai-Hui

    2014-01-01

    A method to predict the risk of lung cancer is proposed, based on two feature selection algorithms: Fisher and ReliefF, and BP Neural Networks. An appropriate quantity of risk factors was chosen for lung cancer risk prediction. The process featured two steps, firstly choosing the risk factors by combining two feature selection algorithms, then providing the predictive value by neural network. Based on the method framework, an algorithm LCRP (lung cancer risk prediction) is presented, to reduce the amount of risk factors collected in practical applications. The proposed method is suitable for health monitoring and self-testing. Experiments showed it can actually provide satisfactory accuracy under low dimensions of risk factors.

  12. Randomized gradient-free method for multiagent optimization over time-varying networks.

    Science.gov (United States)

    Yuan, Deming; Ho, Daniel W C

    2015-06-01

    In this brief, we consider the multiagent optimization over a network where multiple agents try to minimize a sum of nonsmooth but Lipschitz continuous functions, subject to a convex state constraint set. The underlying network topology is modeled as time varying. We propose a randomized derivative-free method, where in each update, the random gradient-free oracles are utilized instead of the subgradients (SGs). In contrast to the existing work, we do not require that agents are able to compute the SGs of their objective functions. We establish the convergence of the method to an approximate solution of the multiagent optimization problem within the error level depending on the smoothing parameter and the Lipschitz constant of each agent's objective function. Finally, a numerical example is provided to demonstrate the effectiveness of the method.

  13. Demonstration of the feasibility of a complete ellipsometric characterization method based on an artificial neural network.

    Science.gov (United States)

    Battie, Yann; Robert, Stéphane; Gereige, Issam; Jamon, Damien; Stchakovsky, Michel

    2009-10-01

    Ellipsometry is an optical technique that is widely used for determining optical and geometrical properties of optical thin films. These properties are in general extracted from the ellipsometric measurement by solving an inverse problem. Classical methods like the Levenberg-Marquardt algorithm are generally too long, depending on direct calculation and are very sensitive to local minima. In this way, the neural network has proved to be an efficient tool for solving these kinds of problems in a very short time. Indeed, it is rapid and less sensitive to local minima than the classical inversion method. We suggest a complete neural ellipsometric characterization method for determining the index dispersion law and the thickness of a simple SiO(2) or photoresist thin layer on Si, SiO(2), and BK7 substrates. The influence of the training couples on the artificial neural network performance is also discussed.

  14. 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.

  15. Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks

    Directory of Open Access Journals (Sweden)

    Dubitzky Werner

    2010-09-01

    Full Text Available Abstract Background A gene-regulatory network (GRN refers to DNA segments that interact through their RNA and protein products and thereby govern the rates at which genes are transcribed. Creating accurate dynamic models of GRNs is gaining importance in biomedical research and development. To improve our understanding of continuous deterministic modeling methods employed to construct dynamic GRN models, we have carried out a comprehensive comparative study of three commonly used systems of ordinary differential equations: The S-system (SS, artificial neural networks (ANNs, and the general rate law of transcription (GRLOT method. These were thoroughly evaluated in terms of their ability to replicate the reference models' regulatory structure and dynamic gene expression behavior under varying conditions. Results While the ANN and GRLOT methods appeared to produce robust models even when the model parameters deviated considerably from those of the reference models, SS-based models exhibited a notable loss of performance even when the parameters of the reverse-engineered models corresponded closely to those of the reference models: this is due to the high number of power terms in the SS-method, and the manner in which they are combined. In cross-method reverse-engineering experiments the different characteristics, biases and idiosynchracies of the methods were revealed. Based on limited training data, with only one experimental condition, all methods produced dynamic models that were able to reproduce the training data accurately. However, an accurate reproduction of regulatory network features was only possible with training data originating from multiple experiments under varying conditions. Conclusions The studied GRN modeling methods produced dynamic GRN models exhibiting marked differences in their ability to replicate the reference models' structure and behavior. Our results suggest that care should be taking when a method is chosen for a

  16. Improved Radio Frequency Identification Indoor Localization Method via Radial Basis Function Neural Network

    Directory of Open Access Journals (Sweden)

    Dongliang Guo

    2014-01-01

    Full Text Available Indoor localization technique has received much attention in recent years. Many techniques have been developed to solve the problem. Among the recent proposed methods, radio frequency identification (RFID indoor localization technology has the advantages of low-cost, noncontact, non-line-of-sight, and high precision. This paper proposed two radial basis function (RBF neural network based indoor localization methods. The RBF neural networks are trained to learn the mapping relationship between received signal strength indication values and position of objects. Traditional method used the received signal strength directly as the input of neural network; we added another input channel by taking the difference of the received signal strength, thus improving the reliability and precision of positioning. Fuzzy clustering is used to determine the center of radial basis function. In order to reduce the impact of signal fading due to non-line-of-sight and multipath transmission in indoor environment, we improved the Gaussian filter to process received signal strength values. The experimental results show that the proposed method outperforms the existing methods as well as improves the reliability and precision of the RFID indoor positioning system.

  17. [Social network analysis: a method to improve safety in healthcare organizations].

    Science.gov (United States)

    Marqués Sánchez, Pilar; González Pérez, Marta Eva; Agra Varela, Yolanda; Vega Núñez, Jorge; Pinto Carral, Arrate; Quiroga Sánchez, Enedina

    2013-01-01

    Patient safety depends on the culture of the healthcare organization involving relationships between professionals. This article proposes that the study of these relations should be conducted from a network perspective and using a methodology called Social Network Analysis (SNA). This methodology includes a set of mathematical constructs grounded in Graph Theory. With the SNA we can know aspects of the individual's position in the network (centrality) or cohesion among team members. Thus, the SNA allows to know aspects related to security such as the kind of links that can increase commitment among professionals, how to build those links, which nodes have more prestige in the team in generating confidence or collaborative network, which professionals serve as intermediaries between the subgroups of a team to transmit information or smooth conflicts, etc. Useful aspects in stablishing a safety culture. The SNA would analyze the relations among professionals, their level of communication to communicate errors and spontaneously seek help and coordination between departments to participate in projects that enhance safety. Thus, they related through a network, using the same language, a fact that helps to build a culture. In summary, we propose an approach to safety culture from a SNA perspective that would complement other commonly used methods.

  18. 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.

  19. On the identification of quark and gluon jets using artificial neural network method

    CERN Document Server

    Zhang, Kun Shi

    2004-01-01

    The identification of quark and gluon jets produced in e^{+}e^{-} collisions using the artificial neural network method is addressed. The structure and the learning algorithm of the BP( back propagation) neural network model is studied. Three characteristic parameters-the average multiplicity and the average transverse momentum of jets and the average value of the angles opposite to the quark or gluon jets are taken as training parameters and are input to the BP network for repeated training. The learning process is ended when the output error of the neural network is less than a preset precision( sigma =0.005). The same training routine is repeated in each of the 8 energy bins ranging from 2.5-22.5 GeV, respectively. The finally updated weights and thresholds of the BP neural network are tested using the quark and gluon jet samples, getting from the nonsymmetric three-jet events produced by the Monte Carlo generator JETSET 7.4. Then the pattern recognition of the mixed sample getting from the combination of ...

  20. Unsupervised Remote Sensing Domain Adaptation Method with Adversarial Network and Auxiliary Task

    Directory of Open Access Journals (Sweden)

    XU Suhui

    2017-12-01

    Full Text Available An important prerequisite when annotating the remote sensing images by machine learning is that there are enough training samples for training, but labeling the samples is very time-consuming. In this paper, we solve the problem of unsupervised learning with small sample size in remote sensing image scene classification by domain adaptation method. A new domain adaptation framework is proposed which combines adversarial network and auxiliary task. Firstly, a novel remote sensing scene classification framework is established based on deep convolution neural networks. Secondly, a domain classifier is added to the network, in order to learn the domain-invariant features. The gradient direction of the domain loss is opposite to the label loss during the back propagation, which makes the domain predictor failed to distinguish the sample's domain. Lastly, we introduce an auxiliary task for the network, which augments the training samples and improves the generalization ability of the network. The experiments demonstrate better results in unsupervised classification with small sample sizes of remote sensing images compared to the baseline unsupervised domain adaptation approaches.

  1. 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.

  2. 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.

  3. Using 3D printed eggs to examine the egg-rejection behaviour of wild birds.

    Science.gov (United States)

    Igic, Branislav; 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.

  4. An novel frequent probability pattern mining algorithm based on circuit simulation method in uncertain biological networks.

    Science.gov (United States)

    He, Jieyue; Wang, Chunyan; Qiu, Kunpu; Zhong, Wei

    2014-01-01

    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. 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. 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. The algorithm of probability graph isomorphism evaluation based on circuit simulation

  5. Frontal-Brainstem Pathways Mediating Placebo Effects on Social Rejection.

    Science.gov (United States)

    Koban, Leonie; Kross, Ethan; Woo, Choong-Wan; Ruzic, Luka; Wager, Tor D

    2017-03-29

    Placebo treatments can strongly affect clinical outcomes, but research on how they shape other life experiences and emotional well-being is in its infancy. We used fMRI in humans to examine placebo effects on a particularly impactful life experience, social pain elicited by a recent romantic rejection. We compared these effects with placebo effects on physical (heat) pain, which are thought to depend on pathways connecting prefrontal cortex and periaqueductal gray (PAG). Placebo treatment, compared with control, reduced both social and physical pain, and increased activity in the dorsolateral prefrontal cortex (dlPFC) in both modalities. Placebo further altered the relationship between affect and both dlPFC and PAG activity during social pain, and effects on behavior were mediated by a pathway connecting dlPFC to the PAG, building on recent work implicating opioidergic PAG activity in the regulation of social pain. These findings suggest that placebo treatments reduce emotional distress by altering affective representations in frontal-brainstem systems. SIGNIFICANCE STATEMENT Placebo effects are improvements due to expectations and the socio-medical context in which treatment takes place. Whereas they have been extensively studied in the context of somatic conditions such as pain, much less is known of how treatment expectations shape the emotional experience of other important stressors and life events. Here, we use brain imaging to show that placebo treatment reduces the painful feelings associated with a recent romantic rejection by recruiting a prefrontal-brainstem network and by shifting the relationship between brain activity and affect. Our findings suggest that this brain network may be important for nonspecific treatment effects across a wide range of therapeutic approaches and mental health conditions. Copyright © 2017 the authors 0270-6474/17/373621-11$15.00/0.

  6. Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods

    Directory of Open Access Journals (Sweden)

    Quan Zou

    2015-01-01

    Full Text Available MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

  7. Background Rejection in the ARA Experiment

    Directory of Open Access Journals (Sweden)

    Pfendner Carl

    2017-01-01

    Full Text Available The Askaryan Radio Array (ARA is a radio frequency observatory under construction at the South Pole that is searching for ultrahigh energy neutrinos via the Askaryan effect. Thermal fluctuations currently dominate the trigger-level background for the observatory and anthropogenic sources also introduce a significant source of noise. By taking advantage of the observatory’s regular geometry and the expected coincident nature of the RF signals arriving from neutrino-induced events, this background can be filtered efficiently. This contribution will discuss techniques developed for the ARA analyses to reject these thermal signals, to reject anthropogenic backgrounds, and to search for neutrino-induced particle showers in the Antarctic ice. The results of a search for neutrinos from GRBs using the prototype station using some of these techniques will be presented.

  8. Comparative Analysis of Neural Network Training Methods in Real-time Radiotherapy.

    Science.gov (United States)

    Nouri, S; Hosseini Pooya, S M; Soltani Nabipour, J

    2017-03-01

    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. 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. 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. The accuracies were obtained 0.8%, 12% and 14% in neural network, genetic and particle swarm optimization algorithms, respectively. The internal target volume (ITV) should be determined based on the applied neural network algorithm on training steps.

  9. The Situation Awareness Weighted Network (SAWN) model and method: Theory and application.

    Science.gov (United States)

    Kalloniatis, Alexander; Ali, Irena; Neville, Timothy; La, Phuong; Macleod, Iain; Zuparic, Mathew; Kohn, Elizabeth

    2017-05-01

    We introduce a novel model and associated data collection method to examine how a distributed organisation of military staff who feed a Common Operating Picture (COP) generates Situation Awareness (SA), a critical component in organisational performance. The proposed empirically derived Situation Awareness Weighted Network (SAWN) model draws on two scientific models of SA, by Endsley involving perception, comprehension and projection, and by Stanton et al. positing that SA exists across a social and semantic network of people and information objects in activities connected across a set of tasks. The output of SAWN is a representation as a weighted semi-bipartite network of the interaction between people ('human nodes') and information artefacts such as documents and system displays ('product nodes'); link weights represent the Endsley levels of SA that individuals acquire from or provide to information objects and other individuals. The SAWN method is illustrated with aggregated empirical data from a case study of Australian military staff undertaking their work during two very different scenarios, during steady-state operations and in a crisis threat context. A key outcome of analysis of the weighted networks is that we are able to quantify flow of SA through an organisation as staff seek to "value-add" in the conduct of their work. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

  10. A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

    Directory of Open Access Journals (Sweden)

    Peixin Zhao

    2013-01-01

    Full Text Available Community detection in social networks plays an important role in cluster analysis. Many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy. In this paper we propose a graph-based clustering method for multidimensional datasets. This novel method has two distinguished features: nonbinary hierarchical tree and the multi-membership clusters. The nonbinary hierarchical tree clearly highlights meaningful clusters, while the multimembership feature may provide more useful service strategies. Experimental results on the customer relationship management confirm the effectiveness of the new method.

  11. A Study on Real-Time Pricing Method of Reactive Power in Voltage Profile Control Method of Future Distribution Network

    Science.gov (United States)

    Koide, Akira; Tsuji, Takao; Oyama, Tsutomu; Hashiguchi, Takuhei; Goda, Tadahiro; Shinji, Takao; Tsujita, Shinsuke

    It is of prime importance to solve the voltage maintenance problem caused by the introduction of a large number of distributed generators. The authors have proposed “voltage profile control method” using reactive power control of distributed generators and developed new systems which can give economical incentives to DG owners who cooperate the voltage profile management in the previous works. However, it is difficult to apply the proposed economical systems to real-time operation because they are based on the optimization technology and the specific amount of incentive is informed after the control action has finished. Therefore, in this paper, we develop a new method that can determine the amount of incentives in real-time and encourage the costumers to cooperate voltage profile control method. The proposed method is tested in one feeder distribution network and its effectiveness is shown.

  12. An observer with controller to detect and reject disturbances

    Science.gov (United States)

    de Jesús Rubio, José; Meléndez, Fidel; Figueroa, Maricela

    2014-03-01

    In this paper, a novel states observer is designed. This observer not only estimates the states, but also detects the disturbances by creating estimated signals. Then, both the observed states and detected disturbances are used in a control law to reject the disturbances, avoiding the requirement to know the states and disturbances. The observer is designed by the combination of the poles assignation and geometric techniques. Both the observer and controller work simultaneously. The proposed method is applied in an active suspension system and a liquid-level hydraulic system.

  13. Increased neural response to peer rejection associated with adolescent depression and pubertal development.

    Science.gov (United States)

    Silk, Jennifer S; Siegle, Greg J; Lee, Kyung Hwa; Nelson, Eric E; Stroud, Laura R; Dahl, Ronald E

    2014-11-01

    Sensitivity to social evaluation has been proposed as a potential marker or risk factor for depression, and has also been theorized to increase with pubertal maturation. This study utilized an ecologically valid paradigm to test the hypothesis that adolescents with major depressive disorder (MDD) would show altered reactivity to peer rejection and acceptance relative to healthy controls in a network of ventral brain regions implicated in affective processing of social information. A total of 48 adolescents (ages 11-17), including 21 with a current diagnosis of MDD and 27 age- and gender-matched controls, received rigged acceptance and rejection feedback from fictitious peers during a simulated online peer interaction during functional neuroimaging. MDD youth showed increased activation to rejection relative to controls in the bilateral amygdala, subgenual anterior cingulate, left anterior insula and left nucleus accumbens. MDD and healthy youth did not differ in response to acceptance. Youth more advanced in pubertal maturation also showed increased reactivity to rejection in the bilateral amygdala/parahippocampal gyrus and the caudate/subgenual anterior cingulate, and these effects remained significant when controlling for chronological age. Findings suggest that increased reactivity to peer rejection is a normative developmental process associated with pubertal development, but is particularly enhanced among youth with depression. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. Cable Overheating Risk Warning Method Based on Impedance Parameter Estimation in Distribution Network

    Science.gov (United States)

    Yu, Zhang; Xiaohui, Song; Jianfang, Li; Fei, Gao

    2017-05-01

    Cable overheating will lead to the cable insulation level reducing, speed up the cable insulation aging, even easy to cause short circuit faults. Cable overheating risk identification and warning is nessesary for distribution network operators. Cable overheating risk warning method based on impedance parameter estimation is proposed in the paper to improve the safty and reliability operation of distribution network. Firstly, cable impedance estimation model is established by using least square method based on the data from distribiton SCADA system to improve the impedance parameter estimation accuracy. Secondly, calculate the threshold value of cable impedance based on the historical data and the forecast value of cable impedance based on the forecasting data in future from distribiton SCADA system. Thirdly, establish risks warning rules library of cable overheating, calculate the cable impedance forecast value and analysis the change rate of impedance, and then warn the overheating risk of cable line based on the overheating risk warning rules library according to the variation relationship between impedance and line temperature rise. Overheating risk warning method is simulated in the paper. The simulation results shows that the method can identify the imedance and forecast the temperature rise of cable line in distribution network accurately. The result of overheating risk warning can provide decision basis for operation maintenance and repair.

  15. Network-Based Method for Identifying Co- Regeneration Genes in Bone, Dentin, Nerve and Vessel Tissues.

    Science.gov (United States)

    Chen, Lei; Pan, Hongying; Zhang, Yu-Hang; Feng, Kaiyan; Kong, XiangYin; Huang, Tao; Cai, Yu-Dong

    2017-10-02

    Bone and dental diseases are serious public health problems. Most current clinical treatments for these diseases can produce side effects. Regeneration is a promising therapy for bone and dental diseases, yielding natural tissue recovery with few side effects. Because soft tissues inside the bone and dentin are densely populated with nerves and vessels, the study of bone and dentin regeneration should also consider the co-regeneration of nerves and vessels. In this study, a network-based method to identify co-regeneration genes for bone, dentin, nerve and vessel was constructed based on an extensive network of protein-protein interactions. Three procedures were applied in the network-based method. The first procedure, searching, sought the shortest paths connecting regeneration genes of one tissue type with regeneration genes of other tissues, thereby extracting possible co-regeneration genes. The second procedure, testing, employed a permutation test to evaluate whether possible genes were false discoveries; these genes were excluded by the testing procedure. The last procedure, screening, employed two rules, the betweenness ratio rule and interaction score rule, to select the most essential genes. A total of seventeen genes were inferred by the method, which were deemed to contribute to co-regeneration of at least two tissues. All these seventeen genes were extensively discussed to validate the utility of the method.

  16. Simulation of two-phase flow in horizontal fracture networks with numerical manifold method

    Science.gov (United States)

    Ma, G. W.; Wang, H. D.; Fan, L. F.; Wang, B.

    2017-10-01

    The paper presents simulation of two-phase flow in discrete fracture networks with numerical manifold method (NMM). Each phase of fluids is considered to be confined within the assumed discrete interfaces in the present method. The homogeneous model is modified to approach the mixed fluids. A new mathematical cover formation for fracture intersection is proposed to satisfy the mass conservation. NMM simulations of two-phase flow in a single fracture, intersection, and fracture network are illustrated graphically and validated by the analytical method or the finite element method. Results show that the motion status of discrete interface significantly depends on the ratio of mobility of two fluids rather than the value of the mobility. The variation of fluid velocity in each fracture segment and the driven fluid content are also influenced by the ratio of mobility. The advantages of NMM in the simulation of two-phase flow in a fracture network are demonstrated in the present study, which can be further developed for practical engineering applications.

  17. NDRC: A Disease-Causing Genes Prioritized Method Based on Network Diffusion and Rank Concordance.

    Science.gov (United States)

    Fang, Minghong; Hu, Xiaohua; Wang, Yan; Zhao, Junmin; Shen, Xianjun; He, Tingting

    2015-07-01

    Disease-causing genes prioritization is very important to understand disease mechanisms and biomedical applications, such as design of drugs. Previous studies have shown that promising candidate genes are mostly ranked according to their relatedness to known disease genes or closely related disease genes. Therefore, a dangling gene (isolated gene) with no edges in the network can not be effectively prioritized. These approaches tend to prioritize those genes that are highly connected in the PPI network while perform poorly when they are applied to loosely connected disease genes. To address these problems, we propose a new disease-causing genes prioritization method that based on network diffusion and rank concordance (NDRC). The method is evaluated by leave-one-out cross validation on 1931 diseases in which at least one gene is known to be involved, and it is able to rank the true causal gene first in 849 of all 2542 cases. The experimental results suggest that NDRC significantly outperforms other existing methods such as RWR, VAVIEN, DADA and PRINCE on identifying loosely connected disease genes and successfully put dangling genes as potential candidate disease genes. Furthermore, we apply NDRC method to study three representative diseases, Meckel syndrome 1, Protein C deficiency and Peroxisome biogenesis disorder 1A (Zellweger). Our study has also found that certain complex disease-causing genes can be divided into several modules that are closely associated with different disease phenotype.

  18. Enhanced method of fast re-routing with load balancing in software-defined networks

    Science.gov (United States)

    Lemeshko, Oleksandr; Yeremenko, Oleksandra

    2017-11-01

    A two-level method of fast re-routing with load balancing in a software-defined network (SDN) is proposed. The novelty of the method consists, firstly, in the introduction of a two-level hierarchy of calculating the routing variables responsible for the formation of the primary and backup paths, and secondly, in ensuring a balanced load of the communication links of the network, which meets the requirements of the traffic engineering concept. The method provides implementation of link, node, path, and bandwidth protection schemes for fast re-routing in SDN. The separation in accordance with the interaction prediction principle along two hierarchical levels of the calculation functions of the primary (lower level) and backup (upper level) routes allowed to abandon the initial sufficiently large and nonlinear optimization problem by transiting to the iterative solution of linear optimization problems of half the dimension. The analysis of the proposed method confirmed its efficiency and effectiveness in terms of obtaining optimal solutions for ensuring balanced load of communication links and implementing the required network element protection schemes for fast re-routing in SDN.

  19. Understanding cancer networks better to implement them more effectively: a mixed methods multi-case study.

    Science.gov (United States)

    Tremblay, Dominique; Touati, Nassera; Roberge, Danièle; Breton, Mylaine; Roch, Geneviève; Denis, Jean-Louis; Candas, Bernard; Francoeur, Danièle

    2016-03-21

    Managed cancer networks are widely promoted in national cancer control programs as an organizational form that enables integrated care as well as enhanced patient outcomes. While national programs are set by policy-makers, the detailed implementation of networks is delegated at the service delivery and institutional levels. It is likely that the capacity to ensure more integrated cancer services requires multi-level governance processes responsive to the strengths and limitations of the contexts and capable of supporting network-based working. Based on an empirical case, this study aims to analyze the implementation of a mandated cancer network, focusing on governance and health services integration as core concepts in the study. This nested multi-case study uses mixed methods to explore the implementation of a mandated cancer network in Quebec, a province of Canada. The case is the National Cancer Network (NCN) subdivided into three micro-cases, each defined by the geographic territory of a health and social services region. For each region, two local health services centers (LHSCs) are selected based on their differences with respect to determining characteristics. Qualitative data will be collected from various sources using three strategies: review of documents, focus groups, and semi-directed interviews with stakeholders. The qualitative data will be supplemented with a survey that will measure the degree of integration as a proxy for implementation of the NCN. A score will be constructed, and then triangulated with the qualitative data, which will have been subjected to content analysis. Qualitative, quantitative, and mixed methods data will be interpreted within and across cases in order to identify governance patterns similarities and differences and degree of integration in contexts. This study is designed to inform decision-making to develop more effective network implementation strategies by thoroughly describing multi-level governance processes of a

  20. Peer acceptance/rejection and academic achievement

    Directory of Open Access Journals (Sweden)

    Spasenović Vera Z.

    2003-01-01

    Full Text Available Considerations of the nature and role of peer relations in child development indicate that peer interaction is an important factor in developing social and cognitive competences. Peer relations not only influence current but also subsequent academic, behavioral and emotional development. Accepted students more often display better academic achievement, whereas the status of rejection is coupled with academic difficulties and lower academic achievement. Peer rejection is a relatively stable characteristic that can be used to predict difficulties in the years to come, such as repeat of a grade, early drop out, unjustified absences, adaptability problems etc. It is considered that correlation between academic achievement and peer group status is mediated by student social behavior at school. The quality of peer relations and academic achievement are mutually influential i.e. peer acceptance serves as a social resource that facilitates academic achievement, but academic achievement has effects on student acceptance. To help students who display difficulties in social relations, various intervention programs have been well thought of so as to contribute to interpersonal efficiency promotion. Concerning interdependence of social behavior, peer status and academic achievement, it is reasonable to expect that positive changes in behavior, frequently leading to the change of rejection status, will produce, directly or indirectly, positive effects on academic achievement too.

  1. Mesenchymal stem cells avoid allogeneic rejection

    Directory of Open Access Journals (Sweden)

    Murphy J Mary

    2005-07-01

    Full Text Available Abstract Adult bone marrow derived mesenchymal stem cells offer the potential to open a new frontier in medicine. Regenerative medicine aims to replace effete cells in a broad range of conditions associated with damaged cartilage, bone, muscle, tendon and ligament. However the normal process of immune rejection of mismatched allogeneic tissue would appear to prevent the realisation of such ambitions. In fact mesenchymal stem cells avoid allogeneic rejection in humans and in animal models. These finding are supported by in vitro co-culture studies. Three broad mechanisms contribute to this effect. Firstly, mesenchymal stem cells are hypoimmunogenic, often lacking MHC-II and costimulatory molecule expression. Secondly, these stem cells prevent T cell responses indirectly through modulation of dendritic cells and directly by disrupting NK as well as CD8+ and CD4+ T cell function. Thirdly, mesenchymal stem cells induce a suppressive local microenvironment through the production of prostaglandins and interleukin-10 as well as by the expression of indoleamine 2,3,-dioxygenase, which depletes the local milieu of tryptophan. Comparison is made to maternal tolerance of the fetal allograft, and contrasted with the immune evasion mechanisms of tumor cells. Mesenchymal stem cells are a highly regulated self-renewing population of cells with potent mechanisms to avoid allogeneic rejection.

  2. When is peer rejection justifiable?: Children's understanding across two cultures.

    Science.gov (United States)

    Park, Yoonjung; Killen, Melanie

    2010-07-01

    This study investigated how Korean (N = 397) and U.S. (N = 333) children and adolescents (10 and 13 years of age) evaluated personality (aggression, shyness) and group (gender, nationality) characteristics as a basis for peer rejection in three contexts (friendship rejection, group exclusion, victimization). Overall, peer rejection based on group membership was viewed as more unfair than peer rejection based on personality traits. Children viewed friendship rejection as more legitimate than group exclusion or victimization and used more personal choice reasoning for friendship rejection than for rejection in any other context. Although there were a few cultural differences, overall, the findings provided support for the cultural generalizability of social reasoning about peer rejection.

  3. Heterosexual Rejection and Mate Choice: A Sociometer Perspective.

    Science.gov (United States)

    Zhang, Lin; Liu, Shen; Li, Yue; Ruan, Lu-Jun

    2015-01-01

    Previous studies about the effects of social rejection on individuals' social behaviors have produced mixed results and tend to study mating behaviors from a static point of view. However, mate selection in essence is a dynamic process, and therefore sociometer theory opens up a new perspective for studying mating and its underlying practices. Based on this theory and using self-perceived mate value in the relationship between heterosexual rejection and mate choice as a mediating role, this current study examined the effects of heterosexual rejection on mate choice in two experiments. Results showed that heterosexual rejection significantly reduced self-perceived mate value, expectation, and behavioral tendencies, while heterosexual acceptance indistinctively increased these measures. Self-perceived mate value did not serve as a mediator in the relationship between heterosexual rejection and mate expectation, but it mediated the relationship between heterosexual rejection and mating behavior tendencies toward potential objects. Moreover, individuals evaded both rejection and irrelevant people when suffering from rejection.

  4. Application of the Intuitionistic Fuzzy InterCriteria Analysis Method with Triples to a Neural Network Preprocessing Procedure.

    Science.gov (United States)

    Sotirov, Sotir; Atanassova, Vassia; Sotirova, Evdokia; Doukovska, Lyubka; Bureva, Veselina; Mavrov, Deyan; Tomov, Jivko

    2017-01-01

    The approach of InterCriteria Analysis (ICA) was applied for the aim of reducing the set of variables on the input of a neural network, taking into account the fact that their large number increases the number of neurons in the network, thus making them unusable for hardware implementation. Here, for the first time, with the help of the ICA method, correlations between triples of the input parameters for training of the neural networks were obtained. In this case, we use the approach of ICA for data preprocessing, which may yield reduction of the total time for training the neural networks, hence, the time for the network's processing of data and images.

  5. MLS-Net and SecureParser®: A New Method for Securing and Segregating Network Data

    Directory of Open Access Journals (Sweden)

    Robert A. Johnson

    2008-10-01

    Full Text Available A new method of network security and virtualization is presented which allows the consolidation of multiple network infrastructures dedicated to single security levels or communities of interest onto a single, virtualized network. An overview of the state of the art of network security protocols is presented, including the use of SSL, IPSec, and HAIPE IS, followed by a discussion of the SecureParser® technology and MLS-Net architecture, which in combination allow the virtualization of local network enclaves.

  6. Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

    Science.gov (United States)

    Dorier, Julien; Crespo, Isaac; Niknejad, Anne; Liechti, Robin; Ebeling, Martin; Xenarios, Ioannis

    2016-10-06

    Prior knowledge networks (PKNs) provide a framework for the development of computational biological models, including Boolean models of regulatory networks which are the focus of this work. PKNs are created by a painstaking process of literature curation, and generally describe all relevant regulatory interactions identified using a variety of experimental conditions and systems, such as specific cell types or tissues. Certain of these regulatory interactions may not occur in all biological contexts of interest, and their presence may dramatically change the dynamical behaviour of the resulting computational model, hindering the elucidation of the underlying mechanisms and reducing the usefulness of model predictions. Methods are therefore required to generate optimized contextual network models from generic PKNs. We developed a new approach to generate and optimize Boolean networks, based on a given PKN. Using a genetic algorithm, a model network is built as a sub-network of the PKN and trained against experimental data to reproduce the experimentally observed behaviour in terms of attractors and the transitions that occur between them under specific perturbations. The resulting model network is therefore contextualized to the experimental conditions and constitutes a dynamical Boolean model closer to the observed biological process used to train the model than the original PKN. Such a model can then be interrogated to simulate response under perturbation, to detect stable states and their properties, to get insights into the underlying mechanisms and to generate new testable hypotheses. Generic PKNs attempt to synthesize knowledge of all interactions occurring in a biological process of interest, irrespective of the specific biological context. This limits their usefulness as a basis for the development of context-specific, predictive dynamical Boolean models. The optimization method presented in this article produces specific, contextualized models from generic

  7. Graft rejection in pediatric penetrating keratoplasty: Clinical features and outcomes

    Directory of Open Access Journals (Sweden)

    Rakhi Kusumesh

    2015-01-01

    Conclusion: This study showed irreversible graft rejection was the leading cause of graft failure of pediatric PK. Though, the incidence (12.1% of graft rejection in current study was not high, but the percentage of reversal (25% was one of the lowest in literature because of delayed presentation and longer interval between corneal graft rejection and treatment. In addition, categorization of the type of graft rejection was very difficult and cumbersome in pediatric patients.

  8. Rejection Sensitivity in Late Adolescence: Social and Emotional Sequelae

    OpenAIRE

    Marston, Emily G.; Hare, Amanda; Allen, Joseph P.

    2010-01-01

    This study used longitudinal, multi-reporter data, in a community sample, to examine the role of rejection sensitivity in late adolescents’ social and emotional development. Rejection sensitivity was linked to a relative increase in adolescent depressive and anxiety symptoms over a three-year period, even after accounting for teens’ baseline level of social competence. Additionally, reciprocal relationships emerged between rejection sensitivity and internalizing symptoms. Rejection sensitivit...

  9. 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

  10. Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications.

    Science.gov (United States)

    Zhao, Mingzhu; Wei, Dongqing

    2015-01-01

    While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. 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. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. 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 article 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 details 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.

  11. Comparison of Iterative Methods for Computing the Pressure Field in a Dynamic Network Model

    DEFF Research Database (Denmark)

    Mogensen, Kristian; Stenby, Erling Halfdan; Banerjee, Srilekha

    1999-01-01

    In dynamic network models, the pressure map (the pressure in the pores) must be evaluated at each time step. This calculation involves the solution of a large number of nonlinear algebraic systems of equations and accounts for more than 80 of the total CPU-time. Each nonlinear system requires...... at least the partial solution of a sequence of linear systems. We present a comparative study of iterative methods for solving these systems, where we apply both standard routines from the public domain package ITPACK 2C and our own routines tailored to the network problem. The conjugate gradient method......, preconditioned by symmetric successive overrelaxation, was found to be consistently faster and more robust than the other solvers tested. In particular, it was found to be much superior to the successive overrelaxation technique currently used by many researchers....

  12. Simulation tests of the optimization method of Hopfield and Tank using neural networks

    Science.gov (United States)

    Paielli, Russell A.

    1988-01-01

    The method proposed by Hopfield and Tank for using the Hopfield neural network with continuous valued neurons to solve the traveling salesman problem is tested by simulation. Several researchers have apparently been unable to successfully repeat the numerical simulation documented by Hopfield and Tank. However, as suggested to the author by Adams, it appears that the reason for those difficulties is that a key parameter value is reported erroneously (by four orders of magnitude) in the original paper. When a reasonable value is used for that parameter, the network performs generally as claimed. Additionally, a new method of using feedback to control the input bias currents to the amplifiers is proposed and successfully tested. This eliminates the need to set the input currents by trial and error.

  13. Design of alluvial Egyptian irrigation canals using artificial neural networks method

    Directory of Open Access Journals (Sweden)

    Hassan Ibrahim Mohamed

    2013-06-01

    Full Text Available In the present study, artificial neural networks method (ANNs is used to estimate the main parameters which used in design of stable alluvial channels. The capability of ANN models to predict the stable alluvial channels dimensions is investigated, where the flow rate and sediment mean grain size were considered as input variables and wetted perimeter, hydraulic radius, and water surface slope were considered as output variables. The used ANN models are based on a back propagation algorithm to train a multi-layer feed-forward network (Levenberg Marquardt algorithm. The proposed models were verified using 311 data sets of field data collected from 61 manmade canals and drains. Several statistical measures and graphical representation are used to check the accuracy of the models in comparison with previous empirical equations. The results of the developed ANN model proved that this technique is reliable in such field compared with previously developed methods.

  14. Method of Performance-Aware Security of Unicast Communication in Hybrid Satellite Networks

    Science.gov (United States)

    Roy-Chowdhury, Ayan (Inventor); Baras, John S. (Inventor)

    2014-01-01

    A method and apparatus utilizes Layered IPSEC (LES) protocol as an alternative to IPSEC for network-layer security including a modification to the Internet Key Exchange protocol. For application-level security of web browsing with acceptable end-to-end delay, the Dual-mode SSL protocol (DSSL) is used instead of SSL. The LES and DSSL protocols achieve desired end-to-end communication security while allowing the TCP and HTTP proxy servers to function correctly.

  15. Optimal convergence of discontinuous Galerkin methods for continuum modeling of supply chain networks

    KAUST Repository

    Zhang, Shuhua

    2014-09-01

    A discontinuous Galerkin method is considered to simulate materials flow in a supply chain network problem which is governed by a system of conservation laws. By means of a novel interpolation and superclose analysis technique, the optimal and superconvergence error estimates are established under two physically meaningful assumptions on the connectivity matrix. Numerical examples are presented to validate the theoretical results. © 2014 Elsevier Ltd. All rights reserved.

  16. Review of Data Preprocessing Methods for Sign Language Recognition Systems based on Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Zorins Aleksejs

    2016-12-01

    Full Text Available The article presents an introductory analysis of relevant research topic for Latvian deaf society, which is the development of the Latvian Sign Language Recognition System. More specifically the data preprocessing methods are discussed in the paper and several approaches are shown with a focus on systems based on artificial neural networks, which are one of the most successful solutions for sign language recognition task.

  17. WHY ENTREPRENEUR OVERCONFIDENCE AFFECT ITS PROJECT FINANCIAL CAPABILITY: EVIDENCE FROM TUNISIA USING THE BAYESIAN NETWORK METHOD

    OpenAIRE

    Salima TAKTAK; AZOUZI Mohamed Ali; Triki, Mohamed

    2013-01-01

    This article discusses the effect of the entrepreneur’s profile on financing his creative project. It analyzes the impact of overconfidence on improving perceptions financing capacity of the project. To analyze this relationship we used networks as Bayesian data analysis method. Our sample is composed of 200 entrepreneurs. Our results show a high level of entrepreneur’s overconfidence positively affects the evaluation of financing capacity of the project.

  18. A study of Time-varying Cost Parameter Estimation Methods in Traffic Networks for Mobile Robots

    OpenAIRE

    Das, Pragna; Xirgo, Lluís Ribas

    2015-01-01

    Industrial robust controlling systems built using automated guided vehicles (AGVs) requires planning which depends on cost parameters like time and energy of the mobile robots functioning in the system. This work addresses the problem of on-line traversal time identification and estimation for proper mobility of mobile robots on systems' traffic networks. Several filtering and estimation methods have been investigated with respect to proper identification of traversal time of arcs of systems'...

  19. Improved methods in neural network-based adaptive output feedback control, with applications to flight control

    Science.gov (United States)

    Kim, Nakwan

    Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

  20. "Geo-statistics methods and neural networks in geophysical applications: A case study"

    Science.gov (United States)

    Rodriguez Sandoval, R.; Urrutia Fucugauchi, J.; Ramirez Cruz, L. C.

    2008-12-01

    The study is focus in the Ebano-Panuco basin of northeastern Mexico, which is being explored for hydrocarbon reservoirs. These reservoirs are in limestones and there is interest in determining porosity and permeability in the carbonate sequences. The porosity maps presented in this study are estimated from application of multiattribute and neural networks techniques, which combine geophysics logs and 3-D seismic data by means of statistical relationships. The multiattribute analysis is a process to predict a volume of any underground petrophysical measurement from well-log and seismic data. The data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs are neutron porosity logs. From the 3-D seismic volume a series of sample attributes is calculated. The objective of this study is to derive a set of attributes and the target log values. The selected set is determined by a process of forward stepwise regression. The analysis can be linear or nonlinear. In the linear mode the method consists of a series of weights derived by least-square minimization. In the nonlinear mode, a neural network is trained using the select attributes as inputs. In this case we used a probabilistic neural network PNN. The method is applied to a real data set from PEMEX. For better reservoir characterization the porosity distribution was estimated using both techniques. The case shown a continues improvement in the prediction of the porosity from the multiattribute to the neural network analysis. The improvement is in the training and the validation, which are important indicators of the reliability of the results. The neural network showed an improvement in resolution over the multiattribute analysis. The final maps provide more realistic results of the porosity distribution.

  1. Gender as predictor of social rejection: the mediating/moderating role of effortful control and parenting

    Directory of Open Access Journals (Sweden)

    Ester Ato

    2014-10-01

    Full Text Available The aim of this work was to analyze the gender differences found in a sample of 474 Spanish children aged between 6 and 8 years with respect to peer rejection using a sociometric status technique. Thus, we analyzed how temperament (Effortful Control and parenting practices (Parental support and Discipline were involved in this relation. To measure social rejection we used the nominations method in the classroom context, while for temperament and parenting practices, parents were given a TMCQ (Temperament in Middle Childhood Questionnaire; Simmonds and Rothbart, 2004 and the Spanish version of the PCRI (Parent-Child Relationship Inventory; Gerard, 1994. Using an statistical modeling approach, we tested various mediation/moderation models until the best one with selected variables was found to explain the relation between these variables. The results confirmed gender differences in social rejection, with boys being rejected more than girls. The model that gave the best fit was the one that placed effortful control latent variable mediating the relation between gender and social rejection and parenting practices as a latent explanatory variable of effortful control. In conclusion, differences between girls and boys in social rejection are to a large extent explained by the significantly lower scores for boys in effortful control construct and, in turn, these lowest scores are explained by negative parental practices, with low levels of support and discipline.

  2. 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.

  3. When Is Peer Rejection Justifiable? Children's Understanding across Two Cultures

    Science.gov (United States)

    Park, Yoonjung; Killen, Melanie

    2010-01-01

    This study investigated how Korean (N = 397) and U.S. (N = 333) children and adolescents (10 and 13 years of age) evaluated personality (aggression, shyness) and group (gender, nationality) characteristics as a basis for peer rejection in three contexts (friendship rejection, group exclusion, victimization). Overall, peer rejection based on…

  4. Children's Coping with "In Vivo" Peer Rejection: An Experimental Investigation

    Science.gov (United States)

    Reijntjes, Albert; Stegge, Hedy; Terwogt, Mark Meerum; Kamphuis, Jan Henk; Telch, Michael J.

    2006-01-01

    We examined children's behavioral coping in response to an "in vivo" peer rejection manipulation. Participants (N = 186) ranging between 10 and 13 years of age, played a computer game based on the television show "Survivor" and were randomized to either peer rejection (i.e., being voted out of the game) or non-rejection control. During a five-min.…

  5. Paying to belong: when does rejection trigger ingratiation?

    Science.gov (United States)

    Romero-Canyas, Rainer; Downey, Geraldine; Reddy, Kavita S; Rodriguez, Sylvia; Cavanaugh, Timothy J; Pelayo, Rosemary

    2010-11-01

    Societies and social scientists have long held the belief that exclusion induces ingratiation and conformity, an idea in contradiction to robust empirical evidence linking rejection with hostility and aggression. The classic literatures on ingratiation and conformity help resolve this contradiction by identifying circumstances under which rejection may trigger efforts to ingratiate. Jointly, findings from these literatures suggest that when people are given an opportunity to impress their rejecters, ingratiation is likely after rejection experiences that are harsh and that occur in important situations that threaten the individual's self-definition. Four studies tested the hypothesis that people high in rejection sensitivity and therefore dispositionally concerned about rejection will utilize opportunities to ingratiate after harsh rejection in situations that are self-defining. In 3 studies of situations that are particularly self-defining for men, rejection predicted ingratiation among men (but not women) who were high in rejection sensitivity. In a 4th study, harsh rejection in a situation particularly self-defining for women predicted ingratiation among highly rejection-sensitive women (but not men). These findings help identify the specific circumstances under which people are willing to act in socially desirable ways toward those who have rejected them harshly.

  6. Cultural Rejection and Re-identification in Minority Group Members.

    Science.gov (United States)

    Diller, Jerry V.

    There is little consistent research available on cultural rejection and re-identification in minority group members, but this report uses case study material to extrapolate three general factors precipitating rejection: self-hatred and negative chauvinism, quality of ethnic experience and rejection of religious experience. A four-step model for…

  7. A Distributed Learning Method for ℓ 1 -Regularized Kernel Machine over Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xinrong Ji

    2016-07-01

    Full Text Available In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ 1 norm regularization ( ℓ 1 -regularized is investigated, and a novel distributed learning algorithm for the ℓ 1 -regularized kernel minimum mean squared error (KMSE machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN test platform further shows the advantages of the proposed algorithm with respect to communication cost.

  8. Estimating the Capacity of Urban Transportation Networks with an Improved Sensitivity Based Method

    Directory of Open Access Journals (Sweden)

    Muqing Du

    2015-01-01

    Full Text Available The throughput of a given transportation network is always of interest to the traffic administrative department, so as to evaluate the benefit of the transportation construction or expansion project before its implementation. The model of the transportation network capacity formulated as a mathematic programming with equilibrium constraint (MPEC well defines this problem. For practical applications, a modified sensitivity analysis based (SAB method is developed to estimate the solution of this bilevel model. The high-efficient origin-based (OB algorithm is extended for the precise solution of the combined model which is integrated in the network capacity model. The sensitivity analysis approach is also modified to simplify the inversion of the Jacobian matrix in large-scale problems. The solution produced in every iteration of SAB is restrained to be feasible to guarantee the success of the heuristic search. From the numerical experiments, the accuracy of the derivatives for the linear approximation could significantly affect the converging of the SAB method. The results also show that the proposed method could obtain good suboptimal solutions from different starting points in the test examples.

  9. Comparative analysis of methods for extracting vessel network on breast MRI images

    Science.gov (United States)

    Gaizer, Bence T.; Vassiou, Katerina G.; Lavdas, Eleftherios; Arvanitis, Dimitrios L.; Fezoulidis, Ioannis V.; Glotsos, Dimitris T.

    2017-11-01

    Digital processing of MRI images aims to provide an automatized diagnostic evaluation of regular health screenings. Cancerous lesions are proven to cause an alteration in the vessel structure of the diseased organ. Currently there are several methods used for extraction of the vessel network in order to quantify its properties. In this work MRI images (Signa HDx 3.0T, GE Healthcare, courtesy of University Hospital of Larissa) of 30 female breasts were subjected to three different vessel extraction algorithms to determine the location of their vascular network. The first method is an experiment to build a graph over known points of the vessel network; the second algorithm aims to determine the direction and diameter of vessels at these points; the third approach is a seed growing algorithm, spreading selection to neighbors of the known vessel pixels. The possibilities shown by the different methods were analyzed, and quantitative measurements were performed. The data provided by these measurements showed no clear correlation with the presence or malignancy of tumors, based on the radiological diagnosis of skilled physicians.

  10. DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

    Directory of Open Access Journals (Sweden)

    Qiao Wei

    2017-01-01

    Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.

  11. An automatic method to generate domain-specific investigator networks using PubMed abstracts

    Directory of Open Access Journals (Sweden)

    Gwinn Marta

    2007-06-01

    Full Text Available Abstract Background Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. Results We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8% and from 94.2% of HuGE PubMed records (accuracy 87.0. We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit, indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70–90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. Conclusion We successfully created a

  12. The heartbrake of social rejection: heart rate deceleration in response to unexpected peer rejection.

    Science.gov (United States)

    Gunther Moor, Bregtje; Crone, Eveline A; van der Molen, Maurits W

    2010-09-01

    Social relationships are vitally important in human life. Social rejection in particular has been conceptualized as a potent social cue resulting in feelings of hurt. Our study investigated the psychophysiological manifestation of hurt feelings by examining the beat-by-beat heart rate response associated with the processing of social rejection. Study participants were presented with a series of unfamiliar faces and were asked to predict whether they would be liked by the other person. Following each judgment, participants were provided with feedback indicating that the person they had viewed had either accepted or rejected them. Feedback was associated with transient heart rate slowing and a return to baseline that was considerably delayed in response to unexpected social rejection. Our results reveal that the processing of unexpected social rejection is associated with a sizable response of the parasympathetic nervous system. These findings are interpreted in terms of a cardiovagal manifestation of a neural mechanism implicated in the central control of autonomic function during cognitive processes and affective regulation.

  13. Securing Body Sensor Networks with Biometric Methods: A New Key Negotiation Method and a Key Sampling Method for Linear Interpolation Encryption

    OpenAIRE

    Huawei Zhao; Chi Chen; Jiankun Hu; Jing Qin

    2015-01-01

    We present two approaches that exploit biometric data to address security problems in the body sensor networks: a new key negotiation scheme based on the fuzzy extractor technology and an improved linear interpolation encryption method. The first approach designs two attack games to give the formal definition of fuzzy negotiation that forms a new key negotiation scheme based on fuzzy extractor technology. According to the definition, we further define a concrete structure of fuzzy negotiation...

  14. How Do Discrepancies between Victimization and Rejection Expectations in Gay and Bisexual Men Relate to Mental Health Problems?

    Directory of Open Access Journals (Sweden)

    Frank A. Sattler

    2017-05-01

    Full Text Available Introduction: Victimization and rejection expectations predict mental health problems in gay and bisexual men. Furthermore, it was shown that victimization predicts rejection expectations. Nevertheless, the levels of these two variables do not necessarily correspond as indicated by low inter-correlations, resulting in the question “How do discrepancies in the two variables relate to mental health problems?” This study tests if non-corresponding levels of victimization and rejection expectations in gay and bisexual men relate to mental health problems differently than corresponding levels of victimization and rejection expectations. It furthermore tests for linear and curvilinear relationships between victimization, rejection expectations, and mental health problems.Methods: Data from N = 1423 gay and bisexual men were obtained online. Victimization and rejection expectations were tested for discrepant values (differing 0.5 SD or more and those that were in agreement (differing less than 0.5: 33.7% of participants were in agreement, 33.0% reported higher rejection expectations than victimization, and 33.3% v.v. Then, a polynomial regression and a surface analysis were conducted.Results: Discrepant values in victimization and rejection expectations or the direction of the discrepancy did not relevantly predict mental health problems. Findings indicate that victimization and rejection expectations predict mental health problems linearly as well as convexly (upward curving in gay and bisexual men.Discussion: This study replicates findings that gay and bisexual men with more experiences of victimization and rejection expectations demonstrated more mental health problems. Furthermore, this study is the first one to find a convex relationship between these predictors and mental health problems, implicating that disproportionally high mental health problems exist in those gay and bisexual men with high levels of victimization and rejection

  15. Evaluation of Vertical Handoff Decision Algorightms Based on MADM Methods for Heterogeneous Wireless Networks

    Directory of Open Access Journals (Sweden)

    E. Stevens-Navarro

    2012-08-01

    Full Text Available In the forthcoming heterogeneous wireless environment, the mobility management of users roaming between differentwireless access technologies is a challenging and important technical issue. New mobile devices such as netbooks,smartphones and tablets allow users to perform vertical handoffs among different wireless networks. The multipleattribute decision making (MADM methods are suitable tools to model and study the vertical handoff process. Hence,recently several MADM methods such as SAW, MEW, TOPSIS, GRA, ELECTRE, VIKOR and WMC have beenproposed for vertical handoff. In this paper, we present an extensive performance evaluation and comparative study ofthe seven MAMD methods by means of numerical simulations in MATLAB. We evaluate the performance of eachvertical handoff method under different applications such as voice, data, and cost-constrained connections. We alsoperform a sensitivity analysis and evaluate the computational complexity of each method in terms of number offloating point operations.

  16. A Least Square-Based Self-Adaptive Localization Method for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Baoguo Yu

    2016-01-01

    Full Text Available In the wireless sensor network (WSN localization methods based on Received Signal Strength Indicator (RSSI, it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

  17. A modular video streaming method for surgical assistance in operating room networks.

    Science.gov (United States)

    Voruganti, Arun Kumar Raj; Mayoral, Rafael; Vazquez, Adrian; Burgert, Oliver

    2010-09-01

    Continuous video is used with increasing frequency in the operating room for minimally invasive laparoscopic and endoscopic procedures. Video data communication in the OR requires device interoperability, efficient data transfer methods, and specialized IT infrastructure. A framework for digital video communication based on a two channel client-server architecture was developed and tested. One channel is used for stream handling and the second channel is used for data streaming. A video stream description (VSD) specification is defined to negotiate video stream characteristics and ensure semantic interoperability. Quality assessment of the streamed data employs an image-based structural quality measure called the Structural Similarity (SSIM) Index. By introducing the stream description and a quality metric, the stream parameters can be modified as needed. The video communication framework ensures interoperability by defining interfaces for each of the streaming architecture modules. To prove the framework's feasibility, two prototype applications were developed and performance tests were performed on a dedicated OR network. The results showed acceptable network performance for streaming video in the OR network under clinically realistic conditions. An OR video communications framework was developed that uses existing OR network infrastructure as an economical alternative to dedicated integrated OR solutions. This framework provides functional and semantic interoperability among imaging modalities for continuous video data communication.

  18. A neural network construction method for surrogate modeling of physics-based analysis

    Science.gov (United States)

    Sung, Woong Je

    In this thesis existing methodologies related to the developmental methods of neural networks have been surveyed and their approaches to network sizing and structuring are carefully observed. This literature review covers the constructive methods, the pruning methods, and the evolutionary methods and questions about the basic assumption intrinsic to the conventional neural network learning paradigm, which is primarily devoted to optimization of connection weights (or synaptic strengths) for the pre-determined connection structure of the network. The main research hypothesis governing this thesis is that, without breaking a prevailing dichotomy between weights and connectivity of the network during learning phase, the efficient design of a task-specific neural network is hard to achieve because, as long as connectivity and weights are searched by separate means, a structural optimization of the neural network requires either repetitive re-training procedures or computationally expensive topological meta-search cycles. The main contribution of this thesis is designing and testing a novel learning mechanism which efficiently learns not only weight parameters but also connection structure from a given training data set, and positioning this learning mechanism within the surrogate modeling practice. In this work, a simple and straightforward extension to the conventional error Back-Propagation (BP) algorithm has been formulated to enable a simultaneous learning for both connectivity and weights of the Generalized Multilayer Perceptron (GMLP) in supervised learning tasks. A particular objective is to achieve a task-specific network having reasonable generalization performance with a minimal training time. The dichotomy between architectural design and weight optimization is reconciled by a mechanism establishing a new connection for a neuron pair which has potentially higher error-gradient than one of the existing connections. Interpreting an instance of the absence of

  19. Timetable-based simulation method for choice set generation in large-scale public transport networks

    DEFF Research Database (Denmark)

    Rasmussen, Thomas Kjær; Anderson, Marie Karen; Nielsen, Otto Anker

    2016-01-01

    The composition and size of the choice sets are a key for the correct estimation of and prediction by route choice models. While existing literature has posed a great deal of attention towards the generation of path choice sets for private transport problems, the same does not apply to public...... transport problems. This study proposes a timetable-based simulation method for generating path choice sets in a multimodal public transport network. Moreover, this study illustrates the feasibility of its implementation by applying the method to reproduce 5131 real-life trips in the Greater Copenhagen Area...

  20. Optimal Power Flow of the Algerian Electrical Network using an Ant Colony Optimization Method

    Directory of Open Access Journals (Sweden)

    Tarek BOUKTIR

    2005-06-01

    Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via an Ant Colony Optimization Meta-heuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the Algerian Electrical Network show that the Ant Colony Optimization method converges quickly to the global optimum.