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Sample records for neural coupling strengths

  1. Cooperative effect of random and time-periodic coupling strength on synchronization transitions in one-way coupled neural system: mean field approach.

    Science.gov (United States)

    Jiancheng, Shi; Min, Luo; Chusheng, Huang

    2017-08-01

    The cooperative effect of random coupling strength and time-periodic coupling strengh on synchronization transitions in one-way coupled neural system has been investigated by mean field approach. Results show that cooperative coupling strength (CCS) plays an active role for the enhancement of synchronization transitions. There exist an optimal frequency of CCS which makes the system display the best CCS-induced synchronization transitions, a critical frequency of CCS which can not further affect the CCS-induced synchronization transitions, and a critical amplitude of CCS which can not occur the CCS-induced synchronization transitions. Meanwhile, noise intensity plays a negative role for the CCS-induced synchronization transitions. Furthermore, it is found that the novel CCS amplitude-induced synchronization transitions and CCS frequency-induced synchronization transitions are found.

  2. Neural Cross-Frequency Coupling Functions

    Directory of Open Access Journals (Sweden)

    Tomislav Stankovski

    2017-06-01

    Full Text Available Although neural interactions are usually characterized only by their coupling strength and directionality, there is often a need to go beyond this by establishing the functional mechanisms of the interaction. We introduce the use of dynamical Bayesian inference for estimation of the coupling functions of neural oscillations in the presence of noise. By grouping the partial functional contributions, the coupling is decomposed into its functional components and its most important characteristics—strength and form—are quantified. The method is applied to characterize the δ-to-α phase-to-phase neural coupling functions from electroencephalographic (EEG data of the human resting state, and the differences that arise when the eyes are either open (EO or closed (EC are evaluated. The δ-to-α phase-to-phase coupling functions were reconstructed, quantified, compared, and followed as they evolved in time. Using phase-shuffled surrogates to test for significance, we show how the strength of the direct coupling, and the similarity and variability of the coupling functions, characterize the EO and EC states for different regions of the brain. We confirm an earlier observation that the direct coupling is stronger during EC, and we show for the first time that the coupling function is significantly less variable. Given the current understanding of the effects of e.g., aging and dementia on δ-waves, as well as the effect of cognitive and emotional tasks on α-waves, one may expect that new insights into the neural mechanisms underlying certain diseases will be obtained from studies of coupling functions. In principle, any pair of coupled oscillations could be studied in the same way as those shown here.

  3. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  4. Coupling strength assumption in statistical energy analysis

    Science.gov (United States)

    Lafont, T.; Totaro, N.; Le Bot, A.

    2017-04-01

    This paper is a discussion of the hypothesis of weak coupling in statistical energy analysis (SEA). The examples of coupled oscillators and statistical ensembles of coupled plates excited by broadband random forces are discussed. In each case, a reference calculation is compared with the SEA calculation. First, it is shown that the main SEA relation, the coupling power proportionality, is always valid for two oscillators irrespective of the coupling strength. But the case of three subsystems, consisting of oscillators or ensembles of plates, indicates that the coupling power proportionality fails when the coupling is strong. Strong coupling leads to non-zero indirect coupling loss factors and, sometimes, even to a reversal of the energy flow direction from low to high vibrational temperature.

  5. Applications of Pulse-Coupled Neural Networks

    CERN Document Server

    Ma, Yide; Wang, Zhaobin

    2011-01-01

    "Applications of Pulse-Coupled Neural Networks" explores the fields of image processing, including image filtering, image segmentation, image fusion, image coding, image retrieval, and biometric recognition, and the role of pulse-coupled neural networks in these fields. This book is intended for researchers and graduate students in artificial intelligence, pattern recognition, electronic engineering, and computer science. Prof. Yide Ma conducts research on intelligent information processing, biomedical image processing, and embedded system development at the School of Information Sci

  6. Unveiling neural coupling within the sensorimotor system : directionality and nonlinearity

    NARCIS (Netherlands)

    Yang, Y.; Dewald, J.P.A.; van der Helm, F.C.T.; Schouten, A.C.

    2017-01-01

    Neural coupling between the central nervous system and the periphery is essential for the neural control of movement. Corticomuscular coherence is a popular linear technique to assess synchronised oscillatory activity in the sensorimotor system. This oscillatory coupling originates from ascending

  7. Chaotic synchronization of nearest-neighbor diffusive coupling Hindmarsh-Rose neural networks in noisy environments

    Energy Technology Data Exchange (ETDEWEB)

    Fang Xiaoling [Institute of Mechanobiology and Medical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, P.O. Box 888, 800 Dongchuan Road, Minhang, Shanghai 200240 (China); Yu Hongjie [Department of Engineering Mechanics, Shanghai Jiao Tong University, Shanghai 200240 (China); Jiang Zonglai [Institute of Mechanobiology and Medical Engineering, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, P.O. Box 888, 800 Dongchuan Road, Minhang, Shanghai 200240 (China)], E-mail: zljiang@sjtu.edu.cn

    2009-03-15

    The chaotic synchronization of Hindmarsh-Rose neural networks linked by a nonlinear coupling function is discussed. The HR neural networks with nearest-neighbor diffusive coupling form are treated as numerical examples. By the construction of a special nonlinear-coupled term, the chaotic system is coupled symmetrically. For three and four neurons network, a certain region of coupling strength corresponding to full synchronization is given, and the effect of network structure and noise position are analyzed. For five and more neurons network, the full synchronization is very difficult to realize. All the results have been proved by the calculation of the maximum conditional Lyapunov exponent.

  8. Estimation of concrete compressive strength using artificial neural network

    Directory of Open Access Journals (Sweden)

    Kostić Srđan

    2015-01-01

    Full Text Available In present paper, concrete compressive strength is evaluated using back propagation feed-forward artificial neural network. Training of neural network is performed using Levenberg-Marquardt learning algorithm for four architectures of artificial neural networks, one, three, eight and twelve nodes in a hidden layer in order to avoid the occurrence of overfitting. Training, validation and testing of neural network is conducted for 75 concrete samples with distinct w/c ratio and amount of superplasticizer of melamine type. These specimens were exposed to different number of freeze/thaw cycles and their compressive strength was determined after 7, 20 and 32 days. The obtained results indicate that neural network with one hidden layer and twelve hidden nodes gives reasonable prediction accuracy in comparison to experimental results (R=0.965, MSE=0.005. These results of the performed analysis are further confirmed by calculating the standard statistical errors: the chosen architecture of neural network shows the smallest value of mean absolute percentage error (MAPE=, variance absolute relative error (VARE and median absolute error (MEDAE, and the highest value of variance accounted for (VAF.

  9. The Effect of Inhibitory Neuron on the Evolution Model of Higher-Order Coupling Neural Oscillator Population

    Science.gov (United States)

    Qi, Yi; Wang, Rubin; Jiao, Xianfa; Du, Ying

    2014-01-01

    We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory coupling strength will cause increase of stable amplitude of average number density. Whether the neural oscillator population is able to enter the new synchronous oscillation or not is determined by excitatory and inhibitory coupling strength. In the presence of external stimulation, the evolution of the average number density is dependent upon the external stimulation and the coupling term in which the dominator will determine the final evolution. PMID:24516505

  10. The Effect of Inhibitory Neuron on the Evolution Model of Higher-Order Coupling Neural Oscillator Population

    Directory of Open Access Journals (Sweden)

    Yi Qi

    2014-01-01

    Full Text Available We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory coupling strength will cause increase of stable amplitude of average number density. Whether the neural oscillator population is able to enter the new synchronous oscillation or not is determined by excitatory and inhibitory coupling strength. In the presence of external stimulation, the evolution of the average number density is dependent upon the external stimulation and the coupling term in which the dominator will determine the final evolution.

  11. Strength at Home Couples Program to Prevent Military Partner Violence

    Science.gov (United States)

    2017-10-01

    AWARD NUMBER: W81XWH-15-1-0374 TITLE: Strength at Home Couples Program to Prevent Military Partner Violence PRINCIPAL INVESTIGATOR: Casey T...SUBTITLE 5a. CONTRACT NUMBER Strength at Home Couples Program to Prevent Military Partner Violence 5b. GRANT NUMBER W81XWH-15-1-0374 5c. PROGRAM...Health 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a. REPORT U b

  12. Neural adaptations underlying cross-education after unilateral strength training.

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    Fimland, Marius S; Helgerud, Jan; Solstad, Gerd Marie; Iversen, Vegard Moe; Leivseth, Gunnar; Hoff, Jan

    2009-12-01

    The purpose of this study was to investigate the effects of 4-week (16 sessions) unilateral, maximal isometric strength training on contralateral neural adaptations. Subjects were randomised to a strength training group (TG, n = 15) or to a control group (CG, n = 11). Both legs of both groups were tested for plantar flexion maximum voluntary isometric contractions (MVCs), surface electromyogram (EMG), H-reflexes and V-waves in the soleus (SOL) and gastrocnemius medialis (GM) superimposed during MVC and normalised by the M-wave (EMG/M(SUP), H(SUP)/M(SUP), V/M(SUP), respectively), before and after the training period. For the untrained leg, the TG increased compared to the CG for MVC torque (33%, P cross-education of strength.

  13. Analysis of Neural-BOLD Coupling through Four Models of the Neural Metabolic Demand

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    Christopher W Tyler

    2015-12-01

    Full Text Available The coupling of the neuronal energetics to the blood-oxygen-level-dependent (BOLD response is still incompletely understood. To address this issue, we compared the fits of four plausible models of neurometabolic coupling dynamics to available data for simultaneous recordings of the local field potential (LFP and the local BOLD response recorded from monkey primary visual cortex over a wide range of stimulus durations. The four models of the metabolic demand driving the BOLD response were: direct coupling with the overall LFP; rectified coupling to the LFP; coupling with a slow adaptive component of the implied neural population response; and coupling with the non-adaptive intracellular input signal defined by the stimulus time course. Taking all stimulus durations into account, the results imply that the BOLD response is most closely coupled with metabolic demand derived from the intracellular input waveform, without significant influence from the adaptive transients and nonlinearities exhibited by the LFP waveform.

  14. Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling

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    Ferris Daniel P

    2010-12-01

    Full Text Available Abstract Background When humans perform rhythmic upper and lower limb locomotor-like movements, there is an excitatory effect of upper limb exertion on lower limb muscle recruitment. To investigate potential neural mechanisms for this behavioral observation, we developed computer simulations modeling interlimb neural pathways among central pattern generators. We hypothesized that enhancement of muscle recruitment from interlimb spinal mechanisms was not sufficient to explain muscle enhancement levels observed in experimental data. Methods We used Matsuoka oscillators for the central pattern generators (CPG and determined parameters that enhanced amplitudes of rhythmic steady state bursts. Potential mechanisms for output enhancement were excitatory and inhibitory sensory feedback gains, excitatory and inhibitory interlimb coupling gains, and coupling geometry. We first simulated the simplest case, a single CPG, and then expanded the model to have two CPGs and lastly four CPGs. In the two and four CPG models, the lower limb CPGs did not receive supraspinal input such that the only mechanisms available for enhancing output were interlimb coupling gains and sensory feedback gains. Results In a two-CPG model with inhibitory sensory feedback gains, only excitatory gains of ipsilateral flexor-extensor/extensor-flexor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 26%. In a two-CPG model with excitatory sensory feedback gains, excitatory gains of contralateral flexor-flexor/extensor-extensor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 100%. However, within a given excitatory sensory feedback gain, enhancement due to excitatory interlimb gains could only reach levels up to 20%. Interconnecting four CPGs to have ipsilateral flexor-extensor/extensor-flexor coupling, contralateral flexor-flexor/extensor-extensor coupling, and bilateral flexor-extensor/extensor-flexor coupling could enhance

  15. Extended Neural Metastability in an Embodied Model of Sensorimotor Coupling

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    Aguilera, Miguel; Bedia, Manuel G.; Barandiaran, Xabier E.

    2016-01-01

    The hypothesis that brain organization is based on mechanisms of metastable synchronization in neural assemblies has been popularized during the last decades of neuroscientific research. Nevertheless, the role of body and environment for understanding the functioning of metastable assemblies is frequently dismissed. The main goal of this paper is to investigate the contribution of sensorimotor coupling to neural and behavioral metastability using a minimal computational model of plastic neural ensembles embedded in a robotic agent in a behavioral preference task. Our hypothesis is that, under some conditions, the metastability of the system is not restricted to the brain but extends to the system composed by the interaction of brain, body and environment. We test this idea, comparing an agent in continuous interaction with its environment in a task demanding behavioral flexibility with an equivalent model from the point of view of “internalist neuroscience.” A statistical characterization of our model and tools from information theory allow us to show how (1) the bidirectional coupling between agent and environment brings the system closer to a regime of criticality and triggers the emergence of additional metastable states which are not found in the brain in isolation but extended to the whole system of sensorimotor interaction, (2) the synaptic plasticity of the agent is fundamental to sustain open structures in the neural controller of the agent flexibly engaging and disengaging different behavioral patterns that sustain sensorimotor metastable states, and (3) these extended metastable states emerge when the agent generates an asymmetrical circular loop of causal interaction with its environment, in which the agent responds to variability of the environment at fast timescales while acting over the environment at slow timescales, suggesting the constitution of the agent as an autonomous entity actively modulating its sensorimotor coupling with the world. We

  16. Chaotic synchronization and control in nonlinear-coupled Hindmarsh-Rose neural systems

    Energy Technology Data Exchange (ETDEWEB)

    Yu Hongjie [Department of Engineering Mechanics, Shanghai Jiao Tong University, 200030 Shanghai (China)]. E-mail: yuhongjie@sjtu.edu.cn; Peng Jianhua [Department of Engineering Mechanics, Shanghai Jiao Tong University, 200030 Shanghai (China)

    2006-07-15

    A new approach for chaotic synchronization of Hindmarsh-Rose (HR) neural networks linked by special nonlinear coupling function is proposed. The method expands SC method in investigation of chaotic synchronization based on the stability criterion. We provide the error evolutional equation to determine the stability of synchronized states, which has very simple forms corresponding to matrix of star coupling coefficients. The synchronization can be achieved without the requirement to calculate the maximum Lyapunov exponents when the coupling strengths are taken as reference values, and there is a region of stability around them. Besides, the stability criterion control method is applied to control chaotic behaviors of individual Hindmarsh-Rose neuron model. The chaotic orbit is stabilized on 5spike/burst orbit embedded in the chaotic attractor by an input of the nonlinear time-continuous feedback perturbation to membrane potential.

  17. Artificial Neural Network Model for Low Strength RC Beam Shear ...

    African Journals Online (AJOL)

    This research was to investigate how the shear strength prediction of low strength reinforced concrete beams will improve under an ANN model. An existing database of 310 reinforced concrete beams without web reinforcement was divided into three sets of training, validation and testing. A total of 224 different architectural ...

  18. Three applications of pulse-coupled neural networks

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    Ranganath, Heggere S.; Banish, Michele R.; Karpinsky, John R.; Clark, Rodney L.; Germany, Glynn A.; Richards, Philip G.

    1999-03-01

    Image segmentation is one of the major application areas for Pulsed Coupled Neural Networks (PCNN). Previous research has shown that the ability of PCNN to ignore minor variations in intensity and small spatial discontinuities in images is beneficial to image segmentation as well as image smoothing. This paper describes research and development projects in progress in which PCNN is used for the segmentation of three different types of digital images. The software for the diagnosis of Pulmonary Embolism from VQ lung scans uses PCNN in single burst mode for segmenting perfusion and ventilation images. The second project is attempting to detect ischemia by comparing 3D SPECT (Single Photon Emission Computed Tomography) images of heart obtained during stress and rest conditions, respectively. The third application is a space science project which deals with the study of global auroral images obtained from Ultraviolet Imager. The paper also describes an hardware implementation of PCNN as an electro-optical chip.

  19. Prediction of Concrete Compressive Strength by Evolutionary Artificial Neural Networks

    National Research Council Canada - National Science Library

    Nikoo, Mehdi; Torabian Moghadam, Farshid; Sadowski, Łukasz

    2015-01-01

    ...) in determining the compressive strength of concrete [1-9]. It is proper to note that there were mostly conventional applications of ANNs not disrupted in facing inaccurate data and information...

  20. Finite-size scaling in the system of coupled oscillators with heterogeneity in coupling strength.

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    Hong, Hyunsuk

    2017-07-01

    We consider a mean-field model of coupled phase oscillators with random heterogeneity in the coupling strength. The system that we investigate here is a minimal model that contains randomness in diverse values of the coupling strength, and it is found to return to the original Kuramoto model [Y. Kuramoto, Prog. Theor. Phys. Suppl. 79, 223 (1984)10.1143/PTPS.79.223] when the coupling heterogeneity disappears. According to one recent paper [H. Hong, H. Chaté, L.-H. Tang, and H. Park, Phys. Rev. E 92, 022122 (2015)10.1103/PhysRevE.92.022122], when the natural frequency of the oscillator in the system is "deterministically" chosen, with no randomness in it, the system is found to exhibit the finite-size scaling exponent ν[over ¯]=5/4. Also, the critical exponent for the dynamic fluctuation of the order parameter is found to be given by γ=1/4, which is different from the critical exponents for the Kuramoto model with the natural frequencies randomly chosen. Originally, the unusual finite-size scaling behavior of the Kuramoto model was reported by Hong et al. [H. Hong, H. Chaté, H. Park, and L.-H. Tang, Phys. Rev. Lett. 99, 184101 (2007)10.1103/PhysRevLett.99.184101], where the scaling behavior is found to be characterized by the unusual exponent ν[over ¯]=5/2. On the other hand, if the randomness in the natural frequency is removed, it is found that the finite-size scaling behavior is characterized by a different exponent, ν[over ¯]=5/4 [H. Hong, H. Chaté, L.-H. Tang, and H. Park, Phys. Rev. E 92, 022122 (2015)10.1103/PhysRevE.92.022122]. Those findings brought about our curiosity and led us to explore the effects of the randomness on the finite-size scaling behavior. In this paper, we pay particular attention to investigating the finite-size scaling and dynamic fluctuation when the randomness in the coupling strength is considered.

  1. Image fusion by pulse couple neural network with shearlet

    Science.gov (United States)

    Geng, Peng; Wang, Zhengyou; Zhang, Zhigang; Xiao, Zhong

    2012-06-01

    The shearlet representation forms a tight frame which decomposes a function into scales and directions, and is optimally sparse in representing images with edges. An image fusion method is proposed based on the shearlet transform. Firstly, transform the image A and image B by the shearlets. Secondly, a pulse couple neural network (PCNN) is used for the frequency subbands, which uses the number of output pulses from the PCNN's neurons to select fusion coefficients. Finally, an inverse shearlet transform is applied on the new fused coefficients to reconstruct the fused image. Some experiments are performed in images such as multi-focus images, multi-sensor images, medical images and multispectral images comparing the proposed algorithm with the wavelet, contourlet and nonsubsampled contourlet method based on the PCNN. The experimental results show that the proposed algorithm can not only extract more important visual information from source images, but also effectively avoid the introduction of artificial information. It significantly outperforms the traditional multiscale transform image fusion methods in terms of both visual quality and objective evaluation criteria such as MI and QAB/F.

  2. Electrolarynx Voice Recognition Utilizing Pulse Coupled Neural Network

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    Fatchul Arifin

    2010-08-01

    Full Text Available The laryngectomies patient has no ability to speak normally because their vocal chords have been removed. The easiest option for the patient to speak again is by using electrolarynx speech. This tool is placed on the lower chin. Vibration of the neck while speaking is used to produce sound. Meanwhile, the technology of "voice recognition" has been growing very rapidly. It is expected that the technology of "voice recognition" can also be used by laryngectomies patients who use electrolarynx.This paper describes a system for electrolarynx speech recognition. Two main parts of the system are feature extraction and pattern recognition. The Pulse Coupled Neural Network – PCNN is used to extract the feature and characteristic of electrolarynx speech. Varying of β (one of PCNN parameter also was conducted. Multi layer perceptron is used to recognize the sound patterns. There are two kinds of recognition conducted in this paper: speech recognition and speaker recognition. The speech recognition recognizes specific speech from every people. Meanwhile, speaker recognition recognizes specific speech from specific person. The system ran well. The "electrolarynx speech recognition" has been tested by recognizing of “A” and "not A" voice. The results showed that the system had 94.4% validation. Meanwhile, the electrolarynx speaker recognition has been tested by recognizing of “saya” voice from some different speakers. The results showed that the system had 92.2% validation. Meanwhile, the best β parameter of PCNN for electrolarynx recognition is 3.

  3. Real-time robot path planning based on a modified pulse-coupled neural network model.

    Science.gov (United States)

    Qu, Hong; Yang, Simon X; Willms, Allan R; Yi, Zhang

    2009-11-01

    This paper presents a modified pulse-coupled neural network (MPCNN) model for real-time collision-free path planning of mobile robots in nonstationary environments. The proposed neural network for robots is topologically organized with only local lateral connections among neurons. It works in dynamic environments and requires no prior knowledge of target or barrier movements. The target neuron fires first, and then the firing event spreads out, through the lateral connections among the neurons, like the propagation of a wave. Obstacles have no connections to their neighbors. Each neuron records its parent, that is, the neighbor that caused it to fire. The real-time optimal path is then the sequence of parents from the robot to the target. In a static case where the barriers and targets are stationary, this paper proves that the generated wave in the network spreads outward with travel times proportional to the linking strength among neurons. Thus, the generated path is always the global shortest path from the robot to the target. In addition, each neuron in the proposed model can propagate a firing event to its neighboring neuron without any comparing computations. The proposed model is applied to generate collision-free paths for a mobile robot to solve a maze-type problem, to circumvent concave U-shaped obstacles, and to track a moving target in an environment with varying obstacles. The effectiveness and efficiency of the proposed approach is demonstrated through simulation and comparison studies.

  4. Resin adhesion strengths to zirconia ceramics after primer treatment with silane coupling monomer or oligomer.

    Science.gov (United States)

    Okada, Masahiro; Inoue, Kazusa; Irie, Masao; Taketa, Hiroaki; Torii, Yasuhiro; Matsumoto, Takuya

    2017-09-26

    Resin bonding to zirconia ceramics is difficult to achieve using the standard methods for conventional silica-based dental ceramics, which employ silane coupling monomers as primers. The hypothesis in this study was that a silane coupling oligomer -a condensed product of silane coupling monomers- would be a more suitable primer for zirconia. To prove this hypothesis, the shear bond strengths between a composite resin and zirconia were compared after applying either a silane coupling monomer or oligomer. The shear bond strength increased after applying a non-activated ethanol solution of the silane coupling oligomer compared with that achieved when applying the monomer. Thermal treatment of the zirconia at 110°C after application of the silane coupling agents was essential to improve the shear bond strength between the composite resin cement and zirconia.

  5. Neural adaptations to strength training: moving beyond transcranial magnetic stimulation and reflex studies.

    Science.gov (United States)

    Carroll, T J; Selvanayagam, V S; Riek, S; Semmler, J G

    2011-06-01

    It has long been believed that training for increased strength not only affects muscle tissue, but also results in adaptive changes in the central nervous system. However, only in the last 10 years has the use of methods to study the neurophysiological details of putative neural adaptations to training become widespread. There are now many published reports that have used single motor unit recordings, electrical stimulation of peripheral nerves, and non-invasive stimulation of the human brain [i.e. transcranial magnetic stimulation (TMS)] to study neural responses to strength training. In this review, we aim to summarize what has been learned from single motor unit, reflex and TMS studies, and identify the most promising avenues to advance our conceptual understanding with these methods. We also consider the few strength training studies that have employed alternative neurophysiological techniques such as functional magnetic resonance imaging and electroencephalography. The nature of the information that these techniques can provide, as well as their major technical and conceptual pitfalls, are briefly described. The overall conclusion of the review is that the current evidence regarding neural adaptations to strength training is inconsistent and incomplete. In order to move forward in our understanding, it will be necessary to design studies that are based on a rigorous consideration of the limitations of the available techniques, and that are specifically targeted to address important conceptual questions. © 2011 The Authors. Acta Physiologica © 2011 Scandinavian Physiological Society.

  6. Bifurcation Analysis on Phase-Amplitude Cross-Frequency Coupling in Neural Networks with Dynamic Synapses

    Science.gov (United States)

    Sase, Takumi; Katori, Yuichi; Komuro, Motomasa; Aihara, Kazuyuki

    2017-01-01

    We investigate a discrete-time network model composed of excitatory and inhibitory neurons and dynamic synapses with the aim at revealing dynamical properties behind oscillatory phenomena possibly related to brain functions. We use a stochastic neural network model to derive the corresponding macroscopic mean field dynamics, and subsequently analyze the dynamical properties of the network. In addition to slow and fast oscillations arising from excitatory and inhibitory networks, respectively, we show that the interaction between these two networks generates phase-amplitude cross-frequency coupling (CFC), in which multiple different frequency components coexist and the amplitude of the fast oscillation is modulated by the phase of the slow oscillation. Furthermore, we clarify the detailed properties of the oscillatory phenomena by applying the bifurcation analysis to the mean field model, and accordingly show that the intermittent and the continuous CFCs can be characterized by an aperiodic orbit on a closed curve and one on a torus, respectively. These two CFC modes switch depending on the coupling strength from the excitatory to inhibitory networks, via the saddle-node cycle bifurcation of a one-dimensional torus in map (MT1SNC), and may be associated with the function of multi-item representation. We believe that the present model might have potential for studying possible functional roles of phase-amplitude CFC in the cerebral cortex. PMID:28424606

  7. Artificial Neural Network-Based Early-Age Concrete Strength Monitoring Using Dynamic Response Signals.

    Science.gov (United States)

    Kim, Junkyeong; Lee, Chaggil; Park, Seunghee

    2017-06-07

    Concrete is one of the most common materials used to construct a variety of civil infrastructures. However, since concrete might be susceptible to brittle fracture, it is essential to confirm the strength of concrete at the early-age stage of the curing process to prevent unexpected collapse. To address this issue, this study proposes a novel method to estimate the early-age strength of concrete, by integrating an artificial neural network algorithm with a dynamic response measurement of the concrete material. The dynamic response signals of the concrete, including both electromechanical impedances and guided ultrasonic waves, are obtained from an embedded piezoelectric sensor module. The cross-correlation coefficient of the electromechanical impedance signals and the amplitude of the guided ultrasonic wave signals are selected to quantify the variation in dynamic responses according to the strength of the concrete. Furthermore, an artificial neural network algorithm is used to verify a relationship between the variation in dynamic response signals and concrete strength. The results of an experimental study confirm that the proposed approach can be effectively applied to estimate the strength of concrete material from the early-age stage of the curing process.

  8. Prediction of concrete strength using ultrasonic pulse velocity and artificial neural networks.

    Science.gov (United States)

    Trtnik, Gregor; Kavcic, Franci; Turk, Goran

    2009-01-01

    Ultrasonic pulse velocity technique is one of the most popular non-destructive techniques used in the assessment of concrete properties. However, it is very difficult to accurately evaluate the concrete compressive strength with this method since the ultrasonic pulse velocity values are affected by a number of factors, which do not necessarily influence the concrete compressive strength in the same way or to the same extent. This paper deals with the analysis of such factors on the velocity-strength relationship. The relationship between ultrasonic pulse velocity, static and dynamic Young's modulus and shear modulus was also analyzed. The influence of aggregate, initial concrete temperature, type of cement, environmental temperature, and w/c ratio was determined by our own experiments. Based on the experimental results, a numerical model was established within the Matlab programming environment. The multi-layer feed-forward neural network was used for this purpose. The paper demonstrates that artificial neural networks can be successfully used in modelling the velocity-strength relationship. This model enables us to easily and reliably estimate the compressive strength of concrete by using only the ultrasonic pulse velocity value and some mix parameters of concrete.

  9. Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network

    Directory of Open Access Journals (Sweden)

    Haiyan Mo

    2013-01-01

    Full Text Available In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model.

  10. Calculations of NMR dipolar coupling strengths in model peptides

    Energy Technology Data Exchange (ETDEWEB)

    Case, David A. [Scripps Research Institute, Department of Molecular Biology (United States)], E-mail: case@scripps.edu

    1999-10-15

    Ab initio MP2 and density functional quantum chemistry calculations are used to explore geometries and vibrational properties of N-methylacetamide and of the alanine dipeptide with backbone angles characteristic of helix and sheet regions in proteins. The results are used to explore one-bond direct dipolar couplings for the N-H, C{alpha}-H{alpha}, C'-N, and C{alpha}-C' bonds, as well as for the two-bond C'-H interaction. Vibrational averaging affects these dipolar couplings, and these effects can be expressed as effective bond lengths that are 0.5-3% larger than the true bond lengths; bending and torsion vibrations have a bigger influence on the effective coupling than do stretching vibrations. Because of zero-point motion, these effects are important even at low temperature. Hydrogen bonding interactions at the amide group also increase the N-H effective bond length. Although vibrational contributions to effective bond lengths are small, they can have a significant influence on the extraction of order parameters from relaxation data, and a knowledge of relative bond lengths is needed when several types of dipolar couplings are to be simultaneously used for refinement. The present computational results are compared to both solid- and liquid-state NMR experiments. The analysis suggests that secondary structural elements in many proteins may be more rigid than is commonly thought.

  11. Strength at Home Couples Program to Prevent Military Partner Violence

    Science.gov (United States)

    2016-10-01

    occurring in military families and to develop skills to build resilience with respect to the impacts of trauma on relationships. 15. SUBJECT TERMS- 16...Couples Program to Prevent Military Partner Violence PT140092, Psychological Health/Traumatic Brain Injury Research Program W81XWH-14-PHTBI-PHRA PI

  12. Precise strength of the $\\pi$NN coupling constant

    CERN Document Server

    Ericson, Torleif Eric Oskar; Rahm, J; Blomgren, J; Olsson, N; Thomas, A W

    1998-01-01

    We report here a preliminary value for the piNN coupling constant deduced from the GMO sumrule for forward piN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives $g^2_c$(GMO) = 13.99(24).

  13. Determination of the quark coupling strength $|V_ub|$ using baryonic decays

    NARCIS (Netherlands)

    Aaij, R.; Raven, G.

    2015-01-01

    In the Standard Model of particle physics, the strength of the couplings of the b quark to the u and c quarks, /Vub/ and /Vcb/, are governed by the coupling of the quarks to the Higgs boson. Using data from the LHCb experiment at the Large Hadron Collider, the probability for

  14. Training-specific functional, neural, and hypertrophic adaptations to explosive- vs. sustained-contraction strength training.

    Science.gov (United States)

    Balshaw, Thomas G; Massey, Garry J; Maden-Wilkinson, Thomas M; Tillin, Neale A; Folland, Jonathan P

    2016-06-01

    Training specificity is considered important for strength training, although the functional and underpinning physiological adaptations to different types of training, including brief explosive contractions, are poorly understood. This study compared the effects of 12 wk of explosive-contraction (ECT, n = 13) vs. sustained-contraction (SCT, n = 16) strength training vs. control (n = 14) on the functional, neural, hypertrophic, and intrinsic contractile characteristics of healthy young men. Training involved 40 isometric knee extension repetitions (3 times/wk): contracting as fast and hard as possible for ∼1 s (ECT) or gradually increasing to 75% of maximum voluntary torque (MVT) before holding for 3 s (SCT). Torque and electromyography during maximum and explosive contractions, torque during evoked octet contractions, and total quadriceps muscle volume (QUADSVOL) were quantified pre and post training. MVT increased more after SCT than ECT [23 vs. 17%; effect size (ES) = 0.69], with similar increases in neural drive, but greater QUADSVOL changes after SCT (8.1 vs. 2.6%; ES = 0.74). ECT improved explosive torque at all time points (17-34%; 0.54 ≤ ES ≤ 0.76) because of increased neural drive (17-28%), whereas only late-phase explosive torque (150 ms, 12%; ES = 1.48) and corresponding neural drive (18%) increased after SCT. Changes in evoked torque indicated slowing of the contractile properties of the muscle-tendon unit after both training interventions. These results showed training-specific functional changes that appeared to be due to distinct neural and hypertrophic adaptations. ECT produced a wider range of functional adaptations than SCT, and given the lesser demands of ECT, this type of training provides a highly efficient means of increasing function. Copyright © 2016 the American Physiological Society.

  15. Lag Synchronization of Memristor-Based Coupled Neural Networks via ω-Measure.

    Science.gov (United States)

    Li, Ning; Cao, Jinde

    2016-03-01

    This paper deals with the lag synchronization problem of memristor-based coupled neural networks with or without parameter mismatch using two different algorithms. Firstly, we consider the memristor-based neural networks with parameter mismatch, lag complete synchronization cannot be achieved due to parameter mismatch, the concept of lag quasi-synchronization is introduced. Based on the ω-measure method and generalized Halanay inequality, the error level is estimated, a new lag quasi-synchronization scheme is proposed to ensure that coupled memristor-based neural networks are in a state of lag synchronization with an error level. Secondly, by constructing Lyapunov functional and applying common Halanary inequality, several lag complete synchronization criteria for the memristor-based neural networks with parameter match are given, which are easy to verify. Finally, two examples are given to illustrate the effectiveness of the proposed lag quasi-synchronization or lag complete synchronization criteria, which well support theoretical results.

  16. Energy efficient neural stimulation: coupling circuit design and membrane biophysics.

    Science.gov (United States)

    Foutz, Thomas J; Ackermann, D Michael; Kilgore, Kevin L; McIntyre, Cameron C

    2012-01-01

    The delivery of therapeutic levels of electrical current to neural tissue is a well-established treatment for numerous indications such as Parkinson's disease and chronic pain. While the neuromodulation medical device industry has experienced steady clinical growth over the last two decades, much of the core technology underlying implanted pulse generators remain unchanged. In this study we propose some new methods for achieving increased energy-efficiency during neural stimulation. The first method exploits the biophysical features of excitable tissue through the use of a centered-triangular stimulation waveform. Neural activation with this waveform is achieved with a statistically significant reduction in energy compared to traditional rectangular waveforms. The second method demonstrates energy savings that could be achieved by advanced circuitry design. We show that the traditional practice of using a fixed compliance voltage for constant-current stimulation results in substantial energy loss. A portion of this energy can be recuperated by adjusting the compliance voltage to real-time requirements. Lastly, we demonstrate the potential impact of axon fiber diameter on defining the energy-optimal pulse-width for stimulation. When designing implantable pulse generators for energy efficiency, we propose that the future combination of a variable compliance system, a centered-triangular stimulus waveform, and an axon diameter specific stimulation pulse-width has great potential to reduce energy consumption and prolong battery life in neuromodulation devices.

  17. Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching.

    Science.gov (United States)

    Wu, Yuanyuan; Cao, Jinde; Li, Qingbo; Alsaedi, Ahmed; Alsaadi, Fuad E

    2017-01-01

    This paper deals with the finite-time synchronization problem for a class of uncertain coupled switched neural networks under asynchronous switching. By constructing appropriate Lyapunov-like functionals and using the average dwell time technique, some sufficient criteria are derived to guarantee the finite-time synchronization of considered uncertain coupled switched neural networks. Meanwhile, the asynchronous switching feedback controller is designed to finite-time synchronize the concerned networks. Finally, two numerical examples are introduced to show the validity of the main results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  18. Bond strength between fiber posts and composite resin core: influence of temperature on silane coupling agents.

    Science.gov (United States)

    Novais, Veridiana Resende; Simamotos Júnior, Paulo Cézar; Rontani, Regina Maria Puppin; Correr-Sobrinho, Lourenço; Soares, Carlos José

    2012-01-01

    This study evaluated the effect of air drying temperature and different silane coupling agents on the bond strength between glass fiber posts and composite resin core. The post surface was cleaned with alcohol and treated with different silane coupling agents, being three prehydrolyzed silanes [Silano (Angelus), Prosil (FGM), RelyX Ceramic Primer (3M ESPE)] and one two-component silane [Silane Coupling Agent (Dentsply)]. Two post-silanization air drying temperatures, 23ºC and 60ºC, were applied. A cylindrical plastic matrix was placed around the silanized post and filled with composite resin. Each bonded post provided 7 slices for push-out testing. Each slice was loaded to failure under compression at a cross-head speed of 0.5 mm/min. Data were analyzed by two-way ANOVA and Scott-Knott tests (α=0.05). Dunnett's test was used to compare the mean of the control group with that of each experimental group. Scanning electron microscopy (SEM) was used to evaluate the interface of the fractured slices. For the 23ºC air drying temperature, the use of RelyX Ceramic Primer resulted in significantly lower bond strength than the other silane coupling agents, while the bond strength with Silane Coupling Agent was the highest of all groups. Only with Silane Coupling Agent, the bond strength for the 23ºC air drying temperature was significantly higher than that for 60ºC air drying. In conclusion, the use of warm air drying after silane application produced no increase in the bond strength between the fiber-reinforced composite post and the composite core. The two-component silane produced higher bond strength than all prehydrolyzed silanes when it was used with air drying at room temperature.

  19. Prediction of Compressive Strength of Concrete Using Artificial Neural Network and Genetic Programming

    Directory of Open Access Journals (Sweden)

    Palika Chopra

    2016-01-01

    Full Text Available An effort has been made to develop concrete compressive strength prediction models with the help of two emerging data mining techniques, namely, Artificial Neural Networks (ANNs and Genetic Programming (GP. The data for analysis and model development was collected at 28-, 56-, and 91-day curing periods through experiments conducted in the laboratory under standard controlled conditions. The developed models have also been tested on in situ concrete data taken from literature. A comparison of the prediction results obtained using both the models is presented and it can be inferred that the ANN model with the training function Levenberg-Marquardt (LM for the prediction of concrete compressive strength is the best prediction tool.

  20. Determination of the quark coupling strength $|V_{ub}|$ using baryonic decays

    CERN Document Server

    Aaij, Roel; Adinolfi, Marco; Affolder, Anthony; Ajaltouni, Ziad; Akar, Simon; Albrecht, Johannes; Alessio, Federico; Alexander, Michael; Ali, Suvayu; Alkhazov, Georgy; Alvarez Cartelle, Paula; Alves Jr, Antonio Augusto; Amato, Sandra; Amerio, Silvia; Amhis, Yasmine; An, Liupan; Anderlini, Lucio; Anderson, Jonathan; Andreotti, Mirco; Andrews, Jason; Appleby, Robert; Aquines Gutierrez, Osvaldo; Archilli, Flavio; Artamonov, Alexander; Artuso, Marina; Aslanides, Elie; Auriemma, Giulio; Baalouch, Marouen; Bachmann, Sebastian; Back, John; Badalov, Alexey; Baesso, Clarissa; Baldini, Wander; Barlow, Roger; Barschel, Colin; Barsuk, Sergey; Barter, William; Batozskaya, Varvara; Battista, Vincenzo; Bay, Aurelio; Beaucourt, Leo; Beddow, John; Bedeschi, Franco; Bediaga, Ignacio; Belyaev, Ivan; Ben-Haim, Eli; Bencivenni, Giovanni; Benson, Sean; Benton, Jack; Berezhnoy, Alexander; Bernet, Roland; Bertolin, Alessandro; Bettler, Marc-Olivier; van Beuzekom, Martinus; Bien, Alexander; Bifani, Simone; Bird, Thomas; Bizzeti, Andrea; Blake, Thomas; Blanc, Frédéric; Blouw, Johan; Blusk, Steven; Bocci, Valerio; Bondar, Alexander; Bondar, Nikolay; Bonivento, Walter; Borghi, Silvia; Borsato, Martino; Bowcock, Themistocles; Bowen, Espen Eie; Bozzi, Concezio; Braun, Svende; Brett, David; Britsch, Markward; Britton, Thomas; Brodzicka, Jolanta; Brook, Nicholas; Bursche, Albert; Buytaert, Jan; Cadeddu, Sandro; Calabrese, Roberto; Calvi, Marta; Calvo Gomez, Miriam; Campana, Pierluigi; Campora Perez, Daniel; Capriotti, Lorenzo; Carbone, Angelo; Carboni, Giovanni; Cardinale, Roberta; Cardini, Alessandro; Carniti, Paolo; Carson, Laurence; Carvalho Akiba, Kazuyoshi; Casanova Mohr, Raimon; Casse, Gianluigi; Cassina, Lorenzo; Castillo Garcia, Lucia; Cattaneo, Marco; Cauet, Christophe; Cavallero, Giovanni; Cenci, Riccardo; Charles, Matthew; Charpentier, Philippe; Chefdeville, Maximilien; Chen, Shanzhen; Cheung, Shu-Faye; Chiapolini, Nicola; Chrzaszcz, Marcin; Cid Vidal, Xabier; Ciezarek, Gregory; Clarke, Peter; Clemencic, Marco; Cliff, Harry; Closier, Joel; Coco, Victor; Cogan, Julien; Cogneras, Eric; Cogoni, Violetta; Cojocariu, Lucian; Collazuol, Gianmaria; Collins, Paula; Comerma-Montells, Albert; Contu, Andrea; Cook, Andrew; Coombes, Matthew; Coquereau, Samuel; Corti, Gloria; Corvo, Marco; Counts, Ian; Couturier, Benjamin; Cowan, Greig; Craik, Daniel Charles; Crocombe, Andrew; Cruz Torres, Melissa Maria; Cunliffe, Samuel; Currie, Robert; D'Ambrosio, Carmelo; Dalseno, Jeremy; David, Pieter; Davis, Adam; De Bruyn, Kristof; De Capua, Stefano; De Cian, Michel; De Miranda, Jussara; De Paula, Leandro; De Silva, Weeraddana; De Simone, Patrizia; Dean, Cameron Thomas; Decamp, Daniel; Deckenhoff, Mirko; Del Buono, Luigi; Déléage, Nicolas; Derkach, Denis; Deschamps, Olivier; Dettori, Francesco; Dey, Biplab; Di Canto, Angelo; Di Ruscio, Francesco; Dijkstra, Hans; Donleavy, Stephanie; Dordei, Francesca; Dorigo, Mirco; Dosil Suárez, Alvaro; Dossett, David; Dovbnya, Anatoliy; Dreimanis, Karlis; Dufour, Laurent; Dujany, Giulio; Dupertuis, Frederic; Durante, Paolo; Dzhelyadin, Rustem; Dziurda, Agnieszka; Dzyuba, Alexey; Easo, Sajan; Egede, Ulrik; Egorychev, Victor; Eidelman, Semen; Eisenhardt, Stephan; Eitschberger, Ulrich; Ekelhof, Robert; Eklund, Lars; El Rifai, Ibrahim; Elsasser, Christian; Ely, Scott; Esen, Sevda; Evans, Hannah Mary; Evans, Timothy; Falabella, Antonio; Färber, Christian; Farinelli, Chiara; Farley, Nathanael; Farry, Stephen; Fay, Robert; Ferguson, Dianne; Fernandez Albor, Victor; Ferreira Rodrigues, Fernando; Ferro-Luzzi, Massimiliano; Filippov, Sergey; Fiore, Marco; Fiorini, Massimiliano; Firlej, Miroslaw; Fitzpatrick, Conor; Fiutowski, Tomasz; Fol, Philip; Fontana, Marianna; Fontanelli, Flavio; Forty, Roger; Francisco, Oscar; Frank, Markus; Frei, Christoph; Frosini, Maddalena; Fu, Jinlin; Furfaro, Emiliano; Gallas Torreira, Abraham; Galli, Domenico; Gallorini, Stefano; Gambetta, Silvia; Gandelman, Miriam; Gandini, Paolo; Gao, Yuanning; García Pardiñas, Julián; Garofoli, Justin; Garra Tico, Jordi; Garrido, Lluis; Gascon, David; Gaspar, Clara; Gastaldi, Ugo; Gauld, Rhorry; Gavardi, Laura; Gazzoni, Giulio; Geraci, Angelo; Gersabeck, Evelina; Gersabeck, Marco; Gershon, Timothy; Ghez, Philippe; Gianelle, Alessio; Gianì, Sebastiana; Gibson, Valerie; Giubega, Lavinia-Helena; Gligorov, Vladimir; Göbel, Carla; Golubkov, Dmitry; Golutvin, Andrey; Gomes, Alvaro; Gotti, Claudio; Grabalosa Gándara, Marc; Graciani Diaz, Ricardo; Granado Cardoso, Luis Alberto; Graugés, Eugeni; Graverini, Elena; Graziani, Giacomo; Grecu, Alexandru; Greening, Edward; Gregson, Sam; Griffith, Peter; Grillo, Lucia; Grünberg, Oliver; Gui, Bin; Gushchin, Evgeny; Guz, Yury; Gys, Thierry; Hadjivasiliou, Christos; Haefeli, Guido; Haen, Christophe; Haines, Susan; Hall, Samuel; Hamilton, Brian; Hampson, Thomas; Han, Xiaoxue; Hansmann-Menzemer, Stephanie; Harnew, Neville; Harnew, Samuel; Harrison, Jonathan; He, Jibo; Head, Timothy; Heijne, Veerle; Hennessy, Karol; Henrard, Pierre; Henry, Louis; Hernando Morata, Jose Angel; van Herwijnen, Eric; Heß, Miriam; Hicheur, Adlène; Hill, Donal; Hoballah, Mostafa; Hombach, Christoph; Hulsbergen, Wouter; Humair, Thibaud; Hussain, Nazim; Hutchcroft, David; Hynds, Daniel; Idzik, Marek; Ilten, Philip; Jacobsson, Richard; Jaeger, Andreas; Jalocha, Pawel; Jans, Eddy; Jawahery, Abolhassan; Jing, Fanfan; John, Malcolm; Johnson, Daniel; Jones, Christopher; Joram, Christian; Jost, Beat; Jurik, Nathan; Kandybei, Sergii; Kanso, Walaa; Karacson, Matthias; Karbach, Moritz; Karodia, Sarah; Kelsey, Matthew; Kenyon, Ian; Kenzie, Matthew; Ketel, Tjeerd; Khanji, Basem; Khurewathanakul, Chitsanu; Klaver, Suzanne; Klimaszewski, Konrad; Kochebina, Olga; Kolpin, Michael; Komarov, Ilya; Koopman, Rose; Koppenburg, Patrick; Korolev, Mikhail; Kravchuk, Leonid; Kreplin, Katharina; Kreps, Michal; Krocker, Georg; Krokovny, Pavel; Kruse, Florian; Kucewicz, Wojciech; Kucharczyk, Marcin; Kudryavtsev, Vasily; Kurek, Krzysztof; Kvaratskheliya, Tengiz; La Thi, Viet Nga; Lacarrere, Daniel; Lafferty, George; Lai, Adriano; Lambert, Dean; Lambert, Robert W; Lanfranchi, Gaia; Langenbruch, Christoph; Langhans, Benedikt; Latham, Thomas; Lazzeroni, Cristina; Le Gac, Renaud; van Leerdam, Jeroen; Lees, Jean-Pierre; Lefèvre, Regis; Leflat, Alexander; Lefrançois, Jacques; Leroy, Olivier; Lesiak, Tadeusz; Leverington, Blake; Li, Yiming; Likhomanenko, Tatiana; Liles, Myfanwy; Lindner, Rolf; Linn, Christian; Lionetto, Federica; Liu, Bo; Lohn, Stefan; Longstaff, Iain; Lopes, Jose; Lowdon, Peter; Lucchesi, Donatella; Luo, Haofei; Lupato, Anna; Luppi, Eleonora; Lupton, Oliver; Machefert, Frederic; Maciuc, Florin; Maev, Oleg; Maguire, Kevin; Malde, Sneha; Malinin, Alexander; Manca, Giulia; Mancinelli, Giampiero; Manning, Peter Michael; Mapelli, Alessandro; Maratas, Jan; Marchand, Jean François; Marconi, Umberto; Marin Benito, Carla; Marino, Pietro; Märki, Raphael; Marks, Jörg; Martellotti, Giuseppe; Martinelli, Maurizio; Martinez Santos, Diego; Martinez Vidal, Fernando; Martins Tostes, Danielle; Massafferri, André; Matev, Rosen; Mathad, Abhijit; Mathe, Zoltan; Matteuzzi, Clara; Mauri, Andrea; Maurin, Brice; Mazurov, Alexander; McCann, Michael; McCarthy, James; McNab, Andrew; McNulty, Ronan; Meadows, Brian; Meier, Frank; Meissner, Marco; Merk, Marcel; Milanes, Diego Alejandro; Minard, Marie-Noelle; Molina Rodriguez, Josue; Monteil, Stephane; Morandin, Mauro; Morawski, Piotr; Mordà, Alessandro; Morello, Michael Joseph; Moron, Jakub; Morris, Adam Benjamin; Mountain, Raymond; Muheim, Franz; Müller, Katharina; Mussini, Manuel; Muster, Bastien; Naik, Paras; Nakada, Tatsuya; Nandakumar, Raja; Nasteva, Irina; Needham, Matthew; Neri, Nicola; Neubert, Sebastian; Neufeld, Niko; Neuner, Max; Nguyen, Anh Duc; Nguyen, Thi-Dung; Nguyen-Mau, Chung; Niess, Valentin; Niet, Ramon; Nikitin, Nikolay; Nikodem, Thomas; Novoselov, Alexey; O'Hanlon, Daniel Patrick; Oblakowska-Mucha, Agnieszka; Obraztsov, Vladimir; Ogilvy, Stephen; Okhrimenko, Oleksandr; Oldeman, Rudolf; Onderwater, Gerco; Osorio Rodrigues, Bruno; Otalora Goicochea, Juan Martin; Otto, Adam; Owen, Patrick; Oyanguren, Maria Arantza; Palano, Antimo; Palombo, Fernando; Palutan, Matteo; Panman, Jacob; Papanestis, Antonios; Pappagallo, Marco; Pappalardo, Luciano; Parkes, Christopher; Passaleva, Giovanni; Patel, Girish; Patel, Mitesh; Patrignani, Claudia; Pearce, Alex; Pellegrino, Antonio; Penso, Gianni; Pepe Altarelli, Monica; Perazzini, Stefano; Perret, Pascal; Pescatore, Luca; Pesen, Erhan; Petridis, Konstantin; Petrolini, Alessandro; Picatoste Olloqui, Eduardo; Pietrzyk, Boleslaw; Pilař, Tomas; Pinci, Davide; Pistone, Alessandro; Playfer, Stephen; Plo Casasus, Maximo; Poikela, Tuomas; Polci, Francesco; Poluektov, Anton; Polyakov, Ivan; Polycarpo, Erica; Popov, Alexander; Popov, Dmitry; Popovici, Bogdan; Potterat, Cédric; Price, Eugenia; Price, Joseph David; Prisciandaro, Jessica; Pritchard, Adrian; Prouve, Claire; Pugatch, Valery; Puig Navarro, Albert; Punzi, Giovanni; Qian, Wenbin; Quagliani, Renato; Rachwal, Bartolomiej; Rademacker, Jonas; Rakotomiaramanana, Barinjaka; Rama, Matteo; Rangel, Murilo; Raniuk, Iurii; Rauschmayr, Nathalie; Raven, Gerhard; Redi, Federico; Reichert, Stefanie; Reid, Matthew; dos Reis, Alberto; Ricciardi, Stefania; Richards, Sophie; Rihl, Mariana; Rinnert, Kurt; Rives Molina, Vincente; Robbe, Patrick; Rodrigues, Ana Barbara; Rodrigues, Eduardo; Rodriguez Lopez, Jairo Alexis; Rodriguez Perez, Pablo; Roiser, Stefan; Romanovsky, Vladimir; Romero Vidal, Antonio; Rotondo, Marcello; Rouvinet, Julien; Ruf, Thomas; Ruiz, Hugo; Ruiz Valls, Pablo; Saborido Silva, Juan Jose; Sagidova, Naylya; Sail, Paul; Saitta, Biagio; Salustino Guimaraes, Valdir; Sanchez Mayordomo, Carlos; Sanmartin Sedes, Brais; Santacesaria, Roberta; Santamarina Rios, Cibran; Santovetti, Emanuele; Sarti, Alessio; Satriano, Celestina; Satta, Alessia; Saunders, Daniel Martin; Savrina, Darya; Schiller, Manuel; Schindler, Heinrich; Schlupp, Maximilian; Schmelling, Michael; Schmidt, Burkhard; Schneider, Olivier; Schopper, Andreas; Schune, Marie Helene; Schwemmer, Rainer; Sciascia, Barbara; Sciubba, Adalberto; Semennikov, Alexander; Sepp, Indrek; Serra, Nicola; Serrano, Justine; Sestini, Lorenzo; Seyfert, Paul; Shapkin, Mikhail; Shapoval, Illya; Shcheglov, Yury; Shears, Tara; Shekhtman, Lev; Shevchenko, Vladimir; Shires, Alexander; Silva Coutinho, Rafael; Simi, Gabriele; Sirendi, Marek; Skidmore, Nicola; Skillicorn, Ian; Skwarnicki, Tomasz; Smith, Anthony; Smith, Edmund; Smith, Eluned; Smith, Jackson; Smith, Mark; Snoek, Hella; Sokoloff, Michael; Soler, Paul; Soomro, Fatima; Souza, Daniel; Souza De Paula, Bruno; Spaan, Bernhard; Spradlin, Patrick; Sridharan, Srikanth; Stagni, Federico; Stahl, Marian; Stahl, Sascha; Steinkamp, Olaf; Stenyakin, Oleg; Sterpka, Christopher Francis; Stevenson, Scott; Stoica, Sabin; Stone, Sheldon; Storaci, Barbara; Stracka, Simone; Straticiuc, Mihai; Straumann, Ulrich; Stroili, Roberto; Sun, Liang; Sutcliffe, William; Swientek, Krzysztof; Swientek, Stefan; Syropoulos, Vasileios; Szczekowski, Marek; Szczypka, Paul; Szumlak, Tomasz; T'Jampens, Stephane; Teklishyn, Maksym; Tellarini, Giulia; Teubert, Frederic; Thomas, Christopher; Thomas, Eric; van Tilburg, Jeroen; Tisserand, Vincent; Tobin, Mark; Todd, Jacob; Tolk, Siim; Tomassetti, Luca; Tonelli, Diego; Topp-Joergensen, Stig; Torr, Nicholas; Tournefier, Edwige; Tourneur, Stephane; Trabelsi, Karim; Tran, Minh Tâm; Tresch, Marco; Trisovic, Ana; Tsaregorodtsev, Andrei; Tsopelas, Panagiotis; Tuning, Niels; Ukleja, Artur; Ustyuzhanin, Andrey; Uwer, Ulrich; Vacca, Claudia; Vagnoni, Vincenzo; Valenti, Giovanni; Vallier, Alexis; Vazquez Gomez, Ricardo; Vazquez Regueiro, Pablo; Vázquez Sierra, Carlos; Vecchi, Stefania; Velthuis, Jaap; Veltri, Michele; Veneziano, Giovanni; Vesterinen, Mika; Viana Barbosa, Joao Vitor; Viaud, Benoit; Vieira, Daniel; Vieites Diaz, Maria; Vilasis-Cardona, Xavier; Vollhardt, Achim; Volyanskyy, Dmytro; Voong, David; Vorobyev, Alexey; Vorobyev, Vitaly; Voß, Christian; de Vries, Jacco; Waldi, Roland; Wallace, Charlotte; Wallace, Ronan; Walsh, John; Wandernoth, Sebastian; Wang, Jianchun; Ward, David; Watson, Nigel; Websdale, David; Weiden, Andreas; Whitehead, Mark; Wiedner, Dirk; Wilkinson, Guy; Wilkinson, Michael; Williams, Mark Richard James; Williams, Matthew; Williams, Mike; Wilschut, Hans; Wilson, Fergus; Wimberley, Jack; Wishahi, Julian; Wislicki, Wojciech; Witek, Mariusz; Wormser, Guy; Wotton, Stephen; Wright, Simon; Wyllie, Kenneth; Xie, Yuehong; Xu, Zhirui; Yang, Zhenwei; Yuan, Xuhao; Yushchenko, Oleg; Zangoli, Maria; Zavertyaev, Mikhail; Zhang, Liming; Zhang, Yanxi; Zhelezov, Alexey; Zhokhov, Anatoly; Zhong, Liang

    2015-01-01

    In the Standard Model of particle physics, the strength of the couplings of the $b$ quark to the $u$ and $c$ quarks, $|V_{ub}|$ and $|V_{cb}|$, are governed by the coupling of the quarks to the Higgs boson. Using data from the LHCb experiment at the Large Hadron Collider, the probability for the $\\Lambda^0_b$ baryon to decay into the $p \\mu^- \\overline{\

  1. Neural substrate for brain stimulation reward in the rat: cathodal and anodal strength-duration properties.

    Science.gov (United States)

    Matthews, G

    1977-08-01

    The trade-off between current strength and duration of a stimulating pulse was studied for the rewarding and priming effects of brain stimulation reward (BSR). With cathodal pulses, strenght-duration functions for BSR had chronaxies of .8-3 msec. No differences were observed between the results for rewarding and priming effects. With anodal pulses. strength-duration curves were parallel to the cathodal curves at pulse durations of .1-5 msec, but at pulse durations greater than 5 msec the anodal curves showed a greater drop in required current intensity than did the cathodal curves. The parallel portion of the anodal curves was interpreted as due to anode-make excitation, and the drop at longer pulse durations was interpreted as due to anode-break excitation. Cathodal strength-duration functions for the motor effect elicited through the BSR electrodes had chronaxies of .15-.48 msec. Measurements of the latency of the muscle twitch confirmed that anode-make and anode-break excitation occurred, the latter becoming evident at pulse durations as brief as .3-.4 msec. The results provide quantitative characterization of cathodal and anodal strength-duration properties of the neural substrate for BSR and are discussed in terms of their value in guiding electrophysiological investigation of that substrate.

  2. G-protein-coupled receptor signaling and neural tube closure defects.

    Science.gov (United States)

    Shimada, Issei S; Mukhopadhyay, Saikat

    2017-01-30

    Disruption of the normal mechanisms that mediate neural tube closure can result in neural tube defects (NTDs) with devastating consequences in affected patients. With the advent of next-generation sequencing, we are increasingly detecting mutations in multiple genes in NTD cases. However, our ability to determine which of these genes contribute to the malformation is limited by our understanding of the pathways controlling neural tube closure. G-protein-coupled receptors (GPCRs) comprise the largest family of transmembrane receptors in humans and have been historically favored as drug targets. Recent studies implicate several GPCRs and downstream signaling pathways in neural tube development and closure. In this review, we will discuss our current understanding of GPCR signaling pathways in pathogenesis of NTDs. Notable examples include the orphan primary cilia-localized GPCR, Gpr161 that regulates the basal suppression machinery of sonic hedgehog pathway by means of activation of cAMP-protein kinase A signaling in the neural tube, and protease-activated receptors that are activated by a local network of membrane-tethered proteases during neural tube closure involving the surface ectoderm. Understanding the role of these GPCR-regulated pathways in neural tube development and closure is essential toward identification of underlying genetic causes to prevent NTDs. Birth Defects Research 109:129-139, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  3. Noise controlled synchronization in potassium coupled neural models

    DEFF Research Database (Denmark)

    Postnov, Dmitry E; Ryazanova, Ludmila S; Zhirin, Roman A

    2007-01-01

    The paper applies biologically plausible models to investigate how noise input to small ensembles of neurons, coupled via the extracellular potassium concentration, can influence their firing patterns. Using the noise intensity and the volume of the extracellular space as control parameters, we...... show that potassium induced depolarization underlies the formation of noise-induced patterns such as delayed firing and synchronization. These phenomena are associated with the appearance of new time scales in the distribution of interspike intervals that may be significant for the spatio...

  4. Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism.

    Science.gov (United States)

    Li, Lulu; Ho, Daniel W C; Cao, Jinde; Lu, Jianquan

    2016-04-01

    Cluster synchronization is a typical collective behavior in coupled dynamical systems, where the synchronization occurs within one group, while there is no synchronization among different groups. In this paper, under event-based mechanism, pinning cluster synchronization in an array of coupled neural networks is studied. A new event-triggered sampled-data transmission strategy, where only local and event-triggering states are utilized to update the broadcasting state of each agent, is proposed to realize cluster synchronization of the coupled neural networks. Furthermore, a self-triggered pinning cluster synchronization algorithm is proposed, and a set of iterative procedures is given to compute the event-triggered time instants. Hence, this will reduce the computational load significantly. Finally, an example is given to demonstrate the effectiveness of the theoretical results. Crown Copyright © 2015. Published by Elsevier Ltd. All rights reserved.

  5. Strength training-induced responses in older adults: attenuation of descending neural drive with age.

    Science.gov (United States)

    Unhjem, Runar; Lundestad, Raymond; Fimland, Marius Steiro; Mosti, Mats Peder; Wang, Eivind

    2015-06-01

    Although reductions in resting H-reflex responses and maximal firing frequency suggest that reduced efferent drive may limit muscle strength in elderly, there are currently no reports of V-wave measurements in elderly, reflecting the magnitude of efferent output to the muscle during maximal contraction. Furthermore, it is uncertain whether potential age-related neural deficiencies can be restored by resistance training. We assessed evoked reflex recordings in the triceps surae muscles during rest and maximal voluntary contraction (MVC), rate of force development (RFD), and muscle mass in seven elderly (74 ± 6 years) males before and after 8 weeks of heavy resistance training, contrasted by seven young (24 ± 4 years) male controls. At baseline, m. soleus (SOL) V/M ratio (0.124 ± 0.082 vs. 0.465 ± 0.197, p elderly compared to young. Also, SOL H-reflex latency (33.29 ± 2.41 vs. 30.29 ± 0.67 ms, p elderly. The reduced neural drive was, despite similar leg muscle mass (10.7 ± 1.2 vs. 11.5 ± 1.4 kg), mirrored by lower MVC (158 ± 48 vs. 240 ± 54 Nm, p elderly. In response to training SOL V/M ratio (0.184 ± 0.092, p elderly, yet only to a level ~40 % of the young. This was accompanied by increased MVC (190 ± 70 Nm, p muscle strength. Furthermore, this motor system impairment can to some extent be improved by heavy resistance training.

  6. Prediction of zeolite-cement-sand unconfined compressive strength using polynomial neural network

    Science.gov (United States)

    MolaAbasi, H.; Shooshpasha, I.

    2016-04-01

    The improvement of local soils with cement and zeolite can provide great benefits, including strengthening slopes in slope stability problems, stabilizing problematic soils and preventing soil liquefaction. Recently, dosage methodologies are being developed for improved soils based on a rational criterion as it exists in concrete technology. There are numerous earlier studies showing the possibility of relating Unconfined Compressive Strength (UCS) and Cemented sand (CS) parameters (voids/cement ratio) as a power function fits. Taking into account the fact that the existing equations are incapable of estimating UCS for zeolite cemented sand mixture (ZCS) well, artificial intelligence methods are used for forecasting them. Polynomial-type neural network is applied to estimate the UCS from more simply determined index properties such as zeolite and cement content, porosity as well as curing time. In order to assess the merits of the proposed approach, a total number of 216 unconfined compressive tests have been done. A comparison is carried out between the experimentally measured UCS with the predictions in order to evaluate the performance of the current method. The results demonstrate that generalized polynomial-type neural network has a great ability for prediction of the UCS. At the end sensitivity analysis of the polynomial model is applied to study the influence of input parameters on model output. The sensitivity analysis reveals that cement and zeolite content have significant influence on predicting UCS.

  7. What the diurnal cycle of precipitation tells us about land-atmosphere coupling strength

    Science.gov (United States)

    Ferguson, Craig; Song, Hyojong; Roundy, Joshua

    2015-04-01

    The key attributes of a coupled forecast model are the coupling strengths between the land-atmosphere and ocean-atmosphere schemes. If a model cannot skillfully capture the diurnal cycle of clouds and precipitation, then it likely cannot be expected to yield accurate long-term climate projections. The seasonal drought forecast skill shortfalls of the U.S. NCEP Coupled Forecast System Version 2 (CFSv2) have been directly linked to its unrealistically strong land-atmosphere coupling strength. Most models can be similarly categorized, which is to say, sensitivity to the land physics (i.e., soil moisture constraints on evapotranspiration) is too strong. In nature, the land signal: noise ratio appears to be at a much lower value. Diagnosing land-atmosphere coupling strength requires at a minimum: surface soil moisture state, surface turbulent heat fluxes, and atmospheric moisture and instability. Full-on diagnosis would entail hacking into the code and inserting a number of tracers. This study addresses the question: What if, given the soil wetness anomaly, model biases in coupling sign and/or strength could be diagnosed from phase shifts in the diurnal precipitation frequency cycle? We use 34-years of output from the North American Regional Reanalysis (NARR) and North American Land Data Assimilation System Phase 2 (NLDAS-2) to investigate the variation in diurnal precipitation frequency cycle between so-called "wet-advantage" and "dry-advantage" coupling regimes over the U.S. southern Great Plains. Wet-advantage occurs when the atmospheric state is closer to the wet adiabatic rate and convection is triggered by a strong increase in the moist static energy from the surface. In contrast, dry-advantage occurs when the atmosphere is drier and the temperature profile is close to the dry adiabatic lapse rate, which favors convection over areas of large boundary layer growth due to high sensible heat fluxes at the surface. We find that there is a significant difference in the

  8. Strain-mediated converse magnetoelectric coupling strength manipulation by a thin titanium layer

    Science.gov (United States)

    Yang, Wei-Gang; Morley, Nicola A.; Sharp, Joanne; Tian, Ye; Rainforth, W. Mark

    2016-01-01

    The manipulation of the strain-mediated magnetoelectric (ME) coupling strength is investigated by inserting a thin Ti layer (0-10 nm) between a 50 nm Co50Fe50 layer and a (011) oriented lead magnesium niobate-lead titanate (PMN-PT) substrate. A record high remanence ratio (Mr/Ms) tunability of 100% has been demonstrated in the 50 nm CoFe/8 nm Ti/PMN-PT heterostructure, when a total in-plane piezoelectric strain of -1821 ppm was applied at an electric field (E-field) of 16 kV/cm. The ME coupling strength is gradually optimized as the Ti layer thickness increases. Magnetic energy calculation showed that with increasing Ti layer thickness the uniaxial magnetic anisotropy energy (Euni) was reduced from 43 ± 1 kJ/m3 to 29.8 ± 1 kJ/m3. The reduction of Euni makes the strain effect dominant in the total magnetic energy, thus gives an obvious enhanced ME coupling strength.

  9. Predicting manual arm strength: A direct comparison between artificial neural network and multiple regression approaches.

    Science.gov (United States)

    La Delfa, Nicholas J; Potvin, Jim R

    2016-02-29

    In ergonomics, strength prediction has typically been accomplished using linked-segment biomechanical models, and independent estimates of strength about each axis of the wrist, elbow and shoulder joints. It has recently been shown that multiple regression approaches, using the simple task-relevant inputs of hand location and force direction, may be a better method for predicting manual arm strength (MAS) capabilities. Artificial neural networks (ANNs) also serve as a powerful data fitting approach, but their application to occupational biomechanics and ergonomics is limited. Therefore, the purpose of this study was to perform a direct comparison between ANN and regression models, by evaluating their ability to predict MAS with identical sets of development and validation MAS data. Multi-directional MAS data were obtained from 95 healthy female participants at 36 hand locations within the reach envelope. ANN and regression models were developed using a random, but identical, sample of 85% of the MAS data (n=456). The remaining 15% of the data (n=80) were used to validate the two approaches. When compared to the development data, the ANN predictions had a much higher explained variance (90.2% vs. 66.5%) and much lower RMSD (9.3N vs. 17.2N), vs. the regression model. The ANN also performed better with the independent validation data (r(2)=78.6%, RMSD=15.1) compared to the regression approach (r(2)=65.3%, RMSD=18.6N). These results suggest that ANNs provide a more accurate and robust alternative to regression approaches, and should be considered more often in biomechanics and ergonomics evaluations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Dynamics in a Delayed Neural Network Model of Two Neurons with Inertial Coupling

    Directory of Open Access Journals (Sweden)

    Changjin Xu

    2012-01-01

    Full Text Available A delayed neural network model of two neurons with inertial coupling is dealt with in this paper. The stability is investigated and Hopf bifurcation is demonstrated. Applying the normal form theory and the center manifold argument, we derive the explicit formulas for determining the properties of the bifurcating periodic solutions. An illustrative example is given to demonstrate the effectiveness of the obtained results.

  11. Blood Cell Segmentation Based on Improved Pulse Coupled Neural Network and Fuzzy Entropy

    OpenAIRE

    Zhanbo Liu; Fang Wang; Shi Yan; Rui Huang

    2016-01-01

    In the field of biomedical image processing, because of the low intensity and brightness of the cell image, and the complex structure of the cell image, the segmentation of cell images is very difficult. A large number of studies have shown that the Pulse Coupled Neural Networks (PCNN) is suitable for image segmentation. However, the traditional PCNN must set a large number of parameters in image segmentation, and the optimal number of iterations cannot be automatically determined. In this pa...

  12. Synchronization in Array of Coupled Neural Networks with Unbounded Distributed Delay and Limited Transmission Efficiency

    Directory of Open Access Journals (Sweden)

    Xinsong Yang

    2013-01-01

    Full Text Available This paper investigates global synchronization in an array of coupled neural networks with time-varying delays and unbounded distributed delays. In the coupled neural networks, limited transmission efficiency between coupled nodes, which makes the model more practical, is considered. Based on a novel integral inequality and the Lyapunov functional method, sufficient synchronization criteria are derived. The derived synchronization criteria are formulated by linear matrix inequalities (LMIs and can be easily verified by using Matlab LMI Toolbox. It is displayed that, when some of the transmission efficiencies are limited, the dynamics of the synchronized state are different from those of the isolated node. Furthermore, the transmission efficiency and inner coupling matrices between nodes play important roles in the final synchronized state. The derivative of the time-varying delay can be any given value, and the time-varying delay can be unbounded. The outer-coupling matrices can be symmetric or asymmetric. Numerical simulations are finally given to demonstrate the effectiveness of the theoretical results.

  13. Strength Analysis of the Carbon-Fiber Reinforced Polymer Impeller Based on Fluid Solid Coupling Method

    Directory of Open Access Journals (Sweden)

    Jinbao Lin

    2014-01-01

    Full Text Available Carbon-fiber reinforced polymer material impeller is designed for the centrifugal pump to deliver corrosive, toxic, and abrasive media in the chemical and pharmaceutical industries. The pressure-velocity coupling fields in the pump are obtained from the CFD simulation. The stress distribution of the impeller couple caused by the flow water pressure and rotation centrifugal force of the blade is analyzed using one-way fluid-solid coupling method. Results show that the strength of the impeller can meet the requirement of the centrifugal pumps, and the largest stress occurred around the blades root on a pressure side of blade surface. Due to the existence of stress concentration at the blades root, the fatigue limit of the impeller would be reduced greatly. In the further structure optimal design, the blade root should be strengthened.

  14. Cross-Coupled Eye Movement Supports Neural Origin of Pattern Strabismus

    Science.gov (United States)

    Ghasia, Fatema F.; Shaikh, Aasef G.; Jacobs, Jonathan; Walker, Mark F.

    2015-01-01

    Purpose. Pattern strabismus describes vertically incomitant horizontal strabismus. Conventional theories emphasized the role of orbital etiologies, such as abnormal fundus torsion and misaligned orbital pulleys as a cause of the pattern strabismus. Experiments in animal models, however, suggested the role of abnormal cross-connections between the neural circuits. We quantitatively assessed eye movements in patients with pattern strabismus with a goal to delineate the role of neural circuits versus orbital etiologies. Methods. We measured saccadic eye movements with high-precision video-oculography in 14 subjects with pattern strabismus, 5 with comitant strabismus, and 15 healthy controls. We assessed change in eye position in the direction orthogonal to that of the desired eye movement (cross-coupled responses). We used fundus photography to quantify the fundus torsion. Results. We found cross-coupling of saccades in all patients with pattern strabismus. The cross-coupled responses were in the same direction in both eyes, but larger in the nonviewing eye. All patients had clinically apparent inferior oblique overaction with abnormal excylotorsion. There was no correlation between the amount of the fundus torsion or the grade of oblique overaction and the severity of cross-coupling. The disconjugacy in the saccade direction and amplitude in pattern strabismics did not have characteristics predicted by clinically apparent inferior oblique overaction. Conclusions. Our results validated primate models of pattern strabismus in human patients. We found no correlation between ocular torsion or oblique overaction and cross-coupling. Therefore, we could not ascribe cross-coupling exclusively to the orbital etiology. Patients with pattern strabismus could have abnormalities in the saccade generators. PMID:26024072

  15. Plasticity in central neural drive with short-term disuse and recovery - effects on muscle strength and influence of aging.

    Science.gov (United States)

    Hvid, L G; Aagaard, P; Ørtenblad, N; Kjaer, M; Suetta, C

    2018-02-21

    While short-term disuse negatively affects mechanical muscle function (e.g. isometric muscle strength) little is known of the relative contribution of adaptions in central neural drive and peripheral muscle contractility. The present study investigated the relative contribution of adaptations in central neural drive and peripheral muscle contractility on changes in isometric muscle strength following short-term unilateral disuse (4 days, knee brace) and subsequent active recovery (7 days, one session of resistance training) in young (n = 11, 24 yrs) and old healthy men (n = 11, 67 yrs). Maximal isometric knee extensor strength (MVC) (isokinetic dynamometer), voluntary muscle activation (superimposed twitch technique), and electrically evoked muscle twitch force (single and doublet twitch stimulation) were assessed prior to and after disuse, and after recovery. Following disuse, relative decreases in MVC did not differ statistically between old (16.4 ± 3.7%, p plasticity in voluntary muscle activation (~central neural drive) is a dominant mechanism affecting short-term disuse- and recovery-induced changes in muscle strength in older adults. Copyright © 2017. Published by Elsevier Inc.

  16. Implications of movement-related cortical potential for understanding neural adaptations in muscle strength tasks

    Science.gov (United States)

    2014-01-01

    This systematic review aims to provide information about the implications of the movement-related cortical potential (MRCP) in acute and chronic responses to the counter resistance training. The structuring of the methods of this study followed the proposals of the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses). It was performed an electronically search in Pubmed/Medline and ISI Web of Knowledge data bases, from 1987 to 2013, besides the manual search in the selected references. The following terms were used: Bereitschaftspotential, MRCP, strength and force. The logical operator “AND” was used to combine descriptors and terms used to search publications. At the end, 11 studies attended all the eligibility criteria and the results demonstrated that the behavior of MRCP is altered because of different factors such as: force level, rate of force development, fatigue induced by exercise, and the specific phase of muscular action, leading to an increase in the amplitude in eccentric actions compared to concentric actions, in acute effects. The long-term adaptations demonstrated that the counter resistance training provokes an attenuation in the amplitude in areas related to the movement, which may be caused by neural adaptation occurred in the motor cortex. PMID:24602228

  17. Synchronization in a neural network of phase oscillators with time delayed coupling

    Science.gov (United States)

    Luzyanina, T. B.

    1994-08-01

    We investigate a neural network model designed as a system of the central oscillator and peripheral oscillators interacting with a time delay τ in a phase-locking loop. The delay corresponds to the finite velocity of signal propagation along nerve fibers. We study the synchronization under various values of τ. It is shown that under some conditions for a finite delay time there exist a multitude of synchronization frequencies in contrast to the case without delay where one has at most one solution. The criteria for the existence of multiple solutions and their stability are found. The asymptotic behavior under increasing connection strengths is analyzed.

  18. Enhanced oscillator strength of interband transitions in coupled Ge /Si quantum dots

    Science.gov (United States)

    Yakimov, A. I.; Bloshsin, A. A.; Dvurechenskii, A. V.

    2008-09-01

    We report a calculation of oscillator strength for the Δ1-Γ25' interband transition in two vertically coupled pyramidal Ge quantum dots embedded in Si. A six-band kṡp formalism was used to study the Γ25' hole states, and a single-band approach was used to obtain the Δ1 electron state interacting with the hole. The elastic strain due to the lattice mismatch between Ge and Si was included into the problem via the Bir-Pikus Hamiltonian. We find that when two dots are brought closely together, the oscillator strength may enlarge by a factor of about 2 as compared to the single-dot system.

  19. The role of frictional strength on plate coupling at the subduction interface

    KAUST Repository

    Tan, Eh

    2012-10-01

    At a subduction zone the amount of friction between the incoming plate and the forearc is an important factor in controlling the dip angle of subduction and the structure of the forearc. In this paper, we investigate the role of the frictional strength of sediments and of the serpentinized peridotite on the evolution of convergent margins. In numerical models, we vary thickness of a serpentinized layer in the mantle wedge (15 to 25km) and the frictional strength of both the sediments and serpentinized mantle (friction angle 1 to 15, or static friction coefficient 0.017 to 0.27) to control the amount of frictional coupling between the plates. With plastic strain weakening in the lithosphere, our numerical models can attain stable subduction geometry over millions of years. We find that the frictional strength of the sediments and serpentinized peridotite exerts the largest control on the dip angle of the subduction interface at seismogenic depths. In the case of low sediment and serpentinite friction, the subduction interface has a shallow dip, while the subduction zone develops an accretionary prism, a broad forearc high, a deep forearc basin, and a shallow trench. In the high friction case, the subduction interface is steep, the trench is deeper, and the accretionary prism, forearc high and basin are all absent. The resultant free-air gravity and topographic signature of these subduction zone models are consistent with observations. We believe that the low-friction model produces a geometry and forearc structure similar to that of accretionary margins. Conversely, models with high friction angles in sediments and serpentinite develop characteristics of an erosional convergent margin. We find that the strength of the subduction interface is critical in controlling the amount of coupling at the seismogenic zone and perhaps ultimately the size of the largest earthquakes at subduction zones. © 2012. American Geophysical Union. All Rights Reserved.

  20. Recovering lateral variations in lithospheric strength from bedrock motion data using a coupled ice sheet-lithosphere model

    National Research Council Canada - National Science Library

    Berg, J. van den; Wal, R.S.W. van de; Oerlemans, J

    2006-01-01

    A vertically integrated two-dimensional ice flow model was coupled to an elastic lithosphere-Earth model to study the effects of lateral variations in lithospheric strength on local bedrock adjustment...

  1. The Effect of Cooling Rate on the Apparent Bond Strength of Porcelain-Metal Couples,

    Science.gov (United States)

    1981-03-06

    IADR Program and Abstracts 53:742, 1974. 10. Dykema, R. W., Johnston, J. F., and Cunningham, D. M.: The veneered gold crown. The Dental Clinics of...AD-A097 492 ARMY INST OF DENTAL RESEARCH WASHINGTON DC F/G 11/2 THE EFFECT OF COOLING RATE ON THE APPARENT BOND STRENGTH OF POR-’ETC(U) MAR 81 J...porcelain- metal couples John W. Guinn, III, B.S., D.D.S. William H. Griswold, B.S., D.D.S. Stanley G. Vermilyea, B.S.,D.M.D., M.S. U.S. Army Dental

  2. FEAMAC-CARES Software Coupling Development Effort for CMC Stochastic-Strength-Based Damage Simulation

    Science.gov (United States)

    Nemeth, Noel N.; Bednarcyk, Brett A.; Pineda, Evan; Arnold, Steven; Mital, Subodh; Murthy, Pappu; Walton, Owen

    2015-01-01

    Reported here is a coupling of two NASA developed codes: CARES (Ceramics Analysis and Reliability Evaluation of Structures) with the MACGMC composite material analysis code. The resulting code is called FEAMACCARES and is constructed as an Abaqus finite element analysis UMAT (user defined material). Here we describe the FEAMACCARES code and an example problem (taken from the open literature) of a laminated CMC in off-axis loading is shown. FEAMACCARES performs stochastic-strength-based damage simulation response of a CMC under multiaxial loading using elastic stiffness reduction of the failed elements.

  3. The increase in surface EMG could be a misleading measure of neural adaptation during the early gains in strength.

    Science.gov (United States)

    Arabadzhiev, Todor I; Dimitrov, Vladimir G; Dimitrov, George V

    2014-08-01

    To test the validity of using the increase in surface EMG as a measure of neural adaptation during the early gains in strength. Simulation of EMG signals detected by surface bipolar electrode with 20-mm inter-pole distance at different radial distances from the muscle and longitudinal distances from the end-plate area. The increases in the root mean square (RMS) of the EMG signal due to possible alteration in the neural drive or elevation of the intracellular negative after-potentials, detected in fast fatigable muscle fibres during post-tetanic potentiation and assumed to accompany post-activation potentiation, were compared. Lengthening of the intracellular action potential (IAP) profile due to elevation of the negative after-potentials could affect amplitude characteristics of surface EMG detected at any axial distance stronger than alteration in the neural drive. This was irrespective of the fact that the elevation of IAP negative after-potential was applied to fast fatigable motor units (MUs) only, while changes in frequency of activation (simulating neural drive changes) were applied to all MUs. In deeper muscles, where the fibre-electrode distance was larger, the peripheral effect was more pronounced. The normalization of EMG amplitude characteristics to an M-wave one could result only in partial elimination of peripheral factor influence The increase in RMS of surface EMG during the early gains in strength should not be directly related to the changes in the neural drive. The relatively small but long-lasting elevated free resting calcium after high-resistance strength training could result in force potentiation and EMG increase.

  4. Inductive coupling between overhead power lines and nearby metallic pipelines. A neural network approach

    Directory of Open Access Journals (Sweden)

    Levente Czumbil

    2015-12-01

    Full Text Available The current paper presents an artificial intelligence based technique applied in the investigation of electromagnetic interference problems between high voltage power lines (HVPL and nearby underground metallic pipelines (MP. An artificial neural network (NN solution has been implemented by the authors to evaluate the inductive coupling between HVPL and MP for different constructive geometries of an electromagnetic interference problem considering a multi-layer soil structure. Obtained results are compared to solutions provided by a finite element method (FEM based analysis and considered as reference. The advantage of the proposed method yields in a simplified computation model compared to FEM, and implicitly a lower computational time.

  5. Optimizing the Flexural Strength of Beams Reinforced with Fiber Reinforced Polymer Bars Using Back-Propagation Neural Networks

    Directory of Open Access Journals (Sweden)

    Bahman O. Taha

    2015-06-01

    Full Text Available The reinforced concrete with fiber reinforced polymer (FRP bars (carbon, aramid, basalt and glass is used in places where a high ratio of strength to weight is required and corrosion is not acceptable. Behavior of structural members using (FRP bars is hard to be modeled using traditional methods because of the high non-linearity relationship among factors influencing the strength of structural members. Back-propagation neural network is a very effective method for modeling such complicated relationships. In this paper, back-propagation neural network is used for modeling the flexural behavior of beams reinforced with (FRP bars. 101 samples of beams reinforced with fiber bars were collected from literatures. Five important factors are taken in consideration for predicting the strength of beams. Two models of Multilayer Perceptron (MLP are created, first with single-hidden layer and the second with two-hidden layers. The two-hidden layer model showed better accuracy ratio than the single-hidden layer model. Parametric study has been done for two-hidden layer model only. Equations are derived to be used instead of the model and the importance of input factors is determined. Results showed that the neural network is successful in modeling the behavior of concrete beams reinforced with different types of (FRP bars.

  6. Impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach.

    Science.gov (United States)

    Chandrasekar, A; Rakkiyappan, R; Cao, Jinde

    2015-10-01

    This paper studies the impulsive synchronization of Markovian jumping randomly coupled neural networks with partly unknown transition probabilities via multiple integral approach. The array of neural networks are coupled in a random fashion which is governed by Bernoulli random variable. The aim of this paper is to obtain the synchronization criteria, which is suitable for both exactly known and partly unknown transition probabilities such that the coupled neural network is synchronized with mixed time-delay. The considered impulsive effects can be synchronized at partly unknown transition probabilities. Besides, a multiple integral approach is also proposed to strengthen the Markovian jumping randomly coupled neural networks with partly unknown transition probabilities. By making use of Kronecker product and some useful integral inequalities, a novel Lyapunov-Krasovskii functional was designed for handling the coupled neural network with mixed delay and then impulsive synchronization criteria are solvable in a set of linear matrix inequalities. Finally, numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Unified synchronization criteria in an array of coupled neural networks with hybrid impulses.

    Science.gov (United States)

    Wang, Nan; Li, Xuechen; Lu, Jianquan; Alsaadi, Fuad E

    2018-02-07

    This paper investigates the problem of globally exponential synchronization of coupled neural networks with hybrid impulses. Two new concepts on average impulsive interval and average impulsive gain are proposed to deal with the difficulties coming from hybrid impulses. By employing the Lyapunov method combined with some mathematical analysis, some efficient unified criteria are obtained to guarantee the globally exponential synchronization of impulsive networks. Our method and criteria are proved to be effective for impulsively coupled neural networks simultaneously with synchronizing impulses and desynchronizing impulses, and we do not need to discuss these two kinds of impulses separately. Moreover, by using our average impulsive interval method, we can obtain an interesting and valuable result for the case of average impulsive interval T a =∞. For some sparse impulsive sequences with T a =∞, the impulses can happen for infinite number of times, but they do not have essential influence on the synchronization property of networks. Finally, numerical examples including scale-free networks are exploited to illustrate our theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.

  8. Impact of vegetation on land-atmosphere coupling strength and its implication for desertification mitigation over East Asia

    Science.gov (United States)

    Myoung, Boksoon; Choi, Yong-Sang; Choi, Suk-Jin; Park, Seon Ki

    2012-06-01

    Desertification of the East Asian drylands and the consequent dust transport have been serious concerns for adjacent Asian countries as well as the western United States. Tree planting has been considered one applicable strategy to mitigate the desertification. However, the desired effect of the tree planting would not be brought to fruition unless the newly planted trees change the coupling characteristics between the land and the atmosphere. Based on this perception, we attempt to clarify the effects of vegetation on the coupling strength between the atmosphere and land surface, and we suggest the most efficient areas of tree planting for desertification mitigation in East Asia. Using regional vegetation-atmosphere coupled model simulations, coupling strength with and without vegetation was computed and compared with each other. An increased vegetation fraction reduces the coupling strength in June, July, and August (JJA), primarily due to decreased evapotranspiration variability. This effect is pronounced over the Manchurian Plains and the highly populated areas of Beijing and Tianjin. The reduced coupling strength tends to weaken feedback between soil moisture and precipitation as a maintenance mechanism of warm season droughts in the midlatitudes and subsequently decrease the probability of droughts, a finding that is reflected in the enhanced JJA mean soil moisture. However, some drylands like the eastern edges of the Gobi desert present marginal or even opposite changes in coupling strength, meaning a limited effect of vegetation on relieving droughts. Therefore, given limited financial and human resources, acupuncture-like afforestation, i.e., concentrated tree planting in a particular region where the coupling strength can be substantially reduced by vegetation, is an effective strategy to secure long-standing desertification mitigation.

  9. Three applications of pulse-coupled neural networks and an optoelectronic hardware implementation

    Science.gov (United States)

    Banish, Michele R.; Ranganath, Heggere S.; Karpinsky, John R.; Clark, Rodney L.; Germany, Glynn A.; Richards, Philip G.

    1999-03-01

    Pulse Coupled Neural Networks have been extended and modified to suit image segmentation applications. Previous research demonstrated the ability of a PCNN to ignore noisy variations in intensity and small spatial discontinuities in images that prove beneficial to image segmentation and image smoothing. This paper describes four research and development projects that relate to PCNN segmentation - three different signal processing applications and a CMOS integrated circuit implementation. The software for the diagnosis of Pulmonary Embolism from VQ lung scans uses PCNN in single burst mode for segmenting perfusion and ventilation images. The second project is attempting to detect ischemia by comparing 3D SPECT images of the heart obtained during stress and rest conditions, respectively. The third application is a space science project which deals with the study of global aurora images obtained from UV Imager. The paper also describes the hardware implementation of PCNN algorithm as an electro-optical chip.

  10. Geometry-invariant texture retrieval using a dual-output pulse-coupled neural network.

    Science.gov (United States)

    Li, Xiaojun; Ma, Yide; Wang, Zhaobin; Yu, Wenrui

    2012-01-01

    This letter proposes a novel dual-output pulse coupled neural network model (DPCNN). The new model is applied to obtain a more stable texture description in the face of the geometric transformation. Time series, which are computed from output binary images of DPCNN, are employed as translation-, rotation-, scale-, and distortion-invariant texture features. In the experiments, DPCNN has been well tested by using Brodatz's album and the VisTex database. Several existing models are compared with the proposed DPCNN model. The experimental results, based on different testing data sets for images with different translations, orientations, scales, and affine transformations, show that our proposed model outperforms existing models in geometry-invariant texture retrieval. Furthermore, the robustness of DPCNN to noisy data is examined in the experiments.

  11. A scheme of de-synchronization in globally coupled neural networks and its possible implications for vagus nerve stimulation

    Energy Technology Data Exchange (ETDEWEB)

    Li Yanlong [Institute of Theoretical Physics, Lanzhou University of Technology, Lanzhou 730050 (China) and Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000 (China)], E-mail: liyl20031@126.com; Wu Min; Ma Jun [Institute of Theoretical Physics, Lanzhou University of Technology, Lanzhou 730050 (China); Chen Zhaoyang [Department of Chemistry, George Washington University, Washington, DC 20052 (United States); Wang Yinghai [Institute of Theoretical Physics, Lanzhou University, Lanzhou 730000 (China)

    2009-02-15

    A scheme of de-synchronization via pulse stimulation is numerically investigated in the Hindmarsh Rose globally coupled neural networks. The simulations show that synchronization evolves into de-synchronization in the globally coupled HR neural network when a part (about 10%) of neurons are stimulated with a pulse current signal. The network de-synchronization appears to be sensitive to the stimulation parameters. For the case of the same stimulation intensity, those weakly coupled networks reach de-synchronization more easily than strongly coupled networks. There exists a homologous asymptotic behavior in the region of higher frequency, and exist the optimal stimulation interval and period of continuous stimulation time when other stimulation parameters remain invariable.

  12. [Effect of amount of silane coupling agent on flexural strength of dental composite resins reinforced with aluminium borate whisker].

    Science.gov (United States)

    Zhu, Ming-yi; Zhang, Xiu-yin

    2015-06-01

    To evaluate the effect of amount of silane coupling agent on flexural strength of dental composite resins reinforced with aluminium borate whisker (ABW). ABW was surface-treated with 0%, 1%, 2%, 3% and 4% silan coupling agent (γ-MPS), and mixed with resin matrix to synthesize 5 groups of composite resins. After heat-cured at 120 degrees centigrade for 1 h, specimens were tested in three-point flexure to measure strength according to ISO-4049. One specimen was selected randomly from each group and observed under scanning electron microscope (SEM). The data was analyzed with SAS 9.2 software package. The flexural strength (117.93±11.9 Mpa) of the group treated with 2% silane coupling agent was the highest, and significantly different from that of the other 4 groups (α=0.01). The amount of silane coupling agent has impact on the flexural strength of dental composite resins reinforced with whiskers; The flexual strength will be reduced whenever the amount is higher or lower than the threshold. Supported by Research Fund of Science and Technology Committee of Shanghai Municipality (08DZ2271100).

  13. Assessment of covalent bond formation between coupling agents and wood by FTIR spectroscopy and pull strength tests

    DEFF Research Database (Denmark)

    Rasmussen, Jonas Stensgaard; Barsberg, Søren Talbro; Venås, Thomas Mark

    2014-01-01

    of ether linkages between lignin and titanium coupling agent. In the present work, changes were found in the attenuated total reflectance-Fourier transform IR (ATR-FTIR) spectra of lignin and wood mixed with silane, and titanium coupling agents, and to a lesser extent for a zirconium coupling agent....... This was seen as evidence for covalent bonds between lignin phenolics and the coupling agents. No spectral changes were observed when the coupling agents were mixed with the wood constituents cellulose and hemicellulose. For verification of the results, a modified EN 311 wet adhesion pull strength test......In the focus was the question whether metal alkoxide coupling agents – titanium, silane, and zirconium – form covalent bonds to wood and how they improve coating adhesion. In a previous work, a downshift of the lignin infrared (IR) band ∼1600 cm-1 was shown to be consistent with the formation...

  14. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  15. A coupled DEM-DFN approach to rock mass strength characterization

    Science.gov (United States)

    Harthong, Barthelemy; Scholtes, Luc; Donze, Frederic

    2013-04-01

    An enhanced version of the discrete element method (DEM) has been specifically developed for the analysis of fractured rock masses [Scholtes L, Donze F, 2012]. In addition to the discrete representation of the intact medium which enables the description of the localized stress-induced damage caused by heterogeneities inherent to rocks, structural defects can be explicitly taken into account in the modeling to represent pre-existing fractures or discontinuities of size typically larger than the discrete element size. From laboratory scale simulations to slope stability case studies, the capability of this approach to simulate the progressive failure mechanisms occurring in jointed rock are presented is assessed on the basis of referenced experiments and in situ observations. For instance, the challenging wing crack extension, typical of brittle material fracturing, can be successfully reproduced under both compressive and shear loading path, as a result of the progressive coalescence of micro-cracks induced by stress concentration at the tips of pre-existing fractures. In this study, the dedicated DEM is coupled to a discrete fracture network (DFN) model to assess the influence of DFN properties on the mechanical behavior of fractured rock masses where progressive failure can occur. The DFN model assumes the distribution of fractures barycentres to be fractal and the distribution of fracture sizes to follow a power-law distribution [Davy P, Le Goc P, Darcel C, Bour O, de Dreuzy JR, Munier R, 2010]. The proposed DEM/DFN model is used to characterize the influence of clustering and size distribution of pre-existing fractures on the strength of fractured rock masses. The results show that the mechanical behaviour of fractured rock masses is mainly dependent on the fracture intensity. However, for a given fracture intensity, the strength can exhibit a 50 per cent variability depending on the size distribution of the pre-existing fractures. This difference can be

  16. Synchronization transitions induced by the fluctuation of adaptive coupling strength in delayed Newman-Watts neuronal networks.

    Science.gov (United States)

    Wang, Qi; Gong, Yubing; Wu, Yanan

    2015-11-01

    Introducing adaptive coupling in delayed neuronal networks and regulating the dissipative parameter (DP) of adaptive coupling by noise, we study the effect of fluctuations of the changing rate of adaptive coupling on the synchronization of the neuronal networks. It is found that time delay can induce synchronization transitions for intermediate DP values, and the synchronization transitions become strongest when DP is optimal. As the intensity of DP noise is varied, the neurons can also exhibit synchronization transitions, and the phenomenon is delay-dependent and is enhanced for certain time delays. Moreover, the synchronization transitions change with the change of DP and become strongest when DP is optimal. These results show that randomly changing adaptive coupling can considerably change the synchronization of the neuronal networks, and hence could play a crucial role in the information processing and transmission in neural systems. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  17. Modeling compressive strength of recycled aggregate concrete by Artificial Neural Network, Model Tree and Non-linear Regression

    Directory of Open Access Journals (Sweden)

    Neela Deshpande

    2014-12-01

    Full Text Available In the recent past Artificial Neural Networks (ANN have emerged out as a promising technique for predicting compressive strength of concrete. In the present study back propagation was used to predict the 28 day compressive strength of recycled aggregate concrete (RAC along with two other data driven techniques namely Model Tree (MT and Non-linear Regression (NLR. Recycled aggregate is the current need of the hour owing to its environmental friendly aspect of re-use of the construction waste. The study observed that, prediction of 28 day compressive strength of RAC was done better by ANN than NLR and MT. The input parameters were cubic meter proportions of Cement, Natural fine aggregate, Natural coarse Aggregates, recycled aggregates, Admixture and Water (also called as raw data. The study also concluded that ANN performs better when non-dimensional parameters like Sand–Aggregate ratio, Water–total materials ratio, Aggregate–Cement ratio, Water–Cement ratio and Replacement ratio of natural aggregates by recycled aggregates, were used as additional input parameters. Study of each network developed using raw data and each non dimensional parameter facilitated in studying the impact of each parameter on the performance of the models developed using ANN, MT and NLR as well as performance of the ANN models developed with limited number of inputs. The results indicate that ANN learn from the examples and grasp the fundamental domain rules governing strength of concrete.

  18. Effect of anatomical variability on neural stimulation strength and focality in electroconvulsive therapy (ECT) and magnetic seizure therapy (MST).

    Science.gov (United States)

    Deng, Zhi-De; Lisanby, Sarah H; Peterchev, Angel V

    2009-01-01

    We present a quantitative comparison of two metrics-neural stimulation strength and focality-in electrocon-vulsive therapy (ECT) and magnetic seizure therapy (MST) using finite-element method (FEM) simulation in a spherical head model. Five stimulation modalities were modeled, including bilateral ECT, unilateral ECT, focal electrically administered seizure therapy (FEAST), and MST with circular and double-cone coils, with stimulation parameters identical to those applied in clinical practice. We further examine the effect on the stimulation metrics of individual-, sex- and age-related variability in tissue layer thickness and conductivity. Neural stimulation by MST is shown to be more focal and superficial than ECT. This result suggests that it may be advantageous to reduce the current used in ECT. The stimulation strength in MST is also less sensitive to variations in head geometry and tissue conductivity than in ECT. Individualization of pulse amplitude in both ECT and MST could compensate for anatomical variability, which could lead to more consistent clinical outcomes.

  19. Recovering lateral variationin lithospheric strength from bedrock motion data using a coupled ice sheet-lithosphere model

    NARCIS (Netherlands)

    van de Berg, W.J.|info:eu-repo/dai/nl/304831611; van de Wal, R.S.W.|info:eu-repo/dai/nl/101899556; Oerlemans, J.|info:eu-repo/dai/nl/06833656X

    2006-01-01

    A vertically integrated two-dimensional ice flow model was coupled to an elastic lithosphere-Earth model to study the effects of lateral variations in lithospheric strength on local bedrock adjustment. We used a synthetic bedrock profile and a synthetic climate to model a characteristic ice sheet

  20. Recovering lateral variations in lithospheric strength from bedrock motion data using a coupled ice sheet-lithosphere model

    NARCIS (Netherlands)

    Berg, J. van den; Wal, R.S.W. van de; Oerlemans, J.

    2006-01-01

    A vertically integrated two-dimensional ice flow model was coupled to an elastic lithosphere-Earth model to study the effects of lateral variations in lithospheric strength on local bedrock adjustment. We used a synthetic bedrock profile and a synthetic climate to model a characteristic ice sheet

  1. Dynamics of Entanglement in Jaynes–Cummings Nodes with Nonidentical Qubit-Field Coupling Strengths

    Directory of Open Access Journals (Sweden)

    Li-Tuo Shen

    2017-07-01

    Full Text Available How to analytically deal with the general entanglement dynamics of separate Jaynes–Cummings nodes with continuous-variable fields is still an open question, and few analytical approaches can be used to solve their general entanglement dynamics. Entanglement dynamics between two separate Jaynes–Cummings nodes are examined in this article. Both vacuum state and coherent state in the initial fields are considered through the numerical and analytical methods. The gap between two nonidentical qubit-field coupling strengths shifts the revival period and changes the revival amplitude of two-qubit entanglement. For vacuum-state fields, the maximal entanglement is fully revived after a gap-dependence period, within which the entanglement nonsmoothly decreases to zero and partly recovers without exhibiting sudden death phenomenon. For strong coherent-state fields, the two-qubit entanglement decays exponentially as the evolution time increases, exhibiting sudden death phenomenon, and the increasing gap accelerates the revival period and amplitude decay of the entanglement, where the numerical and analytical results have an excellent coincidence.

  2. Calculation of marine propeller static strength based on coupled BEM/FEM

    Directory of Open Access Journals (Sweden)

    YE Liyu

    2017-10-01

    Full Text Available [Objectives] The reliability of propeller stress has a great influence on the safe navigation of a ship. To predict propeller stress quickly and accurately,[Methods] a new numerical prediction model is developed by coupling the Boundary Element Method(BEMwith the Finite Element Method (FEM. The low order BEM is used to calculate the hydrodynamic load on the blades, and the Prandtl-Schlichting plate friction resistance formula is used to calculate the viscous load. Next, the calculated hydrodynamic load and viscous correction load are transmitted to the calculation of the Finite Element as surface loads. Considering the particularity of propeller geometry, a continuous contact detection algorithm is developed; an automatic method for generating the finite element mesh is developed for the propeller blade; a code based on the FEM is compiled for predicting blade stress and deformation; the DTRC 4119 propeller model is applied to validate the reliability of the method; and mesh independence is confirmed by comparing the calculated results with different sizes and types of mesh.[Results] The results show that the calculated blade stress and displacement distribution are reliable. This method avoids the process of artificial modeling and finite element mesh generation, and has the advantages of simple program implementation and high calculation efficiency.[Conclusions] The code can be embedded into the code of theoretical and optimized propeller designs, thereby helping to ensure the strength of designed propellers and improve the efficiency of propeller design.

  3. Measurement of Resistance and Strength of Conductor Splices in the MICE Coupling Magnets

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Feng Yu; Pan, Heng; Wu, Hong; Lui, X. K.; Li, E.; Dietderich, Dan; Higley, Hugh; Tam, D. G.; Trillaud, Fredric; Wang, Li; Green, M.A.

    2009-08-19

    The superconducting magnets for the Muon Ionization Cooling Experiment [1] (MICE) use a copper based Nb-Ti conductor with un-insulated dimensions of 0.95 by 1.60 mm. There may be as many as twelve splices in one MICE superconducting coupling coil. These splices are to be wound in the coil. The conductor splices produce Joule heating, which may cause the magnet to quench. A technique of making conductor splices was developed by ICST. Two types of 1-meter long of soldered lap-joints have been tested. Side-by-side splices and up-down one splices were studied theoretically and experimentally using two types of soft solder made of eutectic tin-lead solder and tin-silver solder. The resistances of the splices made by ICST were tested at LBNL at liquid helium temperatures over a range of magnetic fields up to 5 T. The breaking strength of 250 mm long splices was also measured at room temperature and liquid nitrogen temperature.

  4. Self-organization of neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Clark, J.W.; Winston, J.V.; Rafelski, J.

    1984-05-14

    The plastic development of a neural-network model operating autonomously in discrete time is described by the temporal modification of interneuronal coupling strengths according to momentary neural activity. A simple algorithm (brainwashing) is found which, applied to nets with initially quasirandom connectivity, leads to model networks with properties conducive to the simulation of memory and learning phenomena. 18 references, 2 figures.

  5. Class 1 neural excitability, conventional synapses, weakly connected networks, and mathematical foundations of pulse-coupled models.

    Science.gov (United States)

    Izhikevich, E M

    1999-01-01

    Many scientists believe that all pulse-coupled neural networks are toy models that are far away from the biological reality. We show here, however, that a huge class of biophysically detailed and biologically plausible neural-network models can be transformed into a canonical pulse-coupled form by a piece-wise continuous, possibly noninvertible, change of variables. Such transformations exist when a network satisfies a number of conditions; e.g., it is weakly connected; the neurons are Class 1 excitable (i.e., they can generate action potentials with an arbitrary small frequency); and the synapses between neurons are conventional (i.e., axo-dendritic and axo-somatic). Thus, the difference between studying the pulse-coupled model and Hodgkin-Huxley-type neural networks is just a matter of a coordinate change. Therefore, any piece of information about the pulse-coupled model is valuable since it tells something about all weakly connected networks of Class 1 neurons. For example, we show that the pulse-coupled network of identical neurons does not synchronize in-phase. This confirms Ermentrout's result that weakly connected Class 1 neurons are difficult to synchronize, regardless of the equations that describe dynamics of each cell.

  6. Blood Cell Segmentation Based on Improved Pulse Coupled Neural Network and Fuzzy Entropy

    Directory of Open Access Journals (Sweden)

    Zhanbo Liu

    2016-12-01

    Full Text Available In the field of biomedical image processing, because of the low intensity and brightness of the cell image, and the complex structure of the cell image, the segmentation of cell images is very difficult. A large number of studies have shown that the Pulse Coupled Neural Networks (PCNN is suitable for image segmentation. However, the traditional PCNN must set a large number of parameters in image segmentation, and the optimal number of iterations cannot be automatically determined. In this paper, a new improved PCNN model is proposed. The work of improved PCNN includes the acceptance portion of the PCNN model being simplified and the connection portion of PCNN being improved. In addition, the maximum fuzzy entropy is used as the criterion to determine the optimal number of iterations. Experimental results on blood cell image segmentation show that this proposed method can automatically determine the number of loop iterations and automatically select the best threshold. It also has the characteristics of fast convergence, high accuracy and good segmentation effect in blood cell image segmentation processing.

  7. Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover

    Directory of Open Access Journals (Sweden)

    Yueru Ma

    2014-01-01

    Full Text Available The tendency of labor turnover in the Chinese enterprise shows the characteristics of seasonal fluctuations and irregular distribution of various factors, especially the Chinese traditional social and cultural characteristics. In this paper, we present a coupled model for the tendency prediction of labor turnover. In the model, a time series of tendency prediction of labor turnover was expressed as trend item and its random item. Trend item of tendency prediction of labor turnover is predicted using Grey theory. Random item of trend item is calculated by artificial neural network model (ANN. A case study is presented by the data of 24 months in a Chinese matured enterprise. The model uses the advantages of “accumulative generation” of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance, which has a capacity to solve economic problems rapidly, describes the nonlinear relationship easily, and avoids the defects of Grey theory.

  8. Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

    Energy Technology Data Exchange (ETDEWEB)

    Esen, Hikmet; Esen, Mehmet [Department of Mechanical Education, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey); Inalli, Mustafa [Department of Mechanical Engineering, Faculty of Engineering, Firat University, 23279 Elazig (Turkey); Sengur, Abdulkadir [Department of Electronic and Computer Science, Faculty of Technical Education, Firat University, 23119 Elazig (Turkey)

    2008-07-01

    This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R{sup 2}) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (author)

  9. An improved pulse coupled neural network with spectral residual for infrared pedestrian segmentation

    Science.gov (United States)

    He, Fuliang; Guo, Yongcai; Gao, Chao

    2017-12-01

    Pulse coupled neural network (PCNN) has become a significant tool for the infrared pedestrian segmentation, and a variety of relevant methods have been developed at present. However, these existing models commonly have several problems of the poor adaptability of infrared noise, the inaccuracy of segmentation results, and the fairly complex determination of parameters in current methods. This paper presents an improved PCNN model that integrates the simplified framework and spectral residual to alleviate the above problem. In this model, firstly, the weight matrix of the feeding input field is designed by the anisotropic Gaussian kernels (ANGKs), in order to suppress the infrared noise effectively. Secondly, the normalized spectral residual saliency is introduced as linking coefficient to enhance the edges and structural characteristics of segmented pedestrians remarkably. Finally, the improved dynamic threshold based on the average gray values of the iterative segmentation is employed to simplify the original PCNN model. Experiments on the IEEE OTCBVS benchmark and the infrared pedestrian image database built by our laboratory, demonstrate that the superiority of both subjective visual effects and objective quantitative evaluations in information differences and segmentation errors in our model, compared with other classic segmentation methods.

  10. Substrate Coupling Strength of Integrin-Binding Ligands Modulates Adhesion, Spreading, and Differentiation of Human Mesenchymal Stem Cells.

    Science.gov (United States)

    Choi, Chun Kit K; Xu, Yang J; Wang, Ben; Zhu, Meiling; Zhang, Li; Bian, Liming

    2015-10-14

    Substrate stiffness has been shown to regulate the differentiation fate of human mesenchymal stem cells (hMSCs). hMSCs sense and respond to substrate rigidity by exerting traction forces upon the binding between integrins and integrin-specific ligands present on the substrate surface. However, in previous studies, integrin-specific ligands such as Arg-Gly-Asp (RGD) peptides are always grafted to the substrate by a permanent covalent bond. Whether the coupling strength of integrin-specific ligands on substrate will influence cell behaviors has not been explored. In this work, we have developed a facile platform to investigate the effects of varied coupling strength between the RGD peptide and the glass substrate on stem cell behaviors. Glass coverslips are decorated with positive charges by silanization using (3-aminopropyl) triethoxysilane (APTES) to immobilize negatively charged citrate-capped gold nanoparticles (cit-AuNPs) solely via electrostatic interactions. The monolayer of electrostatically immobilized cit-AuNPs is further conjugated with the thiolated RGD peptides through the sulfur-gold bond. The substrate coupling strength of the RGD peptides, which is dependent on the electrostatic interactions between the APTES-treated glass substrate and the cit-AuNPs, is simply tuned by changing the APTES dosage and, hence, the resultant positive charge density on the surface. A total of 0.5% and 12.5% of APTES are used to fabricate low-coupling-strength surfaces (namely, LCS0.5 and LCS12.5), whereas 25% and 50% of APTES are used to fabricate high-coupling-strength surfaces (namely, HCS25 and HCS50). Fluorescence microscopy shows that hMSCs spread well and form stable actin filamentous structure on HCS surfaces but not on LCS surfaces. Remarkably, hMSCs exhibit enhanced osteogenesis on HCS surfaces as revealed by the immunostaining results of multiple early osteogenic markers. These differential behaviors may be governed by Yes-associated protein (YAP), a

  11. artificial neural network model for low strength rc beam shear capacity

    African Journals Online (AJOL)

    User

    not be adequate in predicting the shear capacity of such concrete members. Work by other re- searchers using artificial intelligence to im- prove on theoretical shear modeling did not consider low strength concrete beams made from both conventional and non-conventional aggregates. Such beams are mostly slender with.

  12. artificial neural network model for low strength rc beam shear capacity

    African Journals Online (AJOL)

    User

    searchers using artificial intelligence to im- prove on theoretical shear modeling did not consider low strength concrete beams made from both conventional and non-conventional aggregates. Such beams are mostly slender with effective depths up to 600mm and percent lon- gitudinal reinforcement up to 3%. This research ...

  13. Dual origins of measured phase-amplitude coupling reveal distinct neural mechanisms underlying episodic memory in the human cortex.

    Science.gov (United States)

    Vaz, Alex P; Yaffe, Robert B; Wittig, John H; Inati, Sara K; Zaghloul, Kareem A

    2017-03-01

    Phase-amplitude coupling (PAC) is hypothesized to coordinate neural activity, but its role in successful memory formation in the human cortex is unknown. Measures of PAC are difficult to interpret, however. Both increases and decreases in PAC have been linked to memory encoding, and PAC may arise due to different neural mechanisms. Here, we use a waveform analysis to examine PAC in the human cortex as participants with intracranial electrodes performed a paired associates memory task. We found that successful memory formation exhibited significant decreases in left temporal lobe and prefrontal cortical PAC, and these two regions exhibited changes in PAC within different frequency bands. Two underlying neural mechanisms, nested oscillations and sharp waveforms, were responsible for the changes in these regions. Our data therefore suggest that decreases in measured cortical PAC during episodic memory reflect two distinct underlying mechanisms that are anatomically segregated in the human brain. Published by Elsevier Inc.

  14. Micro mirrors based coupling of light to multi-core fiber realizing in-fiber photonic neural network processor

    Science.gov (United States)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; London, Michael; Zalevsky, Zeev

    2017-02-01

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a DMD based approaches to realize energetically efficient light coupling into a multi-core fiber realizing a unique design for in-fiber optical neural networks. Neurons and synapses are realized as individual cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in Erbium-doped cores mimics synaptic interactions. In order to dynamically and efficiently couple light into the multi-core fiber a DMD based micro mirror device is used to perform proper beam shaping operation. The beam shaping reshapes the light into a large set of points in space matching the positions of the required cores in the entrance plane to the multi-core fiber.

  15. Bonding Strength Effects in Hydro-Mechanical Coupling Transport in Granular Porous Media by Pore-Scale Modeling

    Directory of Open Access Journals (Sweden)

    Zhiqiang Chen

    2016-03-01

    Full Text Available The hydro-mechanical coupling transport process of sand production is numerically investigated with special attention paid to the bonding effect between sand grains. By coupling the lattice Boltzmann method (LBM and the discrete element method (DEM, we are able to capture particles movements and fluid flows simultaneously. In order to account for the bonding effects on sand production, a contact bond model is introduced into the LBM-DEM framework. Our simulations first examine the experimental observation of “initial sand production is evoked by localized failure” and then show that the bonding or cement plays an important role in sand production. Lower bonding strength will lead to more sand production than higher bonding strength. It is also found that the influence of flow rate on sand production depends on the bonding strength in cemented granular media, and for low bonding strength sample, the higher the flow rate is, the more severe the erosion found in localized failure zone becomes.

  16. Neural network evidence for the coupling of presaccadic visual remapping to predictive eye position updating

    Directory of Open Access Journals (Sweden)

    Hrishikesh M Rao

    2016-06-01

    Full Text Available As we look around a scene, we perceive it as continuous and stable even though each saccadic eye movement changes the visual input to the retinas. How the brain achieves this perceptual stabilization is unknown, but a major hypothesis is that it relies on presaccadic remapping, a process in which neurons shift their visual sensitivity to a new location in the scene just before each saccade. This hypothesis is difficult to test in vivo because complete, selective inactivation of remapping is currently intractable. We tested it in silico with a hierarchical, sheet-based neural network model of the visual and oculomotor system. The model generated saccadic commands to move a video camera abruptly. Visual input from the camera and internal copies of the saccadic movement commands, or corollary discharge, converged at a map-level simulation of the frontal eye field (FEF, a primate brain area known to receive such inputs. FEF output was combined with eye position signals to yield a suitable coordinate frame for guiding arm movements of a robot. Our operational definition of perceptual stability was useful stability, quantified as continuously accurate pointing to a visual object despite camera saccades. During training, the emergence of useful stability was correlated tightly with the emergence of presaccadic remapping in the FEF. Remapping depended on corollary discharge but its timing was synchronized to the updating of eye position. When coupled to predictive eye position signals, remapping served to stabilize the target representation for continuously accurate pointing. Graded inactivations of pathways in the model replicated, and helped to interpret, previous in vivo experiments. The results support the hypothesis that visual stability requires presaccadic remapping, provide explanations for the function and timing of remapping, and offer testable hypotheses for in vivo studies. We conclude that remapping allows for seamless coordinate frame

  17. Breast mass segmentation in digital mammography based on pulse coupled neural network and level set method

    Science.gov (United States)

    Xie, Weiying; Ma, Yide; Li, Yunsong

    2015-05-01

    A novel approach to mammographic image segmentation, termed as PCNN-based level set algorithm, is presented in this paper. Just as its name implies, a method based on pulse coupled neural network (PCNN) in conjunction with the variational level set method for medical image segmentation. To date, little work has been done on detecting the initial zero level set contours based on PCNN algorithm for latterly level set evolution. When all the pixels of the input image are fired by PCNN, the small pixel value will be a much more refined segmentation. In mammographic image, the breast tumor presents big pixel value. Additionally, the mammographic image with predominantly dark region, so that we firstly obtain the negative of mammographic image with predominantly dark region except the breast tumor before all the pixels of an input image are fired by PCNN. Therefore, in here, PCNN algorithm is employed to achieve mammary-specific, initial mass contour detection. After that, the initial contours are all extracted. We define the extracted contours as the initial zero level set contours for automatic mass segmentation by variational level set in mammographic image analysis. What's more, a new proposed algorithm improves external energy of variational level set method in terms of mammographic images in low contrast. In accordance with the gray scale of mass region in mammographic image is higher than the region surrounded, so the Laplace operator is used to modify external energy, which could make the bright spot becoming much brighter than the surrounded pixels in the image. A preliminary evaluation of the proposed method performs on a known public database namely MIAS, rather than synthetic images. The experimental results demonstrate that our proposed approach can potentially obtain better masses detection results in terms of sensitivity and specificity. Ultimately, this algorithm could lead to increase both sensitivity and specificity of the physicians' interpretation of

  18. G-protein-coupled receptors and localized signaling in the primary cilium during ventral neural tube patterning.

    Science.gov (United States)

    Hwang, Sun-Hee; Mukhopadhyay, Saikat

    2015-01-01

    The primary cilium is critical in sonic hedgehog (Shh)-dependent ventral patterning of the vertebrate neural tube. Most mutants that cause disruption of the cilium result in decreased Shh signaling in the neural tube. In contrast, mutations in the intraflagellar complex A (IFT-A) and the tubby family protein, Tulp3, result in increased Shh signaling in the neural tube. Proteomic analysis of Tulp3-binding proteins first pointed to the role of the IFT-A complex in trafficking Tulp3 into the cilia. Tulp3 directs trafficking of rhodopsin family G-protein-coupled receptors (GPCRs) to the cilia, suggesting the role of a GPCR in mediating the paradoxical effects of the Tulp3/IFT-A complex in causing increased Shh signaling. Gpr161 has recently been identified as a Tulp3/IFT-A-regulated GPCR that localizes to the primary cilium. A null knock-out mouse model of Gpr161 phenocopies Tulp3 and IFT-A mutants, and causes increased Shh signaling throughout the neural tube. In the absence of Shh, the bifunctional Gli transcription factors are proteolytically processed into repressor forms in a protein kinase A (PKA) -dependent and cilium-dependent manner. Gpr161 activity results in increased cAMP levels in a Gαs -coupled manner, and determines processing of Gli3. Shh signaling also results in removal of Gpr161 from the cilia, suggesting that Gpr161 functions in a positive feedback loop in the Shh pathway. As PKA-null and Gαs mutant embryos also exhibit increased Shh signaling in the neural tube, Gpr161 is a strong candidate for a GPCR that regulates ciliary cAMP levels, and activates PKA in close proximity to the cilia. © 2014 Wiley Periodicals, Inc.

  19. High-Intensity Progressive Resistance Training Increases Strength With No Change in Cardiovascular Function and Autonomic Neural Regulation in Older Adults.

    Science.gov (United States)

    Kanegusuku, Hélcio; Queiroz, Andréia C; Silva, Valdo J; de Mello, Marco T; Ugrinowitsch, Carlos; Forjaz, Cláudia L

    2015-07-01

    The effects of high-intensity progressive resistance training (HIPRT) on cardiovascular function and autonomic neural regulation in older adults are unclear. To investigate this issue, 25 older adults were randomly divided into two groups: control (CON, N = 13, 63 ± 4 years; no training) and HIPRT (N = 12, 64 ± 4 years; 2 sessions/week, 7 exercises, 2–4 sets, 10–4 RM). Before and after four months, maximal strength, quadriceps cross-sectional area (QCSA), clinic and ambulatory blood pressures (BP), systemic hemodynamics, and cardiovascular autonomic modulation were measured. Maximal strength and QCSA increased in the HIPRT group and did not change in the CON group. Clinic and ambulatory BP, cardiac output, systemic vascular resistance, stroke volume, heart rate, and cardiac sympathovagal balance did not change in the HIPRT group or the CON group. In conclusion, HIPRT was effective at increasing muscle mass and strength without promoting changes in cardiovascular function or autonomic neural regulation.

  20. Topology, Cross-Frequency, and Same-Frequency Band Interactions Shape the Generation of Phase-Amplitude Coupling in a Neural Mass Model of a Cortical Column.

    Directory of Open Access Journals (Sweden)

    Roberto C Sotero

    2016-11-01

    Full Text Available Phase-amplitude coupling (PAC, a type of cross-frequency coupling (CFC where the phase of a low-frequency rhythm modulates the amplitude of a higher frequency, is becoming an important indicator of information transmission in the brain. However, the neurobiological mechanisms underlying its generation remain undetermined. A realistic, yet tractable computational model of the phenomenon is thus needed. Here we analyze a neural mass model of a cortical column, comprising fourteen neuronal populations distributed across four layers (L2/3, L4, L5 and L6. A control analysis showed that the conditional transfer entropy (cTE measure is able to correctly estimate the flow of information between neuronal populations. Then, we computed cTE from the phases to the amplitudes of the oscillations generated in the cortical column. This approach provides information regarding directionality by distinguishing PAC from APC (amplitude-phase coupling, i.e. the information transfer from amplitudes to phases, and can be used to estimate other types of CFC such as amplitude-amplitude coupling (AAC and phase-phase coupling (PPC. While experiments often only focus on one or two PAC combinations (e.g., theta-gamma or alpha-gamma, we found that a cortical column can simultaneously generate almost all possible PAC combinations, depending on connectivity parameters, time constants, and external inputs. PAC interactions with and without an anatomical equivalent (direct and indirect interactions, respectively were analyzed. We found that the strength of PAC between two populations was strongly correlated with the strength of the effective connections between the populations and, on average, did not depend on whether the PAC connection was direct or indirect. When considering a cortical column circuit as a complex network, we found that neuronal populations making indirect PAC connections had, on average, higher local clustering coefficient, efficiency, and betweenness

  1. Modelling and Predicting the Breaking Strength and Mass Irregularity of Cotton Rotor-Spun Yarns Containing Cotton Fiber Recovered from Ginning Process by Using Artificial Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Mohsen Shanbeh

    2011-01-01

    Full Text Available One of the main methods to reduce the production costs is waste recycling which is the most important challenge for the future. Cotton wastes collected from ginning process have desirable properties which could be used during spinning process. The purpose of this study was to develop predictive models of breaking strength and mass irregularity (CV% of cotton waste rotor-spun yarns containing cotton waste collected from ginning process by using the artificial neural network trained with backpropagation algorithm. Artificial neural network models have been developed based on rotor diameter, rotor speed, navel type, opener roller speed, ginning waste proportion and yarn linear density as input parameters. The parameters of artificial neural network model, namely, learning, and momentum rate, number of hidden layers and number of hidden processing elements (neurons were optimized to get the best predictive models. The findings showed that the breaking strength and mass irregularity of rotor spun yarns could be predicted satisfactorily by artificial neural network. The maximum error in predicting the breaking strength and mass irregularity of testing data was 8.34% and 6.65%, respectively.

  2. Globally fixed-time synchronization of coupled neutral-type neural network with mixed time-varying delays

    Science.gov (United States)

    2018-01-01

    This paper mainly studies the globally fixed-time synchronization of a class of coupled neutral-type neural networks with mixed time-varying delays via discontinuous feedback controllers. Compared with the traditional neutral-type neural network model, the model in this paper is more general. A class of general discontinuous feedback controllers are designed. With the help of the definition of fixed-time synchronization, the upper right-hand derivative and a defined simple Lyapunov function, some easily verifiable and extensible synchronization criteria are derived to guarantee the fixed-time synchronization between the drive and response systems. Finally, two numerical simulations are given to verify the correctness of the results. PMID:29370248

  3. Bond strength of a dental leucite-based glass ceramic to a resin cement using different silane coupling agents.

    Science.gov (United States)

    Hooshmand, Tabassom; Matinlinna, Jukka P; Keshvad, Alireza; Eskandarion, Solmaz; Zamani, Fereshteh

    2013-01-01

    To evaluate the effect of different types of novel silane coupling agents with two concentrations on the micro-tensile bond strength of a dental glass ceramic with leucite crystals to a dual-cured resin cement using an optimized method of silane application. Leucite-reinforced feldspathic ceramic blocks were fabricated, wet ground and cleansed. The bonding ceramic surfaces were treated with different organosilane solutions as follows: Control silane: Monobond S; methacryloxypropyltrimethoxy silane and experimental silanes with two concentrations (1.0 and 2.5 vol%): amino, isocyanate, styryl, and acrylate silanes. The silane application method consisted of brush application, hot air drying followed by rinsing with hot water and drying. Then a thin layer of an unfilled resin and a dual-cured resin cement was light-cured on the ceramic surfaces. The resin-ceramic blocks were stored in distilled water at 37°C for 24 h and sectioned to produce beam specimens (n=17) with a 1.0 mm(2) cross-sectional area. Specimens were then subjected to thermocycling and tested in a micro-tensile tester device. Data were analyzed using analysis of variance and Tamhane post-hoc test. The mean micro-tensile bond strength value for the styryl silane was significantly higher (P0.05). The micro-tensile bond strength of the leucite-based dental glass ceramic to a resin cement was affected by the type of silane coupling agent and not by the concentration of silane solutions. The best bond strength overall was achieved by methacryloxypropyltrimethoxysilane and experimental styryl silane solutions. Copyright © 2012 Elsevier Ltd. All rights reserved.

  4. Prediction of Tensile Strength of Friction Stir Weld Joints with Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural Network

    Science.gov (United States)

    Dewan, Mohammad W.; Huggett, Daniel J.; Liao, T. Warren; Wahab, Muhammad A.; Okeil, Ayman M.

    2015-01-01

    Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed (??), plunge force (????), and welding speed (??) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (??,??,????,??????) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints.

  5. Multiscale methodology for bone remodelling simulation using coupled finite element and neural network computation.

    Science.gov (United States)

    Hambli, Ridha; Katerchi, Houda; Benhamou, Claude-Laurent

    2011-02-01

    The aim of this paper is to develop a multiscale hierarchical hybrid model based on finite element analysis and neural network computation to link mesoscopic scale (trabecular network level) and macroscopic (whole bone level) to simulate the process of bone remodelling. As whole bone simulation, including the 3D reconstruction of trabecular level bone, is time consuming, finite element calculation is only performed at the macroscopic level, whilst trained neural networks are employed as numerical substitutes for the finite element code needed for the mesoscale prediction. The bone mechanical properties are updated at the macroscopic scale depending on the morphological and mechanical adaptation at the mesoscopic scale computed by the trained neural network. The digital image-based modelling technique using μ-CT and voxel finite element analysis is used to capture volume elements representative of 2 mm³ at the mesoscale level of the femoral head. The input data for the artificial neural network are a set of bone material parameters, boundary conditions and the applied stress. The output data are the updated bone properties and some trabecular bone factors. The current approach is the first model, to our knowledge, that incorporates both finite element analysis and neural network computation to rapidly simulate multilevel bone adaptation.

  6. Extending physical chemistry to populations of living organisms. First step: measuring coupling strength

    CERN Document Server

    Di, Zengru

    2013-01-01

    For any system, whether physical or non-physical, knowledge of the form and strength of inter-individual interactions is a key-information. In an approach based on statistical physics one needs to know the interaction Hamiltonian. For non-physical systems, based on qualitative arguments similar to those used in physical chemistry, interaction strength gives useful clues about the macroscopic properties of the system. Even though our ultimate objective is the understanding of social phenomena, we found that systems composed of insects (or other living organisms) are of great convenience for investigating group effects. In this paper we show how to design experiments that enable us to estimate the strength of interaction in groups of insects. By repeating the same experiments with increasing numbers of insects, ranging from less than 10 to several hundreds, one is able to explore key-properties of the interaction. The data turn out to be consistent with a global correlation that is independent of distance (at l...

  7. Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

    Science.gov (United States)

    Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

    2017-08-01

    Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

  8. Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry.

    Science.gov (United States)

    Tang, Chen; Lu, Wenjing; Chen, Song; Zhang, Zhen; Li, Botao; Wang, Wenping; Han, Lin

    2007-10-20

    We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.

  9. Plant water-stress parameterization determines the strength of land-atmosphere coupling

    Science.gov (United States)

    Combe, Marie; Vilà-Guerau de Arellano, Jordi; Ouwersloot, Huug G.; Peters, Wouter

    2016-04-01

    Land-surface models that are currently used in numerical weather predictions models and earth system models all assume various plant water-stress parameterizations. We investigate the impact of this variety of parametrizations on the performance of atmospheric models. For this, we use a conceptual framework where a convective atmospheric boundary-layer (ABL) model is coupled to a daytime model for the land surface fluxes of carbon, water, and energy. We first validate our coupled model for a set of surface and upper-atmospheric diurnal observations over a grown maize field in the Netherlands. We then perform a sensitivity analysis of this coupled land-atmosphere system by varying the modeled plant water-stress response from a very insensitive to a sensitive response during dry soil conditions. We first propose and verify a feedback diagram that ties plant water-stress response and large-scale atmospheric conditions to the diurnal cycles of ABL CO2, humidity and temperature. Based on our undertanstanding of the diurnal coupled system, we then explore the impact of the assumed water-stress reponse for the development of a dry spell on a synoptic time scale. We find that during a progressive 3-week soil drying caused by evapotranspiration, an insensitive plant will dampen atmospheric heating because the vegetation continues to transpire while soil moisture is available. In contrast, the sensitive plant reduces its transpiration to prevent soil moisture depletion. But when absolute soil moisture comes close to wilting point, the insensitive plant will suddenly close its stomata causing a switch to a land-atmosphere coupling regime dominated by sensible heat exchange. We find that in both cases, our modeled progressive soil moisture depletion contributes to further atmospheric warming up to 6 K, reduced photosynthesis up to 89 %, and CO2 enrichment up to 30 ppm, but the full impact is strongly delayed for the insensitive plant. Finally, we demonstrate that the assumed

  10. Predicting model on ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter based on BP neural network

    Directory of Open Access Journals (Sweden)

    Yu Jingyuan

    2011-08-01

    Full Text Available In present study, BP neural network model was proposed for the prediction of ultimate compressive strength of Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The inputs of the BP neural network model were the applied load on the epispastic polystyrene template (F, centrifugal acceleration (v and sintering temperature (T, while the only output was the ultimate compressive strength (σ. According to the registered BP model, the effects of F, v, T on σ were analyzed. The predicted results agree with the actual data within reasonable experimental error, indicating that the BP model is practically a very useful tool in property prediction and process parameter design of the Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting.

  11. Sampled-Data Synchronization of Markovian Coupled Neural Networks With Mode Delays Based on Mode-Dependent LKF.

    Science.gov (United States)

    Wang, Junyi; Zhang, Huaguang; Wang, Zhanshan; Liu, Zhenwei

    This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.This paper investigates sampled-data synchronization problem of Markovian coupled neural networks with mode-dependent interval time-varying delays and aperiodic sampling intervals based on an enhanced input delay approach. A mode-dependent augmented Lyapunov-Krasovskii functional (LKF) is utilized, which makes the LKF matrices mode-dependent as much as possible. By applying an extended Jensen's integral inequality and Wirtinger's inequality, new delay-dependent synchronization criteria are obtained, which fully utilizes the upper bound on variable sampling interval and the sawtooth structure information of varying input delay. In addition, the desired stochastic sampled-data controllers can be obtained by solving a set of linear matrix inequalities. Finally, two examples are provided to demonstrate the feasibility of the proposed method.

  12. Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients

    CERN Document Server

    Lehnertz, Klaus

    2016-01-01

    Inferring strength and direction of interactions from electroencephalographic (EEG) recordings is of crucial importance to improve our understanding of dynamical interdependencies underlying various physiologic and pathophysiologic conditions in the human epileptic brain. We here use approaches from symbolic analysis to investigate---in a time-resolved manner---weighted and directed, short- to long-ranged interactions between various brain regions constituting the epileptic network. Our observations point to complex spatial-temporal interdependencies underlying the epileptic process and their role in the generation of epileptic seizures, despite the massive reduction of the complex information content of multi-day, multi-channel EEG recordings through symbolisation. We discuss limitations and potential future improvements of this approach.

  13. Bilingualism increases neural response consistency and attentional control: Evidence for sensory and cognitive coupling

    Science.gov (United States)

    Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Auditory processing is presumed to be influenced by cognitive processes – including attentional control – in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] and separately obtained measures of attentional control and language ability in adolescent Spanish-English bilinguals and English monolinguals. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. PMID:24413593

  14. Caco-2 cell permeability modelling: a neural network coupled genetic algorithm approach

    Science.gov (United States)

    Di Fenza, Armida; Alagona, Giuliano; Ghio, Caterina; Leonardi, Riccardo; Giolitti, Alessandro; Madami, Andrea

    2007-04-01

    The ability to cross the intestinal cell membrane is a fundamental prerequisite of a drug compound. However, the experimental measurement of such an important property is a costly and highly time consuming step of the drug development process because it is necessary to synthesize the compound first. Therefore, in silico modelling of intestinal absorption, which can be carried out at very early stages of drug design, is an appealing alternative procedure which is based mainly on multivariate statistical analysis such as partial least squares (PLS) and neural networks (NN). Our implementation of neural network models for the prediction of intestinal absorption is based on the correlation of Caco-2 cell apparent permeability ( P app) values, as a measure of intestinal absorption, to the structures of two different data sets of drug candidates. Several molecular descriptors of the compounds were calculated and the optimal subsets were selected using a genetic algorithm; therefore, the method was indicated as Genetic Algorithm-Neural Network (GA-NN). A methodology combining a genetic algorithm search with neural network analysis applied to the modelling of Caco-2 P app has never been presented before, although the two procedures have been already employed separately. Moreover, we provide new Caco-2 cell permeability measurements for more than two hundred compounds. Interestingly, the selected descriptors show to possess physico-chemical connotations which are in excellent accordance with the well known relevant molecular properties involved in the cellular membrane permeation phenomenon: hydrophilicity, hydrogen bonding propensity, hydrophobicity and molecular size. The predictive ability of the models, although rather good for a preliminary study, is somewhat affected by the poor precision of the experimental Caco-2 measurements. Finally, the generalization ability of one model was checked on an external test set not derived from the data sets used to build the models

  15. Analysis of coherent structures and atmosphere-canopy coupling strength during the CABINEX field campaign

    Directory of Open Access Journals (Sweden)

    A. L. Steiner

    2011-12-01

    Full Text Available Intermittent coherent structures can be responsible for a large fraction of the exchange between a forest canopy and the atmosphere. Quantifying their contribution to momentum and heat fluxes is necessary to interpret measurements of trace gases and aerosols within and above forest canopies. The primary objective of the Community Atmosphere-Biosphere Interactions Experiment (CABINEX field campaign (10 July 2009 to 9 August 2009 was to study the chemistry of volatile organic compounds (VOC within and above a forest canopy. In this manuscript we provide an analysis of coherent structures and canopy-atmosphere exchange during CABINEX to support in-canopy gradient measurements of VOC. We quantify the number and duration of coherent structure events and their percent contribution to momentum and heat fluxes with two methods: (1 quadrant-hole analysis, and (2 wavelet analysis. Despite differences in the duration and number of events, both methods predict that coherent structures contribute 40–50% to momentum fluxes and 44–65% to heat fluxes during the CABINEX campaign. Contributions associated with coherent structures are slightly greater under stable atmospheric conditions. By comparing heat fluxes within and above the canopy, we determine the degree of coupling between upper canopy and atmosphere, and find that they are coupled the majority of the time. Uncoupled canopy-atmosphere events occur in the early morning (4–8 a.m. local time approximately 30% of the time. This study confirms that coherent structures contribute significantly to the exchange of heat and momentum between the canopy and atmosphere at the CABINEX site, and indicates the need to include these transport processes when studying the mixing and chemical reactions of trace gases and aerosols between a forest canopy and the atmosphere.

  16. Analysis of coherent structures and atmosphere-canopy coupling strength during the CABINEX field campaign

    Science.gov (United States)

    Steiner, A. L.; Pressley, S. N.; Botros, A.; Jones, E.; Chung, S. H.; Edburg, S. L.

    2011-12-01

    Intermittent coherent structures can be responsible for a large fraction of the exchange between a forest canopy and the atmosphere. Quantifying their contribution to momentum and heat fluxes is necessary to interpret measurements of trace gases and aerosols within and above forest canopies. The primary objective of the Community Atmosphere-Biosphere Interactions Experiment (CABINEX) field campaign (10 July 2009 to 9 August 2009) was to study the chemistry of volatile organic compounds (VOC) within and above a forest canopy. In this manuscript we provide an analysis of coherent structures and canopy-atmosphere exchange during CABINEX to support in-canopy gradient measurements of VOC. We quantify the number and duration of coherent structure events and their percent contribution to momentum and heat fluxes with two methods: (1) quadrant-hole analysis, and (2) wavelet analysis. Despite differences in the duration and number of events, both methods predict that coherent structures contribute 40-50% to momentum fluxes and 44-65% to heat fluxes during the CABINEX campaign. Contributions associated with coherent structures are slightly greater under stable atmospheric conditions. By comparing heat fluxes within and above the canopy, we determine the degree of coupling between upper canopy and atmosphere, and find that they are coupled the majority of the time. Uncoupled canopy-atmosphere events occur in the early morning (4-8 a.m. local time) approximately 30% of the time. This study confirms that coherent structures contribute significantly to the exchange of heat and momentum between the canopy and atmosphere at the CABINEX site, and indicates the need to include these transport processes when studying the mixing and chemical reactions of trace gases and aerosols between a forest canopy and the atmosphere.

  17. Image processing using pulse-coupled neural networks applications in Python

    CERN Document Server

    Lindblad, Thomas

    2013-01-01

    Image processing algorithms based on the mammalian visual cortex are powerful tools for extraction information and manipulating images. This book reviews the neural theory and translates them into digital models. Applications are given in areas of image recognition, foveation, image fusion and information extraction. The third edition reflects renewed international interest in pulse image processing with updated sections presenting several newly developed applications. This edition also introduces a suite of Python scripts that assist readers in replicating results presented in the text and to further develop their own applications.

  18. Bilingualism increases neural response consistency and attentional control: evidence for sensory and cognitive coupling.

    Science.gov (United States)

    Krizman, Jennifer; Skoe, Erika; Marian, Viorica; Kraus, Nina

    2014-01-01

    Auditory processing is presumed to be influenced by cognitive processes - including attentional control - in a top-down manner. In bilinguals, activation of both languages during daily communication hones inhibitory skills, which subsequently bolster attentional control. We hypothesize that the heightened attentional demands of bilingual communication strengthens connections between cognitive (i.e., attentional control) and auditory processing, leading to greater across-trial consistency in the auditory evoked response (i.e., neural consistency) in bilinguals. To assess this, we collected passively-elicited auditory evoked responses to the syllable [da] in adolescent Spanish-English bilinguals and English monolinguals and separately obtained measures of attentional control and language ability. Bilinguals demonstrated enhanced attentional control and more consistent brainstem and cortical responses. In bilinguals, but not monolinguals, brainstem consistency tracked with language proficiency and attentional control. We interpret these enhancements in neural consistency as the outcome of strengthened attentional control that emerged from experience communicating in two languages. Copyright © 2013 Elsevier Inc. All rights reserved.

  19. The hyperbolic effect of density and strength of inter beta-cell coupling on islet bursting: a theoretical investigation

    Directory of Open Access Journals (Sweden)

    Wang Xujing

    2008-08-01

    Full Text Available Abstract Background Insulin, the principal regulating hormone of blood glucose, is released through the bursting of the pancreatic islets. Increasing evidence indicates the importance of islet morphostructure in its function, and the need of a quantitative investigation. Recently we have studied this problem from the perspective of islet bursting of insulin, utilizing a new 3D hexagonal closest packing (HCP model of islet structure that we have developed. Quantitative non-linear dependence of islet function on its structure was found. In this study, we further investigate two key structural measures: the number of neighboring cells that each β-cell is coupled to, nc, and the coupling strength, gc. Results β-cell clusters of different sizes with number of β-cells nβ ranging from 1–343, nc from 0–12, and gc from 0–1000 pS, were simulated. Three functional measures of islet bursting characteristics – fraction of bursting β-cells fb, synchronization index λ, and bursting period Tb, were quantified. The results revealed a hyperbolic dependence on the combined effect of nc and gc. From this we propose to define a dimensionless cluster coupling index or CCI, as a composite measure for islet morphostructural integrity. We show that the robustness of islet oscillatory bursting depends on CCI, with all three functional measures fb, λ and Tb increasing monotonically with CCI when it is small, and plateau around CCI = 1. Conclusion CCI is a good islet function predictor. It has the potential of linking islet structure and function, and providing insight to identify therapeutic targets for the preservation and restoration of islet β-cell mass and function.

  20. Solitons of the coupled Schrödinger-Korteweg-de Vries system with arbitrary strengths of the nonlinearity and dispersion

    Science.gov (United States)

    Gromov, Evgeny; Malomed, Boris

    2017-11-01

    New two-component soliton solutions of the coupled high-frequency (HF)—low-frequency (LF) system, based on Schrödinger-Korteweg-de Vries (KdV) system with the Zakharov's coupling, are obtained for arbitrary relative strengths of the nonlinearity and dispersion in the LF component. The complex HF field is governed by the linear Schrödinger equation with a potential generated by the real LF component, which, in turn, is governed by the KdV equation including the ponderomotive coupling term, representing the feedback of the HF field onto the LF component. First, we study the evolution of pulse-shaped pulses by means of direct simulations. In the case when the dispersion of the LF component is weak in comparison to its nonlinearity, the input gives rise to several solitons in which the HF component is much broader than its LF counterpart. In the opposite case, the system creates a single soliton with approximately equal widths of both components. Collisions between stable solitons are studied too, with a conclusion that the collisions are inelastic, with a greater soliton getting still stronger, and the smaller one suffering further attenuation. Robust intrinsic modes are excited in the colliding solitons. A new family of approximate analytical two-component soliton solutions with two free parameters is found for an arbitrary relative strength of the nonlinearity and dispersion of the LF component, assuming weak feedback of the HF field onto the LF component. Further, a one-parameter (non-generic) family of exact bright-soliton solutions, with mutually proportional HF and LF components, is produced too. Intrinsic dynamics of the two-component solitons, induced by a shift of their HF component against the LF one, is also studied, by means of numerical simulations, demonstrating excitation of a robust intrinsic mode. In addition to the above-mentioned results for LF-dominated two-component solitons, which always run in one (positive) velocities, we produce HF

  1. Modeling and analysis of porosity and compressive strength of gradient Al2O3-ZrO2 ceramic filter using BP neural network

    Directory of Open Access Journals (Sweden)

    Li Qiang

    2013-07-01

    Full Text Available BP neural network was used in this study to model the porosity and the compressive strength of a gradient Al2O3-ZrO2 ceramic foam filter prepared by centrifugal slip casting. The influences of the load applied on the epispastic polystyrene template (F, the centrifugal acceleration (v and sintering temperature (T on the porosity (P and compressive strength (σ of the sintered products were studied by using the registered three-layer BP model. The accuracy of the model was verified by comparing the BP model predicted results with the experimental ones. Results show that the model prediction agrees with the experimental data within a reasonable experimental error, indicating that the three-layer BP network based modeling is effective in predicting both the properties and processing parameters in designing the gradient Al2O3-ZrO2 ceramic foam filter. The prediction results show that the porosity percentage increases and compressive strength decreases with an increase in the applied load on epispastic polystyrene template. As for the influence of sintering temperature, the porosity percentage decreases monotonically with an increase in sintering temperature, yet the compressive strength first increases and then decreases slightly in a given temperature range. Furthermore, the porosity percentage changes little but the compressive strength first increases and then decreases when the centrifugal acceleration increases.

  2. The Exercising Together project: design and recruitment for a randomized, controlled trial to determine the benefits of partnered strength training for couples coping with prostate cancer.

    Science.gov (United States)

    Winters-Stone, Kerri M; Lyons, Karen S; Nail, Lillian M; Beer, Tomasz M

    2012-03-01

    Prostate cancer can threaten quality of life for the patient and his spouse and the quality of his marital relationship. The purpose of our study is to evaluate the effects of "Exercising Together" - a partnered strength training program for married couples coping with prostate cancer - on the physical and emotional health of prostate cancer survivors (PCS) and their spouses and on marital quality. We are conducting a 6-month randomized controlled trial with two groups: 1) Exercising Together - a progressive, supervised strength training program and 2) a usual care control condition. The primary aims of this exploratory study are to: 1) Determine the effect of partnered strength training on physical and emotional health (muscle strength, physical function, body composition and self-report physical and mental health) in PCS, 2) Determine the effect of partnered strength training on physical and emotional health in spouses and 3) Explore the effect of partnered strength training on marital quality (incongruence, communication, relationship quality, intimacy) of the PCS and spouse. Target accrual has been met in this study with 64 couples enrolled and randomized to exercise (n=32) or usual care (n=32) groups. This study is the first to examine the feasibility of this exercise format in both the chronically ill patient and spouse and explore benefits at the individual and couple level. Copyright © 2011 Elsevier Inc. All rights reserved.

  3. Dispersion of the intrinsic neuronal periods affects the relationship of the entrainment range to the coupling strength in the suprachiasmatic nucleus

    Science.gov (United States)

    Gu, Changgui; Yang, Huijie; Wang, Man

    2017-11-01

    Living beings on the Earth are subjected to and entrained (synchronized) to the natural 24-h light-dark cycle. Interestingly, they can also be entrained to an external artificial cycle of non-24-h periods. The range of these periods is called the entrainment range and it differs among species. In mammals, the entrainment range is regulated by a main clock located in the suprachiasmatic nucleus (SCN) which is composed of 10 000 neurons in the brain. Previous works have found that the entrainment range depends on the cellular coupling strength in the SCN. In particular, the entrainment range decreases with the increase of the cellular coupling strength, provided that all the neuronal oscillators are identical. However, the SCN neurons differ in the intrinsic periods that follow a normal distribution in a range from 22 to 28 h. In the present study, taking the dispersion of the intrinsic neuronal periods into account, we examined the relationship between the entrainment range and the coupling strength. Results from numerical simulations and theoretical analyses both show that the relationship is altered to be paraboliclike if the intrinsic neuronal periods are nonidentical, and the maximal entrainment range is obtained with a suitable coupling strength. Our results shed light on the role of the cellular coupling in the entrainment ability of the SCN network.

  4. Chimera states and collective chaos in pulse-coupled neural networks

    OpenAIRE

    Olmi Simona; Politi Antonio; Torcini Alessandro

    2011-01-01

    Understanding the collective motion of networks of oscillators is crucial in many contexts, starting from neuronal circuits [1]. So far, most of the efforts have been devoted to the characterization of strong forms of synchronization. However, more subtle phenomena, like the onset of coherent oscillations in an ensemble of neurons can also play a relevant role for information coding. A peculiar coherent state, termed Chimera, appears in two symmetrically coupled populations of oscillators, wh...

  5. Modeling of thermal coupling in VO2-based oscillatory neural networks

    Science.gov (United States)

    Velichko, Andrey; Belyaev, Maksim; Putrolaynen, Vadim; Perminov, Valentin; Pergament, Alexander

    2018-01-01

    In this study, we have demonstrated the possibility of using the thermal coupling to control the dynamics of operation of coupled VO2 oscillators. Based on the example of a 'switch-microheater' pair, we have explored the synchronization and dissynchronization modes of a single oscillator with respect to an external harmonic heat impact. The features of changes in the spectra are shown, in particular, the effect of the natural frequency attraction to the affecting signal frequency and the self-oscillation noise reduction effects at synchronization. The time constant of the temperature effect for the considered system configuration is in the range 7-140 μs, which allows operation in the oscillation frequency range of up to ∼70 kHz. A model estimate of the minimum temperature sensitivity of the switch is δTswitch ∼ 0.2 K, and the effective action radius RTC of the switch-to-switch thermal coupling is not less than 25 μm. Nevertheless, as the simulation shows, the frequency range can be significantly extended up to the values of 1-30 GHz if using nanometer-scale switches (heaters).

  6. FAST INdiCATE Trial protocol. Clinical efficacy of functional strength training for upper limb motor recovery early after stroke: neural correlates and prognostic indicators.

    Science.gov (United States)

    Pomeroy, Valerie M; Ward, Nick S; Johansen-Berg, Heidi; van Vliet, Paulette; Burridge, Jane; Hunter, Susan M; Lemon, Roger N; Rothwell, John; Weir, Christopher J; Wing, Alan; Walker, Andrew A; Kennedy, Niamh; Barton, Garry; Greenwood, Richard J; McConnachie, Alex

    2014-02-01

    Functional strength training in addition to conventional physical therapy could enhance upper limb recovery early after stroke more than movement performance therapy plus conventional physical therapy. To determine (a) the relative clinical efficacy of conventional physical therapy combined with functional strength training and conventional physical therapy combined with movement performance therapy for upper limb recovery; (b) the neural correlates of response to conventional physical therapy combined with functional strength training and conventional physical therapy combined with movement performance therapy; (c) whether any one or combination of baseline measures predict motor improvement in response to conventional physical therapy combined with functional strength training or conventional physical therapy combined with movement performance therapy. Randomized, controlled, observer-blind trial. The sample will consist of 288 participants with upper limb paresis resulting from a stroke that occurred within the previous 60 days. All will be allocated to conventional physical therapy combined with functional strength training or conventional physical therapy combined with movement performance therapy. Functional strength training and movement performance therapy will be undertaken for up to 1·5 h/day, five-days/week for six-weeks. Measurements will be undertaken before randomization, six-weeks thereafter, and six-months after stroke. Primary efficacy outcome will be the Action Research Arm Test. Explanatory measurements will include voxel-wise estimates of brain activity during hand movement, brain white matter integrity (fractional anisotropy), and brain-muscle connectivity (e.g. latency of motor evoked potentials). The primary clinical efficacy analysis will compare treatment groups using a multilevel normal linear model adjusting for stratification variables and for which therapist administered the treatment. Effect of conventional physical therapy combined

  7. Measuring speaker-listener neural coupling with functional near infrared spectroscopy.

    Science.gov (United States)

    Liu, Yichuan; Piazza, Elise A; Simony, Erez; Shewokis, Patricia A; Onaral, Banu; Hasson, Uri; Ayaz, Hasan

    2017-02-27

    The present study investigates brain-to-brain coupling, defined as inter-subject correlations in the hemodynamic response, during natural verbal communication. We used functional near-infrared spectroscopy (fNIRS) to record brain activity of 3 speakers telling stories and 15 listeners comprehending audio recordings of these stories. Listeners' brain activity was significantly correlated with speakers' with a delay. This between-brain correlation disappeared when verbal communication failed. We further compared the fNIRS and functional Magnetic Resonance Imaging (fMRI) recordings of listeners comprehending the same story and found a significant relationship between the fNIRS oxygenated-hemoglobin concentration changes and the fMRI BOLD in brain areas associated with speech comprehension. This correlation between fNIRS and fMRI was only present when data from the same story were compared between the two modalities and vanished when data from different stories were compared; this cross-modality consistency further highlights the reliability of the spatiotemporal brain activation pattern as a measure of story comprehension. Our findings suggest that fNIRS can be used for investigating brain-to-brain coupling during verbal communication in natural settings.

  8. Measuring speaker–listener neural coupling with functional near infrared spectroscopy

    Science.gov (United States)

    Liu, Yichuan; Piazza, Elise A.; Simony, Erez; Shewokis, Patricia A.; Onaral, Banu; Hasson, Uri; Ayaz, Hasan

    2017-01-01

    The present study investigates brain-to-brain coupling, defined as inter-subject correlations in the hemodynamic response, during natural verbal communication. We used functional near-infrared spectroscopy (fNIRS) to record brain activity of 3 speakers telling stories and 15 listeners comprehending audio recordings of these stories. Listeners’ brain activity was significantly correlated with speakers’ with a delay. This between-brain correlation disappeared when verbal communication failed. We further compared the fNIRS and functional Magnetic Resonance Imaging (fMRI) recordings of listeners comprehending the same story and found a significant relationship between the fNIRS oxygenated-hemoglobin concentration changes and the fMRI BOLD in brain areas associated with speech comprehension. This correlation between fNIRS and fMRI was only present when data from the same story were compared between the two modalities and vanished when data from different stories were compared; this cross-modality consistency further highlights the reliability of the spatiotemporal brain activation pattern as a measure of story comprehension. Our findings suggest that fNIRS can be used for investigating brain-to-brain coupling during verbal communication in natural settings. PMID:28240295

  9. Ephaptic coupling in cortical neurons

    Directory of Open Access Journals (Sweden)

    Costas Anastassiou

    2014-03-01

    Full Text Available The electrochemical processes that underlie neural function manifest themselves in ceaseless spatial and temporal fluctuations in the extracellular electric field. The local field potential (LFP, used to study neural interactions during various brain states, is regarded as an epiphenomenon of coordinated neural activity. Yet the extracellular field activity feeds back onto the electrical potential across the neuronal membrane via ephaptic coupling (Jefferys et al, Physiol Rev, 1995. The extent to which such ephaptic coupling alters the functioning of individual neurons and neural assemblies under physiological conditions has remained largely speculative despite recent advances (Ozen et al, JNeurosci, 2010; Fröhlich & McCormick, Neuron, 2010, Anastassiou et al, JNeurosci, 2010. To address this question we use a 12-pipette setup that allows independent positioning of each pipette under visual control with μm accuracy, with the flexibility of using an arbitrary number of these as patching, extracellularly stimulating or extracellular recording pipettes only a few μm away from the cell body of patched neurons (Anastassiou et al, Nat Neurosci, 2011. We stimulated in rat somatosensory cortical slices a variety of layer 5 neural types and recorded inside and outside their cell bodies while pharmacologically silencing synaptic transmission. Pyramidal cells couple to the extracellular field distinctly different from interneurons. Ephaptic coupling strength depends both on the field strength (as measured at the neuron soma as well as the spike-history of neurons. In particular, we find that ephaptic coupling strength depends both on the field strength (as measured at the cell body as well as the spike-history of neurons. How do such effects manifest themselves in vivo? We address this question through detailed large-scale simulations from thousands of biophysically realistic and interconnected neurons (Reimann, Anastassiou et al, Neuron, 2013 emulating

  10. The phase difference between neural drives to antagonist muscles in essential tremor is associated with the relative strength of supraspinal and afferent input.

    Science.gov (United States)

    Gallego, Juan A; Dideriksen, Jakob L; Holobar, Ales; Ibáñez, Jaime; Glaser, Vojko; Romero, Juan P; Benito-León, Julián; Pons, José L; Rocon, Eduardo; Farina, Dario

    2015-06-10

    The pathophysiology of essential tremor (ET), the most common movement disorder, is not fully understood. We investigated which factors determine the variability in the phase difference between neural drives to antagonist muscles, a long-standing observation yet unexplained. We used a computational model to simulate the effects of different levels of voluntary and tremulous synaptic input to antagonistic motoneuron pools on the tremor. We compared these simulations to data from 11 human ET patients. In both analyses, the neural drive to muscle was represented as the pooled spike trains of several motor units, which provides an accurate representation of the common synaptic input to motoneurons. The simulations showed that, for each voluntary input level, the phase difference between neural drives to antagonist muscles is determined by the relative strength of the supraspinal tremor input to the motoneuron pools. In addition, when the supraspinal tremor input to one muscle was weak or absent, Ia afferents provided significant common tremor input due to passive stretch. The simulations predicted that without a voluntary drive (rest tremor) the neural drives would be more likely in phase, while a concurrent voluntary input (postural tremor) would lead more frequently to an out-of-phase pattern. The experimental results matched these predictions, showing a significant change in phase difference between postural and rest tremor. They also indicated that the common tremor input is always shared by the antagonistic motoneuron pools, in agreement with the simulations. Our results highlight that the interplay between supraspinal input and spinal afferents is relevant for tremor generation. Copyright © 2015 the authors 0270-6474/15/358925-13$15.00/0.

  11. Not All β-Sheets Are the Same: Amyloid Infrared Spectra, Transition Dipole Strengths, and Couplings Investigated by 2D IR Spectroscopy.

    Science.gov (United States)

    Lomont, Justin P; Ostrander, Joshua S; Ho, Jia-Jung; Petti, Megan K; Zanni, Martin T

    2017-09-28

    We report the transition dipole strengths and frequencies of the amyloid β-sheet amide I mode for the aggregated proteins amyloid-β1-40, calcitonin, α-synuclein, and glucagon. According to standard vibrational coupling models for proteins, the frequencies of canonical β-sheets are set by their size and structural and environmental disorder, which determines the delocalization length of the vibrational excitons. The larger the delocalization the lower the frequency of the main infrared-allowed transition, A⊥. The models also predict an accompanying increase in transition dipole strength. For the proteins measured here, we find no correlation between transition dipole strengths and amyloid β-sheet transition frequency. To understand this observation, we have extracted from the protein data bank crystal structures of amyloid peptides from which we calculate the amide I vibrational couplings, and we use these in a model β-sheet Hamiltonian to simulate amyloid vibrational spectra. We find that the variations in amyloid β-sheet structures (e.g., dihedral angles, interstrand distances, and orientations) create significant differences in the average values for interstrand and nearest neighbor couplings, and that those variations encompass the variation in measured A⊥ frequencies. We also find that off-diagonal disorder about the average values explains the range of transition dipole strengths observed experimentally. Thus, we conclude that the lack of correlation between transition dipole-strength and frequency is caused by variations in amyloid β-sheet structure. Taken together, these results indicate that the amide I frequency is very sensitive to amyloid β-sheet structure, the β-sheets of these 4 proteins are not identical, and the assumption that frequency of amyloids scales with β-sheet size cannot be adopted without an accompanying measurement of transition dipole strengths.

  12. Functional Strength Training and Movement Performance Therapy for Upper Limb Recovery Early Poststroke—Efficacy, Neural Correlates, Predictive Markers, and Cost-Effectiveness: FAST-INdiCATE Trial

    Directory of Open Access Journals (Sweden)

    Susan M. Hunter

    2018-01-01

    Full Text Available BackgroundVariation in physiological deficits underlying upper limb paresis after stroke could influence how people recover and to which physical therapy they best respond.ObjectivesTo determine whether functional strength training (FST improves upper limb recovery more than movement performance therapy (MPT. To identify: (a neural correlates of response and (b whether pre-intervention neural characteristics predict response.DesignExplanatory investigations within a randomised, controlled, observer-blind, and multicentre trial. Randomisation was computer-generated and concealed by an independent facility until baseline measures were completed. Primary time point was outcome, after the 6-week intervention phase. Follow-up was at 6 months after stroke.ParticipantsWith some voluntary muscle contraction in the paretic upper limb, not full dexterity, when recruited up to 60 days after an anterior cerebral circulation territory stroke.InterventionsConventional physical therapy (CPT plus either MPT or FST for up to 90 min-a-day, 5 days-a-week for 6 weeks. FST was “hands-off” progressive resistive exercise cemented into functional task training. MPT was “hands-on” sensory/facilitation techniques for smooth and accurate movement.OutcomesThe primary efficacy measure was the Action Research Arm Test (ARAT. Neural measures: fractional anisotropy (FA corpus callosum midline; asymmetry of corticospinal tracts FA; and resting motor threshold (RMT of motor-evoked potentials.AnalysisCovariance models tested ARAT change from baseline. At outcome: correlation coefficients assessed relationship between change in ARAT and neural measures; an interaction term assessed whether baseline neural characteristics predicted response.Results288 Participants had: mean age of 72.2 (SD 12.5 years and mean ARAT 25.5 (18.2. For 240 participants with ARAT at baseline and outcome the mean change was 9.70 (11.72 for FST + CPT and 7.90 (9.18 for MPT

  13. Adaptive coupling of inferior olive neurons in cerebellar learning.

    Science.gov (United States)

    Tokuda, Isao T; Hoang, Huu; Schweighofer, Nicolas; Kawato, Mitsuo

    2013-11-01

    In the cerebellar learning hypothesis, inferior olive neurons are presumed to transmit high fidelity error signals, despite their low firing rates. The idea of chaotic resonance has been proposed to realize efficient error transmission by desynchronized spiking activities induced by moderate electrical coupling between inferior olive neurons. A recent study suggests that the coupling strength between inferior olive neurons can be adaptive and may decrease during the learning process. We show that such a decrease in coupling strength can be beneficial for motor learning, since efficient coupling strength depends upon the magnitude of the error signals. We introduce a scheme of adaptive coupling that enhances the learning of a neural controller for fast arm movements. Our numerical study supports the view that the controlling strategy of the coupling strength provides an additional degree of freedom to optimize the actual learning in the cerebellum. Copyright © 2012 Elsevier Ltd. All rights reserved.

  14. Back-propagation neural network-based approximate analysis of true stress-strain behaviors of high-strength metallic material

    Energy Technology Data Exchange (ETDEWEB)

    Doh, Jaeh Yeok; Lee, Jong Soo [Yonsei University, Seoul (Korea, Republic of); Lee, Seung Uk [Gyeongbuk Hybrid Technology Institute, Yeongcheon (Korea, Republic of)

    2016-03-15

    In this study, a Back-propagation neural network (BPN) is employed to conduct an approximation of a true stress-strain curve using the load-displacement experimental data of DP590, a high-strength material used in automobile bodies and chassis. The optimized interconnection weights are obtained with hidden layers and output layers of the BPN through intelligent learning and training of the experimental data; by using these weights, a mathematical model of the material's behavior is suggested through this feed-forward neural network. Generally, the material properties from the tensile test cannot be acquired until the fracture regions, since it is difficult to measure the cross-section area of a specimen after diffusion necking. For this reason, the plastic properties of the true stress-strain are extrapolated using the weighted-average method after diffusion necking. The accuracies of BPN-based meta-models for predicting material properties are validated in terms of the Root mean square error (RMSE). By applying the approximate material properties, the reliable finite element solution can be obtained to realize the different shapes of the finite element models. Furthermore, the sensitivity analysis of the approximate meta-model is performed using the first-order approximate derivatives of the BPN and is compared with the results of the finite difference method. In addition, we predict the tension velocity's effect on the material property through a first-order sensitivity analysis.

  15. 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.

  16. The Infuence of Coupling Agent and the Content of Fibers on Tensile Strength and Physical Properties of Cotton Fiber Stem/Recycled Polypropylene Composites

    Directory of Open Access Journals (Sweden)

    Abolfazl Kargarfard

    2013-06-01

    Full Text Available The objective of this study was to investigate the influence of coupling agent and the content of fiber on tensile strength and physical properties of wood/plastic composite produced from recycled polypropylene using mat forming procedure. Recycled polypropylene and three levels of Cotton Fiber Stem (50, 55 and 60% and three levels of MAPP (0, 3 and 5% were used. The results of tensile strength and physical properties were statistically analyzed using factorial experimental design. The results indicated that the tensile strength of composites with increasing MAPP content and decreasing of fiber content was improved However the modulus of tensile reduced significantly when the fibers content reduced. Also, the physical properties of composites were improved with increasing of MAPP consumption. Thickness swelling of composites after 24 hours and water absorption after 2 hours in boiling water showed these properties are lower when 50% fibers is used.

  17. Synchronization of fractional fuzzy cellular neural networks with interactions

    Science.gov (United States)

    Ma, Weiyuan; Li, Changpin; Wu, Yujiang; Wu, Yongqing

    2017-10-01

    In this paper, we introduce fuzzy theory into the fractional cellular neural networks to dynamically enhance the coupling strength and propose a fractional fuzzy neural network model with interactions. Using the Lyapunov principle of fractional differential equations, we design the adaptive control schemes to realize the synchronization and obtain the synchronization criteria. Finally, we provide some numerical examples to show the effectiveness of our obtained results.

  18. [Comparative research for micro-push-out bond strengths of glass fiber posts treated by poly-dopamine or silane coupling agent].

    Science.gov (United States)

    Chen, Qian; Su, Yong-liang; Cai, Qing; Bai, Yun-yang; Su, Jing; Wang, Xin-zhi

    2015-12-18

    To evaluate the micro-push-out bond strengths of prefabricated glass fiber posts with poly-dopamine functionalized to root dentin using resin cements, contrasted with silane treatment. In the study, 30 glass fiber posts were randomly divided into 3 groups (10 posts in each group) for different surface treatments. Group 1, treated with poly-dopa; Group 2, treated with silane coupling agent for 60s; Group 3, no surface treatment (Control group). The 30 extracted human, single-rooted teeth were endodontically treated and a 9 mm post space was prepared in each tooth with post drills provided by the manufacturer. Following post cementation, the specimens were stored in distilled water at 37 °C for 7 days. The micro-push-out bond strengths were tested using a universal testing machine (0.5 mm/min), and the failure modes were examined with a stereomicroscope. The data of the three groups were statistically analyzed using the one-way ANOVA test(α= 0.05). The bond strengths were (7.909 ± 1.987) MPa for Group 1, (5.906 ± 0.620) MPa for Group 2, and 4.678 ± 0.910 MPa for Group 3. The bond strength of poly-dopamine group was significantly higher than that of the silane group (Pfiber posts.

  19. Low absorption state of phycocyanin from Acaryochloris marina antenna system: On the interplay between ionic strength and excitonic coupling

    Science.gov (United States)

    Nganou, Collins

    2013-07-01

    This paper studies the excitonic factor in the excited state energy transfer of phycobilisome (PBS) by using a polarized time-resolved pump-probe and by changing the ionic strength of the cofactors' medium in the PBS of Acaryochloris marina (A. marina). As a result, the interplay between the surrounding medium and the closely excited adjacent cofactors is shown to be a negligible factor of the excitonic decay kinetics at 618 nm of the phycocyanin (PC), while it appears as a driving factor of an increase in excitonic delocalization at 630 nm. The obtained anisotropy values are consistent with the contribution of ionic strength in the excitonic mechanism in PBS. These values were 0.38 in high ionic strength and 0.4 in low ionic strength at 618 nm, and 0.52 in high ionic strength and 0.4 in low ionic strength at 630-635 nm. The anisotropy value of 0.52 in high phosphate is similar at 630 nm and 635 nm, which is consistent with an excitonic delocalization band at 635 nm. The 635 nm band is suggested to show the true low energy level of PC in A. marina PBS. The anisotropy decay kinetic at 630 nm suggests that the excited state population of PC is not all equilibrated in 3 ps because of the existence of the 10 ps decay kinetic component. The presence of the slow kinetic decay component in high, and low ionic strength, is consistent with a 10 and 14 ps energy transfer pathway, while the 450 fs kinetic decay component is consistent with the presence of an additional excitation energy transfer pathway between adjacent α84 and β84. Furthermore, the 450 fs decay kinetic is suggested to be trapped in the trimer, while the 400 fs decay kinetic rules out an excitonic flow from low energy level PC to allophycoyanin. This excitonic flow may occur between β84 in adjacent trimers, towards the low energy state of the PBS rod.

  20. Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection.

    Science.gov (United States)

    Franco, Vanina G; Perín, Juan C; Mantovani, Víctor E; Goicoechea, Héctor C

    2006-01-15

    An experiment was developed as a simple alternative to existing analytical methods for the simultaneous quantitation of glucose (substrate) and glucuronic acid (main product) in the bioprocesses Kombucha by using FTIR spectroscopy coupled to multivariate calibration (partial least-squares, PLS-1 and artificial neural networks, ANNs). Wavelength selection through a novel ranked regions genetic algorithm (RRGA) was used to enhance the predictive ability of the chemometric models. Acceptable results were obtained by using the ANNs models considering the complexity of the sample and the speediness and simplicity of the method. The accuracy on the glucuronic acid determination was calculated by analysing spiked real fermentation samples (recoveries ca. 115%).

  1. Evaluation of Induced Settlements of Piled Rafts in the Coupled Static-Dynamic Loads Using Neural Networks and Evolutionary Polynomial Regression

    Directory of Open Access Journals (Sweden)

    Ali Ghorbani

    2017-01-01

    Full Text Available Coupled Piled Raft Foundations (CPRFs are broadly applied to share heavy loads of superstructures between piles and rafts and reduce total and differential settlements. Settlements induced by static/coupled static-dynamic loads are one of the main concerns of engineers in designing CPRFs. Evaluation of induced settlements of CPRFs has been commonly carried out using three-dimensional finite element/finite difference modeling or through expensive real-scale/prototype model tests. Since the analyses, especially in the case of coupled static-dynamic loads, are not simply conducted, this paper presents two practical methods to gain the values of settlement. First, different nonlinear finite difference models under different static and coupled static-dynamic loads are developed to calculate exerted settlements. Analyses are performed with respect to different axial loads and pile’s configurations, numbers, lengths, diameters, and spacing for both loading cases. Based on the results of well-validated three-dimensional finite difference modeling, artificial neural networks and evolutionary polynomial regressions are then applied and introduced as capable methods to accurately present both static and coupled static-dynamic settlements. Also, using a sensitivity analysis based on Cosine Amplitude Method, axial load is introduced as the most influential parameter, while the ratio l/d is reported as the least effective parameter on the settlements of CPRFs.

  2. The Coupled Effect of Loading Rate and Grain Size on Tensile Strength of Sandstones under Dynamic Disturbance

    Directory of Open Access Journals (Sweden)

    Miao Yu

    2017-01-01

    Full Text Available It is of significance to comprehend the effects of rock microstructure on the tensile strength under different loading rates caused by mining disturbance. So, in this paper, three kinds of sandstones drilled from surrounding rocks in Xiao Jihan Coal to simulate the in situ stress state, whose average grain size is 30 μm (fine grain, FG, 105 μm (medium grain, MG, and 231 μm (Coarse grain, CG, are selected with the calculation of optical microscopic technique and moreover processed to Brazilian disc (BD to study the mechanical response of samples. The dynamic Brazilian tests of samples with three kinds of grain sizes are conducted with the Split Hopkinson Pressure Bar (SHPB driven by pendulum hammer, which can produce four different velocities (V=2.0 m/s, 2.5 m/s, 3.3 m/s, and 4.2 m/s when the incident bar is impacted by pendulum hammer. The incident wave produced by pendulum hammer is a slowly rising stress wave, which allows gradual stress accumulation in the specimen and maintains the load at both ends of the specimen in an equilibrium state. The results show that the dynamic strength of three kinds of BD samples represented loading rates dependence, and FG sandstones are more sensitive for loading rates than MG and CG samples. Moreover, the peak strength is observed to increase linearly with an increasing stress rates, and the relationship between the dynamic BD strength and stress rates can be built through a linear equation. Finally, the failure modes of different grain sizes are discussed and explained by microfailure mechanism.

  3. Physiologically-Based Vision Modeling Applications and Gradient Descent-Based Parameter Adaptation of Pulse Coupled Neural Networks

    Science.gov (United States)

    1997-06-01

    Transactions on Systems, Man and Cybernetics, vol.SMC- 13, no.5, p. 815-26 (1983). 14. Crick , F. and C. Kosch. "The problem of consciousness." Mind and Brain...cortical maps via synchronization," Parallel Pro- cessing in Neural Systems and Computers, p. xv+626, 101-4 (1990). 25. Francis , Gregory and Stephen

  4. Low-Frequency Noise and Offset Rejection in DC-Coupled Neural Amplifiers: A Review and Digitally-Assisted Design Tutorial.

    Science.gov (United States)

    Bagheri, Arezu; Salam, Muhammad Tariqus; Perez Velazquez, Jose Luis; Genov, Roman

    2017-02-01

    We review integrated circuits for low-frequency noise and offset rejection as a motivation for the presented digitally-assisted neural amplifier design methodology. Conventional AC-coupled neural amplifiers inherently reject input DC offset but have key limitations in area, linearity, DC drift, and spectral accuracy. Their chopper stabilization reduces low-frequency intrinsic noise at the cost of degraded area, input impedance and design complexity. DC-coupled implementations with digital high-pass filtering yield improved area, linearity, drift, and spectral accuracy and are inherently suitable for simple chopper stabilization. As a design example, a 56-channel 0.13 [Formula: see text] CMOS intracranial EEG interface is presented. DC offset of up to ±50 mV is rejected by a digital low-pass filter and a 16-bit delta-sigma DAC feeding back into the folding node of a folded-cascode LNA with CMRR of 65 dB. A bank of seven column-parallel fully differential SAR ADCs with ENOB of 6.6 are shared among 56 channels resulting in 0.018 [Formula: see text] effective channel area. Compensation-free direct input chopping yields integrated input-referred noise of 4.2 μVrms over the bandwidth of 1 Hz to 1 kHz. The 8.7 [Formula: see text] chip dissipating 1.07 mW has been validated in vivo in online intracranial EEG monitoring in freely moving rats.

  5. Optimization of auto-induction medium for G-CSF production by Escherichia coli using artificial neural networks coupled with genetic algorithm.

    Science.gov (United States)

    Tian, H; Liu, C; Gao, X D; Yao, W B

    2013-03-01

    Granulocyte colony-stimulating factor (G-CSF) is a cytokine widely used in cancer patients receiving high doses of chemotherapeutic drugs to prevent the chemotherapy-induced suppression of white blood cells. The production of recombinant G-CSF should be increased to meet the increasing market demand. This study aims to model and optimize the carbon source of auto-induction medium to enhance G-CSF production using artificial neural networks coupled with genetic algorithm. In this approach, artificial neural networks served as bioprocess modeling tools, and genetic algorithm (GA) was applied to optimize the established artificial neural network models. Two artificial neural network models were constructed: the back-propagation (BP) network and the radial basis function (RBF) network. The root mean square error, coefficient of determination, and standard error of prediction of the BP model were 0.0375, 0.959, and 8.49 %, respectively, whereas those of the RBF model were 0.0257, 0.980, and 5.82 %, respectively. These values indicated that the RBF model possessed higher fitness and prediction accuracy than the BP model. Under the optimized auto-induction medium, the predicted maximum G-CSF yield by the BP-GA approach was 71.66 %, whereas that by the RBF-GA approach was 75.17 %. These predicted values are in agreement with the experimental results, with 72.4 and 76.014 % for the BP-GA and RBF-GA models, respectively. These results suggest that RBF-GA is superior to BP-GA. The developed approach in this study may be helpful in modeling and optimizing other multivariable, non-linear, and time-variant bioprocesses.

  6. Experimental and Computational Analysis of the Solvent-Dependent O2/Li(+)-O2(-) Redox Couple: Standard Potentials, Coupling Strength, and Implications for Lithium-Oxygen Batteries.

    Science.gov (United States)

    Kwabi, David G; Bryantsev, Vyacheslav S; Batcho, Thomas P; Itkis, Daniil M; Thompson, Carl V; Shao-Horn, Yang

    2016-02-24

    Understanding and controlling the kinetics of O2 reduction in the presence of Li(+)-containing aprotic solvents, to either Li(+)-O2(-) by one-electron reduction or Li2 O2 by two-electron reduction, is instrumental to enhance the discharge voltage and capacity of aprotic Li-O2 batteries. Standard potentials of O2 /Li(+)-O2(-) and O2/O2(-) were experimentally measured and computed using a mixed cluster-continuum model of ion solvation. Increasing combined solvation of Li(+) and O2(-) was found to lower the coupling of Li(+)-O2(-) and the difference between O2/Li(+)-O2(-) and O2/O2(-) potentials. The solvation energy of Li(+) trended with donor number (DN), and varied greater than that of O2 (-) ions, which correlated with acceptor number (AN), explaining a previously reported correlation between Li(+)-O2(-) solubility and DN. These results highlight the importance of the interplay between ion-solvent and ion-ion interactions for manipulating the energetics of intermediate species produced in aprotic metal-oxygen batteries. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization.

    Science.gov (United States)

    Huang, Daizheng; Wu, Zhihui

    2017-01-01

    Accurately predicting the trend of outpatient visits by mathematical modeling can help policy makers manage hospitals effectively, reasonably organize schedules for human resources and finances, and appropriately distribute hospital material resources. In this study, a hybrid method based on empirical mode decomposition and back-propagation artificial neural networks optimized by particle swarm optimization is developed to forecast outpatient visits on the basis of monthly numbers. The data outpatient visits are retrieved from January 2005 to December 2013 and first obtained as the original time series. Second, the original time series is decomposed into a finite and often small number of intrinsic mode functions by the empirical mode decomposition technique. Third, a three-layer back-propagation artificial neural network is constructed to forecast each intrinsic mode functions. To improve network performance and avoid falling into a local minimum, particle swarm optimization is employed to optimize the weights and thresholds of back-propagation artificial neural networks. Finally, the superposition of forecasting results of the intrinsic mode functions is regarded as the ultimate forecasting value. Simulation indicates that the proposed method attains a better performance index than the other four methods.

  8. Analysis of coherent structures and atmosphere-canopy coupling strength during the CABINEX field campaign: implications for atmospheric chemistry

    Science.gov (United States)

    Steiner, A. L.; Pressley, S. N.; Botros, A.; Jones, E.; Chung, S. H.; Edburg, S. L.

    2011-07-01

    Intermittent coherent structures can be responsible for a large fraction of the chemical exchange between the vegetation canopy and the atmosphere. Quantifying their contribution to fluxes is necessary to interpret measurements of trace gases and aerosols within and above forest canopies. The primary objective of the Community Atmosphere-Biosphere Interactions Experiment (CABINEX) field campaign (10 July 2009 to 9 August 2009) was to study the chemistry of volatile organic compounds (VOC) within and above a forest canopy. In this manuscript, we provide an analysis of coherent structures and canopy-atmosphere exchange during CABINEX to support in-canopy gradient measurements of VOC. We quantify the number and duration of coherent structure events and their percent contribution to momentum and heat fluxes with two methods: (1) quadrant-hole analysis and (2) wavelet analysis. Despite differences in the duration and number of events, both methods predict that coherent structures contribute 40-50 % to total momentum fluxes and 44-65 % to total heat fluxes during the CABINEX campaign. Contributions associated with coherent structures are slightly greater under stable rather than unstable conditions. By comparing heat fluxes within and above the canopy, we determine the degree of coupling between upper canopy and atmosphere and find that they are coupled to the majority of the campaign time period. Uncoupled canopy-atmosphere events occur in the early morning (04:00-08:00 LT) approximately 30 % of the time. This study confirms that coherent structures contribute significantly to the exchange of heat and momentum between the canopy and atmosphere at the CABINEX site, and indicates the need to include these transport processes when studying the mixing and chemical reactions of trace gases and aerosols between a forest canopy and the atmosphere.

  9. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Science.gov (United States)

    Ziv, Omer; Zaritsky, Assaf; Yaffe, Yakey; Mutukula, Naresh; Edri, Reuven; Elkabetz, Yechiel

    2015-10-01

    Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  10. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Directory of Open Access Journals (Sweden)

    Omer Ziv

    2015-10-01

    Full Text Available Neural stem cells (NSCs are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  11. Neural inhibition during maximal eccentric and concentric quadriceps contraction: effects of resistance training

    DEFF Research Database (Denmark)

    Aagaard, Per; Simonsen, E.B.; Andersen, J.L.

    2000-01-01

    neuromuscular activation, muscle strength, neural efferent drive, eccentric activation deficiency, force inhibition......neuromuscular activation, muscle strength, neural efferent drive, eccentric activation deficiency, force inhibition...

  12. Analysis of Pull-In Instability of Geometrically Nonlinear Microbeam Using Radial Basis Artificial Neural Network Based on Couple Stress Theory

    Directory of Open Access Journals (Sweden)

    Mohammad Heidari

    2014-01-01

    Full Text Available The static pull-in instability of beam-type microelectromechanical systems (MEMS is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.

  13. Analysis of pull-in instability of geometrically nonlinear microbeam using radial basis artificial neural network based on couple stress theory.

    Science.gov (United States)

    Heidari, Mohammad; Heidari, Ali; Homaei, Hadi

    2014-01-01

    The static pull-in instability of beam-type microelectromechanical systems (MEMS) is theoretically investigated. Two engineering cases including cantilever and double cantilever microbeam are considered. Considering the midplane stretching as the source of the nonlinearity in the beam behavior, a nonlinear size-dependent Euler-Bernoulli beam model is used based on a modified couple stress theory, capable of capturing the size effect. By selecting a range of geometric parameters such as beam lengths, width, thickness, gaps, and size effect, we identify the static pull-in instability voltage. A MAPLE package is employed to solve the nonlinear differential governing equations to obtain the static pull-in instability voltage of microbeams. Radial basis function artificial neural network with two functions has been used for modeling the static pull-in instability of microcantilever beam. The network has four inputs of length, width, gap, and the ratio of height to scale parameter of beam as the independent process variables, and the output is static pull-in voltage of microbeam. Numerical data, employed for training the network, and capabilities of the model have been verified in predicting the pull-in instability behavior. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 4.55% in predicting pull-in voltage of cantilever microbeam. Further analysis of pull-in instability of beam under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach. The results reveal significant influences of size effect and geometric parameters on the static pull-in instability voltage of MEMS.

  14. Retention Strength of PMMA/UDMA-Based Crowns Bonded to Dentin: Impact of Different Coupling Agents for Pretreatment

    Directory of Open Access Journals (Sweden)

    Bogna Stawarczyk

    2015-11-01

    Full Text Available Computer aided design/computer aided manufacturing (CAD/CAM polymers for long-term dental restorations benefit from enhanced mechanical properties. However, the quantification of their bonding properties on teeth is lacking. Therefore, the aim of this study was to determine the retention strength (RS of differently pretreated new developed polymethylmethacrylate/ urethanedimethacrylate-based CAD/CAM polymer bonded on dentin. In summary, 120 human caries-free molars were prepared, and polymeric crowns were milled and pretreated (n = 20: visio.link (VL, Scotchbond Universal (SU, Monobond Plus/Heliobond (MH, Margin Bond (MB, Margin Bond mixed with acetone (1:1 (MBA or not pretreated (CG. Half of the specimens were cemented using Variolink II and the other half with RelyX Ultimate. Specimens were stored for 24 h in distilled water and thermal cycled (5000 ×, 5 °C/55 °C. The retention load was measured and failure types were defined. RS was calculated and analyzed using both two- and one-way ANOVA with a post-hoc Scheffé-test, unpaired t-test, Kaplan–Meier with Breslow–Gehan test and chi-squared test (p < 0.05. Crowns bonded using RelyX Ultimate showed higher RS than those bonded using Variolink II. The pretreatment showed no impact on the RS. However, survival analysis within Variolink II found an impact of pretreatment. The median RS for MH was the lowest and statistically different from MB, MBA and CG. For Variolink II MH had the poorest survival as the estimated cumulative failure function of the debonded crown increased very quickly with increasing TBS. Within the RelyX Ultimate groups, no significant differences were determined. The newly developed CAD/CAM polymer showed the highest bonding properties after cementation using RelyX Ultimate.

  15. Retention Strength of PMMA/UDMA-Based Crowns Bonded to Dentin: Impact of Different Coupling Agents for Pretreatment.

    Science.gov (United States)

    Stawarczyk, Bogna; Teuss, Simona; Eichberger, Marlis; Roos, Malgorzata; Keul, Christine

    2015-11-06

    Computer aided design/computer aided manufacturing (CAD/CAM) polymers for long-term dental restorations benefit from enhanced mechanical properties. However, the quantification of their bonding properties on teeth is lacking. Therefore, the aim of this study was to determine the retention strength (RS) of differently pretreated new developed polymethylmethacrylate/ urethanedimethacrylate-based CAD/CAM polymer bonded on dentin. In summary, 120 human caries-free molars were prepared, and polymeric crowns were milled and pretreated (n = 20): visio.link (VL), Scotchbond Universal (SU), Monobond Plus/Heliobond (MH), Margin Bond (MB), Margin Bond mixed with acetone (1:1) (MBA) or not pretreated (CG). Half of the specimens were cemented using Variolink II and the other half with RelyX Ultimate. Specimens were stored for 24 h in distilled water and thermal cycled (5000 ×, 5 °C/55 °C). The retention load was measured and failure types were defined. RS was calculated and analyzed using both two- and one-way ANOVA with a post-hoc Scheffé-test, unpaired t-test, Kaplan-Meier with Breslow-Gehan test and chi-squared test (p < 0.05). Crowns bonded using RelyX Ultimate showed higher RS than those bonded using Variolink II. The pretreatment showed no impact on the RS. However, survival analysis within Variolink II found an impact of pretreatment. The median RS for MH was the lowest and statistically different from MB, MBA and CG. For Variolink II MH had the poorest survival as the estimated cumulative failure function of the debonded crown increased very quickly with increasing TBS. Within the RelyX Ultimate groups, no significant differences were determined. The newly developed CAD/CAM polymer showed the highest bonding properties after cementation using RelyX Ultimate.

  16. The orphan G-protein-coupled receptor-encoding gene V28 is closely related to genes for chemokine receptors and is expressed in lymphoid and neural tissues.

    Science.gov (United States)

    Raport, C J; Schweickart, V L; Eddy, R L; Shows, T B; Gray, P W

    1995-10-03

    A polymerase chain reaction (PCR) strategy with degenerate primers was used to identify novel G-protein-coupled receptor-encoding genes from human genomic DNA. One of the isolated clones, termed V28, showed high sequence similarity to the genes encoding human chemokine receptors for monocyte chemoattractant protein 1 (MCP-1) and macrophage inflammatory protein 1 alpha (MIP-1 alpha)/RANTES, and to the rat orphan receptor-encoding gene RBS11. When RNA was analyzed by Northern blot, V28 was found to be most highly expressed in neural and lymphoid tissues. Myeloid cell lines, particularly THP.1 cells, showed especially high expression of V28. We have mapped V28 to human chromosome 3p21-3pter, near the MIP-1 alpha/RANTES receptor-encoding gene.

  17. Analysis of Drug Design for a Selection of G Protein-Coupled Neuro-Receptors Using Neural Network Techniques

    DEFF Research Database (Denmark)

    Agerskov, Claus; Mortensen, Rasmus M.; Bohr, Henrik G.

    2015-01-01

    mu-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drug...... networks, trained with greedy learning algorithms, showed superior performance in prediction over the simple feedback NNs. The best networks obtained scores of more than 90 % accuracy in predicting the degree of binding drug molecules to the mentioned receptors and with a maximal Matthew's coefficient of 0...... computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques....

  18. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  19. A wavelet transform coupled with a fuzzy neural network for prediction of significant st segmental changes in the ecg.

    Science.gov (United States)

    Compe, Victor P

    2008-01-01

    The leading cause of death in the United States for people 65 and over is heart disease. A significant factor contributing to this disease process is the damage caused by an infarction, which can manifest as an abnormality in the ST segment of an Electrocardiogram (ECG). This research will develop a pattern recognition model that will be capable of detecting these critical changes. This model will be developed using a feature extraction scheme based upon Wavelet analysis and a classification scheme based upon a Fuzzy Neural Network design. These schemes will be implemented using software tools available from MatLab. Evaluation of the model will be accomplished by simulation (MatLab) with representative ECG samples obtained from a database (e.g. MIT-BIH) that have been universally accepted for such a purpose. This model could be available for implementation into a device used in the pre-hospital setting that would provide the capability of early detection of critical ST changes. Accurate detection of these abnormalities can provide the means for establishing guidelines to determine a treatment protocol that may save lives.

  20. Prediction of Welded Joint Strength in Plasma Arc Welding: A Comparative Study Using Back-Propagation and Radial Basis Neural Networks

    Science.gov (United States)

    Srinivas, Kadivendi; Vundavilli, Pandu R.; Manzoor Hussain, M.; Saiteja, M.

    2016-09-01

    Welding input parameters such as current, gas flow rate and torch angle play a significant role in determination of qualitative mechanical properties of weld joint. Traditionally, it is necessary to determine the weld input parameters for every new welded product to obtain a quality weld joint which is time consuming. In the present work, the effect of plasma arc welding parameters on mild steel was studied using a neural network approach. To obtain a response equation that governs the input-output relationships, conventional regression analysis was also performed. The experimental data was constructed based on Taguchi design and the training data required for neural networks were randomly generated, by varying the input variables within their respective ranges. The responses were calculated for each combination of input variables by using the response equations obtained through the conventional regression analysis. The performances in Levenberg-Marquardt back propagation neural network and radial basis neural network (RBNN) were compared on various randomly generated test cases, which are different from the training cases. From the results, it is interesting to note that for the above said test cases RBNN analysis gave improved training results compared to that of feed forward back propagation neural network analysis. Also, RBNN analysis proved a pattern of increasing performance as the data points moved away from the initial input values.

  1. Neural coupling between contralesional motor and frontoparietal networks correlates with motor ability in individuals with chronic stroke.

    Science.gov (United States)

    Lam, Timothy K; Dawson, Deirdre R; Honjo, Kie; Ross, Bernhard; Binns, Malcolm A; Stuss, Donald T; Black, Sandra E; Chen, J Jean; Levine, Brian T; Fujioka, Takako; Chen, Joyce L

    2018-01-15

    Movement is traditionally viewed as a process that involves motor brain regions. However, movement also implicates non-motor regions such as prefrontal and parietal cortex, regions whose integrity may thus be important for motor recovery after stroke. Importantly, focal brain damage can affect neural functioning within and between distinct brain networks implicated in the damage. The aim of this study is to investigate how resting state connectivity (rs-connectivity) within and between motor and frontoparietal networks are affected post-stroke in correlation with motor outcome. Twenty-seven participants with chronic stroke with unilateral upper limb deficits underwent motor assessments and magnetic resonance imaging. Participants completed the Chedoke-McMaster Stroke Assessment as a measure of arm (CMSA-Arm) and hand (CMSA-Hand) impairment and the Action Research Arm Test (ARAT) as a measure of motor function. We used a seed-based rs-connectivity approach defining the motor (seed=contralesional primary motor cortex (M1)) and frontoparietal (seed=contralesional dorsolateral prefrontal cortex (DLPFC)) networks. We analyzed the rs-connectivity within each network (intra-network connectivity) and between both networks (inter-network connectivity), and performed correlations between: a) intra-network connectivity and motor assessment scores; b) inter-network connectivity and motor assessment scores. We found: a) Participants with high rs-connectivity within the motor network (between M1 and supplementary motor area) have higher CMSA-Hand stage (z=3.62, p=0.003) and higher ARAT score (z=3.41, p=0.02). Rs-connectivity within the motor network was not significantly correlated with CMSA-Arm stage (z=1.83, p>0.05); b) Participants with high rs-connectivity within the frontoparietal network (between DLPFC and mid-ventrolateral prefrontal cortex) have higher CMSA-Hand stage (z=3.64, p=0.01). Rs-connectivity within the frontoparietal network was not significantly correlated

  2. Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm.

    Science.gov (United States)

    Jacob, Samuel; Banerjee, Rintu

    2016-08-01

    A novel approach to overcome the acidification problem has been attempted in the present study by codigesting industrial potato waste (PW) with Pistia stratiotes (PS, an aquatic weed). The effectiveness of codigestion of the weed and PW was tested in an equal (1:1) proportion by weight with substrate concentration of 5g total solid (TS)/L (2.5gPW+2.5gPS) which resulted in enhancement of methane yield by 76.45% as compared to monodigestion of PW with a positive synergistic effect. Optimization of process parameters was conducted using central composite design (CCD) based response surface methodology (RSM) and artificial neural network (ANN) coupled genetic algorithm (GA) model. Upon comparison of these two optimization techniques, ANN-GA model obtained through feed forward back propagation methodology was found to be efficient and yielded 447.4±21.43LCH4/kgVSfed (0.279gCH4/kgCODvs) which is 6% higher as compared to the CCD-RSM based approach. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

    Directory of Open Access Journals (Sweden)

    Mihail Lucian Birsa

    2011-10-01

    Full Text Available In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen, or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry and GC-MS (gas chromatography-mass spectrometry spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA. The scores of the forensic compounds on the main principal components (PCs were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%, as well as a good selectivity (a rate of true negatives TN

  4. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    In contemporary consciousness studies the phenomenon of neural plasticity has received little attention despite the fact that neural plasticity is of still increased interest in neuroscience. We will, however, argue that neural plasticity could be of great importance to consciousness studies....... If consciousness is related to neural processes it seems, at least prima facie, that the ability of the neural structures to change should be reflected in a theory of this relationship "Neural plasticity" refers to the fact that the brain can change due to its own activity. The brain is not static but rather...... a dynamic entity, which physical structure changes according to its use and environment. This change may take the form of growth of new neurons, the creation of new networks and structures, and change within network structures, that is, changes in synaptic strengths. Plasticity raises questions about...

  5. Applications of Box–Behnken experimental design coupled with artificial neural networks for biosorption of low concentrations of cadmium using Spirulina (Arthrospira spp.

    Directory of Open Access Journals (Sweden)

    R.R. Siva Kiran

    2017-03-01

    Full Text Available The present study deals with the application of artificial intelligence techniques coupled with Box–Behnken (BB design to model the process parameters for biosorption of cadmium using live Spirulina (Arthrospira spp. as adsorbent in open race way pond with Zarrouk medium. The biomass concentration of Spirulina spp. decreased to half at 4 ppm Cd (II after 8 days. Based on the LCt50 values, 3.69 ppm (8th day, Spirulina (Arthospira maxima showed maximum tolerance. Considerable growth and bioaccumulation of Spirulina spp. is observed below 1 ppm and tolerant up to 3 ppm. The cadmium adsorption on Spirulina spp. showed good correlation (R2 = 0.99 when applied to Freundlich equation and data fit into pseudo second order kinetics. A four factorial, three blocks and three level Box–Behnken design with initial concentration (1 ppb to 5 ppb, biosorbant dosage (0.1 gdw to 0.2 gdw, agitation speed (12 rpm to 16 rpm and pH (6 to 8 as independent variables and percentage adsorption as dependent variable were selected for study. The data were further processed using artificial neural network model and DIRECT algorithm for better optimization. The final Cd (II concentration of <0.5 ppb was achieved with 1 ppb initial concentration under optimal conditions. A continuous desorption process was also developed for removal of cadmium from Spirulina (Arthrospira sp.

  6. Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open pit mine

    Science.gov (United States)

    Bahrami, Saeed; Doulati Ardejani, Faramarz; Baafi, Ernest

    2016-05-01

    In this study, hybrid models are designed to predict groundwater inflow to an advancing open pit mine and the hydraulic head (HH) in observation wells at different distances from the centre of the pit during its advance. Hybrid methods coupling artificial neural network (ANN) with genetic algorithm (GA) methods (ANN-GA), and simulated annealing (SA) methods (ANN-SA), were utilised. Ratios of depth of pit penetration in aquifer to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the HH in the observation wells to the distance of observation wells from the centre of the pit were used as inputs to the networks. To achieve the objective two hybrid models consisting of ANN-GA and ANN-SA with 4-5-3-1 arrangement were designed. In addition, by switching the last argument of the input layer with the argument of the output layer of two earlier models, two new models were developed to predict the HH in the observation wells for the period of the mining process. The accuracy and reliability of models are verified by field data, results of a numerical finite element model using SEEP/W, outputs of simple ANNs and some well-known analytical solutions. Predicted results obtained by the hybrid methods are closer to the field data compared to the outputs of analytical and simple ANN models. Results show that despite the use of fewer and simpler parameters by the hybrid models, the ANN-GA and to some extent the ANN-SA have the ability to compete with the numerical models.

  7. Measurements of the Higgs boson production and decay rates and coupling strengths using pp collision data at √(s) = 7 and 8 TeV in the ATLAS experiment

    Energy Technology Data Exchange (ETDEWEB)

    Aad, G. [CPPM, Aix-Marseille Univ. et CNRS/IN2P3, Marseille (France); Abbott, B. [Oklahoma Univ., Norman, OK (United States). Homer L. Dodge Dept. of Physics and Astronomy; Abdallah, J. [Academia Sinica, Taipei (China). Inst. of Physics; Collaboration: ATLAS Publications; and others

    2016-01-15

    Combined analyses of the Higgs boson production and decay rates as well as its coupling strengths to vector bosons and fermions are presented. The combinations include the results of the analyses of the H → γγ, ZZ*, WW*, Zγ, b anti b, ττ and μμ decay modes, and the constraints on the associated production with a pair of top quarks and on the off-shell coupling strengths of the Higgs boson. The results are based on the LHC proton-proton collision datasets, with integrated luminosities of up to 4.7 fb{sup -1} at √(s) = 7 TeV and 20.3 fb{sup -1} at √(s) = 8 TeV, recorded by the ATLAS detector in 2011 and 2012. Combining all production modes and decay channels, the measured signal yield, normalised to the Standard Model expectation, is 1.18{sub -0.14}{sup +0.15}. The observed Higgs boson production and decay rates are interpreted in a leading-order coupling framework, exploring a wide range of benchmark coupling models both with and without assumptions on the Higgs boson width and on the Standard Model particle content in loop processes. The data are found to be compatible with the Standard Model expectations for a Higgs boson at a mass of 125.36 GeV for all models considered. (orig.)

  8. Individual differences in sound-in-noise perception are related to the strength of short-latency neural responses to noise.

    Directory of Open Access Journals (Sweden)

    Ekaterina Vinnik

    Full Text Available Important sounds can be easily missed or misidentified in the presence of extraneous noise. We describe an auditory illusion in which a continuous ongoing tone becomes inaudible during a brief, non-masking noise burst more than one octave away, which is unexpected given the frequency resolution of human hearing. Participants strongly susceptible to this illusory discontinuity did not perceive illusory auditory continuity (in which a sound subjectively continues during a burst of masking noise when the noises were short, yet did so at longer noise durations. Participants who were not prone to illusory discontinuity showed robust early electroencephalographic responses at 40-66 ms after noise burst onset, whereas those prone to the illusion lacked these early responses. These data suggest that short-latency neural responses to auditory scene components reflect subsequent individual differences in the parsing of auditory scenes.

  9. Death and rebirth of neural activity in sparse inhibitory networks

    Science.gov (United States)

    Angulo-Garcia, David; Luccioli, Stefano; Olmi, Simona; Torcini, Alessandro

    2017-05-01

    Inhibition is a key aspect of neural dynamics playing a fundamental role for the emergence of neural rhythms and the implementation of various information coding strategies. Inhibitory populations are present in several brain structures, and the comprehension of their dynamics is strategical for the understanding of neural processing. In this paper, we clarify the mechanisms underlying a general phenomenon present in pulse-coupled heterogeneous inhibitory networks: inhibition can induce not only suppression of neural activity, as expected, but can also promote neural re-activation. In particular, for globally coupled systems, the number of firing neurons monotonically reduces upon increasing the strength of inhibition (neuronal death). However, the random pruning of connections is able to reverse the action of inhibition, i.e. in a random sparse network a sufficiently strong synaptic strength can surprisingly promote, rather than depress, the activity of neurons (neuronal rebirth). Thus, the number of firing neurons reaches a minimum value at some intermediate synaptic strength. We show that this minimum signals a transition from a regime dominated by neurons with a higher firing activity to a phase where all neurons are effectively sub-threshold and their irregular firing is driven by current fluctuations. We explain the origin of the transition by deriving a mean field formulation of the problem able to provide the fraction of active neurons as well as the first two moments of their firing statistics. The introduction of a synaptic time scale does not modify the main aspects of the reported phenomenon. However, for sufficiently slow synapses the transition becomes dramatic, and the system passes from a perfectly regular evolution to irregular bursting dynamics. In this latter regime the model provides predictions consistent with experimental findings for a specific class of neurons, namely the medium spiny neurons in the striatum.

  10. Role of feed forward neural networks coupled with genetic algorithm in capitalizing of intracellular alpha-galactosidase production by Acinetobacter sp.

    Science.gov (United States)

    Edupuganti, Sirisha; Potumarthi, Ravichandra; Sathish, Thadikamala; Mangamoori, Lakshmi Narasu

    2014-01-01

    Alpha-galactosidase production in submerged fermentation by Acinetobacter sp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between fermentation parameters and enzyme yield. The predicted values were further optimized by genetic algorithm (GA). The predictability of neural networks was further analysed by using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2-value for training and testing data. Using hybrid neural networks and genetic algorithm, alpha-galactosidase production was improved from 7.5 U/mL to 10.2 U/mL.

  11. Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network.

    Science.gov (United States)

    De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

    2016-11-10

    A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

  12. Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Luigi Alberto Ciro De Filippis

    2016-11-01

    Full Text Available A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable and the mechanical properties (output responses of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls. The simulation model was based on the adoption of the Artificial Neural Networks (ANNs characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration.

  13. Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network

    Science.gov (United States)

    De Filippis, Luigi Alberto Ciro; Serio, Livia Maria; Facchini, Francesco; Mummolo, Giovanni; Ludovico, Antonio Domenico

    2016-01-01

    A simulation model was developed for the monitoring, controlling and optimization of the Friction Stir Welding (FSW) process. This approach, using the FSW technique, allows identifying the correlation between the process parameters (input variable) and the mechanical properties (output responses) of the welded AA5754 H111 aluminum plates. The optimization of technological parameters is a basic requirement for increasing the seam quality, since it promotes a stable and defect-free process. Both the tool rotation and the travel speed, the position of the samples extracted from the weld bead and the thermal data, detected with thermographic techniques for on-line control of the joints, were varied to build the experimental plans. The quality of joints was evaluated through destructive and non-destructive tests (visual tests, macro graphic analysis, tensile tests, indentation Vickers hardness tests and t thermographic controls). The simulation model was based on the adoption of the Artificial Neural Networks (ANNs) characterized by back-propagation learning algorithm with different types of architecture, which were able to predict with good reliability the FSW process parameters for the welding of the AA5754 H111 aluminum plates in Butt-Joint configuration. PMID:28774035

  14. The neural mobilization technique modulates the expression of endogenous opioids in the periaqueductal gray and improves muscle strength and mobility in rats with neuropathic pain.

    Science.gov (United States)

    Santos, Fabio Martinez; Grecco, Leandro Henrique; Pereira, Marcelo Gomes; Oliveira, Mara Evany; Rocha, Priscila Abreu; Silva, Joyce Teixeira; Martins, Daniel Oliveira; Miyabara, Elen Haruka; Chacur, Marucia

    2014-05-13

    The neural mobilization (NM) technique is a noninvasive method that has been proven to be clinically effective in reducing pain; however, the molecular mechanisms involved remain poorly understood. The aim of this study was to analyze whether NM alters the expression of the mu-opioid receptor (MOR), the delta-opioid receptor (DOR) and the Kappa-opioid receptor (KOR) in the periaqueductal gray (PAG) and improves locomotion and muscle force after chronic constriction injury (CCI) in rats. The CCI was imposed on adult male rats followed by 10 sessions of NM every other day, starting 14 days after the CCI injury. At the end of the sessions, the PAG was analyzed using Western blot assays for opioid receptors. Locomotion was analyzed by the Sciatic functional index (SFI), and muscle force was analyzed by the BIOPAC system. An improvement in locomotion was observed in animals treated with NM compared with injured animals. Animals treated with NM showed an increase in maximal tetanic force of the tibialis anterior muscle of 172% (p < 0.001) compared with the CCI group. We also observed a decrease of 53% (p < 0.001) and 23% (p < 0.05) in DOR and KOR levels, respectively, after CCI injury compared to those from naive animals and an increase of 17% (p < 0.05) in KOR expression only after NM treatment compared to naive animals. There were no significant changes in MOR expression in the PAG. These data provide evidence that a non-pharmacological NM technique facilitates pain relief by endogenous analgesic modulation.

  15. Effects of hydrodynamic mixing intensity coupled with ionic strength on the initial stage dynamics of bridging flocculation of polystyrene latex particles with polyelectrolyte

    NARCIS (Netherlands)

    Adachi, Y.; Matsumoto, T.; Cohen Stuart, M.A.

    2002-01-01

    Effects of hydrodynamic mixing intensity on the initial stage dynamics of bridging flocculation induced by adsorbing polyelectrolyte were analyzed as an extension of previous report on the effect of ionic strength (J. Coll. Int. Sci. 204 (1998) 328). Mixing condition were changed by adopting forked

  16. Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks.

    Science.gov (United States)

    Arbabi, Vahid; Pouran, Behdad; Campoli, Gianni; Weinans, Harrie; Zadpoor, Amir A

    2016-03-21

    One of the most widely used techniques to determine the mechanical properties of cartilage is based on indentation tests and interpretation of the obtained force-time or displacement-time data. In the current computational approaches, one needs to simulate the indentation test with finite element models and use an optimization algorithm to estimate the mechanical properties of cartilage. The modeling procedure is cumbersome, and the simulations need to be repeated for every new experiment. For the first time, we propose a method for fast and accurate estimation of the mechanical and physical properties of cartilage as a poroelastic material with the aid of artificial neural networks. In our study, we used finite element models to simulate the indentation for poroelastic materials with wide combinations of mechanical and physical properties. The obtained force-time curves are then divided into three parts: the first two parts of the data is used for training and validation of an artificial neural network, while the third part is used for testing the trained network. The trained neural network receives the force-time curves as the input and provides the properties of cartilage as the output. We observed that the trained network could accurately predict the properties of cartilage within the range of properties for which it was trained. The mechanical and physical properties of cartilage could therefore be estimated very fast, since no additional finite element modeling is required once the neural network is trained. The robustness of the trained artificial neural network in determining the properties of cartilage based on noisy force-time data was assessed by introducing noise to the simulated force-time data. We found that the training procedure could be optimized so as to maximize the robustness of the neural network against noisy force-time data. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. Strength of the rare-earth-transition-metal exchange coupling in hard magnetic materials, an experimental approach based on high-field magnetisation measurements: Application to Er sub 2 Fe sub 14 B

    Energy Technology Data Exchange (ETDEWEB)

    Verhoef, R.; Radwanski, R.J.; Franse, J.J.M. (Natuurkundig Lab., Univ. van Amsterdam (Netherlands))

    1990-09-01

    A new experimental technique is presented to determine the intersublattice molecular-field coefficient, n{sub RT}, in heavy rare-earth-transition-metal intermetallic compounds. The technique is based on high-field magnetisation measurements on finely powdered polycrystalline material that is free to rotate in the sampleholder. Experimental results are reported for a number of Er{sub 2}Fe{sub 14-x}Mn{sub x}B compounds. The strength of the R-T exchange coupling is not affected by the Mn substitution, and a value of 0.445 Tkg/Am{sup 2} has been deduced for the coefficient n{sub RT}. (orig.).

  18. A case study of coupling upflow anaerobic sludge blanket (UASB) and ANITA™ Mox process to treat high-strength landfill leachate.

    Science.gov (United States)

    Lu, Ting; George, Biju; Zhao, Hong; Liu, Wenjun

    2016-01-01

    A pilot study was conducted to study the treatability of high-strength landfill leachate by a combined process including upflow anaerobic sludge blanket (UASB), carbon removal (C-stage) moving bed biofilm reactor (MBBR) and ANITA™ Mox process. The major innovation on this pilot study is the patent-pending process invented by Veolia that integrates the above three unit processes with an effluent recycle stream, which not only maintains the low hydraulic retention time to enhance the treatment performance but also reduces inhibiting effect from chemicals present in the high-strength leachate. This pilot study has demonstrated that the combined process was capable of treating high-strength leachate with efficient chemical oxygen demand (COD) and nitrogen removals. The COD removal efficiency by the UASB was 93% (from 45,000 to 3,000 mg/L) at a loading rate of 10 kg/(m(3)·d). The C-stage MBBR removed an additional 500 to 1,000 mg/L of COD at a surface removal rate (SRR) of 5 g/(m(2)·d) and precipitated 400 mg/L of calcium. The total inorganic nitrogen removal efficiency by the ANITA Mox reactor was about 70% at SRR of 1.0 g/(m(2)·d).

  19. Measurements of the Higgs boson production and decay rates and coupling strengths using $pp$ collision data at $\\sqrt{s}=7$ and $8$ TeV in the ATLAS experiment

    CERN Document Server

    Aad, Georges; Abdallah, Jalal; Abdinov, Ovsat; Aben, Rosemarie; Abolins, Maris; AbouZeid, Ossama; Abramowicz, Halina; Abreu, Henso; Abreu, Ricardo; Abulaiti, Yiming; Acharya, Bobby Samir; Adamczyk, Leszek; Adams, David; Adelman, Jahred; Adomeit, Stefanie; Adye, Tim; Affolder, Tony; Agatonovic-Jovin, Tatjana; Aguilar-Saavedra, Juan Antonio; Ahlen, Steven; Ahmadov, Faig; Aielli, Giulio; Akerstedt, Henrik; Åkesson, Torsten Paul Ake; Akimoto, Ginga; Akimov, Andrei; Alberghi, Gian Luigi; Albert, Justin; Albrand, Solveig; Alconada Verzini, Maria Josefina; Aleksa, Martin; Aleksandrov, Igor; Alexa, Calin; Alexander, Gideon; Alexopoulos, Theodoros; Alhroob, Muhammad; Alimonti, Gianluca; Alio, Lion; Alison, John; Alkire, Steven Patrick; Allbrooke, Benedict; Allport, Phillip; Aloisio, Alberto; Alonso, Alejandro; Alonso, Francisco; Alpigiani, Cristiano; Altheimer, Andrew David; Alvarez Gonzalez, Barbara; Άlvarez Piqueras, Damián; Alviggi, Mariagrazia; Amadio, Brian Thomas; Amako, Katsuya; Amaral Coutinho, Yara; Amelung, Christoph; Amidei, Dante; Amor Dos Santos, Susana Patricia; Amorim, Antonio; Amoroso, Simone; Amram, Nir; Amundsen, Glenn; Anastopoulos, Christos; Ancu, Lucian Stefan; Andari, Nansi; Andeen, Timothy; Anders, Christoph Falk; Anders, Gabriel; Anders, John Kenneth; Anderson, Kelby; Andreazza, Attilio; Andrei, George Victor; Angelidakis, Stylianos; Angelozzi, Ivan; Anger, Philipp; Angerami, Aaron; Anghinolfi, Francis; Anisenkov, Alexey; Anjos, Nuno; Annovi, Alberto; Antonelli, Mario; Antonov, Alexey; Antos, Jaroslav; Anulli, Fabio; Aoki, Masato; Aperio Bella, Ludovica; Arabidze, Giorgi; Arai, Yasuo; Araque, Juan Pedro; Arce, Ayana; Arduh, Francisco Anuar; Arguin, Jean-Francois; Argyropoulos, Spyridon; Arik, Metin; Armbruster, Aaron James; Arnaez, Olivier; Arnal, Vanessa; Arnold, Hannah; Arratia, Miguel; Arslan, Ozan; Artamonov, Andrei; Artoni, Giacomo; Asai, Shoji; Asbah, Nedaa; Ashkenazi, Adi; Åsman, Barbro; Asquith, Lily; Assamagan, Ketevi; Astalos, Robert; Atkinson, Markus; Atlay, Naim Bora; Auerbach, Benjamin; Augsten, Kamil; Aurousseau, Mathieu; Avolio, Giuseppe; Axen, Bradley; Ayoub, Mohamad Kassem; Azuelos, Georges; Baak, Max; Baas, Alessandra; Bacci, Cesare; Bachacou, Henri; Bachas, Konstantinos; Backes, Moritz; Backhaus, Malte; Bagiacchi, Paolo; Bagnaia, Paolo; Bai, Yu; Bain, Travis; Baines, John; Baker, Oliver Keith; Balek, Petr; Balestri, Thomas; Balli, Fabrice; Banas, Elzbieta; Banerjee, Swagato; Bannoura, Arwa A E; Bansil, Hardeep Singh; Barak, Liron; Barberio, Elisabetta Luigia; Barberis, Dario; Barbero, Marlon; Barillari, Teresa; Barisonzi, Marcello; Barklow, Timothy; Barlow, Nick; Barnes, Sarah Louise; Barnett, Bruce; Barnett, Michael; Barnovska, Zuzana; Baroncelli, Antonio; Barone, Gaetano; Barr, Alan; Barreiro, Fernando; Barreiro Guimarães da Costa, João; Bartoldus, Rainer; Barton, Adam Edward; Bartos, Pavol; Basalaev, Artem; Bassalat, Ahmed; Basye, Austin; Bates, Richard; Batista, Santiago Juan; Batley, Richard; Battaglia, Marco; Bauce, Matteo; Bauer, Florian; Bawa, Harinder Singh; Beacham, James Baker; Beattie, Michael David; Beau, Tristan; Beauchemin, Pierre-Hugues; Beccherle, Roberto; Bechtle, Philip; Beck, Hans Peter; Becker, Anne Kathrin; Becker, Maurice; Becker, Sebastian; Beckingham, Matthew; Becot, Cyril; Beddall, Andrew; Beddall, Ayda; Bednyakov, Vadim; Bee, Christopher; Beemster, Lars; Beermann, Thomas; Begel, Michael; Behr, Janna Katharina; Belanger-Champagne, Camille; Bell, William; Bella, Gideon; Bellagamba, Lorenzo; Bellerive, Alain; Bellomo, Massimiliano; Belotskiy, Konstantin; Beltramello, Olga; Benary, Odette; Benchekroun, Driss; Bender, Michael; Bendtz, Katarina; Benekos, Nektarios; Benhammou, Yan; Benhar Noccioli, Eleonora; Benitez Garcia, Jorge-Armando; Benjamin, Douglas; Bensinger, James; Bentvelsen, Stan; Beresford, Lydia; Beretta, Matteo; Berge, David; Bergeaas Kuutmann, Elin; Berger, Nicolas; Berghaus, Frank; Beringer, Jürg; Bernard, Clare; Bernard, Nathan Rogers; Bernius, Catrin; Bernlochner, Florian Urs; Berry, Tracey; Berta, Peter; Bertella, Claudia; Bertoli, Gabriele; Bertolucci, Federico; Bertsche, Carolyn; Bertsche, David; Besana, Maria Ilaria; Besjes, Geert-Jan; Bessidskaia Bylund, Olga; Bessner, Martin Florian; Besson, Nathalie; Betancourt, Christopher; Bethke, Siegfried; Bevan, Adrian John; Bhimji, Wahid; Bianchi, Riccardo-Maria; Bianchini, Louis; Bianco, Michele; Biebel, Otmar; Bieniek, Stephen Paul; Biglietti, Michela; Bilbao De Mendizabal, Javier; Bilokon, Halina; Bindi, Marcello; Binet, Sebastien; Bingul, Ahmet; Bini, Cesare; Black, Curtis; Black, James; Black, Kevin; Blackburn, Daniel; Blair, Robert; Blanchard, Jean-Baptiste; Blanco, Jacobo Ezequiel; Blazek, Tomas; Bloch, Ingo; Blocker, Craig; Blum, Walter; Blumenschein, Ulrike; Bobbink, Gerjan; Bobrovnikov, Victor; Bocchetta, Simona Serena; Bocci, Andrea; Bock, Christopher; Boehler, Michael; Bogaerts, Joannes Andreas; Bogdanchikov, Alexander; Bohm, Christian; Boisvert, Veronique; Bold, Tomasz; Boldea, Venera; Boldyrev, Alexey; Bomben, Marco; Bona, Marcella; Boonekamp, Maarten; Borisov, Anatoly; Borissov, Guennadi; Borroni, Sara; Bortfeldt, Jonathan; Bortolotto, Valerio; Bos, Kors; Boscherini, Davide; Bosman, Martine; Boudreau, Joseph; Bouffard, Julian; Bouhova-Thacker, Evelina Vassileva; Boumediene, Djamel Eddine; Bourdarios, Claire; Bousson, Nicolas; Boveia, Antonio; Boyd, James; Boyko, Igor; Bozic, Ivan; Bracinik, Juraj; Brandt, Andrew; Brandt, Gerhard; Brandt, Oleg; Bratzler, Uwe; Brau, Benjamin; Brau, James; Braun, Helmut; Brazzale, Simone Federico; Brendlinger, Kurt; Brennan, Amelia Jean; Brenner, Lydia; Brenner, Richard; Bressler, Shikma; Bristow, Kieran; Bristow, Timothy Michael; Britton, Dave; Britzger, Daniel; Brochu, Frederic; Brock, Ian; Brock, Raymond; Bronner, Johanna; Brooijmans, Gustaaf; Brooks, Timothy; Brooks, William; Brosamer, Jacquelyn; Brost, Elizabeth; Brown, Jonathan; Bruckman de Renstrom, Pawel; Bruncko, Dusan; Bruneliere, Renaud; Bruni, Alessia; Bruni, Graziano; Bruschi, Marco; Bryngemark, Lene; Buanes, Trygve; Buat, Quentin; Buchholz, Peter; Buckley, Andrew; Buda, Stelian Ioan; Budagov, Ioulian; Buehrer, Felix; Bugge, Lars; Bugge, Magnar Kopangen; Bulekov, Oleg; Bullock, Daniel; Burckhart, Helfried; Burdin, Sergey; Burghgrave, Blake; Burke, Stephen; Burmeister, Ingo; Busato, Emmanuel; Büscher, Daniel; Büscher, Volker; Bussey, Peter; Butler, John; Butt, Aatif Imtiaz; Buttar, Craig; Butterworth, Jonathan; Butti, Pierfrancesco; Buttinger, William; Buzatu, Adrian; Buzykaev, Aleksey; Cabrera Urbán, Susana; Caforio, Davide; Cairo, Valentina; Cakir, Orhan; Calafiura, Paolo; Calandri, Alessandro; Calderini, Giovanni; Calfayan, Philippe; Caloba, Luiz; Calvet, David; Calvet, Samuel; Camacho Toro, Reina; Camarda, Stefano; Camarri, Paolo; Cameron, David; Caminada, Lea Michaela; Caminal Armadans, Roger; Campana, Simone; Campanelli, Mario; Campoverde, Angel; Canale, Vincenzo; Canepa, Anadi; Cano Bret, Marc; Cantero, Josu; Cantrill, Robert; Cao, Tingting; Capeans Garrido, Maria Del Mar; Caprini, Irinel; Caprini, Mihai; Capua, Marcella; Caputo, Regina; Cardarelli, Roberto; Carli, Tancredi; Carlino, Gianpaolo; Carminati, Leonardo; Caron, Sascha; Carquin, Edson; Carrillo-Montoya, German D; Carter, Janet; Carvalho, João; Casadei, Diego; Casado, Maria Pilar; Casolino, Mirkoantonio; Castaneda-Miranda, Elizabeth; Castelli, Angelantonio; Castillo Gimenez, Victoria; Castro, Nuno Filipe; Catastini, Pierluigi; Catinaccio, Andrea; Catmore, James; Cattai, Ariella; Caudron, Julien; Cavaliere, Viviana; Cavalli, Donatella; Cavalli-Sforza, Matteo; Cavasinni, Vincenzo; Ceradini, Filippo; Cerio, Benjamin; Cerny, Karel; Santiago Cerqueira, Augusto; Cerri, Alessandro; Cerrito, Lucio; Cerutti, Fabio; Cerv, Matevz; Cervelli, Alberto; Cetin, Serkant Ali; Chafaq, Aziz; Chakraborty, Dhiman; Chalupkova, Ina; Chang, Philip; Chapleau, Bertrand; Chapman, John Derek; Charlton, Dave; Chau, Chav Chhiv; Chavez Barajas, Carlos Alberto; Cheatham, Susan; Chegwidden, Andrew; Chekanov, Sergei; Chekulaev, Sergey; Chelkov, Gueorgui; Chelstowska, Magda Anna; Chen, Chunhui; Chen, Hucheng; Chen, Karen; Chen, Liming; Chen, Shenjian; Chen, Xin; Chen, Ye; Cheng, Hok Chuen; Cheng, Yangyang; Cheplakov, Alexander; Cheremushkina, Evgenia; Cherkaoui El Moursli, Rajaa; Chernyatin, Valeriy; Cheu, Elliott; Chevalier, Laurent; Chiarella, Vitaliano; Childers, John Taylor; Chiodini, Gabriele; Chisholm, Andrew; Chislett, Rebecca Thalatta; Chitan, Adrian; Chizhov, Mihail; Choi, Kyungeon; Chouridou, Sofia; Chow, Bonnie Kar Bo; Christodoulou, Valentinos; Chromek-Burckhart, Doris; Chu, Ming-Lee; Chudoba, Jiri; Chuinard, Annabelle Julia; Chwastowski, Janusz; Chytka, Ladislav; Ciapetti, Guido; Ciftci, Abbas Kenan; Cinca, Diane; Cindro, Vladimir; Cioara, Irina Antonela; Ciocio, Alessandra; Citron, Zvi Hirsh; Ciubancan, Mihai; Clark, Allan G; Clark, Brian Lee; Clark, Philip James; Clarke, Robert; Cleland, Bill; Clement, Christophe; Coadou, Yann; Cobal, Marina; Coccaro, Andrea; Cochran, James H; Coffey, Laurel; Cogan, Joshua Godfrey; Cole, Brian; Cole, Stephen; Colijn, Auke-Pieter; Collot, Johann; Colombo, Tommaso; Compostella, Gabriele; Conde Muiño, Patricia; Coniavitis, Elias; Connell, Simon Henry; Connelly, Ian; Consonni, Sofia Maria; Consorti, Valerio; Constantinescu, Serban; Conta, Claudio; Conti, Geraldine; Conventi, Francesco; Cooke, Mark; Cooper, Ben; Cooper-Sarkar, Amanda; Cornelissen, Thijs; Corradi, Massimo; Corriveau, Francois; Corso-Radu, Alina; Cortes-Gonzalez, Arely; Cortiana, Giorgio; Costa, Giuseppe; Costa, María José; Costanzo, Davide; Côté, David; Cottin, Giovanna; Cowan, Glen; Cox, Brian; Cranmer, Kyle; Cree, Graham; Crépé-Renaudin, Sabine; Crescioli, Francesco; Cribbs, Wayne Allen; Crispin Ortuzar, Mireia; Cristinziani, Markus; Croft, Vince; Crosetti, Giovanni; Cuhadar Donszelmann, Tulay; Cummings, Jane; Curatolo, Maria; Cuthbert, Cameron; Czirr, Hendrik; Czodrowski, Patrick; D'Auria, Saverio; D'Onofrio, Monica; Da Cunha Sargedas De Sousa, Mario Jose; Da Via, Cinzia; Dabrowski, Wladyslaw; Dafinca, Alexandru; Dai, Tiesheng; Dale, Orjan; Dallaire, Frederick; Dallapiccola, Carlo; Dam, Mogens; Dandoy, Jeffrey Rogers; Dang, Nguyen Phuong; Daniells, Andrew Christopher; Danninger, Matthias; Dano Hoffmann, Maria; Dao, Valerio; Darbo, Giovanni; Darmora, Smita; Dassoulas, James; Dattagupta, Aparajita; Davey, Will; David, Claire; Davidek, Tomas; Davies, Eleanor; Davies, Merlin; Davison, Peter; Davygora, Yuriy; Dawe, Edmund; Dawson, Ian; Daya-Ishmukhametova, Rozmin; De, Kaushik; de Asmundis, Riccardo; De Castro, Stefano; De Cecco, Sandro; De Groot, Nicolo; de Jong, Paul; De la Torre, Hector; De Lorenzi, Francesco; De Nooij, Lucie; De Pedis, Daniele; De Salvo, Alessandro; De Sanctis, Umberto; De Santo, Antonella; De Vivie De Regie, Jean-Baptiste; Dearnaley, William James; Debbe, Ramiro; Debenedetti, Chiara; Dedovich, Dmitri; Deigaard, Ingrid; Del Peso, Jose; Del Prete, Tarcisio; Delgove, David; Deliot, Frederic; Delitzsch, Chris Malena; Deliyergiyev, Maksym; Dell'Acqua, Andrea; Dell'Asta, Lidia; Dell'Orso, Mauro; Della Pietra, Massimo; della Volpe, Domenico; Delmastro, Marco; Delsart, Pierre-Antoine; Deluca, Carolina; DeMarco, David; Demers, Sarah; Demichev, Mikhail; Demilly, Aurelien; Denisov, Sergey; Derendarz, Dominik; Derkaoui, Jamal Eddine; Derue, Frederic; Dervan, Paul; Desch, Klaus Kurt; Deterre, Cecile; Deviveiros, Pier-Olivier; Dewhurst, Alastair; Dhaliwal, Saminder; Di Ciaccio, Anna; Di Ciaccio, Lucia; Di Domenico, Antonio; Di Donato, Camilla; Di Girolamo, Alessandro; Di Girolamo, Beniamino; Di Mattia, Alessandro; Di Micco, Biagio; Di Nardo, Roberto; Di Simone, Andrea; Di Sipio, Riccardo; Di Valentino, David; Diaconu, Cristinel; Diamond, Miriam; Dias, Flavia; Diaz, Marco Aurelio; Diehl, Edward; Dietrich, Janet; Diglio, Sara; Dimitrievska, Aleksandra; Dingfelder, Jochen; Dita, Petre; Dita, Sanda; Dittus, Fridolin; Djama, Fares; Djobava, Tamar; Djuvsland, Julia Isabell; Barros do Vale, Maria Aline; Dobos, Daniel; Dobre, Monica; Doglioni, Caterina; Dohmae, Takeshi; Dolejsi, Jiri; Dolezal, Zdenek; Dolgoshein, Boris; Donadelli, Marisilvia; Donati, Simone; Dondero, Paolo; Donini, Julien; Dopke, Jens; Doria, Alessandra; Dova, Maria-Teresa; Doyle, Tony; Drechsler, Eric; Dris, Manolis; Dubreuil, Emmanuelle; Duchovni, Ehud; Duckeck, Guenter; Ducu, Otilia Anamaria; Duda, Dominik; Dudarev, Alexey; Duflot, Laurent; Duguid, Liam; Dührssen, Michael; Dunford, Monica; Duran Yildiz, Hatice; Düren, Michael; Durglishvili, Archil; Duschinger, Dirk; Dyndal, Mateusz; Eckardt, Christoph; Ecker, Katharina Maria; Edgar, Ryan Christopher; Edson, William; Edwards, Nicholas Charles; Ehrenfeld, Wolfgang; Eifert, Till; Eigen, Gerald; Einsweiler, Kevin; Ekelof, Tord; El Kacimi, Mohamed; Ellert, Mattias; Elles, Sabine; Ellinghaus, Frank; Elliot, Alison; Ellis, Nicolas; Elmsheuser, Johannes; Elsing, Markus; Emeliyanov, Dmitry; Enari, Yuji; Endner, Oliver Chris; Endo, Masaki; Erdmann, Johannes; Ereditato, Antonio; Ernis, Gunar; Ernst, Jesse; Ernst, Michael; Errede, Steven; Ertel, Eugen; Escalier, Marc; Esch, Hendrik; Escobar, Carlos; Esposito, Bellisario; Etienvre, Anne-Isabelle; Etzion, Erez; Evans, Hal; Ezhilov, Alexey; Fabbri, Laura; Facini, Gabriel; Fakhrutdinov, Rinat; Falciano, Speranza; Falla, Rebecca Jane; Faltova, Jana; Fang, Yaquan; Fanti, Marcello; Farbin, Amir; Farilla, Addolorata; Farooque, Trisha; Farrell, Steven; Farrington, Sinead; Farthouat, Philippe; Fassi, Farida; Fassnacht, Patrick; Fassouliotis, Dimitrios; Faucci Giannelli, Michele; Favareto, Andrea; Fayard, Louis; Federic, Pavol; Fedin, Oleg; Fedorko, Wojciech; Feigl, Simon; Feligioni, Lorenzo; Feng, Cunfeng; Feng, Eric; Feng, Haolu; Fenyuk, Alexander; Fernandez Martinez, Patricia; Fernandez Perez, Sonia; Ferrando, James; Ferrari, Arnaud; Ferrari, Pamela; Ferrari, Roberto; Ferreira de Lima, Danilo Enoque; Ferrer, Antonio; Ferrere, Didier; Ferretti, Claudio; Ferretto Parodi, Andrea; Fiascaris, Maria; Fiedler, Frank; Filipčič, Andrej; Filipuzzi, Marco; Filthaut, Frank; Fincke-Keeler, Margret; Finelli, Kevin Daniel; Fiolhais, Miguel; Fiorini, Luca; Firan, Ana; Fischer, Adam; Fischer, Cora; Fischer, Julia; Fisher, Wade Cameron; Fitzgerald, Eric Andrew; Flechl, Martin; Fleck, Ivor; Fleischmann, Philipp; Fleischmann, Sebastian; Fletcher, Gareth Thomas; Fletcher, Gregory; Flick, Tobias; Floderus, Anders; Flores Castillo, Luis; Flowerdew, Michael; Formica, Andrea; Forti, Alessandra; Fournier, Daniel; Fox, Harald; Fracchia, Silvia; Francavilla, Paolo; Franchini, Matteo; Francis, David; Franconi, Laura; Franklin, Melissa; Fraternali, Marco; Freeborn, David; French, Sky; Friedrich, Felix; Froidevaux, Daniel; Frost, James; Fukunaga, Chikara; Fullana Torregrosa, Esteban; Fulsom, Bryan Gregory; Fuster, Juan; Gabaldon, Carolina; Gabizon, Ofir; Gabrielli, Alessandro; Gabrielli, Andrea; Gadatsch, Stefan; Gadomski, Szymon; Gagliardi, Guido; Gagnon, Pauline; Galea, Cristina; Galhardo, Bruno; Gallas, Elizabeth; Gallop, Bruce; Gallus, Petr; Galster, Gorm Aske Gram Krohn; Gan, KK; Gao, Jun; Gao, Yanyan; Gao, Yongsheng; Garay Walls, Francisca; Garberson, Ford; García, Carmen; García Navarro, José Enrique; Garcia-Sciveres, Maurice; Gardner, Robert; Garelli, Nicoletta; Garonne, Vincent; Gatti, Claudio; Gaudiello, Andrea; Gaudio, Gabriella; Gaur, Bakul; Gauthier, Lea; Gauzzi, Paolo; Gavrilenko, Igor; Gay, Colin; Gaycken, Goetz; Gazis, Evangelos; Ge, Peng; Gecse, Zoltan; Gee, Norman; Geerts, Daniël Alphonsus Adrianus; Geich-Gimbel, Christoph; Geisler, Manuel Patrice; Gemme, Claudia; Genest, Marie-Hélène; Gentile, Simonetta; George, Matthias; George, Simon; Gerbaudo, Davide; Gershon, Avi; Ghazlane, Hamid; Giacobbe, Benedetto; Giagu, Stefano; Giangiobbe, Vincent; Giannetti, Paola; Gibbard, Bruce; Gibson, Stephen; Gilchriese, Murdock; Gillam, Thomas; Gillberg, Dag; Gilles, Geoffrey; Gingrich, Douglas; Giokaris, Nikos; Giordani, MarioPaolo; Giorgi, Filippo Maria; Giorgi, Francesco Michelangelo; Giraud, Pierre-Francois; Giromini, Paolo; Giugni, Danilo; Giuliani, Claudia; Giulini, Maddalena; Gjelsten, Børge Kile; Gkaitatzis, Stamatios; Gkialas, Ioannis; Gkougkousis, Evangelos Leonidas; Gladilin, Leonid; Glasman, Claudia; Glatzer, Julian; Glaysher, Paul; Glazov, Alexandre; Goblirsch-Kolb, Maximilian; Goddard, Jack Robert; Godlewski, Jan; Goldfarb, Steven; Golling, Tobias; Golubkov, Dmitry; Gomes, Agostinho; Gonçalo, Ricardo; Goncalves Pinto Firmino Da Costa, Joao; Gonella, Laura; González de la Hoz, Santiago; Gonzalez Parra, Garoe; Gonzalez-Sevilla, Sergio; Goossens, Luc; Gorbounov, Petr Andreevich; Gordon, Howard; Gorelov, Igor; Gorini, Benedetto; Gorini, Edoardo; Gorišek, Andrej; Gornicki, Edward; Goshaw, Alfred; Gössling, Claus; Gostkin, Mikhail Ivanovitch; Goujdami, Driss; Goussiou, Anna; Govender, Nicolin; Grabas, Herve Marie Xavier; Graber, Lars; Grabowska-Bold, Iwona; Grafström, Per; Grahn, Karl-Johan; Gramling, Johanna; Gramstad, Eirik; Grancagnolo, Sergio; Grassi, Valerio; Gratchev, Vadim; Gray, Heather; Graziani, Enrico; Greenwood, Zeno Dixon; Gregersen, Kristian; Gregor, Ingrid-Maria; Grenier, Philippe; Griffiths, Justin; Grillo, Alexander; Grimm, Kathryn; Grinstein, Sebastian; Gris, Philippe Luc Yves; Grivaz, Jean-Francois; Grohs, Johannes Philipp; Grohsjean, Alexander; Gross, Eilam; Grosse-Knetter, Joern; Grossi, Giulio Cornelio; Grout, Zara Jane; Guan, Liang; Guenther, Jaroslav; Guescini, Francesco; Guest, Daniel; Gueta, Orel; Guido, Elisa; Guillemin, Thibault; Guindon, Stefan; Gul, Umar; Gumpert, Christian; Guo, Jun; Gupta, Shaun; Gutierrez, Phillip; Gutierrez Ortiz, Nicolas Gilberto; Gutschow, Christian; Guyot, Claude; Gwenlan, Claire; Gwilliam, Carl; Haas, Andy; Haber, Carl; Hadavand, Haleh Khani; Haddad, Nacim; Haefner, Petra; Hageböck, Stephan; Hajduk, Zbigniew; Hakobyan, Hrachya; Haleem, Mahsana; Haley, Joseph; Hall, David; Halladjian, Garabed; Hallewell, Gregory David; Hamacher, Klaus; Hamal, Petr; Hamano, Kenji; Hamer, Matthias; Hamilton, Andrew; Hamity, Guillermo Nicolas; Hamnett, Phillip George; Han, Liang; Hanagaki, Kazunori; Hanawa, Keita; Hance, Michael; Hanke, Paul; Hanna, Remie; Hansen, Jørgen Beck; Hansen, Jorn Dines; Hansen, Maike Christina; Hansen, Peter Henrik; Hara, Kazuhiko; Hard, Andrew; Harenberg, Torsten; Hariri, Faten; Harkusha, Siarhei; Harrington, Robert; Harrison, Paul Fraser; Hartjes, Fred; Hasegawa, Makoto; Hasegawa, Satoshi; Hasegawa, Yoji; Hasib, A; Hassani, Samira; Haug, Sigve; Hauser, Reiner; Hauswald, Lorenz; Havranek, Miroslav; Hawkes, Christopher; Hawkings, Richard John; Hawkins, Anthony David; Hayashi, Takayasu; Hayden, Daniel; Hays, Chris; Hays, Jonathan Michael; Hayward, Helen; Haywood, Stephen; Head, Simon; Heck, Tobias; Hedberg, Vincent; Heelan, Louise; Heim, Sarah; Heim, Timon; Heinemann, Beate; Heinrich, Lukas; Hejbal, Jiri; Helary, Louis; Hellman, Sten; Hellmich, Dennis; Helsens, Clement; Henderson, James; Henderson, Robert; Heng, Yang; Hengler, Christopher; Henrichs, Anna; Henriques Correia, Ana Maria; Henrot-Versille, Sophie; Herbert, Geoffrey Henry; Hernández Jiménez, Yesenia; Herrberg-Schubert, Ruth; Herten, Gregor; Hertenberger, Ralf; Hervas, Luis; Hesketh, Gavin Grant; Hessey, Nigel; Hetherly, Jeffrey Wayne; Hickling, Robert; Higón-Rodriguez, Emilio; Hill, Ewan; Hill, John; Hiller, Karl Heinz; Hillier, Stephen; Hinchliffe, Ian; Hines, Elizabeth; Hinman, Rachel Reisner; Hirose, Minoru; Hirschbuehl, Dominic; Hobbs, John; Hod, Noam; Hodgkinson, Mark; Hodgson, Paul; Hoecker, Andreas; Hoeferkamp, Martin; Hoenig, Friedrich; Hohlfeld, Marc; Hohn, David; Holmes, Tova Ray; Homann, Michael; Hong, Tae Min; Hooft van Huysduynen, Loek; Hopkins, Walter; Horii, Yasuyuki; Horton, Arthur James; Hostachy, Jean-Yves; Hou, Suen; Hoummada, Abdeslam; Howard, Jacob; Howarth, James; Hrabovsky, Miroslav; Hristova, Ivana; Hrivnac, Julius; Hryn'ova, Tetiana; Hrynevich, Aliaksei; Hsu, Catherine; Hsu, Pai-hsien Jennifer; Hsu, Shih-Chieh; Hu, Diedi; Hu, Qipeng; Hu, Xueye; Huang, Yanping; Hubacek, Zdenek; Hubaut, Fabrice; Huegging, Fabian; Huffman, Todd Brian; Hughes, Emlyn; Hughes, Gareth; Huhtinen, Mika; Hülsing, Tobias Alexander; Huseynov, Nazim; Huston, Joey; Huth, John; Iacobucci, Giuseppe; Iakovidis, Georgios; Ibragimov, Iskander; Iconomidou-Fayard, Lydia; Ideal, Emma; Idrissi, Zineb; Iengo, Paolo; Igonkina, Olga; Iizawa, Tomoya; Ikegami, Yoichi; Ikematsu, Katsumasa; Ikeno, Masahiro; Ilchenko, Iurii; Iliadis, Dimitrios; Ilic, Nikolina; Inamaru, Yuki; Ince, Tayfun; Ioannou, Pavlos; Iodice, Mauro; Iordanidou, Kalliopi; Ippolito, Valerio; Irles Quiles, Adrian; Isaksson, Charlie; Ishino, Masaya; Ishitsuka, Masaki; Ishmukhametov, Renat; Issever, Cigdem; Istin, Serhat; Iturbe Ponce, Julia Mariana; Iuppa, Roberto; Ivarsson, Jenny; Iwanski, Wieslaw; Iwasaki, Hiroyuki; Izen, Joseph; Izzo, Vincenzo; Jabbar, Samina; Jackson, Brett; Jackson, Matthew; Jackson, Paul; Jaekel, Martin; Jain, Vivek; Jakobs, Karl; Jakobsen, Sune; Jakoubek, Tomas; Jakubek, Jan; Jamin, David Olivier; Jana, Dilip; Jansen, Eric; Jansky, Roland; Janssen, Jens; Janus, Michel; Jarlskog, Göran; Javadov, Namig; Javůrek, Tomáš; Jeanty, Laura; Jejelava, Juansher; Jeng, Geng-yuan; Jennens, David; Jenni, Peter; Jentzsch, Jennifer; Jeske, Carl; Jézéquel, Stéphane; Ji, Haoshuang; Jia, Jiangyong; Jiang, Yi; Jiggins, Stephen; Jimenez Pena, Javier; Jin, Shan; Jinaru, Adam; Jinnouchi, Osamu; Joergensen, Morten Dam; Johansson, Per; Johns, Kenneth; Jon-And, Kerstin; Jones, Graham; Jones, Roger; Jones, Tim; Jongmanns, Jan; Jorge, Pedro; Joshi, Kiran Daniel; Jovicevic, Jelena; Ju, Xiangyang; Jung, Christian; Jussel, Patrick; Juste Rozas, Aurelio; Kaci, Mohammed; Kaczmarska, Anna; Kado, Marumi; Kagan, Harris; Kagan, Michael; Kahn, Sebastien Jonathan; Kajomovitz, Enrique; Kalderon, Charles William; Kama, Sami; Kamenshchikov, Andrey; Kanaya, Naoko; Kaneda, Michiru; Kaneti, Steven; Kantserov, Vadim; Kanzaki, Junichi; Kaplan, Benjamin; Kapliy, Anton; Kar, Deepak; Karakostas, Konstantinos; Karamaoun, Andrew; Karastathis, Nikolaos; Kareem, Mohammad Jawad; Karnevskiy, Mikhail; Karpov, Sergey; Karpova, Zoya; Karthik, Krishnaiyengar; Kartvelishvili, Vakhtang; Karyukhin, Andrey; Kashif, Lashkar; Kass, Richard; Kastanas, Alex; Kataoka, Yousuke; Katre, Akshay; Katzy, Judith; Kawagoe, Kiyotomo; Kawamoto, Tatsuo; Kawamura, Gen; Kazama, Shingo; Kazanin, Vassili; Kazarinov, Makhail; Keeler, Richard; Kehoe, Robert; Keller, John; Kempster, Jacob Julian; Keoshkerian, Houry; Kepka, Oldrich; Kerševan, Borut Paul; Kersten, Susanne; Keyes, Robert; Khalil-zada, Farkhad; Khandanyan, Hovhannes; Khanov, Alexander; Kharlamov, Alexey; Khoo, Teng Jian; Khovanskiy, Valery; Khramov, Evgeniy; Khubua, Jemal; Kim, Hee Yeun; Kim, Hyeon Jin; Kim, Shinhong; Kim, Young-Kee; Kimura, Naoki; Kind, Oliver Maria; King, Barry; King, Matthew; King, Robert Steven Beaufoy; King, Samuel Burton; Kirk, Julie; Kiryunin, Andrey; Kishimoto, Tomoe; Kisielewska, Danuta; Kiss, Florian; Kiuchi, Kenji; Kivernyk, Oleh; Kladiva, Eduard; Klein, Matthew Henry; Klein, Max; Klein, Uta; Kleinknecht, Konrad; Klimek, Pawel; Klimentov, Alexei; Klingenberg, Reiner; Klinger, Joel Alexander; Klioutchnikova, Tatiana; Kluge, Eike-Erik; Kluit, Peter; Kluth, Stefan; Kneringer, Emmerich; Knoops, Edith; Knue, Andrea; Kobayashi, Aine; Kobayashi, Dai; Kobayashi, Tomio; Kobel, Michael; Kocian, Martin; Kodys, Peter; Koffas, Thomas; Koffeman, Els; Kogan, Lucy Anne; Kohlmann, Simon; Kohout, Zdenek; Kohriki, Takashi; Koi, Tatsumi; Kolanoski, Hermann; Koletsou, Iro; Komar, Aston; Komori, Yuto; Kondo, Takahiko; Kondrashova, Nataliia; Köneke, Karsten; König, Adriaan; König, Sebastian; Kono, Takanori; Konoplich, Rostislav; Konstantinidis, Nikolaos; Kopeliansky, Revital; Koperny, Stefan; Köpke, Lutz; Kopp, Anna Katharina; Korcyl, Krzysztof; Kordas, Kostantinos; Korn, Andreas; Korol, Aleksandr; Korolkov, Ilya; Korolkova, Elena; Kortner, Oliver; Kortner, Sandra; Kosek, Tomas; Kostyukhin, Vadim; Kotov, Vladislav; Kotwal, Ashutosh; Kourkoumeli-Charalampidi, Athina; Kourkoumelis, Christine; Kouskoura, Vasiliki; Koutsman, Alex; Kowalewski, Robert Victor; Kowalski, Tadeusz; Kozanecki, Witold; Kozhin, Anatoly; Kramarenko, Viktor; Kramberger, Gregor; Krasnopevtsev, Dimitriy; Krasznahorkay, Attila; Kraus, Jana; Kravchenko, Anton; Kreiss, Sven; Kretz, Moritz; Kretzschmar, Jan; Kreutzfeldt, Kristof; Krieger, Peter; Krizka, Karol; Kroeninger, Kevin; Kroha, Hubert; Kroll, Joe; Kroseberg, Juergen; Krstic, Jelena; Kruchonak, Uladzimir; Krüger, Hans; Krumnack, Nils; Krumshteyn, Zinovii; Kruse, Amanda; Kruse, Mark; Kruskal, Michael; Kubota, Takashi; Kucuk, Hilal; Kuday, Sinan; Kuehn, Susanne; Kugel, Andreas; Kuger, Fabian; Kuhl, Andrew; Kuhl, Thorsten; Kukhtin, Victor; Kulchitsky, Yuri; Kuleshov, Sergey; Kuna, Marine; Kunigo, Takuto; Kupco, Alexander; Kurashige, Hisaya; Kurochkin, Yurii; Kurumida, Rie; Kus, Vlastimil; Kuwertz, Emma Sian; Kuze, Masahiro; Kvita, Jiri; Kwan, Tony; Kyriazopoulos, Dimitrios; La Rosa, Alessandro; La Rosa Navarro, Jose Luis; La Rotonda, Laura; Lacasta, Carlos; Lacava, Francesco; Lacey, James; Lacker, Heiko; Lacour, Didier; Lacuesta, Vicente Ramón; Ladygin, Evgueni; Lafaye, Remi; Laforge, Bertrand; Lagouri, Theodota; Lai, Stanley; Lambourne, Luke; Lammers, Sabine; Lampen, Caleb; Lampl, Walter; Lançon, Eric; Landgraf, Ulrich; Landon, Murrough; Lang, Valerie Susanne; Lange, J örn Christian; Lankford, Andrew; Lanni, Francesco; Lantzsch, Kerstin; Laplace, Sandrine; Lapoire, Cecile; Laporte, Jean-Francois; Lari, Tommaso; Lasagni Manghi, Federico; Lassnig, Mario; Laurelli, Paolo; Lavrijsen, Wim; Law, Alexander; Laycock, Paul; Lazovich, Tomo; Le Dortz, Olivier; Le Guirriec, Emmanuel; Le Menedeu, Eve; LeBlanc, Matthew Edgar; LeCompte, Thomas; Ledroit-Guillon, Fabienne Agnes Marie; Lee, Claire Alexandra; Lee, Shih-Chang; Lee, Lawrence; Lefebvre, Guillaume; Lefebvre, Michel; Legger, Federica; Leggett, Charles; Lehan, Allan; Lehmann Miotto, Giovanna; Lei, Xiaowen; Leight, William Axel; Leisos, Antonios; Leister, Andrew Gerard; Leite, Marco Aurelio Lisboa; Leitner, Rupert; Lellouch, Daniel; Lemmer, Boris; Leney, Katharine; Lenz, Tatjana; Lenzi, Bruno; Leone, Robert; Leone, Sandra; Leonidopoulos, Christos; Leontsinis, Stefanos; Leroy, Claude; Lester, Christopher; Levchenko, Mikhail; Levêque, Jessica; Levin, Daniel; Levinson, Lorne; Levy, Mark; Lewis, Adrian; Leyko, Agnieszka; Leyton, Michael; Li, Bing; Li, Haifeng; Li, Ho Ling; Li, Lei; Li, Liang; Li, Shu; Li, Yichen; Liang, Zhijun; Liao, Hongbo; Liberti, Barbara; Liblong, Aaron; Lichard, Peter; Lie, Ki; Liebal, Jessica; Liebig, Wolfgang; Limbach, Christian; Limosani, Antonio; Lin, Simon; Lin, Tai-Hua; Linde, Frank; Lindquist, Brian Edward; Linnemann, James; Lipeles, Elliot; Lipniacka, Anna; Lisovyi, Mykhailo; Liss, Tony; Lissauer, David; Lister, Alison; Litke, Alan; Liu, Bo; Liu, Dong; Liu, Jian; Liu, Jianbei; Liu, Kun; Liu, Lulu; Liu, Miaoyuan; Liu, Minghui; Liu, Yanwen; Livan, Michele; Lleres, Annick; Llorente Merino, Javier; Lloyd, Stephen; Lo Sterzo, Francesco; Lobodzinska, Ewelina; Loch, Peter; Lockman, William; Loebinger, Fred; Loevschall-Jensen, Ask Emil; Loginov, Andrey; Lohse, Thomas; Lohwasser, Kristin; Lokajicek, Milos; Long, Brian Alexander; Long, Jonathan; Long, Robin Eamonn; Looper, Kristina Anne; Lopes, Lourenco; Lopez Mateos, David; Lopez Paredes, Brais; Lopez Paz, Ivan; Lorenz, Jeanette; Lorenzo Martinez, Narei; Losada, Marta; Loscutoff, Peter; Lösel, Philipp Jonathan; Lou, XinChou; Lounis, Abdenour; Love, Jeremy; Love, Peter; Lu, Nan; Lubatti, Henry; Luci, Claudio; Lucotte, Arnaud; Luehring, Frederick; Lukas, Wolfgang; Luminari, Lamberto; Lundberg, Olof; Lund-Jensen, Bengt; Lynn, David; Lysak, Roman; Lytken, Else; Ma, Hong; Ma, Lian Liang; Maccarrone, Giovanni; Macchiolo, Anna; Macdonald, Calum Michael; Machado Miguens, Joana; Macina, Daniela; Madaffari, Daniele; Madar, Romain; Maddocks, Harvey Jonathan; Mader, Wolfgang; Madsen, Alexander; Maeland, Steffen; Maeno, Tadashi; Maevskiy, Artem; Magradze, Erekle; Mahboubi, Kambiz; Mahlstedt, Joern; Maiani, Camilla; Maidantchik, Carmen; Maier, Andreas Alexander; Maier, Thomas; Maio, Amélia; Majewski, Stephanie; Makida, Yasuhiro; Makovec, Nikola; Malaescu, Bogdan; Malecki, Pawel; Maleev, Victor; Malek, Fairouz; Mallik, Usha; Malon, David; Malone, Caitlin; Maltezos, Stavros; Malyshev, Vladimir; Malyukov, Sergei; Mamuzic, Judita; Mancini, Giada; Mandelli, Beatrice; Mandelli, Luciano; Mandić, Igor; Mandrysch, Rocco; Maneira, José; Manfredini, Alessandro; Manhaes de Andrade Filho, Luciano; Manjarres Ramos, Joany; Mann, Alexander; Manning, Peter; Manousakis-Katsikakis, Arkadios; Mansoulie, Bruno; Mantifel, Rodger; Mantoani, Matteo; Mapelli, Livio; March, Luis; Marchiori, Giovanni; Marcisovsky, Michal; Marino, Christopher; Marjanovic, Marija; Marroquim, Fernando; Marsden, Stephen Philip; Marshall, Zach; Marti, Lukas Fritz; Marti-Garcia, Salvador; Martin, Brian Thomas; Martin, Tim; Martin, Victoria Jane; Martin dit Latour, Bertrand; Martinez, Mario; Martin-Haugh, Stewart; Martoiu, Victor Sorin; Martyniuk, Alex; Marx, Marilyn; Marzano, Francesco; Marzin, Antoine; Masetti, Lucia; Mashimo, Tetsuro; Mashinistov, Ruslan; Masik, Jiri; Maslennikov, Alexey; Massa, Ignazio; Massa, Lorenzo; Massol, Nicolas; Mastrandrea, Paolo; Mastroberardino, Anna; Masubuchi, Tatsuya; Mättig, Peter; Mattmann, Johannes; Maurer, Julien; Maxfield, Stephen; Maximov, Dmitriy; Mazini, Rachid; Mazza, Simone Michele; Mazzaferro, Luca; Mc Goldrick, Garrin; Mc Kee, Shawn Patrick; McCarn, Allison; McCarthy, Robert; McCarthy, Tom; McCubbin, Norman; McFarlane, Kenneth; Mcfayden, Josh; Mchedlidze, Gvantsa; McMahon, Steve; McPherson, Robert; Medinnis, Michael; Meehan, Samuel; Mehlhase, Sascha; Mehta, Andrew; Meier, Karlheinz; Meineck, Christian; Meirose, Bernhard; Mellado Garcia, Bruce Rafael; Meloni, Federico; Mengarelli, Alberto; Menke, Sven; Meoni, Evelin; Mercurio, Kevin Michael; Mergelmeyer, Sebastian; Mermod, Philippe; Merola, Leonardo; Meroni, Chiara; Merritt, Frank; Messina, Andrea; Metcalfe, Jessica; Mete, Alaettin Serhan; Meyer, Carsten; Meyer, Christopher; Meyer, Jean-Pierre; Meyer, Jochen; Middleton, Robin; Miglioranzi, Silvia; Mijović, Liza; Mikenberg, Giora; Mikestikova, Marcela; Mikuž, Marko; Milesi, Marco; Milic, Adriana; Miller, David; Mills, Corrinne; Milov, Alexander; Milstead, David; Minaenko, Andrey; Minami, Yuto; Minashvili, Irakli; Mincer, Allen; Mindur, Bartosz; Mineev, Mikhail; Ming, Yao; Mir, Lluisa-Maria; Mitani, Takashi; Mitrevski, Jovan; Mitsou, Vasiliki A; Miucci, Antonio; Miyagawa, Paul; Mjörnmark, Jan-Ulf; Moa, Torbjoern; Mochizuki, Kazuya; Mohapatra, Soumya; Mohr, Wolfgang; Molander, Simon; Moles-Valls, Regina; Mönig, Klaus; Monini, Caterina; Monk, James; Monnier, Emmanuel; Montejo Berlingen, Javier; Monticelli, Fernando; Monzani, Simone; Moore, Roger; Morange, Nicolas; Moreno, Deywis; Moreno Llácer, María; Morettini, Paolo; Morgenstern, Marcus; Morii, Masahiro; Morinaga, Masahiro; Morisbak, Vanja; Moritz, Sebastian; Morley, Anthony Keith; Mornacchi, Giuseppe; Morris, John; Mortensen, Simon Stark; Morton, Alexander; Morvaj, Ljiljana; Mosidze, Maia; Moss, Josh; Motohashi, Kazuki; Mount, Richard; Mountricha, Eleni; Mouraviev, Sergei; Moyse, Edward; Muanza, Steve; Mudd, Richard; Mueller, Felix; Mueller, James; Mueller, Klemens; Mueller, Ralph Soeren Peter; Mueller, Thibaut; Muenstermann, Daniel; Mullen, Paul; Munwes, Yonathan; Murillo Quijada, Javier Alberto; Murray, Bill; Musheghyan, Haykuhi; Musto, Elisa; Myagkov, Alexey; Myska, Miroslav; Nackenhorst, Olaf; Nadal, Jordi; Nagai, Koichi; Nagai, Ryo; Nagai, Yoshikazu; Nagano, Kunihiro; Nagarkar, Advait; Nagasaka, Yasushi; Nagata, Kazuki; Nagel, Martin; Nagy, Elemer; Nairz, Armin Michael; Nakahama, Yu; Nakamura, Koji; Nakamura, Tomoaki; Nakano, Itsuo; Namasivayam, Harisankar; Naranjo Garcia, Roger Felipe; Narayan, Rohin; Naumann, Thomas; Navarro, Gabriela; Nayyar, Ruchika; Neal, Homer; Nechaeva, Polina; Neep, Thomas James; Nef, Pascal Daniel; Negri, Andrea; Negrini, Matteo; Nektarijevic, Snezana; Nellist, Clara; Nelson, Andrew; Nemecek, Stanislav; Nemethy, Peter; Nepomuceno, Andre Asevedo; Nessi, Marzio; Neubauer, Mark; Neumann, Manuel; Neves, Ricardo; Nevski, Pavel; Newman, Paul; Nguyen, Duong Hai; Nickerson, Richard; Nicolaidou, Rosy; Nicquevert, Bertrand; Nielsen, Jason; Nikiforou, Nikiforos; Nikiforov, Andriy; Nikolaenko, Vladimir; Nikolic-Audit, Irena; Nikolopoulos, Konstantinos; Nilsen, Jon Kerr; Nilsson, Paul; Ninomiya, Yoichi; Nisati, Aleandro; Nisius, Richard; Nobe, Takuya; Nomachi, Masaharu; Nomidis, Ioannis; Nooney, Tamsin; Norberg, Scarlet; Nordberg, Markus; Novgorodova, Olga; Nowak, Sebastian; Nozaki, Mitsuaki; Nozka, Libor; Ntekas, Konstantinos; Nunes Hanninger, Guilherme; Nunnemann, Thomas; Nurse, Emily; Nuti, Francesco; O'Brien, Brendan Joseph; O'grady, Fionnbarr; O'Neil, Dugan; O'Shea, Val; Oakham, Gerald; Oberlack, Horst; Obermann, Theresa; Ocariz, Jose; Ochi, Atsuhiko; Ochoa, Ines; Ochoa-Ricoux, Juan Pedro; Oda, Susumu; Odaka, Shigeru; Ogren, Harold; Oh, Alexander; Oh, Seog; Ohm, Christian; Ohman, Henrik; Oide, Hideyuki; Okamura, Wataru; Okawa, Hideki; Okumura, Yasuyuki; Okuyama, Toyonobu; Olariu, Albert; Olivares Pino, Sebastian Andres; Oliveira Damazio, Denis; Oliver Garcia, Elena; Olszewski, Andrzej; Olszowska, Jolanta; Onofre, António; Onyisi, Peter; Oram, Christopher; 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Pingel, Almut; Pinto, Belmiro; Pires, Sylvestre; Pitt, Michael; Pizio, Caterina; Plazak, Lukas; Pleier, Marc-Andre; Pleskot, Vojtech; Plotnikova, Elena; Plucinski, Pawel; Pluth, Daniel; Poettgen, Ruth; Poggioli, Luc; Pohl, David-leon; Polesello, Giacomo; Policicchio, Antonio; Polifka, Richard; Polini, Alessandro; Pollard, Christopher Samuel; Polychronakos, Venetios; Pommès, Kathy; Pontecorvo, Ludovico; Pope, Bernard; Popeneciu, Gabriel Alexandru; Popovic, Dragan; Poppleton, Alan; Pospisil, Stanislav; Potamianos, Karolos; Potrap, Igor; Potter, Christina; Potter, Christopher; Poulard, Gilbert; Poveda, Joaquin; Pozdnyakov, Valery; Pralavorio, Pascal; Pranko, Aliaksandr; Prasad, Srivas; Prell, Soeren; Price, Darren; Price, Lawrence; Primavera, Margherita; Prince, Sebastien; Proissl, Manuel; Prokofiev, Kirill; Prokoshin, Fedor; Protopapadaki, Eftychia-sofia; Protopopescu, Serban; Proudfoot, James; Przybycien, Mariusz; Ptacek, Elizabeth; Puddu, Daniele; Pueschel, Elisa; Puldon, David; Purohit, Milind; 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Shushkevich, Stanislav; Sicho, Petr; Sidiropoulou, Ourania; Sidorov, Dmitri; Sidoti, Antonio; Siegert, Frank; Sijacki, Djordje; Silva, José; Silver, Yiftah; Silverstein, Samuel; Simak, Vladislav; Simard, Olivier; Simic, Ljiljana; Simion, Stefan; Simioni, Eduard; Simmons, Brinick; Simon, Dorian; Simoniello, Rosa; Sinervo, Pekka; Sinev, Nikolai; Siragusa, Giovanni; Sisakyan, Alexei; Sivoklokov, Serguei; Sjölin, Jörgen; Sjursen, Therese; Skinner, Malcolm Bruce; Skottowe, Hugh Philip; Skubic, Patrick; Slater, Mark; Slavicek, Tomas; Slawinska, Magdalena; Sliwa, Krzysztof; Smakhtin, Vladimir; Smart, Ben; Smestad, Lillian; Smirnov, Sergei; Smirnov, Yury; Smirnova, Lidia; Smirnova, Oxana; Smith, Matthew; Smith, Russell; Smizanska, Maria; Smolek, Karel; Snesarev, Andrei; Snidero, Giacomo; Snyder, Scott; Sobie, Randall; Socher, Felix; Soffer, Abner; Soh, Dart-yin; Solans, Carlos; Solar, Michael; Solc, Jaroslav; Soldatov, Evgeny; Soldevila, Urmila; Solodkov, Alexander; Soloshenko, Alexei; Solovyanov, Oleg; Solovyev, Victor; Sommer, Philip; Song, Hong Ye; Soni, Nitesh; Sood, Alexander; Sopczak, Andre; Sopko, Bruno; Sopko, Vit; Sorin, Veronica; Sosa, David; Sosebee, Mark; Sotiropoulou, Calliope Louisa; Soualah, Rachik; Soueid, Paul; Soukharev, Andrey; South, David; Sowden, Benjamin; Spagnolo, Stefania; Spalla, Margherita; Spanò, Francesco; Spearman, William Robert; Spettel, Fabian; Spighi, Roberto; Spigo, Giancarlo; Spiller, Laurence Anthony; Spousta, Martin; Spreitzer, Teresa; St Denis, Richard Dante; Staerz, Steffen; Stahlman, Jonathan; Stamen, Rainer; Stamm, Soren; Stanecka, Ewa; Stanescu, Cristian; Stanescu-Bellu, Madalina; Stanitzki, Marcel Michael; Stapnes, Steinar; Starchenko, Evgeny; Stark, Jan; Staroba, Pavel; Starovoitov, Pavel; Staszewski, Rafal; Stavina, Pavel; Steinberg, Peter; Stelzer, Bernd; Stelzer, Harald Joerg; Stelzer-Chilton, Oliver; Stenzel, Hasko; Stern, Sebastian; Stewart, Graeme; Stillings, Jan Andre; Stockton, Mark; Stoebe, Michael; Stoicea, Gabriel; Stolte, Philipp; 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Yu, Jie; Yuan, Li; Yurkewicz, Adam; Yusuff, Imran; Zabinski, Bartlomiej; Zaidan, Remi; Zaitsev, Alexander; Zalieckas, Justas; Zaman, Aungshuman; Zambito, Stefano; Zanello, Lucia; Zanzi, Daniele; Zeitnitz, Christian; Zeman, Martin; Zemla, Andrzej; Zengel, Keith; Zenin, Oleg; Ženiš, Tibor; Zerwas, Dirk; Zhang, Dongliang; Zhang, Fangzhou; Zhang, Jinlong; Zhang, Lei; Zhang, Ruiqi; Zhang, Xueyao; Zhang, Zhiqing; Zhao, Xiandong; Zhao, Yongke; Zhao, Zhengguo; Zhemchugov, Alexey; Zhong, Jiahang; Zhou, Bing; Zhou, Chen; Zhou, Lei; Zhou, Li; Zhou, Ning; Zhu, Cheng Guang; Zhu, Hongbo; Zhu, Junjie; Zhu, Yingchun; Zhuang, Xuai; Zhukov, Konstantin; Zibell, Andre; Zieminska, Daria; Zimine, Nikolai; Zimmermann, Christoph; Zimmermann, Stephanie; Zinonos, Zinonas; Zinser, Markus; Ziolkowski, Michael; Živković, Lidija; Zobernig, Georg; Zoccoli, Antonio; zur Nedden, Martin; Zurzolo, Giovanni; Zwalinski, Lukasz

    2016-01-05

    Combined analyses of the Higgs boson production and decay rates as well as its coupling strengths to vector bosons and fermions are presented. The combinations include the results of the analyses of the $H\\to\\gamma\\gamma,\\, ZZ^*,\\, WW^*,\\, Z\\gamma,\\, b\\bar{b},\\, \\tau\\tau$ and $\\mu\\mu$ decay modes, and the constraints on the associated production with a pair of top quarks and on the off-shell coupling strengths of the Higgs boson. The results are based on the LHC proton--proton collision datasets, with integrated luminosities of up to 4.7 fb$^{-1}$ at $\\sqrt{s}=7$ TeV and 20.3 fb$^{-1}$ at $\\sqrt{s}=8$ TeV, recorded by the ATLAS detector in 2011 and 2012. Combining all production modes and decay channels, the measured signal yield, normalised to the Standard Model expectation, is $1.18^{+0.15}_{-0.14}$. The data provide unequivocal confirmation of the gluon fusion production of the Higgs boson, strong evidence of vector-boson fusion production and support Standard Model assumptions of production in association...

  20. Weak electric fields detectability in a noisy neural network.

    Science.gov (United States)

    Zhao, Jia; Deng, Bin; Qin, Yingmei; Men, Cong; Wang, Jiang; Wei, Xile; Sun, Jianbing

    2017-02-01

    We investigate the detectability of weak electric field in a noisy neural network based on Izhikevich neuron model systematically. The neural network is composed of excitatory and inhibitory neurons with similar ratio as that in the mammalian neocortex, and the axonal conduction delays between neurons are also considered. It is found that the noise intensity can modulate the detectability of weak electric field. Stochastic resonance (SR) phenomenon induced by white noise is observed when the weak electric field is added to the network. It is interesting that SR almost disappeared when the connections between neurons are cancelled, suggesting the amplification effects of the neural coupling on the synchronization of neuronal spiking. Furthermore, the network parameters, such as the connection probability, the synaptic coupling strength, the scale of neuron population and the neuron heterogeneity, can also affect the detectability of the weak electric field. Finally, the model sensitivity is studied in detail, and results show that the neural network model has an optimal region for the detectability of weak electric field signal.

  1. Potential Mechanisms and Functions of Intermittent Neural Synchronization

    Directory of Open Access Journals (Sweden)

    Sungwoo Ahn

    2017-05-01

    Full Text Available Neural synchronization is believed to play an important role in different brain functions. Synchrony in cortical and subcortical circuits is frequently variable in time and not perfect. Few long intervals of desynchronized dynamics may be functionally different from many short desynchronized intervals although the average synchrony may be the same. Recent analysis of imperfect synchrony in different neural systems reported one common feature: neural oscillations may go out of synchrony frequently, but primarily for a short time interval. This study explores potential mechanisms and functional advantages of this short desynchronizations dynamics using computational neuroscience techniques. We show that short desynchronizations are exhibited in coupled neurons if their delayed rectifier potassium current has relatively large values of the voltage-dependent activation time-constant. The delayed activation of potassium current is associated with generation of quickly-rising action potential. This “spikiness” is a very general property of neurons. This may explain why very different neural systems exhibit short desynchronization dynamics. We also show how the distribution of desynchronization durations may be independent of the synchronization strength. Finally, we show that short desynchronization dynamics requires weaker synaptic input to reach a pre-set synchrony level. Thus, this dynamics allows for efficient regulation of synchrony and may promote efficient formation of synchronous neural assemblies.

  2. Strength Training

    Science.gov (United States)

    ... big difference between strength training, powerlifting, and competitive bodybuilding! Strength training uses resistance methods like free weights, ... a person can lift at one time. Competitive bodybuilding involves evaluating muscle definition and symmetry, as well ...

  3. Behavioral patterns and in-situ target strength of the hairtail ( Trichiurus lepturus) via coupling of scientific echosounder and acoustic camera data

    Science.gov (United States)

    Hwang, Kangseok; Yoon, Eun-A.; Kang, Sukyung; Cha, Hyungkee; Lee, Kyounghoon

    2017-12-01

    The present study focuses on the influence of target strength (TS) changes in the swimming angle of the hairtail ( Trichiurus lepturus). We measured in-situ TS at 38 and 120 kHz with luring lamps at a fishing ground for jigging boats near the coastal waters of Jeju-do in Korea. Swimming angle and size of hairtails were measured using an acoustic camera. Results showed that mean preanal length was estimated to be 13.5 cm (SD = 2.7 cm) and mean swimming tilt angle was estimated to be 43.9° (SD = 17.6°). The mean TS values were -35.7 and -41.2 dB at 38 and 120 kHz, respectively. The results will assist in understanding the influence of swimming angle on the TS of hairtails and, thus, improve the accuracy of biomass estimates.

  4. Behavioral patterns and in-situ target strength of the hairtail (Trichiurus lepturus) via coupling of scientific echosounder and acoustic camera data

    Science.gov (United States)

    Hwang, Kangseok; Yoon, Eun-A.; Kang, Sukyung; Cha, Hyungkee; Lee, Kyounghoon

    2017-11-01

    The present study focuses on the influence of target strength (TS) changes in the swimming angle of the hairtail (Trichiurus lepturus). We measured in-situ TS at 38 and 120 kHz with luring lamps at a fishing ground for jigging boats near the coastal waters of Jeju-do in Korea. Swimming angle and size of hairtails were measured using an acoustic camera. Results showed that mean preanal length was estimated to be 13.5 cm (SD = 2.7 cm) and mean swimming tilt angle was estimated to be 43.9° (SD = 17.6°). The mean TS values were -35.7 and -41.2 dB at 38 and 120 kHz, respectively. The results will assist in understanding the influence of swimming angle on the TS of hairtails and, thus, improve the accuracy of biomass estimates.

  5. The strength of the R - T exchange coupling in R sub 2 Fe sub 14 B compounds; an approach based on high-field magnetization measurements

    Energy Technology Data Exchange (ETDEWEB)

    Verhoef, R.; Quang, P.H.; Franse, J.J.M.; Radwanski, R.J. (Natuurkundig Laboratorium der Universiteit van Amsterdam, Valckenierstraat 65, 1018 XE Amsterdam, The Netherlands (NL))

    1990-05-01

    By performing high-field magnetization measurements on Gd{sub 2}Fe{sub 14{minus}{ital x}}Mn{sub {ital x}}B and Tb{sub 2}Fe{sub 14{minus}{ital x}}Mn{sub {ital x}}B powdered samples that are oriented in the sample holder by the applied field it is possible to improve the accuracy of earlier reported values for the {ital R}-{ital T} exchange-coupling constant of these R{sub 2}Fe{sub 14}B compounds. In addition, the two-sublattice model that has been applied previously has been extended to a three-sublattice model with two magnetic rare-earth sublattices for the Er and Tm compounds in an attempt to describe the experimentally observed low-field susceptibility. It turns out however, that mainly the iron-manganese sublattice is responsible for this susceptibility.

  6. Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data.

    Science.gov (United States)

    Chatterjee, Sankhadeep; Dey, Nilanjan; Shi, Fuqian; Ashour, Amira S; Fong, Simon James; Sen, Soumya

    2017-09-11

    Dengue fever detection and classification have a vital role due to the recent outbreaks of different kinds of dengue fever. Recently, the advancement in the microarray technology can be employed for such classification process. Several studies have established that the gene selection phase takes a significant role in the classifier performance. Subsequently, the current study focused on detecting two different variations, namely, dengue fever (DF) and dengue hemorrhagic fever (DHF). A modified bag-of-features method has been proposed to select the most promising genes in the classification process. Afterward, a modified cuckoo search optimization algorithm has been engaged to support the artificial neural (ANN-MCS) to classify the unknown subjects into three different classes namely, DF, DHF, and another class containing convalescent and normal cases. The proposed method has been compared with other three well-known classifiers, namely, multilayer perceptron feed-forward network (MLP-FFN), artificial neural network (ANN) trained with cuckoo search (ANN-CS), and ANN trained with PSO (ANN-PSO). Experiments have been carried out with different number of clusters for the initial bag-of-features-based feature selection phase. After obtaining the reduced dataset, the hybrid ANN-MCS model has been employed for the classification process. The results have been compared in terms of the confusion matrix-based performance measuring metrics. The experimental results indicated a highly statistically significant improvement with the proposed classifier over the traditional ANN-CS model.

  7. Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model

    Directory of Open Access Journals (Sweden)

    Yaojie Yue

    2016-12-01

    Full Text Available Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS, a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008. Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is

  8. Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

    How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

  9. Phase-flip bifurcation in a coupled Josephson junction neuron system

    Energy Technology Data Exchange (ETDEWEB)

    Segall, Kenneth, E-mail: ksegall@colgate.edu [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States); Guo, Siyang; Crotty, Patrick [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States); Schult, Dan [Department of Mathematics, Colgate University, Hamilton, NY 13346 (United States); Miller, Max [Department of Physics and Astronomy, Colgate University, Hamilton, NY 13346 (United States)

    2014-12-15

    Aiming to understand group behaviors and dynamics of neural networks, we have previously proposed the Josephson junction neuron (JJ neuron) as a fast analog model that mimics a biological neuron using superconducting Josephson junctions. In this study, we further analyze the dynamics of the JJ neuron numerically by coupling one JJ neuron to another. In this coupled system we observe a phase-flip bifurcation, where the neurons synchronize out-of-phase at weak coupling and in-phase at strong coupling. We verify this by simulation of the circuit equations and construct a bifurcation diagram for varying coupling strength using the phase response curve and spike phase difference map. The phase-flip bifurcation could be observed experimentally using standard digital superconducting circuitry.

  10. Coupling a Neural Network-Based forward Model and a Bayesian Inversion Approach to Retrieve Wind Field from Spaceborne Polarimetric Radiometers.

    Science.gov (United States)

    Pulvirenti, Luca; Pierdicca, Nazzareno; Marzano, Frank S

    2008-12-03

    A simulation study to assess the potentiality of sea surface wind vector estimation based on the approximation of the forward model through Neural Networks and on the Bayesian theory of parameter estimation is presented. A polarimetric microwave radiometer has been considered and its observations have been simulated by means of the two scale model. To perform the simulations, the atmospheric and surface parameters have been derived from ECMWF analysis fields. To retrieve wind speed, Minimum Variance (MV) and Maximum Posterior Probability (MAP) criteria have been used while, for wind direction, a Maximum Likelihood (ML) criterion has been exploited. To minimize the cost function of MAP and ML, conventional Gradient Descent method, as well as Simulated Annealing optimization technique, have been employed. Results have shown that the standard deviation of the wind speed retrieval error is approximately 1.1 m/s for the best estimator. As for the wind direction, the standard deviation of the estimation error is less than 13° for wind speeds larger than 6 m/s. For lower wind velocities, the wind direction signal is too weak to ensure reliable retrievals. A method to deal with the non-uniqueness of the wind direction solution has been also developed. A test on a case study has yielded encouraging results.

  11. Reduction in LFP cross-frequency coupling between theta and gamma rhythms associated with impaired STP and LTP in a rat model of brain ischemia

    Directory of Open Access Journals (Sweden)

    Tao eZhang

    2013-04-01

    Full Text Available The theta-gamma cross-frequency coupling (CFC in hippocampus was reported to reflect memory process. In this study, we measured the CFC of hippocampal local field potentials (LFPs in a two-vessel occlusion (2VO rat model, combined with both amplitude and phase properties and associated with short and long-term plasticity indicating the memory function. Male Wistar rats were used and a 2VO model was established. STP and LTP were recorded in hippocampal CA3-CA1 pathway after LFPs were collected in both CA3 and CA1. Based on the data of relative power spectra and phase synchronization, it suggested that both the amplitude and phase coupling of either theta or gamma rhythm were involved in modulating the neural network in 2VO rats. In order to determine whether the CFC was also implicated in neural impairment in 2VO rats, the coupling of CA3 theta–CA1 gamma was measured by both phase-phase coupling (n:m phase synchronization and phase-amplitude coupling. The attenuated CFC strength in 2VO rats implied the impaired neural communication in the coordination of theta-gamma entraining process. Moreover, compared with modulation index (MI a novel algorithm named cross frequency conditional mutual information (CF-CMI, was developed to focus on the coupling between theta phase and the phase of gamma amplitude. The results suggest that the reduced CFC strength probably attributed to the disruption of the phase of CA1 gamma envelop. In conclusion, it implied that the phase coupling and CFC of hippocampal theta and gamma played an important role in supporting functions of neural network. Furthermore, synaptic plasticity on CA3-CA1 pathway was reduced in line with the decreased CFC strength from CA3 to CA1. It partly supported our hypothesis that directional CFC indicator might probably be used as a measure of synaptic plasticity.

  12. Adaptive Neurons For Artificial Neural Networks

    Science.gov (United States)

    Tawel, Raoul

    1990-01-01

    Training time decreases dramatically. In improved mathematical model of neural-network processor, temperature of neurons (in addition to connection strengths, also called weights, of synapses) varied during supervised-learning phase of operation according to mathematical formalism and not heuristic rule. Evidence that biological neural networks also process information at neuronal level.

  13. Evolution of N-converting bacteria during the start-up of anaerobic digestion coupled biological nitrogen removal pilot-scale bioreactors treating high-strength animal waste slurry.

    Science.gov (United States)

    Anceno, Alfredo J; Rouseau, Pierre; Béline, Fabrice; Shipin, Oleg V; Dabert, Patrick

    2009-07-01

    Animal wastes have been successfully employed in anaerobic biogas production, viewed as a pragmatic approach to rationalize energy costs in animal farms. Effluents resulting from that process however are still high in nitrogen such that attempts were made to couple biological nitrogen removal (BNR) with anaerobic digestion (AD). The demand for organic substrate in such system is partitioned between the anaerobic metabolism in AD and the heterotrophic denitrification cascade following the autotrophic nitrification in BNR. Investigation of underlying N-converting taxa with respect to process conditions is therefore critical in optimizing N-removal in such treatment system. In this study, a pilot-scale intermittently aerated BNR bioreactor was started up either independently or in series with the AD bioreactor to treat high-strength swine waste slurry. The compositions of NH(3)-oxidizing bacteria (AOB), NO(2)(-)-oxidizing bacteria (NOB) and denitrifiers (nosZ gene) were profiled by polymerase chain reaction-capillary electrophoresis/single strand conformation polymorphism (PCR-CE/SSCP) technique and clone library analysis. Performance data suggested that these two process configurations significantly differ in the modes of biological N-removal. PCR-CE/SSCP based profiling of the underlying nitrifying bacteria also revealed the selection of distinct taxa between process configurations. Under the investigated process conditions, correlation of performance data and composition of underlying nitrifiers suggest that the stand-alone BNR bioreactor tended to favor N-removal via NO(3)(-) whereas the coupled bioreactors could be optimized to achieve the same via a NO(2)(-) shortcut.

  14. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  15. Modeling of inter-neuronal coupling medium and its impact on neuronal synchronization.

    Science.gov (United States)

    Iqbal, Muhammad; Rehan, Muhammad; Hong, Keum-Shik

    2017-01-01

    In this paper, modeling of the coupling medium between two neurons, the effects of the model parameters on the synchronization of those neurons, and compensation of coupling strength deficiency in synchronization are studied. Our study exploits the inter-neuronal coupling medium and investigates its intrinsic properties in order to get insight into neuronal-information transmittance and, there from, brain-information processing. A novel electrical model of the coupling medium that represents a well-known RLC circuit attributable to the coupling medium's intrinsic resistive, inductive, and capacitive properties is derived. Surprisingly, the integration of such properties reveals the existence of a natural three-term control strategy, referred to in the literature as the proportional integral derivative (PID) controller, which can be responsible for synchronization between two neurons. Consequently, brain-information processing can rely on a large number of PID controllers based on the coupling medium properties responsible for the coherent behavior of neurons in a neural network. Herein, the effects of the coupling model (or natural PID controller) parameters are studied and, further, a supervisory mechanism is proposed that follows a learning and adaptation policy based on the particle swarm optimization algorithm for compensation of the coupling strength deficiency.

  16. Modeling of inter-neuronal coupling medium and its impact on neuronal synchronization

    Science.gov (United States)

    Iqbal, Muhammad; Hong, Keum-Shik

    2017-01-01

    In this paper, modeling of the coupling medium between two neurons, the effects of the model parameters on the synchronization of those neurons, and compensation of coupling strength deficiency in synchronization are studied. Our study exploits the inter-neuronal coupling medium and investigates its intrinsic properties in order to get insight into neuronal-information transmittance and, there from, brain-information processing. A novel electrical model of the coupling medium that represents a well-known RLC circuit attributable to the coupling medium’s intrinsic resistive, inductive, and capacitive properties is derived. Surprisingly, the integration of such properties reveals the existence of a natural three-term control strategy, referred to in the literature as the proportional integral derivative (PID) controller, which can be responsible for synchronization between two neurons. Consequently, brain-information processing can rely on a large number of PID controllers based on the coupling medium properties responsible for the coherent behavior of neurons in a neural network. Herein, the effects of the coupling model (or natural PID controller) parameters are studied and, further, a supervisory mechanism is proposed that follows a learning and adaptation policy based on the particle swarm optimization algorithm for compensation of the coupling strength deficiency. PMID:28486505

  17. Quantitative structure-retention relationship model for the determination of naratriptan hydrochloride and its impurities based on artificial neural networks coupled with genetic algorithm.

    Science.gov (United States)

    Mizera, Mikołaj; Krause, Anna; Zalewski, Przemysław; Skibiński, Robert; Cielecka-Piontek, Judyta

    2017-03-01

    Mathematical modeling of Quantitative Structure - Property Relationships met great interest in fields of in silico drug design and more recently, pharmaceutical analysis. In our approach we proposed automated method of creation Quantitative Structure-Retention Relationship (QSRR) for analysis of triptans, selective serotonin 5-HT1 receptor agonists used for the treatment of acute headache. The method was created using hybrid machine learning approach, namely Genetic algorithm (GA) coupled with artificial neutral networks (ANN). Performance of proposed hybrid GA-ANN model was evaluated with predicting relative retention times of naratriptan hydrochloride impurities. Several ANN types were coupled with GA and tested: single-layer ANN (SL-ANN), double-layer ANN (D-ANN) and higher order architectures: pi-sigma ANN (PS-ANN) and sigma-pi-sigma ANN (SPS-ANN). Partial Least Squares (PLS) method was used as a reference. The separation of naratriptan hydrochloride and its related products (impurities and degradation products) was obtained by developing a gradient high-performance liquid chromatography method with diode-array detector (HPLC-DAD). Degradation products during acid-basic hydrolysis were identified with an electrospray ionization tandem mass spectrometry (Q-TOF-MS/MS) detector. Independent data for outer validation of QSRR model was obtained from the determination of related products of sumatriptan succinate via an HPLC-DAD method. Accuracy of QSRR was measured by inner-validation on naratriptan data and outer validation on sumatriptan succinate samples. The best performing model were PS-ANN and SPS-ANN with mean errors of 8% (Q2=0.87) and 15% (Q2=0.77) on an inner-validation data set, respectively. Validation on similar samples from an outer validation data set of sumatriptan succinate impurities gave mean errors of 18% (R(2)pred=0.64) and 17% (R(2)pred=0.63) for the PS-ANN and SPS-ANN models, respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Electronic properties of quasi one-dimensional quantum wire models under equal coupling strength superpositions of Rashba and Dresselhaus spin-orbit interactions in the presence of an in-plane magnetic field

    Energy Technology Data Exchange (ETDEWEB)

    Papp, E. [Physics Department, West University of Timisoara, RO-300223, Timisoara (Romania); Micu, C.; Racolta, D. [Faculty of Science, North University of Baia Mare, RO-430122, Baia Mare (Romania)

    2013-11-13

    In this paper one deals with the theoretical derivation of energy bands and of related wavefunctions characterizing quasi 1D semiconductor heterostructures, such as InAs quantum wire models. Such models get characterized this time by equal coupling strength superpositions of Rashba and Dresselhaus spin-orbit interactions of dimensionless magnitude a under the influence of in-plane magnetic fields of magnitude B. We found that the orientations of the field can be selected by virtue of symmetry requirements. For this purpose one resorts to spin conservations, but alternative conditions providing sensible simplifications of the energy-band formula can be reasonably accounted for. Besides the wavenumber k relying on the 1D electron, one deals with the spin-like s=±1 factors in the front of the square root term of the energy. Having obtained the spinorial wavefunction, opens the way to the derivation of spin precession effects. For this purpose one resorts to the projections of the wavenumber operator on complementary spin states. Such projections are responsible for related displacements proceeding along the Ox-axis. This results in a 2D rotation matrix providing both the precession angle as well as the precession axis.

  19. Optimization of the Production of Extracellular Polysaccharide from the Shiitake Medicinal Mushroom Lentinus edodes (Agaricomycetes) Using Mutation and a Genetic Algorithm-Coupled Artificial Neural Network (GA-ANN).

    Science.gov (United States)

    Adeeyo, Adeyemi Ojutalayo; Lateef, Agbaje; Gueguim-Kana, Evariste Bosco

    2016-01-01

    Exopolysaccharide (EPS) production by a strain of Lentinus edodes was studied via the effects of treatments with ultraviolet (UV) irradiation and acridine orange. Furthermore, optimization of EPS production was studied using a genetic algorithm coupled with an artificial neural network in submerged fermentation. Exposure to irradiation and acridine orange resulted in improved EPS production (2.783 and 5.548 g/L, respectively) when compared with the wild strain (1.044 g/L), whereas optimization led to improved productivity (23.21 g/L). The EPS produced by various strains also demonstrated good DPPH scavenging activities of 45.40-88.90%, and also inhibited the growth of Escherichia coli and Klebsiella pneumoniae. This study shows that multistep optimization schemes involving physical-chemical mutation and media optimization can be an attractive strategy for improving the yield of bioactives from medicinal mushrooms. To the best of our knowledge, this report presents the first reference of a multistep approach to optimizing EPS production in L. edodes.

  20. predicting flexural strength river gravel using multi ravel using multi

    African Journals Online (AJOL)

    eobe

    determination of flexural determination of flexural strength of concrete mate strength of concrete mate ... computational model, based on artificial neural ne strength of concrete materials made from prevalent coarse aggregate com ...... of Date Palm Wood Fibre-Recycled Low Density. Polyethylene Composite Using Artificial ...

  1. Comparative Expression Study of the Endo–G Protein Coupled Receptor (GPCR) Repertoire in Human Glioblastoma Cancer Stem-like Cells, U87-MG Cells and Non Malignant Cells of Neural Origin Unveils New Potential Therapeutic Targets

    Science.gov (United States)

    Lennon, Sarah; Carapito, Christine; Dong, Jihu; Van Dorsselaer, Alain; Junier, Marie-Pierre; Chneiweiss, Hervé; Cianférani, Sarah; Haiech, Jacques; Kilhoffer, Marie-Claude

    2014-01-01

    Glioblastomas (GBMs) are highly aggressive, invasive brain tumors with bad prognosis and unmet medical need. These tumors are heterogeneous being constituted by a variety of cells in different states of differentiation. Among these, cells endowed with stem properties, tumor initiating/propagating properties and particularly resistant to chemo- and radiotherapies are designed as the real culprits for tumor maintenance and relapse after treatment. These cells, termed cancer stem-like cells, have been designed as prominent targets for new and more efficient cancer therapies. G-protein coupled receptors (GPCRs), a family of membrane receptors, play a prominent role in cell signaling, cell communication and crosstalk with the microenvironment. Their role in cancer has been highlighted but remains largely unexplored. Here, we report a descriptive study of the differential expression of the endo-GPCR repertoire in human glioblastoma cancer stem-like cells (GSCs), U-87 MG cells, human astrocytes and fetal neural stem cells (f-NSCs). The endo-GPCR transcriptome has been studied using Taqman Low Density Arrays. Of the 356 GPCRs investigated, 138 were retained for comparative studies between the different cell types. At the transcriptomic level, eight GPCRs were specifically expressed/overexpressed in GSCs. Seventeen GPCRs appeared specifically expressed in cells with stem properties (GSCs and f-NSCs). Results of GPCR expression at the protein level using mass spectrometry and proteomic analysis are also presented. The comparative GPCR expression study presented here gives clues for new pathways specifically used by GSCs and unveils novel potential therapeutic targets. PMID:24662753

  2. Effect of inhibitory firing pattern on coherence resonance in random neural networks

    Science.gov (United States)

    Yu, Haitao; Zhang, Lianghao; Guo, Xinmeng; Wang, Jiang; Cao, Yibin; Liu, Jing

    2018-01-01

    The effect of inhibitory firing patterns on coherence resonance (CR) in random neuronal network is systematically studied. Spiking and bursting are two main types of firing pattern considered in this work. Numerical results show that, irrespective of the inhibitory firing patterns, the regularity of network is maximized by an optimal intensity of external noise, indicating the occurrence of coherence resonance. Moreover, the firing pattern of inhibitory neuron indeed has a significant influence on coherence resonance, but the efficacy is determined by network property. In the network with strong coupling strength but weak inhibition, bursting neurons largely increase the amplitude of resonance, while they can decrease the noise intensity that induced coherence resonance within the neural system of strong inhibition. Different temporal windows of inhibition induced by different inhibitory neurons may account for the above observations. The network structure also plays a constructive role in the coherence resonance. There exists an optimal network topology to maximize the regularity of the neural systems.

  3. predicting the compressive strength of concretes made with granite

    African Journals Online (AJOL)

    2013-03-01

    Mar 1, 2013 ... computational model based on artificial neural networks for the determination of the compressive strength of concrete ... (relative) error of 1.149, while the neural network model prediction has a sum of squares error of. 0.299 and a mean .... tion vary from region to region and from country to country. Hence ...

  4. Endogenous field feedback promotes the detectability for exogenous electric signal in the hybrid coupled population

    Energy Technology Data Exchange (ETDEWEB)

    Wei, Xile; Zhang, Danhong; Wang, Jiang; Yu, Haitao, E-mail: htyu@tju.edu.cn [Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072 (China); Lu, Meili [School of Informational Technology and Engineering, Tianjin University of Technology and Education, Tianjin 300222 (China); Che, Yanqiu [School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin 300222 (China)

    2015-01-15

    This paper presents the endogenous electric field in chemical or electrical synaptic coupled networks, aiming to study the role of endogenous field feedback in the signal propagation in neural systems. It shows that the feedback of endogenous fields to network activities can reduce the required energy of the noise and enhance the transmission of input signals in hybrid coupled populations. As a common and important nonsynaptic interactive method among neurons, particularly, the endogenous filed feedback can not only promote the detectability of exogenous weak signal in hybrid coupled neural population but also enhance the robustness of the detectability against noise. Furthermore, with the increasing of field coupling strengths, the endogenous field feedback is conductive to the stochastic resonance by facilitating the transition of cluster activities from the no spiking to spiking regions. Distinct from synaptic coupling, the endogenous field feedback can play a role as internal driving force to boost the population activities, which is similar to the noise. Thus, it can help to transmit exogenous weak signals within the network in the absence of noise drive via the stochastic-like resonance.

  5. Chaotic Motion of Nonlinearly Coupled Quintic Oscillators | Adeloye ...

    African Journals Online (AJOL)

    With a fixed energy, we investigate the motion of two nonlinearly coupled quintic oscillators for various values of the coupling strength with the aid of the Poincare surface of section. It is observed that chaotic motion sets in for coupling strength as low as 0.001. The degree of chaoticity generally increases as the coupling ...

  6. ESTIMATION OF SHEAR STRENGTH PARAMETERS OF ...

    African Journals Online (AJOL)

    This research work seeks to develop models for predicting the shear strength parameters (cohesion and angle of friction) of lateritic soils in central and southern areas of Delta State using artificial neural network modeling technique. The application of these models will help reduce cost and time in acquiring geotechnical ...

  7. Synchrony, waves and ripple in spatially coupled Kuramoto oscillators with Mexican hat connectivity.

    Science.gov (United States)

    Heitmann, Stewart; Ermentrout, G Bard

    2015-06-01

    Spatiotemporal waves of synchronized activity are known to arise in oscillatory neural networks with lateral inhibitory coupling. How such patterns respond to dynamic changes in coupling strength is largely unexplored. The present study uses analysis and simulation to investigate the evolution of wave patterns when the strength of lateral inhibition is varied dynamically. Neural synchronization was modeled by a spatial ring of Kuramoto oscillators with Mexican hat lateral coupling. Broad bands of coexisting stable wave solutions were observed at all levels of inhibition. The stability of these waves was formally analyzed in both the infinite ring and the finite ring. The broad range of multi-stability predicted hysteresis in transitions between neighboring wave solutions when inhibition is slowly varied. Numerical simulation confirmed the predicted transitions when inhibition was ramped down from a high initial value. However, non-wave solutions emerged from the uniform solution when inhibition was ramped upward from zero. These solutions correspond to spatially periodic deviations of phase that we call ripple states. Numerical continuation showed that stable ripple states emerge from synchrony via a supercritical pitchfork bifurcation. The normal form of this bifurcation was derived analytically, and its predictions compared against the numerical results. Ripple states were also found to bifurcate from wave solutions, but these were locally unstable. Simulation also confirmed the existence of hysteresis and ripple states in two spatial dimensions. Our findings show that spatial synchronization patterns can remain structurally stable despite substantial changes in network connectivity.

  8. Gestural coupling and social cognition

    DEFF Research Database (Denmark)

    Michael, John; Krueger, Joel William

    2012-01-01

    Social cognition researchers have become increasingly interested in the ways that behavioral, physiological, and neural coupling facilitate social interaction and interpersonal understanding. We distinguish two ways of conceptualizing the role of such coupling processes in social cognition: strong...... and interpersonal understanding. We conclude that investigations of coupling processes within social interaction should inform rather than marginalize or eliminate investigation of higher-level individual cognition...... and moderate interactionism. According to strong interactionism (SI), low-level coupling processes are alternatives to higher-level individual cognitive processes; the former at least sometimes render the latter superfluous. Moderate interactionism (MI) on the other hand, is an integrative approach. Its...

  9. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  10. A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia.

    Science.gov (United States)

    Floares, Alexandru George

    2008-01-01

    Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which can be used to reverse engineer neural networks. The RODES algorithm automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks.

  11. Predicting Flexural Strength of Concretes Incorporating River Gravel ...

    African Journals Online (AJOL)

    This work shows the development of a computational model, based on artificial neural networks for the determination of flexural strength of concrete materials made from ... The result of the study has adequately demonstrated a cheap, simple, very quick and accurate alternative to experimental method of concrete strength ...

  12. Predicting the Compressive Strength of Concretes Made with ...

    African Journals Online (AJOL)

    This research seeks to develop a computational model based on arti cial neural networks for the determination of the compressive strength of concrete materials ... The result of the study has ably demonstrated a cheap, simple, very quick and accurate alternative to experimental method of concrete strength determination.

  13. Estimación de la resistencia a la penetración de suelos usando redes neuronales artificiales Prediction of the soils penetration strength using artificial neural networks

    Directory of Open Access Journals (Sweden)

    Nidia Johana Valdés Holguín

    2011-07-01

    Full Text Available Las redes neuronales artificiales, simuladoras del proceso de aprendizaje de las neuronas biológicas, han sido utilizadas con éxito en el cálculo de parámetros en diversos problemas de ingeniería en que las variables involucradas tienen una alta relación no lineal entre sí y la modelación no permite representar el problema mediante una función matemática de fácil deducción. En la ciencia del suelo la predicción de algunas propiedades involucra diversas variables que hacen de su estimación por medio de modelos matemáticos un proceso complejo, y trasladan la solución del problema al campo de la inteligencia artificial. En el presente artículo se reporta la elaboración de redes neuronales artificiales para la estimación de la resistencia a la penetración a diferentes profundidades de un suelo; se consideran como variables influyentes el contenido de humedad, la densidad, la carga estática y la presión de inflado. Los resultados muestran una mejor estimación para profundidades entre 20 cm y 30 cm.Artificial Neural Networks simulate the learning process of biological neurons, and these have been successfully used in the computation of parameters on several engineering problems where exist a strong nonlinear relation among the variables. In soil science, estimation of some properties involves variables that are complicated to estimate using mathematical models, so the solution for the problems fall into the field of Artificial Intelligence. The present paper reports the elaboration of an Artificial Neural Network for the estimation of penetration resistance of soil at different depths, considering as influential variables humidity, density, static load, and inflate pressure. The best estimation results were obtained at a depth of 20-30 cm.

  14. Autonomous Navigation Apparatus With Neural Network for a Mobile Vehicle

    Science.gov (United States)

    Quraishi, Naveed (Inventor)

    1996-01-01

    An autonomous navigation system for a mobile vehicle arranged to move within an environment includes a plurality of sensors arranged on the vehicle and at least one neural network including an input layer coupled to the sensors, a hidden layer coupled to the input layer, and an output layer coupled to the hidden layer. The neural network produces output signals representing respective positions of the vehicle, such as the X coordinate, the Y coordinate, and the angular orientation of the vehicle. A plurality of patch locations within the environment are used to train the neural networks to produce the correct outputs in response to the distances sensed.

  15. Neural Tube Defects

    Science.gov (United States)

    ... vitamin, before and during pregnancy prevents most neural tube defects. Neural tube defects are usually diagnosed before the infant is ... or imaging tests. There is no cure for neural tube defects. The nerve damage and loss of function ...

  16. Anger in brain and body: the neural and physiological perturbation of decision-making by emotion

    National Research Council Canada - National Science Library

    Garfinkel, Sarah N; Zorab, Emma; Navaratnam, Nakulan; Engels, Miriam; Mallorquí-Bagué, Núria; Minati, Ludovico; Dowell, Nicholas G; Brosschot, Jos F; Thayer, Julian F; Critchley, Hugo D

    2016-01-01

    Emotion and cognition are dynamically coupled to bodily arousal: the induction of anger, even unconsciously, can reprioritise neural and physiological resources toward action states that bias cognitive processes...

  17. Active voltammetric microsensors with neural signal processing.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    1998-12-11

    Many industrial and environmental processes, including bioremediation, would benefit from the feedback and control information provided by a local multi-analyte chemical sensor. For most processes, such a sensor would need to be rugged enough to be placed in situ for long-term remote monitoring, and inexpensive enough to be fielded in useful numbers. The multi-analyte capability is difficult to obtain from common passive sensors, but can be provided by an active device that produces a spectrum-type response. Such new active gas microsensor technology has been developed at Argonne National Laboratory. The technology couples an electrocatalytic ceramic-metallic (cermet) microsensor with a voltammetric measurement technique and advanced neural signal processing. It has been demonstrated to be flexible, rugged, and very economical to produce and deploy. Both narrow interest detectors and wide spectrum instruments have been developed around this technology. Much of this technology's strength lies in the active measurement technique employed. The technique involves applying voltammetry to a miniature electrocatalytic cell to produce unique chemical ''signatures'' from the analytes. These signatures are processed with neural pattern recognition algorithms to identify and quantify the components in the analyte. The neural signal processing allows for innovative sampling and analysis strategies to be employed with the microsensor. In most situations, the whole response signature from the voltammogram can be used to identify, classify, and quantify an analyte, without dissecting it into component parts. This allows an instrument to be calibrated once for a specific gas or mixture of gases by simple exposure to a multi-component standard rather than by a series of individual gases. The sampled unknown analytes can vary in composition or in concentration, the calibration, sensing, and processing methods of these active voltammetric microsensors can

  18. Fiber optically coupled radioluminescence detectors: A short review of key strengths and weaknesses of BCF-60 and Al2O3:C scintillating-material based systems in radiotherapy dosimetry applications

    DEFF Research Database (Denmark)

    Buranurak, Siritorn; Andersen, Claus E.

    2017-01-01

    time resolution. In particular, the all-optical nature of these detectors is an advantage for in vivo measurements due to the absence of high voltage supply or electrical wire that could cause harm to the patient or disturb the treatment. Basically, fiber-coupled luminescence detector systems function...

  19. Cockayne syndrome b maintains neural precursor function.

    Science.gov (United States)

    Sacco, Raffaele; Tamblyn, Laura; Rajakulendran, Nishani; Bralha, Fernando N; Tropepe, Vincent; Laposa, Rebecca R

    2013-02-01

    Neurodevelopmental defects are observed in the hereditary disorder Cockayne syndrome (CS). The gene most frequently mutated in CS, Cockayne Syndrome B (CSB), is required for the repair of bulky DNA adducts in transcribed genes during transcription-coupled nucleotide excision repair. CSB also plays a role in chromatin remodeling and mitochondrial function. The role of CSB in neural development is poorly understood. Here we report that the abundance of neural progenitors is normal in Csb(-/-) mice and the frequency of apoptotic cells in the neurogenic niche of the adult subependymal zone is similar in Csb(-/-) and wild type mice. Both embryonic and adult Csb(-/-) neural precursors exhibited defective self-renewal in the neurosphere assay. In Csb(-/-) neural precursors, self-renewal progressively decreased in serially passaged neurospheres. The data also indicate that Csb and the nucleotide excision repair protein Xpa preserve embryonic neural stem cell self-renewal after UV DNA damage. Although Csb(-/-) neural precursors do not exhibit altered neuronal lineage commitment after low-dose UV (1J/m(2)) in vitro, neurons differentiated in vitro from Csb(-/-) neural precursors that had been irradiated with 1J/m(2) UV exhibited defective neurite outgrowth. These findings identify a function for Csb in neural precursors. Copyright © 2012 Elsevier B.V. All rights reserved.

  20. [Neural repair].

    Science.gov (United States)

    Kitada, Masaaki; Dezawa, Mari

    2008-05-01

    Recent progress of stem cell biology gives us the hope for neural repair. We have established methods to specifically induce functional Schwann cells and neurons from bone marrow stromal cells (MSCs). The effectiveness of these induced cells was evaluated by grafting them either into peripheral nerve injury, spinal cord injury, or Parkinson' s disease animal models. MSCs-derived Schwann cells supported axonal regeneration and re-constructed myelin to facilitate the functional recovery in peripheral and spinal cord injury. MSCs-derived dopaminergic neurons integrated into host striatum and contributed to behavioral repair. In this review, we introduce the differentiation potential of MSCs and finally discuss about their benefits and drawbacks of these induction systems for cell-based therapy in neuro-traumatic and neuro-degenerative diseases.

  1. Stochastic resonance can enhance information transmission in neural networks.

    Science.gov (United States)

    Kawaguchi, Minato; Mino, Hiroyuki; Durand, Dominique M

    2011-07-01

    Stochastic resonance (SR) is a noise-induced phenomenon whereby signal detection can be improved by the addition of background noise in nonlinear systems. SR can also improve the transmission of information within single neurons. Since information processing in the brain is carried out by neural networks and noise is present throughout the brain, the hypothesis that noise and coupling play an important role in the control of information processing within a population of neurons to control was tested. Using computer simulations, we investigate the effect of noise on the transmission of information in an array of neurons, known as array-enhanced SR (AESR) in an interconnected population of hippocampal neurons. A subthreshold synaptic current (signal) modeled by a filtered homogeneous Poisson process was applied to a distal position in each of the apical dendrites, while background synaptic signals (uncorrelated noise) were presented to the midpoint in the basal dendrite. The transmembrane potentials were recorded in each cell of an array of CA1 neuron models, in order to determine spike firing times and to estimate the total and noise entropies from the spike firing times. The results show that the mutual information is maximized for a specific amplitude of uncorrelated noise, implying the presence of AESR. The results also show that the maximum mutual information increases with increased numbers of neurons and the strength of connections. Moreover, the relative levels of excitation and inhibition modulate the mutual information transfer. It is concluded that uncorrelated noise can enhance information transmission of subthreshold synaptic input currents in a population of hippocampal CA1 neuron models. Therefore, endogenous neural noise could play an important role in neural tissue by modulating the transfer of information across the network. © 2011 IEEE

  2. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  3. Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy

    Science.gov (United States)

    Maleki, E.

    2015-12-01

    Friction stir welding (FSW) is a relatively new solid-state joining technique that is widely adopted in manufacturing and industry fields to join different metallic alloys that are hard to weld by conventional fusion welding. Friction stir welding is a very complex process comprising several highly coupled physical phenomena. The complex geometry of some kinds of joints makes it difficult to develop an overall governing equations system for theoretical behavior analyse of the friction stir welded joints. Weld quality is predominantly affected by welding effective parameters, and the experiments are often time consuming and costly. On the other hand, employing artificial intelligence (AI) systems such as artificial neural networks (ANNs) as an efficient approach to solve the science and engineering problems is considerable. In present study modeling of FSW effective parameters by ANNs is investigated. To train the networks, experimental test results on thirty AA-7075-T6 specimens are considered, and the networks are developed based on back propagation (BP) algorithm. ANNs testing are carried out using different experimental data that they are not used during networks training. In this paper, rotational speed of tool, welding speed, axial force, shoulder diameter, pin diameter and tool hardness are regarded as inputs of the ANNs. Yield strength, tensile strength, notch-tensile strength and hardness of welding zone are gathered as outputs of neural networks. According to the obtained results, predicted values for the hardness of welding zone, yield strength, tensile strength and notch-tensile strength have the least mean relative error (MRE), respectively. Comparison of the predicted and the experimental results confirms that the networks are adjusted carefully, and the ANN can be used for modeling of FSW effective parameters.

  4. Mixed Mode Oscillations and Synchronous Activity in Noise Induced Modified Morris-Lecar Neural System

    Science.gov (United States)

    Upadhyay, Ranjit Kumar; Mondal, Argha; Teka, Wondimu W.

    The modified three-dimensional (3D) Morris-Lecar (M-L) model is very useful to understand the spiking activities of neurons. The present article addresses the random dynamical behavior of a modified M-L model driven by a white Gaussian noise with mean zero and unit spectral density. The applied stimulus can be expressed as a random term. Such random perturbations are represented by a white Gaussian noise current added through the electrical potential of membrane of the excitatory principal cells. The properties of the stochastic system (perturbed one) and noise induced mixed mode oscillation are analyzed. The Lyapunov spectrum is computed to present the nature of the system dynamics. The noise intensity is varied while keeping fixed the predominant parameters of the model in their ranges and also observed the changes in the dynamical behavior of the system. The dynamical synchronization is studied in the coupled M-L systems interconnected by excitatory and inhibitory neurons with noisy electrical coupling and verified with similarity functions. This result suggests the potential benefits of noise and noise induced oscillations which have been observed in real neurons and how that affects the dynamics of the neural model as well as the coupled systems. The analysis reports that the modified M-L system which has the limit cycle behavior can show a type of phase locking behavior which follows either period adding (i.e. 1:1, 2:1, 3:1, 4:1) sequences or Farey sequences. For the coupled neural systems, complete synchronization is shown for sufficient noisy coupling strength.

  5. Neurodynamics in Randomly Coupled Circle Maps

    Science.gov (United States)

    Matsuno, Tetsuya; Toko, Kiyoshi; Yamafuji, Kaoru

    1996-05-01

    The dynamics of retrieval processes in a system composed of coupled circle maps is studied by means of a statistical method and numerical simulations. Phase patterns are embedded in coupling parameters so that the system may work as an associative memory system. A parameter, which is an amplification factor multiplied to all the coupling strengths, is introduced for investigating the effect of the strength of the coupling nonlinearity on the behavior of the system concerned. The statistical method provides a set of time evolution equations representing the macroscopic behavior. It is found that the storage capacity is considerably enhanced by the introduced amplification factor. It is also shown that the system exhibits macroscopic chaotic oscillations when the strength of the coupling is sufficiently large. Moreover, the clustering is observed, as in other types of the globally coupled nonlinear systems.

  6. Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

    OpenAIRE

    Davis, Tyler; Xue, Gui; Love, Bradley C.; Preston, Alison. R.; Poldrack, Russell A

    2014-01-01

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categoriza...

  7. Learning alters theta amplitude, theta-gamma coupling and neuronal synchronization in inferotemporal cortex

    Directory of Open Access Journals (Sweden)

    Nicol Alister U

    2011-06-01

    Full Text Available Abstract Background How oscillatory brain rhythms alone, or in combination, influence cortical information processing to support learning has yet to be fully established. Local field potential and multi-unit neuronal activity recordings were made from 64-electrode arrays in the inferotemporal cortex of conscious sheep during and after visual discrimination learning of face or object pairs. A neural network model has been developed to simulate and aid functional interpretation of learning-evoked changes. Results Following learning the amplitude of theta (4-8 Hz, but not gamma (30-70 Hz oscillations was increased, as was the ratio of theta to gamma. Over 75% of electrodes showed significant coupling between theta phase and gamma amplitude (theta-nested gamma. The strength of this coupling was also increased following learning and this was not simply a consequence of increased theta amplitude. Actual discrimination performance was significantly correlated with theta and theta-gamma coupling changes. Neuronal activity was phase-locked with theta but learning had no effect on firing rates or the magnitude or latencies of visual evoked potentials during stimuli. The neural network model developed showed that a combination of fast and slow inhibitory interneurons could generate theta-nested gamma. By increasing N-methyl-D-aspartate receptor sensitivity in the model similar changes were produced as in inferotemporal cortex after learning. The model showed that these changes could potentiate the firing of downstream neurons by a temporal desynchronization of excitatory neuron output without increasing the firing frequencies of the latter. This desynchronization effect was confirmed in IT neuronal activity following learning and its magnitude was correlated with discrimination performance. Conclusions Face discrimination learning produces significant increases in both theta amplitude and the strength of theta-gamma coupling in the inferotemporal cortex

  8. Variety of alternative stable phase-locking in networks of electrically coupled relaxation oscillators.

    Directory of Open Access Journals (Sweden)

    Pierre Meyrand

    Full Text Available We studied the dynamics of a large-scale model network comprised of oscillating electrically coupled neurons. Cells are modeled as relaxation oscillators with short duty cycle, so they can be considered either as models of pacemaker cells, spiking cells with fast regenerative and slow recovery variables or firing rate models of excitatory cells with synaptic depression or cellular adaptation. It was already shown that electrically coupled relaxation oscillators exhibit not only synchrony but also anti-phase behavior if electrical coupling is weak. We show that a much wider spectrum of spatiotemporal patterns of activity can emerge in a network of electrically coupled cells as a result of switching from synchrony, produced by short external signals of different spatial profiles. The variety of patterns increases with decreasing rate of neuronal firing (or duty cycle and with decreasing strength of electrical coupling. We study also the effect of network topology--from all-to-all--to pure ring connectivity, where only the closest neighbors are coupled. We show that the ring topology promotes anti-phase behavior as compared to all-to-all coupling. It also gives rise to a hierarchical organization of activity: during each of the main phases of a given pattern cells fire in a particular sequence determined by the local connectivity. We have analyzed the behavior of the network using geometric phase plane methods and we give heuristic explanations of our findings. Our results show that complex spatiotemporal activity patterns can emerge due to the action of stochastic or sensory stimuli in neural networks without chemical synapses, where each cell is equally coupled to others via gap junctions. This suggests that in developing nervous systems where only electrical coupling is present such a mechanism can lead to the establishment of proto-networks generating premature multiphase oscillations whereas the subsequent emergence of chemical synapses would

  9. Probabilistic graphs using coupled random variables

    Science.gov (United States)

    Nelson, Kenric P.; Barbu, Madalina; Scannell, Brian J.

    2014-05-01

    Neural network design has utilized flexible nonlinear processes which can mimic biological systems, but has suffered from a lack of traceability in the resulting network. Graphical probabilistic models ground network design in probabilistic reasoning, but the restrictions reduce the expressive capability of each node making network designs complex. The ability to model coupled random variables using the calculus of nonextensive statistical mechanics provides a neural node design incorporating nonlinear coupling between input states while maintaining the rigor of probabilistic reasoning. A generalization of Bayes rule using the coupled product enables a single node to model correlation between hundreds of random variables. A coupled Markov random field is designed for the inferencing and classification of UCI's MLR `Multiple Features Data Set' such that thousands of linear correlation parameters can be replaced with a single coupling parameter with just a (3%, 4%) reduction in (classification, inference) performance.

  10. Survey on Neural Networks Used for Medical Image Processing.

    Science.gov (United States)

    Shi, Zhenghao; He, Lifeng; Suzuki, Kenji; Nakamura, Tsuyoshi; Itoh, Hidenori

    2009-02-01

    This paper aims to present a review of neural networks used in medical image processing. We classify neural networks by its processing goals and the nature of medical images. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of neural network application for medical image processing and an outlook for the future research are also discussed. By this survey, we try to answer the following two important questions: (1) What are the major applications of neural networks in medical image processing now and in the nearby future? (2) What are the major strengths and weakness of applying neural networks for solving medical image processing tasks? We believe that this would be very helpful researchers who are involved in medical image processing with neural network techniques.

  11. Entrepreneurial Couples

    DEFF Research Database (Denmark)

    Dahl, Michael S.; Van Praag, Mirjam; Thompson, Peter

    2015-01-01

    We study possible motivations for co-entreprenurial couples to start up a joint firm, using a sample of 1,069 Danish couples that established a joint enterprise between 2001 and 2010. We compare their pre-entry characteristics, firm performance and post-dissolution private and financial outcomes...... with a selected set of comparable firms and couples. We find evidence that couples often establish a business together because one spouse – most commonly the female – has limited outside opportunities in the labor market. However, the financial benefits for each of the spouses, and especially the female...

  12. Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete.

    Science.gov (United States)

    Cai, Guanjing; Zheng, Wei; Yang, Xujun; Zhang, Bangzhou; Zheng, Tianling

    2014-05-24

    Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms.

  13. Assessing Landslide Hazard Using Artificial Neural Network

    DEFF Research Database (Denmark)

    Farrokhzad, Farzad; Choobbasti, Asskar Janalizadeh; Barari, Amin

    2011-01-01

    failure" which is main concentration of the current research and "liquefaction failure". Shear failures along shear planes occur when the shear stress along the sliding surfaces exceed the effective shear strength. These slides have been referred to as landslide. An expert system based on artificial...... neural network has been developed for use in the stability evaluation of slopes under various geological conditions and engineering requirements. The Artificial neural network model of this research uses slope characteristics as input and leads to the output in form of the probability of failure...

  14. Fiber optically coupled radioluminescence detectors: A short review of key strengths and weaknesses of BCF-60 and Al2O3:C scintillating-material based systems in radiotherapy dosimetry applications

    Science.gov (United States)

    Buranurak, S.; Andersen, C. E.

    2017-06-01

    Radiotherapy technologies have improved for several decades aiming to effectively destroy cancerous tissues without overdosing surrounding healthy tissues. In order to fulfil this requirement, accurate and precise dosimetry systems play an important role. Throughout the years, ionization chambers have been used as a standard detector for basic linear accelerator calibrations and reference dosimetry in hospitals. However, they are not ideal for all treatment modalities: and limitations and difficulties have been reported in case of (i) small treatment fields, (ii) strong magnetic field used in the new hybrid MRI LINAC/cobalt systems, and (iii) in vivo measurements due to safety-issues related to the high operating voltage. Fiber optically coupled luminescence detectors provide a promising supplement to ionization chambers by offering the capability of real-time in vivo dose monitoring with high time resolution. In particular, the all-optical nature of these detectors is an advantage for in vivo measurements due to the absence of high voltage supply or electrical wire that could cause harm to the patient or disturb the treatment. Basically, fiber-coupled luminescence detector systems function by radiation-induced generation of radioluminescence from a sub-mm size organic/inorganic phosphor. A thin optical fiber cable is used for guiding the radioluminescence to a photomultiplier tube or similar sensitive light detection systems. The measured light intensity is proportional to dose rate. Throughout the years, developments and research of the fiber detector systems have undergone in several groups worldwide. In this article, the in-house developed fiber detector systems based on two luminescence phosphors of (i) BCF-60 polystyrene-based organic plastic scintillator and (ii) carbon-doped aluminum oxide crystal (Al2O3:C) are reviewed with comparison to the same material-based systems reported in the literature. The potential use of these detectors for reference

  15. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  16. Introduction to neural networks

    CERN Document Server

    James, Frederick E

    1994-02-02

    1. Introduction and overview of Artificial Neural Networks. 2,3. The Feed-forward Network as an inverse Problem, and results on the computational complexity of network training. 4.Physics applications of neural networks.

  17. A hyperstable neural network for the modelling and control of ...

    Indian Academy of Sciences (India)

    A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other ...

  18. Task-dependent modulation of oscillatory neural activity during movements

    DEFF Research Database (Denmark)

    Herz, D. M.; Christensen, M. S.; Reck, C.

    2011-01-01

    -dependent modulation of frequency coupling within this network. To this end we recorded 122-multichannel EEG in 13 healthy subjects while they performed three simple motor tasks. EEG data source modeling using individual MR images was carried out with a multiple source beamformer approach. A bilateral motor network...... for inferring on architecture and coupling parameters of neural networks....

  19. Entrepreneurial Couples

    DEFF Research Database (Denmark)

    Dahl, Michael S.; Van Praag, Mirjam; Thompson, Peter

    2015-01-01

    a labor market position for (female) spouses with limited alternative opportunities. This decision has positive effects: the financial benefits for each of the spouses, and especially the fe-male, are larger in co-entrepreneurial firms, both during the life of the business and post-dissolution. This also......We study motivations for and outcomes of couples starting up a joint firm, using a sample of 1,069 Danish couples that established a joint enterprise between 2001 and 2010, while comparing them to a set of comparable firms and couples. The main motivation for joint entrepreneurship is to create...

  20. Vibrational coupling in plasmonic molecules.

    Science.gov (United States)

    Yi, Chongyue; Dongare, Pratiksha D; Su, Man-Nung; Wang, Wenxiao; Chakraborty, Debadi; Wen, Fangfang; Chang, Wei-Shun; Sader, John E; Nordlander, Peter; Halas, Naomi J; Link, Stephan

    2017-10-31

    Plasmon hybridization theory, inspired by molecular orbital theory, has been extremely successful in describing the near-field coupling in clusters of plasmonic nanoparticles, also known as plasmonic molecules. However, the vibrational modes of plasmonic molecules have been virtually unexplored. By designing precisely configured plasmonic molecules of varying complexity and probing them at the individual plasmonic molecule level, intramolecular coupling of acoustic modes, mediated by the underlying substrate, is observed. The strength of this coupling can be manipulated through the configuration of the plasmonic molecules. Surprisingly, classical continuum elastic theory fails to account for the experimental trends, which are well described by a simple coupled oscillator picture that assumes the vibrational coupling is mediated by coherent phonons with low energies. These findings provide a route to the systematic optical control of the gigahertz response of metallic nanostructures, opening the door to new optomechanical device strategies. Published under the PNAS license.

  1. Strengths-based Learning

    DEFF Research Database (Denmark)

    Ledertoug, Mette Marie

    Strength-based learning - Children͛s Character Strengths as Means to their Learning Potential͛ is a Ph.D.-project aiming to create a strength-based mindset in school settings and at the same time introducing strength-based interventions as specific tools to improve both learning and well......-being. The Ph.D.-project in Strength-based learning took place in a Danish school with 750 pupils age 6-16 and a similar school was functioning as a control group. The presentation will focus on both the aware-explore-apply processes and the practical implications for the schools involved, and on measurable...

  2. Strength Modeling Report

    Science.gov (United States)

    Badler, N. I.; Lee, P.; Wong, S.

    1985-01-01

    Strength modeling is a complex and multi-dimensional issue. There are numerous parameters to the problem of characterizing human strength, most notably: (1) position and orientation of body joints; (2) isometric versus dynamic strength; (3) effector force versus joint torque; (4) instantaneous versus steady force; (5) active force versus reactive force; (6) presence or absence of gravity; (7) body somatotype and composition; (8) body (segment) masses; (9) muscle group envolvement; (10) muscle size; (11) fatigue; and (12) practice (training) or familiarity. In surveying the available literature on strength measurement and modeling an attempt was made to examine as many of these parameters as possible. The conclusions reached at this point toward the feasibility of implementing computationally reasonable human strength models. The assessment of accuracy of any model against a specific individual, however, will probably not be possible on any realistic scale. Taken statistically, strength modeling may be an effective tool for general questions of task feasibility and strength requirements.

  3. Entrepreneurial Couples

    DEFF Research Database (Denmark)

    Dahl, Michael S.; Van Praag, Mirjam; Thompson, Peter

    with a selected set of comparable firms and couples. We find evidence that couples often establish a business together because one spouse - most commonly the female - has limited outside opportunities in the labor market. However, the financial benefits for each of the spouses, and especially the female......We study possible motivations for co-entrepenurial couples to start up a joint firm, us-ing a sample of 1,069 Danish couples that established a joint enterprise between 2001 and 2010. We compare their pre-entry characteristics, firm performance and post-dissolution private and financial outcomes......, are larger in co-entrepreneurial firms, both during the life of the business and post-dissolution. The start-up of co-entrepreneurial firms seems therefore a sound in-vestment in the human capital of both spouses as well as in the reduction of income inequality in the household. We find no evidence of non...

  4. Entrepreneurial Couples

    DEFF Research Database (Denmark)

    Dahl, Michael S.; Van Praag, Mirjam; Thompson, Peter

    with a selected set of comparable firms and couples. We find evidence that couples often establish a business together because one spouse – most commonly the female – has limited outside opportunities in the labor market. However, the financial benefits for each of the spouses, and especially the female......We study possible motivations for co-entrepenurial couples to start up a joint firm, using a sample of 1,069 Danish couples that established a joint enterprise between 2001 and 2010. We compare their pre-entry characteristics, firm performance and postdissolution private and financial outcomes......, are larger in co-entrepreneurial firms, both during the life of the business and post-dissolution. The start-up of co-entrepreneurial firms seems therefore a sound investment in the human capital of both spouses as well as in the reduction of income inequality in the household. We find no evidence of non...

  5. Functional neural anatomy of talent.

    Science.gov (United States)

    Kalbfleisch, M Layne

    2004-03-01

    The terms gifted, talented, and intelligent all have meanings that suggest an individual's highly proficient or exceptional performance in one or more specific areas of strength. Other than Spearman's g, which theorizes about a general elevated level of potential or ability, more contemporary theories of intelligence are based on theoretical models that define ability or intelligence according to a priori categories of specific performance. Recent studies in cognitive neuroscience report on the neural basis of g from various perspectives such as the neural speed theory and the efficiency of prefrontal function. Exceptional talent is the result of interactions between goal-directed behavior and nonvolitional perceptual processes in the brain that have yet to be fully characterized and understood by the fields of psychology and cognitive neuroscience. Some developmental studies report differences in region-specific neural activation, recruitment patterns, and reaction times in subjects who are identified with high IQ scores according to traditional scales of assessment such as the WISC-III or Stanford-Binet. Although as cases of savants and prodigies illustrate, talent is not synonymous with high IQ. This review synthesizes information from the fields of psychometrics and gifted education, with findings from the neurosciences on the neural basis of intelligence, creativity, profiles of expert performers, cognitive function, and plasticity to suggest a paradigm for investigating talent as the maximal and productive use of either or both of one's high level of general intelligence or domain-specific ability. Anat Rec (Part B: New Anat) 277B:21-36, 2004. Copyright 2004 Wiley-Liss, Inc.

  6. Synchronization of indirectly coupled Lorenz oscillators: An ...

    Indian Academy of Sciences (India)

    [7], the magnetoencephalographic activity of Parkinsonian patients [8], electrosensitive cells of paddlefish [9] etc. In the context of the coupling strength and the nature of the coupling, different types of synchronizations studied in literature are: complete or iden- tical [10], in-phase [11], anti-phase [12], lag [13], generalized ...

  7. predicting the compressive strength of concretes made with granite

    African Journals Online (AJOL)

    2013-03-01

    Mar 1, 2013 ... to civil engineers and construction professionals, and would help in economically determining the strength, as well as economical selection of appropriate mix of construction materials, a prelude to building strong and cheap buildings and structures. Keywords: artificial neural network, concrete, granite, ...

  8. Forex Market Prediction Using NARX Neural Network with Bagging

    Directory of Open Access Journals (Sweden)

    Shahbazi Nima

    2016-01-01

    Full Text Available We propose a new methodfor predicting movements in Forex market based on NARX neural network withtime shifting bagging techniqueand financial indicators, such as relative strength index and stochastic indicators. Neural networks have prominent learning ability but they often exhibit bad and unpredictable performance for noisy data. When compared with the static neural networks, our method significantly reducesthe error rate of the responseandimproves the performance of the prediction. We tested three different types ofarchitecture for predicting the response and determined the best network approach. We applied our method to prediction the hourly foreign exchange rates and found remarkable predictability in comprehensive experiments with 2 different foreign exchange rates (GBPUSD and EURUSD.

  9. The strength compass

    DEFF Research Database (Denmark)

    Ledertoug, Mette Marie

    Individual paper presentation: The ‘Strength Compass’. The results of a PhDresearch project among schoolchildren (age 6-16) identifying VIAstrengths concerning age, gender, mother-tongue-langue and possible child psychiatric diagnosis. Strengths-based interventions in schools have a theoretical...... the results for strengths display for children aged 6-16 in different categories: • Different age groups – are the same strengths present in both small children and youths? • Gender – Do the results show differences between the two genders? • Danish as a mother- tongue language. Do the results show any...

  10. Prediction of properties of polymer concrete composite with tire rubber using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Diaconescu, Rodica-Mariana, E-mail: rodicamdiaconescu@yahoo.com [“Gheorghe Asachi” Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection, B-dul Prof.dr.doc. D. Mangeron 73, Iasi 700050 (Romania); Barbuta, Marinela, E-mail: barbuta31bmc@yahoo.com [“Gheorghe Asachi” Technical University of Iasi, Faculty of Civil Engineering and Services, B-dul Prof.dr.doc. D. Mangeron 1, Iasi 700050 (Romania); Harja, Maria, E-mail: maria_harja06@yahoo.com [“Gheorghe Asachi” Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection, B-dul Prof.dr.doc. D. Mangeron 73, Iasi 700050 (Romania)

    2013-11-20

    Highlights: ► Using waste a new composite material was obtained with specific characteristics. ► The objective was to maximize tire powder content with the minimum resin content. ► By direct modeling, the maximum compressive strength was obtained for 30% tire powder. ► Inverse neural modeling was used for obtaining maximum values of strengths. -- Abstract: The neural network method was used to investigate the influence of filler and resin content on the mechanical properties of polymer concrete with powdered tire waste. The mechanical strengths of 10 experimentally determined combinations using mixed epoxy resin, aggregates and tire powder as filler were optimized using direct neural modeling and inverse neural modeling, by imposing a minimum cost (content in resin). Direct neural modeling gave the optimum composition for obtaining maximum values for compressive strength, flexural strength and split tensile strength. Inverse neural modeling analyzed the possibility of obtaining maximum values of mechanical properties by variations in the dosages of the epoxy resin and tire powder. Neural network modeling generated the mixes with the lowest cost and maximum strength. The modeling method has shown that two mechanical properties can be simultaneously optimized in the investigation domain. From direct modeling, the maximum compressive strength was obtained for a composition with 0.215 (fraction weight) epoxy resin and 0.3 (fraction weight) tire powder. Maximum flexural strength was obtained for experimental values of 0.23 epoxy resin and 0.17 tire powder with a severe reduction noted for smaller resin dosages. The maximum split tensile strength was obtained for a resin dosage of 0.24 and tire powder dosage of 0.17.

  11. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  12. Frequency mode excitations in two-dimensional Hindmarsh-Rose neural networks

    Science.gov (United States)

    Tabi, Conrad Bertrand; Etémé, Armand Sylvin; Mohamadou, Alidou

    2017-05-01

    In this work, we explicitly show the existence of two frequency regimes in a two-dimensional Hindmarsh-Rose neural network. Each of the regimes, through the semi-discrete approximation, is shown to be described by a two-dimensional complex Ginzburg-Landau equation. The modulational instability phenomenon for the two regimes is studied, with consideration given to the coupling intensities among neighboring neurons. Analytical solutions are also investigated, along with their propagation in the two frequency regimes. These waves, depending on the coupling strength, are identified as breathers, impulses and trains of soliton-like structures. Although the waves in two regimes appear in some common regions of parameters, some phase differences are noticed and the global dynamics of the system is highly influenced by the values of the coupling terms. For some values of such parameters, the high-frequency regime displays modulated trains of waves, while the low-frequency dynamics keeps the original asymmetric character of action potentials. We argue that in a wide range of pathological situations, strong interactions among neurons can be responsible for some pathological states, including schizophrenia and epilepsy.

  13. Anisotropic Concrete Compressive Strength

    DEFF Research Database (Denmark)

    Gustenhoff Hansen, Søren; Jørgensen, Henrik Brøner; Hoang, Linh Cao

    2017-01-01

    When the load carrying capacity of existing concrete structures is (re-)assessed it is often based on compressive strength of cores drilled out from the structure. Existing studies show that the core compressive strength is anisotropic; i.e. it depends on whether the cores are drilled parallel...

  14. Neural correlates of treatment outcome in major depression.

    LENUS (Irish Health Repository)

    Lisiecka, Danuta

    2012-02-01

    There is a need to identify clinically useful biomarkers in major depressive disorder (MDD). In this context the functional connectivity of the orbitofrontal cortex (OFC) to other areas of the affect regulation circuit is of interest. The aim of this study was to identify neural changes during antidepressant treatment and correlates associated with the treatment outcome. In an exploratory analysis it was investigated whether functional connectivity measures moderated a response to mirtazapine and venlafaxine. Twenty-three drug-free patients with MDD were recruited from the Department of Psychiatry and Psychotherapy of the Ludwig-Maximilians University in Munich. The patients were subjected to a 4-wk randomized clinical trial with two common antidepressants, venlafaxine or mirtazapine. Functional connectivity of the OFC, derived from functional magnetic resonance imaging with an emotional face-matching task, was measured before and after the trial. Higher OFC connectivity with the left motor areas and the OFC regions prior to the trial characterized responders (p<0.05, false discovery rate). The treatment non-responders were characterized by higher OFC-cerebellum connectivity. The strength of response was positively correlated with functional coupling between left OFC and the caudate nuclei and thalami. Differences in longitudinal changes were detected between venlafaxine and mirtazapine treatment in the motor areas, cerebellum, cingulate gyrus and angular gyrus. These results indicate that OFC functional connectivity might be useful as a marker for therapy response to mirtazapine and venlafaxine and to reconstruct the differences in their mechanism of action.

  15. Fuzzy and neural control

    Science.gov (United States)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  16. A new measure for the strength of electrical synapses

    Directory of Open Access Journals (Sweden)

    Julie S Haas

    2015-09-01

    Full Text Available Electrical synapses, like chemical synapses, mediate intraneuronal communication. Electrical synapses are typically quantified by subthreshold measurements of coupling, which fall short in describing their impact on spiking activity in coupled neighbors. Here we describe a novel measurement for electrical synapse strength that directly evaluates the effect of synaptically transmitted activity on spike timing. This method, also applicable to neurotransmitter-based synapses, communicates the considerable strength of electrical synapses. For electrical synapses measured in rodent slices of the thalamic reticular nucleus, spike timing is modulated by tens of ms by activity in a coupled neighbor.

  17. What Is Neural Plasticity?

    Science.gov (United States)

    von Bernhardi, Rommy; Bernhardi, Laura Eugenín-von; Eugenín, Jaime

    2017-01-01

    "Neural plasticity" refers to the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. As the various chapters in this volume show, plasticity is a key component of neural development and normal functioning of the nervous system, as well as a response to the changing environment, aging, or pathological insult. This chapter discusses how plasticity is necessary not only for neural networks to acquire new functional properties, but also for them to remain robust and stable. The article also reviews the seminal proposals developed over the years that have driven experiments and strongly influenced concepts of neural plasticity.

  18. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  19. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V......This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...

  20. Dynamics of macro- and microscopic neural networks

    DEFF Research Database (Denmark)

    Mikkelsen, Kaare

    2014-01-01

    GN), which is a class of signals with a non-trivial low-frequency component. It is assumed that certain characteristica about the low-frequency component can yield information about the neural processes behind the signal. The method has been used in a range of different studies over the course of the past 10...... that the method continues to find use, of which examples are presented. In the second part of the thesis, numerical simulations of networks of neurons are described. To simplify the analysis, a relatively simpled neuron model - Leaky Integrate and Fire - is chosen. The strengths of the connections between...... shown that the syncronizing effect of the plasticity disappears when the strengths of the connections are frozen in time. Subsequently, the so-called ``Sisyphus'' mechanism is discussed, which is shown to cause slow fluctuations in the both the network synchronization and the strengths...

  1. Global neural pattern similarity as a common basis for categorization and recognition memory.

    Science.gov (United States)

    Davis, Tyler; Xue, Gui; Love, Bradley C; Preston, Alison R; Poldrack, Russell A

    2014-05-28

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. Copyright © 2014 the authors 0270-6474/14/347472-13$15.00/0.

  2. Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

    Science.gov (United States)

    Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

    2014-01-01

    Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

  3. Qualitative analysis and control of complex neural networks with delays

    CERN Document Server

    Wang, Zhanshan; Zheng, Chengde

    2016-01-01

    This book focuses on the stability of the dynamical neural system, synchronization of the coupling neural system and their applications in automation control and electrical engineering. The redefined concept of stability, synchronization and consensus are adopted to provide a better explanation of the complex neural network. Researchers in the fields of dynamical systems, computer science, electrical engineering and mathematics will benefit from the discussions on complex systems. The book will also help readers to better understand the theory behind the control technique and its design.

  4. Noise shaping in neural populations.

    Science.gov (United States)

    Avila Akerberg, Oscar; Chacron, Maurice J

    2009-01-01

    Many neurons display intrinsic interspike interval correlations in their spike trains. However, the effects of such correlations on information transmission in neural populations are not well understood. We quantified signal processing using linear response theory supported by numerical simulations in networks composed of two different models: One model generates a renewal process where interspike intervals are not correlated while the other generates a nonrenewal process where subsequent interspike intervals are negatively correlated. Our results show that the fractional rate of increase in information rate as a function of network size and stimulus intensity is lower for the nonrenewal model than for the renewal one. We show that this is mostly due to the lower amount of effective noise in the nonrenewal model. We also show the surprising result that coupling has opposite effects in renewal and nonrenewal networks: Excitatory (inhibitory coupling) will decrease (increase) the information rate in renewal networks while inhibitory (excitatory coupling) will decrease (increase) the information rate in nonrenewal networks. We discuss these results and their applicability to other classes of excitable systems.

  5. Bridging the divide between sensory integration and binding theory: Using a binding-like neural synchronization mechanism to model sensory enhancements during multisensory interactions.

    Science.gov (United States)

    Billock, Vincent A; Tsou, Brian H

    2014-07-01

    Neural information combination problems are ubiquitous in cognitive neuroscience. Two important disciplines, although conceptually similar, take radically different approaches to these problems. Sensory binding theory is largely grounded in synchronization of neurons responding to different aspects of a stimulus, resulting in a coherent percept. Sensory integration focuses more on the influences of the senses on each other and is largely grounded in the study of neurons that respond to more than one sense. It would be desirable to bridge these disciplines, so that insights gleaned from either could be harnessed by the other. To link these two fields, we used a binding-like oscillatory synchronization mechanism to simulate neurons in rattlesnake that are driven by one sense but modulated by another. Mutual excitatory coupling produces synchronized trains of action potentials with enhanced firing rates. The same neural synchronization mechanism models the behavior of a population of cells in cat visual cortex that are modulated by auditory activation. The coupling strength of the synchronizing neurons is crucial to the outcome; a criterion of strong coupling (kept weak enough to avoid seriously distorting action potential amplitude) results in intensity-dependent sensory enhancement-the principle of inverse effectiveness-a key property of sensory integration.

  6. Asymmetrically coupled resonators for mass sensing

    Science.gov (United States)

    Marquez, S.; Alvarez, M.; Plaza, J. A.; Villanueva, L. G.; Dominguez, C.; Lechuga, L. M.

    2017-09-01

    Mechanically coupled resonators have been applied in the last years to the development of nanomechanical mass-sensors based on the detection of the different vibration modes of the system by measuring on a single resonator. Their sensitivity and capability for detecting multiple analytes strongly depends on the design and coupling strength between the mechanically coupled resonators in an array format. We present a theoretical and experimental study of the behavior of an asymmetrically coupled array of four different resonators. These doubly clamped beam resonators are elastically coupled by an overhang region of varying length along the transversal axis of the array. The results show that parameters such as the gap between microbeams and the overhang length affect the coupling strength, tuning the system from highly disordered and highly localized (weak coupling) to highly delocalized (strong coupling). In the strong coupling and partially localized case, the distances between resonant peaks are larger, reaching higher eigenfrequency values. In this case, relative changes in a specific eigenstate, due to an added mass, can be markedly large due to the energy distribution over a single microbeam. A strong coupling also facilitates performing the detection on the relative frequency shift mode, which can usually be resolved with better precision than the amplitude changes.

  7. Neural Networks: Implementations and Applications

    NARCIS (Netherlands)

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  8. Collective isospin-spin excitations and Gamow-Teller strength

    CERN Document Server

    Gaarde, C; Goodman, C D; Goulding, C A; Horen, D J; Larsen, J S; Masterson, T; Rapaport, J; Sugarbaker, E R; Taddeucci, T N

    1981-01-01

    The (p, n) reaction at intermediate energies is a sensitive tool for the study of isospin-spin correlations in nuclei. For heavy nuclei the neutron-spectra are at forward angle dominated by transitions corresponding to excitation of collective states carrying a significant part of total sum rule strength. The zero degree spectra give information on the Gamow-Teller strength distribution. The analysis shows that only 30-50% of the strength of observed, and the coupling to the Delta -resonance could be responsible for part of the missing strength. (16 refs).

  9. Nonlinear Analysis of a Cross-Coupled Quadrature Harmonic Oscillator

    DEFF Research Database (Denmark)

    Djurhuus, Torsten; Krozer, Viktor; Vidkjær, Jens

    2004-01-01

    We derive the dynamic equations governing the cross-coupled quadrature oscillator leading to an expression for the trade-off between signal quadrature and close-in phase noise. The theory shows that nonlinearity in the coupling transconductance results in AM-PM noise close to the carrier, which...... increases with the coupling strength. The results are compared with SPECTRE RF simulations....

  10. LIPSS results for photons coupling to light neutral scalar bosons

    Energy Technology Data Exchange (ETDEWEB)

    Andrei Afanasev; Oliver K. Baker; Kevin Beard; George Biallas; James Boyce; Minarni Minarni; Roopchan Ramdon; Michelle D. Shinn; Penny Slocum

    2008-06-01

    The LIPSS search for a light neutral scalar boson coupling to optical photons is reported. The search covers a region of parameter space of approximately 1.0 meV and coupling strength greater than 10^-6 GeV^-1. The LIPSS results show no evidence for scalar coupling in this region of parameter space.

  11. Localized chaoticity in two linearly coupled inverted double-well ...

    African Journals Online (AJOL)

    Two linearly coupled inverted double-well oscillators for a fixed energy and varying coupling strength were studied. The dynamics yielded a chaotic system in which the Poincare surface was characterised by two non-mixing regions, one of regular motion and the other region that became chaotic as the coupling increased.

  12. Anisotropic Concrete Compressive Strength

    DEFF Research Database (Denmark)

    Gustenhoff Hansen, Søren; Jørgensen, Henrik Brøner; Hoang, Linh Cao

    2017-01-01

    When the load carrying capacity of existing concrete structures is (re-)assessed it is often based on compressive strength of cores drilled out from the structure. Existing studies show that the core compressive strength is anisotropic; i.e. it depends on whether the cores are drilled parallel...... correlation to the curing time. The experiments show no correlation between the anisotropy and the curing time and a small strength difference between the two drilling directions. The literature shows variations on which drilling direction that is strongest. Based on a Monto Carlo simulation of the expected...

  13. Strength of human pulleys.

    Science.gov (United States)

    Manske, P R; Lesker, P A

    1977-06-01

    The length, breaking stength, and tensile strength of each of the annular fibroosseous pulleys of digital flexor sheath in ten fresh human cadaver specimens were measured. The first annular pulley and the fourth annular pulley were found to be the strongest, while the second annular pulley was the weakest. The design of artificial pulleys should reproduce the strength of the first annular and fourth annular pulleys. Suggested minimum requirements for the breaking strength of artificial implant pulleys may be made based on these studies.

  14. Functional alterations in neural substrates of geometric reasoning in adults with high-functioning autism.

    Directory of Open Access Journals (Sweden)

    Takashi Yamada

    Full Text Available Individuals with autism spectrum condition (ASC are known to excel in some perceptual cognitive tasks, but such developed functions have been often regarded as "islets of abilities" that do not significantly contribute to broader intellectual capacities. However, recent behavioral studies have reported that individuals with ASC have advantages for performing Raven's (Standard Progressive Matrices (RPM/RSPM, a standard neuropsychological test for general fluid intelligence, raising the possibility that ASC's cognitive strength can be utilized for more general purposes like novel problem solving. Here, the brain activity of 25 adults with high-functioning ASC and 26 matched normal controls (NC was measured using functional magnetic resonance imaging (fMRI to examine neural substrates of geometric reasoning during the engagement of a modified version of the RSPM test. Among the frontal and parietal brain regions involved in fluid intelligence, ASC showed larger activation in the left lateral occipitotemporal cortex (LOTC during an analytic condition with moderate difficulty than NC. Activation in the left LOTC and ventrolateral prefrontal cortex (VLPFC increased with task difficulty in NC, whereas such modulation of activity was absent in ASC. Furthermore, functional connectivity analysis revealed a significant reduction of activation coupling between the left inferior parietal cortex and the right anterior prefrontal cortex during both figural and analytic conditions in ASC. These results indicate altered pattern of functional specialization and integration in the neural system for geometric reasoning in ASC, which may explain its atypical cognitive pattern, including performance on the Raven's Matrices test.

  15. Synchronization and redundancy: implications for robustness of neural learning and decision making.

    Science.gov (United States)

    Bouvrie, Jake; Slotine, Jean-Jacques

    2011-11-01

    Learning and decision making in the brain are key processes critical to survival, and yet are processes implemented by nonideal biological building blocks that can impose significant error. We explore quantitatively how the brain might cope with this inherent source of error by taking advantage of two ubiquitous mechanisms, redundancy and synchronization. In particular we consider a neural process whose goal is to learn a decision function by implementing a nonlinear gradient dynamics. The dynamics, however, are assumed to be corrupted by perturbations modeling the error, which might be incurred due to limitations of the biology, intrinsic neuronal noise, and imperfect measurements. We show that error, and the associated uncertainty surrounding a learned solution, can be controlled in large part by trading off synchronization strength among multiple redundant neural systems against the noise amplitude. The impact of the coupling between such redundant systems is quantified by the spectrum of the network Laplacian, and we discuss the role of network topology in synchronization and in reducing the effect of noise. We discuss range of situations in which the mechanisms we model arise in brain science and draw attention to experimental evidence suggesting that cortical circuits capable of implementing the computations of interest here can be found on several scales. Finally, simulations comparing theoretical bounds to the relevant empirical quantities show that the theoretical estimates we derive can be tight.

  16. Individual differences in speech-in-noise perception parallel neural speech processing and attention in preschoolers.

    Science.gov (United States)

    Thompson, Elaine C; Woodruff Carr, Kali; White-Schwoch, Travis; Otto-Meyer, Sebastian; Kraus, Nina

    2017-02-01

    From bustling classrooms to unruly lunchrooms, school settings are noisy. To learn effectively in the unwelcome company of numerous distractions, children must clearly perceive speech in noise. In older children and adults, speech-in-noise perception is supported by sensory and cognitive processes, but the correlates underlying this critical listening skill in young children (3-5 year olds) remain undetermined. Employing a longitudinal design (two evaluations separated by ∼12 months), we followed a cohort of 59 preschoolers, ages 3.0-4.9, assessing word-in-noise perception, cognitive abilities (intelligence, short-term memory, attention), and neural responses to speech. Results reveal changes in word-in-noise perception parallel changes in processing of the fundamental frequency (F0), an acoustic cue known for playing a role central to speaker identification and auditory scene analysis. Four unique developmental trajectories (speech-in-noise perception groups) confirm this relationship, in that improvements and declines in word-in-noise perception couple with enhancements and diminishments of F0 encoding, respectively. Improvements in word-in-noise perception also pair with gains in attention. Word-in-noise perception does not relate to strength of neural harmonic representation or short-term memory. These findings reinforce previously-reported roles of F0 and attention in hearing speech in noise in older children and adults, and extend this relationship to preschool children. Copyright © 2016 Elsevier B.V. All rights reserved.

  17. Temporal-spatial characteristics of phase-amplitude coupling in electrocorticogram for human temporal lobe epilepsy.

    Science.gov (United States)

    Zhang, Ruihua; Ren, Ye; Liu, Chunyan; Xu, Na; Li, Xiaoli; Cong, Fengyu; Ristaniemi, Tapani; Wang, YuPing

    2017-09-01

    Neural activity of the epileptic human brain contains low- and high-frequency oscillations in different frequency bands, some of which have been used as reliable biomarkers of the epileptogenic brain areas. However, the relationship between the low- and high-frequency oscillations in different cortical areas during the period from pre-seizure to post-seizure has not been completely clarified. We recorded electrocorticogram data from the temporal lobe and hippocampus of seven patients with temporal lobe epilepsy. The modulation index based on the Kullback-Leibler distance and the phase-amplitude coupling co-modulogram were adopted to quantify the coupling strength between the phase of low-frequency oscillations (0.2-10Hz) and the amplitude of high-frequency oscillations (11-400Hz) in different seizure epochs. The time-varying phase-amplitude modulogram was used to analyze the phase-amplitude coupling pattern during the entire period from pre-seizure to post-seizure in both the left and right temporal lobe and hippocampus. Channels with strong modulation index were compared with the seizure onset channels identified by the neurosurgeons and the resection channels in the clinical surgery. The phase-amplitude coupling strength (modulation index) increased significantly in the mid-seizure epoch and decrease significantly in seizure termination and post-seizure epochs (ptemporal cortex and hippocampus. The "fall-max" phase-amplitude modulation pattern, i.e., high-frequency amplitudes were largest in the low-frequency phase range [-π, 0], which corresponded to the falling edges of low-frequency oscillations, appeared in the middle period of the seizures at epileptic focus channels. Channels with strong modulation index appeared on the corresponding left or right temporal cortex of surgical resection and overlapped with the clinical resection zones in all patients. The "fall-max" pattern between the phase of low-frequency oscillation and amplitude of high

  18. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  19. Kunstige neurale net

    DEFF Research Database (Denmark)

    Hørning, Annette

    1994-01-01

    Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse.......Artiklen beskæftiger sig med muligheden for at anvende kunstige neurale net i forbindelse med datamatisk procession af naturligt sprog, specielt automatisk talegenkendelse....

  20. Fast and robust global decoupling with coupling angle modulation

    Directory of Open Access Journals (Sweden)

    Y. Luo

    2005-07-01

    Full Text Available We describe a fast and robust global decoupling scheme, coupling angle modulation. This novel technique introduces an extra rotating coupling coefficient into the coupled optics to determine the global decoupling strengths. The eigentune split is used as the observable during the modulation. The two eigentunes are tracked with a high-resolution phase locked loop tune measurement system. In the article, the principle of coupling angle modulation is presented, followed by its application to the Relativistic Heavy Ion Collider (RHIC. Coupling angle modulation coupling correction has been used for the global coupling correction on the nonstop RHIC ramp.

  1. Synchronization of Switched Neural Networks With Communication Delays via the Event-Triggered Control.

    Science.gov (United States)

    Wen, Shiping; Zeng, Zhigang; Chen, Michael Z Q; Huang, Tingwen

    2017-10-01

    This paper addresses the issue of synchronization of switched delayed neural networks with communication delays via event-triggered control. For synchronizing coupled switched neural networks, we propose a novel event-triggered control law which could greatly reduce the number of control updates for synchronization tasks of coupled switched neural networks involving embedded microprocessors with limited on-board resources. The control signals are driven by properly defined events, which depend on the measurement errors and current-sampled states. By using a delay system method, a novel model of synchronization error system with delays is proposed with the communication delays and event-triggered control in the unified framework for coupled switched neural networks. The criteria are derived for the event-triggered synchronization analysis and control synthesis of switched neural networks via the Lyapunov-Krasovskii functional method and free weighting matrix approach. A numerical example is elaborated on to illustrate the effectiveness of the derived results.

  2. Cotton genotypes selection through artificial neural networks.

    Science.gov (United States)

    Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

    2017-09-27

    Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

  3. The effects of strength training on finger strength and hand dexterity in healthy elderly individuals.

    Science.gov (United States)

    Olafsdottir, Halla B; Zatsiorsky, Vladimir M; Latash, Mark L

    2008-10-01

    We investigated the effect of 6 wk of strength training on maximal pressing (MVC) force, indexes of finger individuation (enslaving), and performance in accurate force production tests and in functional hand tests in healthy, physically fit, elderly individuals. Twelve participants (average age 76 yr) exercised with both hands. One of the hands exercised by pressing with the proximal phalanges (targeting mainly intrinsic hand muscles), whereas the other hand exercised by pressing with the finger tips (targeting mainly extrinsic hand muscles). Training led to higher MVC forces, higher enslaving indexes, and improved performance on the pegboard grooved test. Changes in an index of multi-finger force stabilizing synergy showed a significant correlation with changes in the index of force variability in the accurate force production test. Strong transfer effects were seen to the site that did not perform strength training exercise within each hand. Effects of exercise at the proximal site were somewhat stronger compared with those of exercise at the finger tips, although the differences did not reach significance level. Control tests showed that repetitive testing by itself did not significantly change the maximal finger force and enslaving. The results suggest that strength training is an effective way to improve finger strength. It can also lead to changes in finger interaction and in performance of accurate force production tasks. Adaptations at a neural level are likely to mediate the observed effects. Overall, the data suggest that strength training can also improve the hand function of less healthy elderly subjects.

  4. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  5. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  6. Investigation of sputtered iridium oxide as a stimulating/sensing material for neural prostheses

    Science.gov (United States)

    Negi, Sandeep

    Neural interface devices are developed for neuroscience and neuroprosthetics applications to record and stimulate nerve signals. Microelectrodes represent the direct interface between the biological tissue and the electronic system in neural prostheses that serve to record electrical signals from the nerves to obtain information from the natural sensors of the body or the motor fibers of the muscles. Also, the microelectrodes can inject charge into the targeted tissue to functionally excite nerves and muscles by electrical stimulation. Overall, the neural microelectrodes have to measure electrical potentials and have to exchange charge between the solid state of the electrode and the fluid state of the electrolyte in the body. Therefore, the interface between the microelectrode and biological fluid is a critical factor for the performance of the neural device. The interface properties depend mainly on the physical, electrical and chemical property of the electrode material. Even though a large selection of electrode materials has been tested for this purpose, to date no electrode material or coating process presented in scientific literature has been identified or qualified for long-term stimulation and recording neural signals. In this work, sputtered iridium oxide film (SIROF) was investigated as a potential electrode material. SIROF was deposited on the microelectrodes by reactive pulsed DC sputtering. The deposition parameters and corresponding film properties were examined and correlated with the stimulation and recording characteristics. Furthermore, for chronic applications, the stability of SIROF was investigated and stimulation protocol was determined for damage threshold of the film. The sputtering pressure was varied to obtain SIROF with desired properties. The SIROF properties were optimized based on its ability to inject charge in the tissue and its mechanical strength. The electrochemical characterization of SIROF was studied by electrochemical

  7. Strength of Fibrous Composites

    CERN Document Server

    Huang, Zheng-Ming

    2012-01-01

    "Strength of Fibrous Composites" addresses evaluation of the strength of a fibrous composite by using its constituent material properties and its fiber architecture parameters. Having gone through the book, a reader is able to predict the progressive failure behavior and ultimate strength of a fibrous laminate subjected to an arbitrary load condition in terms of the constituent fiber and matrix properties, as well as fiber geometric parameters. The book is useful to researchers and engineers working on design and analysis for composite materials. Dr. Zheng-Ming Huang is a professor at the School of Aerospace Engineering & Applied Mechanics, Tongji University, China. Mr. Ye-Xin Zhou is a PhD candidate at the Department of Mechanical Engineering, the University of Hong Kong, China.

  8. Strong Coupling Holography

    CERN Document Server

    Dvali, Gia

    2009-01-01

    We show that whenever a 4-dimensional theory with N particle species emerges as a consistent low energy description of a 3-brane embedded in an asymptotically-flat (4+d)-dimensional space, the holographic scale of high-dimensional gravity sets the strong coupling scale of the 4D theory. This connection persists in the limit in which gravity can be consistently decoupled. We demonstrate this effect for orbifold planes, as well as for the solitonic branes and string theoretic D-branes. In all cases the emergence of a 4D strong coupling scale from bulk holography is a persistent phenomenon. The effect turns out to be insensitive even to such extreme deformations of the brane action that seemingly shield 4D theory from the bulk gravity effects. A well understood example of such deformation is given by large 4D Einstein term in the 3-brane action, which is known to suppress the strength of 5D gravity at short distances and change the 5D Newton's law into the four-dimensional one. Nevertheless, we observe that the ...

  9. A review of evidence linking disrupted neural plasticity to schizophrenia.

    Science.gov (United States)

    Voineskos, Daphne; Rogasch, Nigel C; Rajji, Tarek K; Fitzgerald, Paul B; Daskalakis, Zafiris J

    2013-02-01

    The adaptations resulting from neural plasticity lead to changes in cognition and behaviour, which are strengthened through repeated exposure to the novel environment or stimulus. Learning and memory have been hypothesized to occur through modifications of the strength of neural circuits, particularly in the hippocampus and cortex. Cognitive deficits, specifically in executive functioning and negative symptoms, may be a corollary to deficits in neural plasticity. Moreover, the main excitatory and inhibitory neurotransmitters associated with neural plasticity have also been extensively investigated for their role in the cognitive deficits associated with schizophrenia. Transcranial magnetic stimulation (TMS) represents some of the most promising approaches to directly explore the physiological manifestations of neural plasticity in the human brain. Three TMS paradigms (use-dependent plasticity, paired associative stimulation, and repetitive TMS) have been used to evaluate neurophysiological measures of neural plasticity in the healthy brain and in patients with schizophrenia, and to examine the brain's responses to such stimulation. In schizophrenia, deficits in neural plasticity have been consistently shown which parallel the molecular evidence appearing to be entwined with this debilitating disorder. Such pathophysiology may underlie the learning and memory deficits that are key symptoms of this disorder and may even be a key mechanism involved in treatment with antipsychotics.

  10. Search for anomalous Wtb couplings and top FCNC in t-channel single-top-quark events

    CERN Document Server

    CMS Collaboration

    2014-01-01

    Single-top-quark events in the $t$-channel are used to probe Wtb anomalous couplings and to search for top quark Flavor Changing Neutral Current (FCNC) interactions in proton-proton collisions at $\\sqrt{s}=7$ TeV. The analyzed data correspond to an integrated luminosity of 5~fb$^{-1}$. Events with the top quark decaying into a muon, neutrino and b-quark are selected. A Bayesian neural network is used to discriminate between signal and backgrounds. The observed event yields are consistent with SM prediction, and exclusion limits at 95\\% C.L. are determined. The exclusion limits on anomalous right vector and left tensor couplings of the Wtb vertex are found to be $|f_{V}^{R}|< 0.34$ and $|f_{T}^{L}|<0.09$. In the scenarios with FCNC tcg and tug couplings, limits on the coupling strengths are found to be $\\kappa_{tug}/\\Lambda < 1.8 \\cdot 10^{-2}~ \\mathrm{TeV^{-1}},\\ \\kappa_{tcg}/\\Lambda < 5.6 \\cdot 10^{-2} ~ \\mathrm{TeV^{-1}}$ which corresponds to limits on the branching ratios $Br(t~\\rightarrow~u+g)...

  11. [A Structural Equation Model on Family Strength of Married Working Women].

    Science.gov (United States)

    Hong, Yeong Seon; Han, Kuem Sun

    2015-12-01

    The purpose of this study was to identify the effect of predictive factors related to family strength and develop a structural equation model that explains family strength among married working women. A hypothesized model was developed based on literature reviews and predictors of family strength by Yoo. This constructed model was built of an eight pathway form. Two exogenous variables included in this model were ego-resilience and family support. Three endogenous variables included in this model were functional couple communication, family stress and family strength. Data were collected using a self-report questionnaire from 319 married working women who were 30~40 of age and lived in cities of Chungnam province in Korea. Data were analyzed with PASW/WIN 18.0 and AMOS 18.0 programs. Family support had a positive direct, indirect and total effect on family strength. Family stress had a negative direct, indirect and total effect on family strength. Functional couple communication had a positive direct and total effect on family strength. These predictive variables of family strength explained 61.8% of model. The results of the study show a structural equation model for family strength of married working women and that predicting factors for family strength are family support, family stress, and functional couple communication. To improve family strength of married working women, the results of this study suggest nursing access and mediative programs to improve family support and functional couple communication, and reduce family stress.

  12. High strength alloys

    Energy Technology Data Exchange (ETDEWEB)

    Maziasz, Phillip James; Shingledecker, John Paul; Santella, Michael Leonard; Schneibel, Joachim Hugo; Sikka, Vinod Kumar; Vinegar, Harold J.; John, Randy Carl; Kim, Dong Sub

    2012-06-05

    High strength metal alloys are described herein. At least one composition of a metal alloy includes chromium, nickel, copper, manganese, silicon, niobium, tungsten and iron. System, methods, and heaters that include the high strength metal alloys are described herein. At least one heater system may include a canister at least partially made from material containing at least one of the metal alloys. At least one system for heating a subterranean formation may include a tublar that is at least partially made from a material containing at least one of the metal alloys.

  13. High strength alloys

    Energy Technology Data Exchange (ETDEWEB)

    Maziasz, Phillip James [Oak Ridge, TN; Shingledecker, John Paul [Knoxville, TN; Santella, Michael Leonard [Knoxville, TN; Schneibel, Joachim Hugo [Knoxville, TN; Sikka, Vinod Kumar [Oak Ridge, TN; Vinegar, Harold J [Bellaire, TX; John, Randy Carl [Houston, TX; Kim, Dong Sub [Sugar Land, TX

    2010-08-31

    High strength metal alloys are described herein. At least one composition of a metal alloy includes chromium, nickel, copper, manganese, silicon, niobium, tungsten and iron. System, methods, and heaters that include the high strength metal alloys are described herein. At least one heater system may include a canister at least partially made from material containing at least one of the metal alloys. At least one system for heating a subterranean formation may include a tubular that is at least partially made from a material containing at least one of the metal alloys.

  14. Hand grip strength

    DEFF Research Database (Denmark)

    Frederiksen, Henrik; Gaist, David; Petersen, Hans Christian

    2002-01-01

    in life is a major problem in terms of prevalence, morbidity, functional limitations, and quality of life. It is therefore of interest to find a phenotype reflecting physical functioning which has a relatively high heritability and which can be measured in large samples. Hand grip strength is known......-55%). A powerful design to detect genes associated with a phenotype is obtained using the extreme discordant and concordant sib pairs, of whom 28 and 77 dizygotic twin pairs, respectively, were found in this study. Hence grip strength is a suitable phenotype for identifying genetic variants of importance to mid...

  15. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  16. [Neural codes for perception].

    Science.gov (United States)

    Romo, R; Salinas, E; Hernández, A; Zainos, A; Lemus, L; de Lafuente, V; Luna, R

    This article describes experiments designed to show the neural codes associated with the perception and processing of tactile information. The results of these experiments have shown the neural activity correlated with tactile perception. The neurones of the primary somatosensory cortex (S1) represent the physical attributes of tactile perception. We found that these representations correlated with tactile perception. By means of intracortical microstimulation we demonstrated the causal relationship between S1 activity and tactile perception. In the motor areas of the frontal lobe is to be found the connection between sensorial and motor representation whilst decisions are being taken. S1 generates neural representations of the somatosensory stimuli which seen to be sufficient for tactile perception. These neural representations are subsequently processed by central areas to S1 and seem useful in perception, memory and decision making.

  17. Neural Oscillators Programming Simplified

    Directory of Open Access Journals (Sweden)

    Patrick McDowell

    2012-01-01

    Full Text Available The neurological mechanism used for generating rhythmic patterns for functions such as swallowing, walking, and chewing has been modeled computationally by the neural oscillator. It has been widely studied by biologists to model various aspects of organisms and by computer scientists and robotics engineers as a method for controlling and coordinating the gaits of walking robots. Although there has been significant study in this area, it is difficult to find basic guidelines for programming neural oscillators. In this paper, the authors approach neural oscillators from a programmer’s point of view, providing background and examples for developing neural oscillators to generate rhythmic patterns that can be used in biological modeling and robotics applications.

  18. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  19. Neural cryptography with feedback

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  20. Neural network applications

    Science.gov (United States)

    Padgett, Mary L.; Desai, Utpal; Roppel, T.A.; White, Charles R.

    1993-01-01

    A design procedure is suggested for neural networks which accommodates the inclusion of such knowledge-based systems techniques as fuzzy logic and pairwise comparisons. The use of these procedures in the design of applications combines qualitative and quantitative factors with empirical data to yield a model with justifiable design and parameter selection procedures. The procedure is especially relevant to areas of back-propagation neural network design which are highly responsive to the use of precisely recorded expert knowledge.

  1. Building Neural Net Software

    OpenAIRE

    Neto, João Pedro; Costa, José Félix

    1999-01-01

    In a recent paper [Neto et al. 97] we showed that programming languages can be translated on recurrent (analog, rational weighted) neural nets. The goal was not efficiency but simplicity. Indeed we used a number-theoretic approach to machine programming, where (integer) numbers were coded in a unary fashion, introducing a exponential slow down in the computations, with respect to a two-symbol tape Turing machine. Implementation of programming languages in neural nets turns to be not only theo...

  2. NEMEFO: NEural MEteorological FOrecast

    Energy Technology Data Exchange (ETDEWEB)

    Pasero, E.; Moniaci, W.; Meindl, T.; Montuori, A. [Polytechnic of Turin (Italy). Dept. of Electronics

    2004-07-01

    Artificial Neural Systems are a well-known technique used to classify and recognize objects. Introducing the time dimension they can be used to forecast numerical series. NEMEFO is a ''nowcasting'' tool, which uses both statistical and neural systems to forecast meteorological data in a restricted area close to a meteorological weather station in a short time range (3 hours). Ice, fog, rain are typical events which can be anticipated by NEMEFO. (orig.)

  3. Cross-education strength and activation after eccentric exercise.

    Science.gov (United States)

    Lepley, Lindsey K; Palmieri-Smith, Riann M

    2014-01-01

    the unexercised knee was noted, suggesting that strength gains may have occurred because of enhanced neural activity. This type of therapy may be a useful addition to rehabilitation programs designed to improve quadriceps strength.

  4. Delay-induced cluster patterns in coupled Cayley tree networks

    Science.gov (United States)

    Singh, A.; Jalan, S.

    2013-07-01

    We study effects of delay in diffusively coupled logistic maps on the Cayley tree networks. We find that smaller coupling values exhibit sensitiveness to value of delay, and lead to different cluster patterns of self-organized and driven types. Whereas larger coupling strengths exhibit robustness against change in delay values, and lead to stable driven clusters comprising nodes from last generation of the Cayley tree. Furthermore, introduction of delay exhibits suppression as well as enhancement of synchronization depending upon coupling strength values. To the end we discuss the importance of results to understand conflicts and cooperations observed in family business.

  5. Mechanisms of neural reorganization in chronic stroke subjects after virtual reality training.

    Science.gov (United States)

    Saleh, S; Bagce, H; Qiu, Q; Fluet, G; Merians, A; Adamovich, S; Tunik, E

    2011-01-01

    This study investigates patterns of brain reorganization in chronic stroke subjects after two weeks of robot-assisted arm and hand training in virtual reality (VR). Four subjects were studied with event-related fMRI while doing simple paretic hand finger movements before (double baseline) and after training. Bilateral hand movements were recorded and used to provide real-time feedback to subjects during scanning to eliminate performance confounds on fMRI results. The kinematic parameters of each movement were also used in the general linear model with the BOLD signal to investigate training-induced changes in neuromotor coupling. Univariate analysis showed an increase in BOLD signal in the ipsilesional hemisphere in two subjects and a decrease in activity in the other two subjects. Seed voxel based functional connectivity analysis revealed an increase in connectivity between ipsilesional motor cortex and bilateral sensorimotor cortex during finger movements in all four subjects. Hemispheric laterality index values showed a tendency to decrease reflecting a reduction in the over-dominance of the contralesional hemisphere. The study is novel in terms of 1) tracking finger movement during a motor task in the scanner, 2) monitoring motor performance during the experiment and 3) giving online visual feedback of subjects' movement. This pilot study introduces a novel approach to study neural plasticity by combining measures of regional intensity, interregional interactions (using functional connectivity analysis and hemispheric laterality index), and modulation in the strength of neuromotor coupling.

  6. Intersegmental coordination of cockroach locomotion: adaptive control of centrally coupled pattern generator circuits

    Directory of Open Access Journals (Sweden)

    Einat eFuchs

    2011-01-01

    Full Text Available Animals’ ability to demonstrate both stereotyped and adaptive locomotor behavior is largely dependent on the interplay between centrally-generated motor patterns and the sensory inputs that shape them. We utilized a combined experimental and theoretical approach to investigate the relative importance of CPG interconnections vs. intersegmental afferents in the cockroach: an animal that is renowned for rapid and stable locomotion. We simultaneously recorded coxal levator and depressor motor neurons (MN in the thoracic ganglia of Periplaneta americana, while sensory feedback was completely blocked or allowed only from one intact stepping leg. In the absence of sensory feedback, we observed a coordination pattern with consistent phase relationship that shares similarities with a double tripod gait, suggesting central, feedforward control. This intersegmental coordination pattern was then reinforced in the presence of sensory feedback from a single stepping leg. Specifically, we report on transient stabilization of phase differences between activity recorded in the middle and hind thoracic MN following individual front-leg steps, suggesting a role for afferent phasic information in the coordination of motor circuits at the different hemiganglia. Data were further analyzed using stochastic models of coupled oscillators and maximum likelihood techniques to estimate underlying physiological parameters, such as uncoupled endogenous frequencies of hemisegmental oscillators and coupling strengths and directions. We found that descending ipsilateral coupling is stronger than ascending coupling, while left-right coupling in both the meso- and meta-thoracic ganglia appear to be symmetrical. We discuss our results in comparison with recent findings in stick insects that share similar neural and body architectures, and argue that the two species may exemplify opposite extremes of a fast-slow locomotion continuum, mediated through different intersegmental

  7. The Strength Compass

    DEFF Research Database (Denmark)

    Ledertoug, Mette Marie

    present in both small children and youths? Gender: Do the results show differences between the two genders? Danish as a mother- tongue language: Do the results show any differences in the strengths display when considering different language and cultural backgrounds? Children with Special Needs: Do...

  8. Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

    Science.gov (United States)

    Safiuddin, Md.; Raman, Sudharshan N.; Abdus Salam, Md.; Jumaat, Mohd. Zamin

    2016-01-01

    Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC) containing palm oil fuel ash (POFA). The present study has used artificial neural networks (ANN) to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70%) of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE) and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R2) for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN. PMID:28773520

  9. Modeling of Compressive Strength for Self-Consolidating High-Strength Concrete Incorporating Palm Oil Fuel Ash

    Directory of Open Access Journals (Sweden)

    Md. Safiuddin

    2016-05-01

    Full Text Available Modeling is a very useful method for the performance prediction of concrete. Most of the models available in literature are related to the compressive strength because it is a major mechanical property used in concrete design. Many attempts were taken to develop suitable mathematical models for the prediction of compressive strength of different concretes, but not for self-consolidating high-strength concrete (SCHSC containing palm oil fuel ash (POFA. The present study has used artificial neural networks (ANN to predict the compressive strength of SCHSC incorporating POFA. The ANN model has been developed and validated in this research using the mix proportioning and experimental strength data of 20 different SCHSC mixes. Seventy percent (70% of the data were used to carry out the training of the ANN model. The remaining 30% of the data were used for testing the model. The training of the ANN model was stopped when the root mean square error (RMSE and the percentage of good patterns was 0.001 and ≈100%, respectively. The predicted compressive strength values obtained from the trained ANN model were much closer to the experimental values of compressive strength. The coefficient of determination (R2 for the relationship between the predicted and experimental compressive strengths was 0.9486, which shows the higher degree of accuracy of the network pattern. Furthermore, the predicted compressive strength was found very close to the experimental compressive strength during the testing process of the ANN model. The absolute and percentage relative errors in the testing process were significantly low with a mean value of 1.74 MPa and 3.13%, respectively, which indicated that the compressive strength of SCHSC including POFA can be efficiently predicted by the ANN.

  10. Weaknesses, strengths and needs in fertility care according to patients.

    Science.gov (United States)

    van Empel, Inge W H; Nelen, Willianne L D M; Tepe, Eveline T; van Laarhoven, Esther A P; Verhaak, Christianne M; Kremer, Jan A M

    2010-01-01

    The patients' role in assessing health care quality is increasingly recognized. Measuring patients' specific experiences and needs generates concrete information for care improvement, whereas satisfaction surveys only give an overoptimistic, undifferentiating picture. Therefore, this study aimed to investigate possible weaknesses, strengths and needs in fertility care by measuring patients' specific experiences. Mixed (qualitative and quantitative) methods were used to identify weaknesses, strengths and needs in fertility care. Four focus groups with 21 infertile patients were used for documenting care aspects relevant to patients. The fully transcribed qualitative results were analysed and converted into a 124-item questionnaire, to investigate whether these aspects were regarded as weaknesses, strengths or needs in fertility care. The questionnaire was distributed to 369 eligible couples attending 13 Dutch fertility clinics. Descriptive statistics were used to determine the quantity of the weaknesses, strengths and needs. Overall, 286 women (78%) and 280 men (76%) completed the questionnaire. Patients experienced many weaknesses in fertility care, mostly regarding emotional support and continuity of care. Respect and autonomy and partner involvement were considered strengths in current care. Furthermore, women expressed their need for more doctors' continuity during their treatment, and couples strongly desired to have free access to their own medical record. The questionnaire's internal consistency and construct validity were sufficient. Infertile couples experience strengths, but also many weaknesses and needs in current fertility care. Lack of patient centredness seems to be a major cause herein. Using mixed methods is a sensitive means for identifying these weaknesses and needs.

  11. Coherent Electronic Coupling versus Localization in Individual Molecular Dimers

    NARCIS (Netherlands)

    Lippitz, Markus; Hübner, Christian G.; Christ, Thomas; Eichner, Holger; Bordat, Patrice; Herrmann, Andreas; Müllen, Klaus; Basché, Thomas

    2004-01-01

    We have investigated electronic excitation transfer in individual molecular dimers by time and spectrally resolved confocal fluorescence microscopy. The single molecule measurements allow for directly probing the distribution of the electronic coupling strengths due to static disorder in the polymer

  12. Stereo, Shading, and Surfaces: Curvature Constraints Couple Neural Computations

    Science.gov (United States)

    2014-04-23

    reinforce weak or missing responses. How might con- straints ci;j be designed for such a task? Do they resemble third-order edge statistics? We apply...image into cortical coordinates [Fig. 6(b) and (d)] reveals a rich connection to Gestalt principles [121]. Good continuation [125] for curvesVthat slow... gestalt laws for the perceptual organization of contours,’’ J. Vis., vol. 2, no. 4, 2002, DOI: 10.1167/2.4.5. [28] J. H. Elder and R. M. Goldberg

  13. Artificial neural network coupled with wavelet transform for ...

    Indian Academy of Sciences (India)

    Artificial Intelligence (AI) techniques are known to have great abilities in estimating nonlinear time series where they have attracted attention from various areas including hydrologic engineer- ing. In hydrologic field, they employ available his- torical time series for simulating the system. In this aspect, ANN and some other ...

  14. Analysis of Synchronization for Coupled Hybrid Systems

    DEFF Research Database (Denmark)

    Li, Zheng; Wisniewski, Rafal

    2006-01-01

    In the control systems with coupled multi-subsystem, the subsystems might be synchronized (i.e. all the subsystems have the same operation states), which results in negative influence to the whole system. For example, in the supermarket refrigeration systems, the synchronized switch of each...... subsystem will cause low efficiency, inferior control performance and a high wear on the compressor. This paper takes the supermarket refrigeration systems as an example to analyze the synchronization and its coupling strengths of coupled hybrid systems, which may provide a base for further research...

  15. Bimanual training in stroke: How do coupling and symmetry-breaking matter?

    Directory of Open Access Journals (Sweden)

    Berton Eric

    2011-01-01

    Full Text Available Abstract Background The dramatic consequences of stroke on patient autonomy in daily living activities urged the need for new reliable therapeutic strategies. Recently, bimanual training has emerged as a promising tool to improve the functional recovery of upper-limbs in stroke patients. However, who could benefit from bimanual therapy and how it could be used as a part of a more complete rehabilitation protocol remain largely unknown. A possible reason explaining this situation is that coupling and symmetry-breaking mechanisms, two fundamental principles governing bimanual behaviour, have been largely under-explored in both research and rehabilitation in stroke. Discussion Bimanual coordination emerges as an active, task-specific assembling process where the limbs are constrained to act as a single unit by virtue of mutual coupling. Consequently, exploring, assessing, re-establishing and exploiting functional bimanual synergies following stroke, require moving beyond the classical characterization of performance of each limb in separate and isolated fashion, to study coupling signatures at both neural and behavioural levels. Grounded on the conceptual framework of the dynamic system approach to bimanual coordination, we debated on two main assumptions: 1 stroke-induced impairment of bimanual coordination might be anticipated/understood by comparing, in join protocols, changes in coupling strength and asymmetry of bimanual discrete movements observed in healthy people and those observed in stroke; 2 understanding/predicting behavioural manifestations of decrease in bimanual coupling strength and/or increase in interlimb asymmetry might constitute an operational prerequisite to adapt therapy and better target training at the specific needs of each patient. We believe that these statements draw new directions for experimental and clinical studies and contribute in promoting bimanual training as an efficient and adequate tool to facilitate the

  16. Conducting Polymers for Neural Prosthetic and Neural Interface Applications

    Science.gov (United States)

    2015-01-01

    Neural interfacing devices are an artificial mechanism for restoring or supplementing the function of the nervous system lost as a result of injury or disease. Conducting polymers (CPs) are gaining significant attention due to their capacity to meet the performance criteria of a number of neuronal therapies including recording and stimulating neural activity, the regeneration of neural tissue and the delivery of bioactive molecules for mediating device-tissue interactions. CPs form a flexible platform technology that enables the development of tailored materials for a range of neuronal diagnostic and treatment therapies. In this review the application of CPs for neural prostheses and other neural interfacing devices are discussed, with a specific focus on neural recording, neural stimulation, neural regeneration, and therapeutic drug delivery. PMID:26414302

  17. Trail making test performance in youth varies as a function of anatomical coupling between the prefrontal cortex and distributed cortical regions

    Directory of Open Access Journals (Sweden)

    Nancy Raitano Lee

    2014-07-01

    Full Text Available While researchers have gained a richer understanding of the neural correlates of executive function in adulthood, much less is known about how these abilities are represented in the developing brain and what structural brain networks underlie them. Thus, the current study examined how individual differences in executive function, as measured by the Trail Making Test (TMT, relate to structural covariance in the pediatric brain. The sample included 146 unrelated, typically developing youth (80 females, ages 9-14 years, who completed a structural MRI scan of the brain and the Halstead-Reitan TMT (intermediate form. TMT scores used to index executive function included those that evaluated set-shifting ability: Trails B time (number-letter sequencing and the difference in time between Trails B and A (number sequencing only. Anatomical coupling was measured by examining correlations between mean cortical thickness (MCT across the entire cortical ribbon and individual vertex thickness measured at ~81,000 vertices. To examine how TMT scores related to anatomical coupling strength, linear regression was utilized and the interaction between age-normed TMT scores and both age and sex-normed MCT was used to predict vertex thickness. Results revealed that stronger Trails B scores were associated with greater anatomical coupling between a large swath of prefrontal cortex and the rest of cortex. For the difference between Trails B and A, a network of regions in the frontal, temporal and parietal lobes was found to be more tightly coupled with the rest of cortex in stronger performers. This study is the first to highlight the importance of structural covariance in the prediction of individual differences in executive function skills in youth. Thus, it adds to the growing literature on the neural correlates of childhood executive functions and identifies neuroanatomic coupling as a biological substrate that may contribute to executive function and dysfunction in

  18. Strengths only or strengths and relative weaknesses? A preliminary study.

    Science.gov (United States)

    Rust, Teri; Diessner, Rhett; Reade, Lindsay

    2009-10-01

    Does working on developing character strengths and relative character weaknesses cause lower life satisfaction than working on developing character strengths only? The present study provides a preliminary answer. After 76 college students completed the Values in Action Inventory of Strengths (C. Peterson & M. E. P. Seligman, 2004), the authors randomly assigned them to work on 2 character strengths or on 1 character strength and 1 relative weakness. Combined, these groups showed significant gains on the Satisfaction With Life Scale (E. Diener, R. A. Emmons, R. J. Larsen, & S. Griffin, 1985), compared with a 32-student no-treatment group. However, there was no significant difference in gain scores between the 2-strengths group and the 1-character-strength-and-1-relative-character-weakness group. The authors discuss how focusing on relative character weaknesses (along with strengths) does not diminish-and may assist in increasing-life satisfaction.

  19. Hyperbolic Hopfield neural networks.

    Science.gov (United States)

    Kobayashi, M

    2013-02-01

    In recent years, several neural networks using Clifford algebra have been studied. Clifford algebra is also called geometric algebra. Complex-valued Hopfield neural networks (CHNNs) are the most popular neural networks using Clifford algebra. The aim of this brief is to construct hyperbolic HNNs (HHNNs) as an analog of CHNNs. Hyperbolic algebra is a Clifford algebra based on Lorentzian geometry. In this brief, a hyperbolic neuron is defined in a manner analogous to a phasor neuron, which is a typical complex-valued neuron model. HHNNs share common concepts with CHNNs, such as the angle and energy. However, HHNNs and CHNNs are different in several aspects. The states of hyperbolic neurons do not form a circle, and, therefore, the start and end states are not identical. In the quantized version, unlike complex-valued neurons, hyperbolic neurons have an infinite number of states.

  20. Neural Semantic Encoders.

    Science.gov (United States)

    Munkhdalai, Tsendsuren; Yu, Hong

    2017-04-01

    We present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory that evolves over time and maintains the understanding of input sequences through read, compose and write operations. NSE can also access multiple and shared memories. In this paper, we demonstrated the effectiveness and the flexibility of NSE on five different natural language tasks: natural language inference, question answering, sentence classification, document sentiment analysis and machine translation where NSE achieved state-of-the-art performance when evaluated on publically available benchmarks. For example, our shared-memory model showed an encouraging result on neural machine translation, improving an attention-based baseline by approximately 1.0 BLEU.

  1. Convolutional neural networks and face recognition task

    Science.gov (United States)

    Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

    2017-09-01

    Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

  2. Universal Stabilization of a Parametrically Coupled Qubit

    Science.gov (United States)

    Lu, Yao; Chakram, S.; Leung, N.; Earnest, N.; Naik, R. K.; Huang, Ziwen; Groszkowski, Peter; Kapit, Eliot; Koch, Jens; Schuster, David I.

    2017-10-01

    We autonomously stabilize arbitrary states of a qubit through parametric modulation of the coupling between a fixed frequency qubit and resonator. The coupling modulation is achieved with a tunable coupling design, in which the qubit and the resonator are connected in parallel to a superconducting quantum interference device. This allows for quasistatic tuning of the qubit-cavity coupling strength from 12 MHz to more than 300 MHz. Additionally, the coupling can be dynamically modulated, allowing for single-photon exchange in 6 ns. Qubit coherence times exceeding 20 μ s are maintained over the majority of the range of tuning, limited primarily by the Purcell effect. The parametric stabilization technique realized using the tunable coupler involves engineering the qubit bath through a combination of photon nonconserving sideband interactions realized by flux modulation, and direct qubit Rabi driving. We demonstrate that the qubit can be stabilized to arbitrary states on the Bloch sphere with a worst-case fidelity exceeding 80%.

  3. Distorted Character Recognition Via An Associative Neural Network

    Science.gov (United States)

    Messner, Richard A.; Szu, Harold H.

    1987-03-01

    The purpose of this paper is two-fold. First, it is intended to provide some preliminary results of a character recognition scheme which has foundations in on-going neural network architecture modeling, and secondly, to apply some of the neural network results in a real application area where thirty years of effort has had little effect on providing the machine an ability to recognize distorted objects within the same object class. It is the author's belief that the time is ripe to start applying in ernest the results of over twenty years of effort in neural modeling to some of the more difficult problems which seem so hard to solve by conventional means. The character recognition scheme proposed utilizes a preprocessing stage which performs a 2-dimensional Walsh transform of an input cartesian image field, then sequency filters this spectrum into three feature bands. Various features are then extracted and organized into three sets of feature vectors. These vector patterns that are stored and recalled associatively. Two possible associative neural memory models are proposed for further investigation. The first being an outer-product linear matrix associative memory with a threshold function controlling the strength of the output pattern (similar to Kohonen's crosscorrelation approach [1]). The second approach is based upon a modified version of Grossberg's neural architecture [2] which provides better self-organizing properties due to its adaptive nature. Preliminary results of the sequency filtering and feature extraction preprocessing stage and discussion about the use of the proposed neural architectures is included.

  4. Functional and structural alterations in the cingulate motor area relate to decreased fronto-striatal coupling in major depressive disorder with psychomotor disturbances

    Directory of Open Access Journals (Sweden)

    Benny eLiberg

    2014-12-01

    Full Text Available Psychomotor disturbances are a classic feature of major depressive disorders. These can manifest as lack of facial expressions and decreased speech production, reduced body posture and mobility, and slowed voluntary movement. The neural correlates of psychomotor disturbances in depression are poorly understood but it has been suggested that outputs from the cingulate motor area (CMA to striatal motor regions, including the putamen, could be involved. We used functional and structural magnetic resonance imaging to conduct a region-of-interest analysis to test the hypotheses that neural activation patterns related to motor production and gray matter volumes in the CMA would be different between depressed subjects displaying psychomotor disturbances (n=13 and matched healthy controls (n=13. In addition, we conducted a psychophysiological interaction analysis to assess the functional coupling related to self-paced finger-tapping between the caudal CMA and the posterior putamen in patients compared to controls. We found a cluster of increased neural activation, adjacent to a cluster of decreased gray matter volume in the caudal CMA in patients compared to controls. The functional coupling between the left caudal CMA and the left putamen during finger-tapping task performance was additionally decreased in patients compared to controls. In addition, the strength of the functional coupling between the left caudal CMA and the left putamen was negatively correlated with the severity of psychomotor disturbances in the patient group. In conclusion, we found converging evidence for involvement of the caudal CMA and putamen in the generation of psychomotor disturbances in depression.

  5. A neural network model for texture discrimination.

    Science.gov (United States)

    Xing, J; Gerstein, G L

    1993-01-01

    A model of texture discrimination in visual cortex was built using a feedforward network with lateral interactions among relatively realistic spiking neural elements. The elements have various membrane currents, equilibrium potentials and time constants, with action potentials and synapses. The model is derived from the modified programs of MacGregor (1987). Gabor-like filters are applied to overlapping regions in the original image; the neural network with lateral excitatory and inhibitory interactions then compares and adjusts the Gabor amplitudes in order to produce the actual texture discrimination. Finally, a combination layer selects and groups various representations in the output of the network to form the final transformed image material. We show that both texture segmentation and detection of texture boundaries can be represented in the firing activity of such a network for a wide variety of synthetic to natural images. Performance details depend most strongly on the global balance of strengths of the excitatory and inhibitory lateral interconnections. The spatial distribution of lateral connective strengths has relatively little effect. Detailed temporal firing activities of single elements in the lateral connected network were examined under various stimulus conditions. Results show (as in area 17 of cortex) that a single element's response to image features local to its receptive field can be altered by changes in the global context.

  6. The neural crest and neural crest cells: discovery and significance ...

    Indian Academy of Sciences (India)

    In this paper I provide a brief overview of the major phases of investigation into the neural crest and the major players involved, discuss how the origin of the neural crest relates to the origin of the nervous system in vertebrate embryos, discuss the impact on the germ-layer theory of the discovery of the neural crest and of ...

  7. A neural circuit covarying with social hierarchy in macaques.

    Directory of Open Access Journals (Sweden)

    MaryAnn P Noonan

    2014-09-01

    Full Text Available Despite widespread interest in social dominance, little is known of its neural correlates in primates. We hypothesized that social status in primates might be related to individual variation in subcortical brain regions implicated in other aspects of social and emotional behavior in other mammals. To examine this possibility we used magnetic resonance imaging (MRI, which affords the taking of quantitative measurements noninvasively, both of brain structure and of brain function, across many regions simultaneously. We carried out a series of tests of structural and functional MRI (fMRI data in 25 group-living macaques. First, a deformation-based morphometric (DBM approach was used to show that gray matter in the amygdala, brainstem in the vicinity of the raphe nucleus, and reticular formation, hypothalamus, and septum/striatum of the left hemisphere was correlated with social status. Second, similar correlations were found in the same areas in the other hemisphere. Third, similar correlations were found in a second data set acquired several months later from a subset of the same animals. Fourth, the strength of coupling between fMRI-measured activity in the same areas was correlated with social status. The network of subcortical areas, however, had no relationship with the sizes of individuals' social networks, suggesting the areas had a simple and direct relationship with social status. By contrast a second circuit in cortex, comprising the midsuperior temporal sulcus and anterior and dorsal prefrontal cortex, covaried with both individuals' social statuses and the social network sizes they experienced. This cortical circuit may be linked to the social cognitive processes that are taxed by life in more complex social networks and that must also be used if an animal is to achieve a high social status.

  8. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  9. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  10. Beam echoes in the presence of coupling

    Energy Technology Data Exchange (ETDEWEB)

    Gross, Axel [Case Western Reserve U.

    2017-10-03

    Transverse beam echoes could provide a new technique of measuring diusion characteristics orders of magnitude faster than the current methods; however, their interaction with many accelerator parameters is poorly understood. Using a program written in C, we explored the relationship between coupling and echo strength. We found that echoes could be generated in both dimensions, even with a dipole kick in only one dimension. We found that the echo eects are not destroyed even when there is strong coupling, falling o only at extremely high coupling values. We found that at intermediate values of skew quadrupole strength, the decoherence time of the beam is greatly increased, causing a destruction of the echo eects. We found that this is caused by a narrowing of the tune width of the particles. Results from this study will help to provide recommendations to IOTA (Integrable Optics Test Accelerator) for their upcoming echo experiment.

  11. Neuromuscular adaptation during prolonged strength training, detraining and re-strength-training in middle-aged and elderly people.

    Science.gov (United States)

    Häkkinen, K; Alen, M; Kallinen, M; Newton, R U; Kraemer, W J

    2000-09-01

    Effects of a 24-week strength training performed twice weekly (24 ST) (combined with explosive exercises) followed by either a 3-week detraining (3 DT) and a 21-week re-strength-training (21 RST) (experiment A) or by a 24-week detraining (24 DT) (experiment B) on neural activation of the agonist and antagonist leg extensors, muscle cross-sectional area (CSA) of the quadriceps femoris, maximal isometric and one repetition maximum (1-RM) strength and jumping (J) and walking (W) performances were examined. A group of middle-aged (M, 37-44 years, n = 12) and elderly (E, 62-77, n = 10) and another group of M (35-45, n = 7) and E (63-78, n = 7) served as subjects. In experiment A, the 1-RM increased substantially during 24 ST in M (27%, Pelderly during the initial training phases. Neural adaptation seemed to play a greater role than muscle hypertrophy. Short-term detraining led to only minor changes, while prolonged detraining resulted in muscle atrophy and decreased voluntary strength, but explosive jumping and walking actions in both age groups appeared to remain elevated for quite a long time by compensatory types of physical activities when performed on a regular basis.

  12. Incorporating Ideological Context in Counseling Couples Experiencing Infertility

    Science.gov (United States)

    Burnett, Judith A.; Panchal, Krishna

    2008-01-01

    This article describes the influence of ideological values on couples' experience of infertility. Contextual issues are considered in terms of how they influence medical decision making as well as emotional factors. Strength-based counseling interventions that attend to couples' diverse values are described. Last, implications for counselors,…

  13. Optimisation of hardness and tensile strength of friction stir welded ...

    African Journals Online (AJOL)

    Optimisation of hardness and tensile strength of friction stir welded AA6061 alloy using response surface methodology coupled with grey relational analysis and principle component ... Response Surface Methodology (RSM) was adopted to develop mathematical model between the response and process parameters.

  14. Strength Development for Young Adolescents

    Science.gov (United States)

    McDaniel, Larry W.; Jackson, Allen; Gaudet, Laura

    2009-01-01

    Participation in strength training is important for older children or young adolescences who wish to improve fitness or participate in sports. When designing strength training programs for our youth this age group is immature anatomically, physiologically, and psychologically. For the younger or inexperienced group the strength training activities…

  15. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  16. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  17. Neural Tube Defects

    Science.gov (United States)

    ... pregnancies each year in the United States. A baby’s neural tube normally develops into the brain and spinal cord. ... fluid in the brain. This is called hydrocephalus. Babies with this condition are treated with surgery to insert a tube (called a shunt) into the brain. The shunt ...

  18. Coupling-induced population synchronization in an excitatory population of subthreshold Izhikevich neurons.

    Science.gov (United States)

    Kim, Sang-Yoon; Lim, Woochang

    2013-12-01

    We consider an excitatory population of subthreshold Izhikevich neurons which exhibit noise-induced firings. By varying the coupling strength J, we investigate population synchronization between the noise-induced firings which may be used for efficient cognitive processing such as sensory perception, multisensory binding, selective attention, and memory formation. As J is increased, rich types of population synchronization (e.g., spike, burst, and fast spike synchronization) are found to occur. Transitions between population synchronization and incoherence are well described in terms of an order parameter [Formula: see text]. As a final step, the coupling induces oscillator death (quenching of noise-induced spikings) because each neuron is attracted to a noisy equilibrium state. The oscillator death leads to a transition from firing to non-firing states at the population level, which may be well described in terms of the time-averaged population spike rate [Formula: see text]. In addition to the statistical-mechanical analysis using [Formula: see text] and [Formula: see text], each population and individual state are also characterized by using the techniques of nonlinear dynamics such as the raster plot of neural spikes, the time series of the membrane potential, and the phase portrait. We note that population synchronization of noise-induced firings may lead to emergence of synchronous brain rhythms in a noisy environment, associated with diverse cognitive functions.

  19. Spike-train bifurcation scaling in two coupled chaotic neurons

    Energy Technology Data Exchange (ETDEWEB)

    Huerta, R.; Rabinovich, M.I. [Institute for Nonlinear Science, University of California, San Diego, La Jolla, California 92093-0402 (United States); Abarbanel, H.D. [Department of Physics and Marine Physical Laboratory, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92093-0402 (United States); Bazhenov, M. [Howard Hughes Medical Institute, The Salk Institute, Computational Neurobiology Laboratory, La Jolla, California 92037 (United States)

    1997-03-01

    We investigate the variation of the out-of-phase periodic rhythm produced by two chaotic neurons {bold (}Hindmarsh-Rose neurons [J. L. Hindmarsh and R. M. Rose, Proc. R. Soc. London B {bold 221}, 87 (1984)]{bold )} coupled by electrical and reciprocally synaptic connections. The exploration of a two-parametric bifurcation diagram, as a function of the strength of the electrical and inhibitory coupling, reveals that the periodic rhythms associated to the limit cycles bounded by saddle-node bifurcations, undergo a strong variation as a function of small changes of electrical coupling. We found that there is a scaling law for the bifurcations of the limit cycles as a function of the strength of both couplings. From the functional point of view of this mixed typed of coupling, the small variation of electrical coupling provides a high sensitivity for period regulation inside the regime of out-of-phase synchronization. {copyright} {ital 1997} {ital The American Physical Society}

  20. The fracture strength and frictional strength of Weber Sandstone

    Science.gov (United States)

    Byerlee, J.D.

    1975-01-01

    The fracture strength and frictional strength of Weber Sandstone have been measured as a function of confining pressure and pore pressure. Both the fracture strength and the frictional strength obey the law of effective stress, that is, the strength is determined not by the confining pressure alone but by the difference between the confining pressure and the pore pressure. The fracture strength of the rock varies by as much as 20 per cent depending on the cement between the grains, but the frictional strength is independent of lithology. Over the range 0 2 kb, ??=0??5 + 0??6??n. This relationship also holds for other rocks such as gabbro, dunite, serpentinite, granite and limestone. ?? 1975.

  1. Fractional dynamical model for neurovascular coupling

    KAUST Repository

    Belkhatir, Zehor

    2014-08-01

    The neurovascular coupling is a key mechanism linking the neural activity to the hemodynamic behavior. Modeling of this coupling is very important to understand the brain function but it is at the same time very complex due to the complexity of the involved phenomena. Many studies have reported a time delay between the neural activity and the cerebral blood flow, which has been described by adding a delay parameter in some of the existing models. An alternative approach is proposed in this paper, where a fractional system is used to model the neurovascular coupling. Thanks to its nonlocal property, a fractional derivative is suitable for modeling the phenomena with delay. The proposed model is coupled with the first version of the well-known balloon model, which relates the cerebral blood flow to the Blood Oxygen Level Dependent (BOLD) signal measured using functional Magnetic Resonance Imaging (fMRI). Through some numerical simulations, the properties of the fractional model are explained and some preliminary comparisons to a real BOLD data set are provided. © 2014 IEEE.

  2. Effect of spatially correlated noise on stochastic synchronization in globally coupled FitzHugh-Nagumo neuron systems

    Directory of Open Access Journals (Sweden)

    Yange Shao

    2014-01-01

    Full Text Available The phenomenon of stochastic synchronization in globally coupled FitzHugh-Nagumo (FHN neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation (DMA and direct simulation (DS. Results from DMA are in good quantitative or qualitative agreement with those from DS for weak noise intensity and larger system size. Whether the consisting single FHN neuron is staying at the resting state, subthreshold oscillatory regime, or the spiking state, our investigation shows that the synchronization ratio of the globally coupled system becomes higher as the noise correlation coefficient increases, and thus we conclude that spatial correlation has an active effect on stochastic synchronization, and the neurons can achieve complete synchronization in the sense of statistics when the noise correlation coefficient tends to one. Our investigation also discloses that the noise spatial correlation plays the same beneficial role as the global coupling strength in enhancing stochastic synchronization in the ensemble. The result might be useful in understanding the information coding mechanism in neural systems.

  3. Linking Neural and Symbolic Representation and Processing of Conceptual Structures

    Directory of Open Access Journals (Sweden)

    Frank van der Velde

    2017-08-01

    Full Text Available We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like structures. First is the Neural Blackboard Architecture (NBA, which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking, which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.

  4. Neural basis of multisensory looming signals.

    Science.gov (United States)

    Tyll, Sascha; Bonath, Björn; Schoenfeld, Mircea Ariel; Heinze, Hans-Jochen; Ohl, Frank W; Noesselt, Tömme

    2013-01-15

    Approaching or looming signals are often related to extremely relevant environmental events (e.g. threats or collisions) making these signals critical for survival. However, the neural network underlying multisensory looming processing is not yet fully understood. Using functional magnetic resonance imaging (fMRI) we identified the neural correlates of audiovisual looming processing in humans: audiovisual looming (vs. receding) signals enhance fMRI-responses in low-level visual and auditory areas plus multisensory cortex (superior temporal sulcus; plus parietal and frontal structures). When characterizing the fMRI-response profiles for multisensory looming stimuli, we found significant enhancements relative to the mean and maximum of unisensory responses in looming-sensitive visual and auditory cortex plus STS. Superadditive enhancements were observed in visual cortex. Subject-specific region-of-interest analyses further revealed superadditive response profiles within all sensory-specific looming-sensitive structures plus bilateral STS for audiovisual looming vs. summed unisensory looming conditions. Finally, we observed enhanced connectivity of bilateral STS with low-level visual areas in the context of looming processing. This enhanced coupling of STS with unisensory regions might potentially serve to enhance the salience of unisensory stimulus features and is accompanied by superadditive fMRI-responses. We suggest that this preference in neural signaling for looming stimuli effectively informs animals to avoid potential threats or collisions. Copyright © 2012 Elsevier Inc. All rights reserved.

  5. Coupling among Electroencephalogram Gamma Signals on a Short Time Scale

    Directory of Open Access Journals (Sweden)

    Michael P. McAssey

    2010-01-01

    coupling states among several signals are also identified, using a mixed multivariate beta distribution to model coupling strength across multiple gamma signals with reference to a common base signal. We first apply our variable-window method to simulated signals and compare its performance to a fixed-window approach. We then focus on gamma signals recorded in two regions of the rat hippocampus. Our results indicate that this may be a useful method for mapping coupling patterns among signals in EEG datasets.

  6. Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks

    Science.gov (United States)

    Aguiar, Manuela A. D.; Dias, Ana Paula S.; Ferreira, Flora

    2017-01-01

    We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks.

  7. Patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks.

    Science.gov (United States)

    Aguiar, Manuela A D; Dias, Ana Paula S; Ferreira, Flora

    2017-01-01

    We consider feed-forward and auto-regulation feed-forward neural (weighted) coupled cell networks. In feed-forward neural networks, cells are arranged in layers such that the cells of the first layer have empty input set and cells of each other layer receive only inputs from cells of the previous layer. An auto-regulation feed-forward neural coupled cell network is a feed-forward neural network where additionally some cells of the first layer have auto-regulation, that is, they have a self-loop. Given a network structure, a robust pattern of synchrony is a space defined in terms of equalities of cell coordinates that is flow-invariant for any coupled cell system (with additive input structure) associated with the network. In this paper, we describe the robust patterns of synchrony for feed-forward and auto-regulation feed-forward neural networks. Regarding feed-forward neural networks, we show that only cells in the same layer can synchronize. On the other hand, in the presence of auto-regulation, we prove that cells in different layers can synchronize in a robust way and we give a characterization of the possible patterns of synchrony that can occur for auto-regulation feed-forward neural networks.

  8. Role of astrocytes in neurovascular coupling.

    Science.gov (United States)

    Petzold, Gabor C; Murthy, Venkatesh N

    2011-09-08

    Neural activity is intimately tied to blood flow in the brain. This coupling is specific enough in space and time that modern imaging methods use local hemodynamics as a measure of brain activity. In this review, we discuss recent evidence indicating that neuronal activity is coupled to local blood flow changes through an intermediary, the astrocyte. We highlight unresolved issues regarding the role of astrocytes and propose ways to address them using novel techniques. Our focus is on cellular level analysis in vivo, but we also relate mechanistic insights gained from ex vivo experiments to native tissue. We also review some strategies to harness advances in optical and genetic methods to study neurovascular coupling in the intact brain. Copyright © 2011 Elsevier Inc. All rights reserved.

  9. Neural substrates of context- and person-dependent altruistic punishment.

    Science.gov (United States)

    Wang, Lili; Lu, Xiaping; Gu, Ruolei; Zhu, Ruida; Xu, Rui; Broster, Lucas S; Feng, Chunliang

    2017-11-01

    Human altruistic behaviors are heterogeneous across both contexts and people, whereas the neural signatures underlying the heterogeneity remain to be elucidated. To address this issue, we examined the neural signatures underlying the context- and person-dependent altruistic punishment, conjoining event-related fMRI with both task-based and resting-state functional connectivity (RSFC). Acting as an impartial third party, participants decided how to punish norm violators either alone or in the presence of putative others. We found that the presence of others decreased altruistic punishment due to diffusion of responsibility. Those behavioral effects paralleled altered neural responses in the dorsal anterior cingulate cortex (dACC) and putamen. Further, we identified modulation of responsibility diffusion on task-based functional connectivity of dACC with the brain regions implicated in reward processing (i.e., posterior cingulate cortex and amygdala/orbital frontal cortex). Finally, the RSFC results revealed that (i) increased intrinsic connectivity strengths of the putamen with temporoparietal junction and dorsolateral PFC were associated with attenuated responsibility diffusion in altruistic punishment and (ii) increased putamen-dorsomedial PFC connectivity strengths were associated with reduced responsibility diffusion in self-reported responsibility. Taken together, our findings elucidate the context- and person-dependent altruistic behaviors as well as associated neural substrates and thus provide a potential neurocognitive mechanism of heterogeneous human altruistic behaviors. Hum Brain Mapp 38:5535-5550, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  10. On strength of porous material

    DEFF Research Database (Denmark)

    Nielsen, Lauge Fuglsang

    1999-01-01

    The question of non-destructive testing of porous materials has always been of interest for the engineering profession. A number of empirically based MOE-MOR relations between stiffness (Modulus Of Elasticity) and strength (Modulus OF Rupture) of materials have been established in order to control...... to the theoretical research on non-destructive testing of such materials relating strength to stiffness and pore geometry.It is demonstrated that solutions for stiffness, tensile strength, and pore strength (damaging pore pressure, frost, fire) for some ideal porous materials can be determined theoretically only...... from knowing about pore geometry, solid phase stiffness, and zero-porosity strength. Pore geometry is the very important common denominator which controls both both stiffness and strength.The accurate results obtained are finally used to suggest generalizations with respect to strength in general...

  11. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Novel coupling scheme to control dynamics of coupled discrete systems

    Science.gov (United States)

    Shekatkar, Snehal M.; Ambika, G.

    2015-08-01

    We present a new coupling scheme to control spatio-temporal patterns and chimeras on 1-d and 2-d lattices and random networks of discrete dynamical systems. The scheme involves coupling with an external lattice or network of damped systems. When the system network and external network are set in a feedback loop, the system network can be controlled to a homogeneous steady state or synchronized periodic state with suppression of the chaotic dynamics of the individual units. The control scheme has the advantage that its design does not require any prior information about the system dynamics or its parameters and works effectively for a range of parameters of the control network. We analyze the stability of the controlled steady state or amplitude death state of lattices using the theory of circulant matrices and Routh-Hurwitz criterion for discrete systems and this helps to isolate regions of effective control in the relevant parameter planes. The conditions thus obtained are found to agree well with those obtained from direct numerical simulations in the specific context of lattices with logistic map and Henon map as on-site system dynamics. We show how chimera states developed in an experimentally realizable 2-d lattice can be controlled using this scheme. We propose this mechanism can provide a phenomenological model for the control of spatio-temporal patterns in coupled neurons due to non-synaptic coupling with the extra cellular medium. We extend the control scheme to regulate dynamics on random networks and adapt the master stability function method to analyze the stability of the controlled state for various topologies and coupling strengths.

  13. Water's Hydrogen Bond Strength

    CERN Document Server

    Chaplin, Martin

    2007-01-01

    Water is necessary both for the evolution of life and its continuance. It possesses particular properties that cannot be found in other materials and that are required for life-giving processes. These properties are brought about by the hydrogen bonded environment particularly evident in liquid water. Each liquid water molecule is involved in about four hydrogen bonds with strengths considerably less than covalent bonds but considerably greater than the natural thermal energy. These hydrogen bonds are roughly tetrahedrally arranged such that when strongly formed the local clustering expands, decreasing the density. Such low density structuring naturally occurs at low and supercooled temperatures and gives rise to many physical and chemical properties that evidence the particular uniqueness of liquid water. If aqueous hydrogen bonds were actually somewhat stronger then water would behave similar to a glass, whereas if they were weaker then water would be a gas and only exist as a liquid at sub-zero temperature...

  14. Failing to learn from negative prediction errors: Obesity is associated with alterations in a fundamental neural learning mechanism.

    Science.gov (United States)

    Mathar, David; Neumann, Jane; Villringer, Arno; Horstmann, Annette

    2017-10-01

    Prediction errors (PEs) encode the difference between expected and actual action outcomes in the brain via dopaminergic modulation. Integration of these learning signals ensures efficient behavioral adaptation. Obesity has recently been linked to altered dopaminergic fronto-striatal circuits, thus implying impairments in cognitive domains that rely on its integrity. 28 obese and 30 lean human participants performed an implicit stimulus-response learning paradigm inside an fMRI scanner. Computational modeling and psycho-physiological interaction (PPI) analysis was utilized for assessing PE-related learning and associated functional connectivity. We show that human obesity is associated with insufficient incorporation of negative PEs into behavioral adaptation even in a non-food context, suggesting differences in a fundamental neural learning mechanism. Obese subjects were less efficient in using negative PEs to improve implicit learning performance, despite proper coding of PEs in striatum. We further observed lower functional coupling between ventral striatum and supplementary motor area in obese subjects subsequent to negative PEs. Importantly, strength of functional coupling predicted task performance and negative PE utilization. These findings show that obesity is linked to insufficient behavioral adaptation specifically in response to negative PEs, and to associated alterations in function and connectivity within the fronto-striatal system. Recognition of neural differences as a central characteristic of obesity hopefully paves the way to rethink established intervention strategies: Differential behavioral sensitivity to negative and positive PEs should be considered when designing intervention programs. Measures relying on penalization of unwanted behavior may prove less effective in obese subjects than alternative approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Shear bond strength of acrylic teeth to acrylic denture base after different surface conditioning methods.

    Science.gov (United States)

    Madhav, Gajula Venu; Raj, Soundar; Yadav, Naveen; Mudgal, Ishitha; Mehta, Nidhi; Tatwadiya, Riddhi

    2013-09-01

    Acrylic resin ruled the dental profession for 60 years, and this success is attributed to its aesthetics, handling properties, physical and biological compatibility, its stability in oral environment and its cost effectiveness. The objective of this study is to evaluate and compare the bond strength of acrylic resin teeth treated with various conditioning materials like monomer and silane coupling agent. METHDOLOGY: A study was carried out in which 96 samples were grouped into 3 groups with a sample size of 32 each (16 premolars, 16 molars). They were conditioned with different conditioning materials i,e monomer and silane coupling agent. Monomer, Silane coupling agent are coated on the ridge lap area before thermocycling and cured according to the manufacturer recommendations. The samples are retained from the fask; trimmed and polished. The samples are then subjected to shear bond strength using the Insteron Universal Testing Machine. In the present study it was found that application of monomer increased the bond strength between acrylic teeth and denture base, when compared to the conventionally processed samples. However it was found that application of silane coupling agent further increased the shear bond strength between acrylic teeth and denture base. Interprations and Within the confnes of this study it is found that there was a signifcant improvement in the bond strength between the acrylic teeth and denture base when silane coupling agent and monomer were used as surface conditioning material. The order of shear strength of samples is control > monomer > silane coupling agent.

  16. Neural network technologies

    Science.gov (United States)

    Villarreal, James A.

    1991-01-01

    A whole new arena of computer technologies is now beginning to form. Still in its infancy, neural network technology is a biologically inspired methodology which draws on nature's own cognitive processes. The Software Technology Branch has provided a software tool, Neural Execution and Training System (NETS), to industry, government, and academia to facilitate and expedite the use of this technology. NETS is written in the C programming language and can be executed on a variety of machines. Once a network has been debugged, NETS can produce a C source code which implements the network. This code can then be incorporated into other software systems. Described here are various software projects currently under development with NETS and the anticipated future enhancements to NETS and the technology.

  17. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  18. Neural tube defects

    Directory of Open Access Journals (Sweden)

    M.E. Marshall

    1981-09-01

    Full Text Available Neural tube defects refer to any defect in the morphogenesis of the neural tube, the most common types being spina bifida and anencephaly. Spina bifida has been recognised in skeletons found in north-eastern Morocco and estimated to have an age of almost 12 000 years. It was also known to the ancient Greek and Arabian physicians who thought that the bony defect was due to the tumour. The term spina bifida was first used by Professor Nicolai Tulp of Amsterdam in 1652. Many other terms have been used to describe this defect, but spina bifida remains the most useful general term, as it describes the separation of the vertebral elements in the midline.

  19. Hypercapnic normalization of BOLD fMRI: comparison across field strengths and pulse sequences

    DEFF Research Database (Denmark)

    Cohen, Eric R.; Rostrup, Egill; Sidaros, Karam

    2004-01-01

    The blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal response to neural stimulation is influenced by many factors that are unrelated to the stimulus. These factors are physiological, such as the resting venous cerebral blood volume (CBV(v)) and vessel...... size, as well as experimental, such as pulse sequence and static magnetic field strength (B(0)). Thus, it is difficult to compare task-induced fMRI signals across subjects, field strengths, and pulse sequences. This problem can be overcome by normalizing the neural activity-induced BOLD fMRI response...... by a global hypercapnia-induced BOLD signal. To demonstrate the effectiveness of the BOLD normalization approach, gradient-echo BOLD fMRI at 1.5, 4, and 7 T and spin-echo BOLD fMRI at 4 T were performed in human subjects. For neural stimulation, subjects performed sequential finger movements at 2 Hz, while...

  20. Multiple Rabi Splittings under Ultrastrong Vibrational Coupling.

    Science.gov (United States)

    George, Jino; Chervy, Thibault; Shalabney, Atef; Devaux, Eloïse; Hiura, Hidefumi; Genet, Cyriaque; Ebbesen, Thomas W

    2016-10-07

    From the high vibrational dipolar strength offered by molecular liquids, we demonstrate that a molecular vibration can be ultrastrongly coupled to multiple IR cavity modes, with Rabi splittings reaching 24% of the vibration frequencies. As a proof of the ultrastrong coupling regime, our experimental data unambiguously reveal the contributions to the polaritonic dynamics coming from the antiresonant terms in the interaction energy and from the dipolar self-energy of the molecular vibrations themselves. In particular, we measure the opening of a genuine vibrational polaritonic band gap of ca. 60 meV. We also demonstrate that the multimode splitting effect defines a whole vibrational ladder of heavy polaritonic states perfectly resolved. These findings reveal the broad possibilities in the vibrational ultrastrong coupling regime which impact both the optical and the molecular properties of such coupled systems, in particular, in the context of mode-selective chemistry.

  1. Neural networks for triggering

    Energy Technology Data Exchange (ETDEWEB)

    Denby, B. (Fermi National Accelerator Lab., Batavia, IL (USA)); Campbell, M. (Michigan Univ., Ann Arbor, MI (USA)); Bedeschi, F. (Istituto Nazionale di Fisica Nucleare, Pisa (Italy)); Chriss, N.; Bowers, C. (Chicago Univ., IL (USA)); Nesti, F. (Scuola Normale Superiore, Pisa (Italy))

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab.

  2. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  3. Neurally-mediated sincope.

    Science.gov (United States)

    Can, I; Cytron, J; Jhanjee, R; Nguyen, J; Benditt, D G

    2009-08-01

    Syncope is a syndrome characterized by a relatively sudden, temporary and self-terminating loss of consciousness; the causes may vary, but they have in common a temporary inadequacy of cerebral nutrient flow, usually due to a fall in systemic arterial pressure. However, while syncope is a common problem, it is only one explanation for episodic transient loss of consciousness (TLOC). Consequently, diagnostic evaluation should start with a broad consideration of real or seemingly real TLOC. Among those patients in whom TLOC is deemed to be due to ''true syncope'', the focus may then reasonably turn to assessing the various possible causes; in this regard, the neurally-mediated syncope syndromes are among the most frequently encountered. There are three common variations: vasovagal syncope (often termed the ''common'' faint), carotid sinus syndrome, and the so-called ''situational faints''. Defining whether the cause is due to a neurally-mediated reflex relies heavily on careful history taking and selected testing (e.g., tilt-test, carotid massage). These steps are important. Despite the fact that neurally-mediated faints are usually relatively benign from a mortality perspective, they are nevertheless only infrequently an isolated event; neurally-mediated syncope tends to recur, and physical injury resulting from falls or accidents, diminished quality-of-life, and possible restriction from employment or avocation are real concerns. Consequently, defining the specific form and developing an effective treatment strategy are crucial. In every case the goal should be to determine the cause of syncope with sufficient confidence to provide patients and family members with a reliable assessment of prognosis, recurrence risk, and treatment options.

  4. The Neural Noisy Channel

    OpenAIRE

    Yu, Lei; Blunsom, Phil; Dyer, Chris; Grefenstette, Edward; Kocisky, Tomas

    2016-01-01

    We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct models which can suffer from explaining-away effects during training, noisy channel models must produce outputs that explain their inputs, and their component models can be trained with not only paired training samples but also unpaired samples from the marginal output distribution. Using a latent variable to control ...

  5. COLLISION STRENGTHS AND EFFECTIVE COLLISION STRENGTHS FOR TRANSITIONS WITHIN THE GROUND-STATE CONFIGURATION OF S III

    Energy Technology Data Exchange (ETDEWEB)

    Hudson, C. E.; Ramsbottom, C. A.; Scott, M. P., E-mail: c.hudson@qub.ac.uk, E-mail: c.ramsbottom@qub.ac.uk, E-mail: p.scott@qub.ac.uk [Department of Applied Maths and Theoretical Physics, The Queen' s University of Belfast, Belfast BT7 1NN (United Kingdom)

    2012-05-01

    We have carried out a 29-state R-matrix calculation in order to calculate collision strengths and effective collision strengths for the electron impact excitation of S III. The recently developed parallel RMATRX II suite of codes have been used, which perform the calculation in intermediate coupling. Collision strengths have been generated over an electron energy range of 0-12 Ryd, and effective collision strength data have been calculated from these at electron temperatures in the range 1000-100,000 K. Results are here presented for the fine-structure transitions between the ground-state configurations of 3s {sup 2}3p {sup 23} P{sub 0,1,2}, {sup 1}D{sub 2}, and {sup 1} S{sub 0}, and the values given resolve a discrepancy between two previous R-matrix calculations.

  6. Wireless Concrete Strength Monitoring of Wind Turbine Foundations

    Directory of Open Access Journals (Sweden)

    Marcus Perry

    2017-12-01

    Full Text Available Wind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete’s initial performance, and this can encourage both costly over-design and inaccurate prognoses of structural health. Here, we demonstrate the field application of a dense, wireless thermocouple network to monitor the strength development of an onshore, reinforced-concrete wind turbine foundation. Up-to-date methods in fly ash concrete strength and maturity modelling are used to estimate the distribution and evolution of foundation strength over 29 days of curing. Strength estimates are verified by core samples, extracted from the foundation base. In addition, an artificial neural network, trained using temperature data, is exploited to demonstrate that distributed concrete strengths can be estimated for foundations using only sparse thermocouple data. Our techniques provide a practical alternative to computational models, and could assist site operators in making more informed decisions about foundation design, construction, operation and maintenance.

  7. Wireless Concrete Strength Monitoring of Wind Turbine Foundations.

    Science.gov (United States)

    Perry, Marcus; Fusiek, Grzegorz; Niewczas, Pawel; Rubert, Tim; McAlorum, Jack

    2017-12-16

    Wind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete's initial performance, and this can encourage both costly over-design and inaccurate prognoses of structural health. Here, we demonstrate the field application of a dense, wireless thermocouple network to monitor the strength development of an onshore, reinforced-concrete wind turbine foundation. Up-to-date methods in fly ash concrete strength and maturity modelling are used to estimate the distribution and evolution of foundation strength over 29 days of curing. Strength estimates are verified by core samples, extracted from the foundation base. In addition, an artificial neural network, trained using temperature data, is exploited to demonstrate that distributed concrete strengths can be estimated for foundations using only sparse thermocouple data. Our techniques provide a practical alternative to computational models, and could assist site operators in making more informed decisions about foundation design, construction, operation and maintenance.

  8. Neural Based Orthogonal Data Fitting The EXIN Neural Networks

    CERN Document Server

    Cirrincione, Giansalvo

    2008-01-01

    Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

  9. A Canonical Password Strength Measure

    OpenAIRE

    Panferov, Eugene

    2015-01-01

    We notice that the "password security" discourse is missing the most fundamental notion of the "password strength" -- it was never properly defined. We propose a canonical definition of the "password strength", based on the assessment of the efficiency of a set of possible guessing attack. Unlike naive password strength assessments our metric takes into account the attacker's strategy, and we demonstrate the necessity of that feature. This paper does NOT advise you to include "at least three ...

  10. Commitee III.1 Ultimate Strength

    DEFF Research Database (Denmark)

    Jensen, Jørgen Juncher

    1997-01-01

    This report addresses the subject of ductile collapse of ships and offshore structures and their components due to buckling and excessive yielding under overload conditions. Consideration is given to load-deflection predictions for components with fabrication imperfections and in-service damage a...... and to the ultimate strength and post-ultimate behaviour of structural systems in order to identify the reserve strength. The effect of uncertainties in the modelling on the strength predictions is highlighted in two design examples....

  11. Action Potential Modulation of Neural Spin Networks Suggests Possible Role of Spin

    CERN Document Server

    Hu, H P

    2004-01-01

    In this paper we show that nuclear spin networks in neural membranes are modulated by action potentials through J-coupling, dipolar coupling and chemical shielding tensors and perturbed by microscopically strong and fluctuating internal magnetic fields produced largely by paramagnetic oxygen. We suggest that these spin networks could be involved in brain functions since said modulation inputs information carried by the neural spike trains into them, said perturbation activates various dynamics within them and the combination of the two likely produce stochastic resonance thus synchronizing said dynamics to the neural firings. Although quantum coherence is desirable and may indeed exist, it is not required for these spin networks to serve as the subatomic components for the conventional neural networks.

  12. Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control.

    Science.gov (United States)

    Guo, Zhenyuan; Yang, Shaofu; Wang, Jun

    2016-12-01

    This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Vibronic coupling in icosahedral systems

    CERN Document Server

    Huang, R

    2001-01-01

    consideration. In this thesis, an explanation based on the competition between the two tunneling paths on the lowest APES is proposed. It is assumed that tunneling occurs along paths of steepest descent. The reversal in sign of the tunneling splitting, calculated using the WKB method, in the icosahedral H x h Jahn-Teller system is explained in terms of different tunneling paths along which the system moves as the strength of the vibronic coupling K sub h sub sub 1 changes. It is found that this sign reversal occurs when K sub h sub sub 1 /(h/2 pi)w approx 4.1. This result is very near to the original result of K sub h sub sub 1 /(h/2 pi)w approx 3.8 obtained using a totally different method. This reversal can be explained as follows; for weak vibronic coupling, the H symmetry state is dominated by tunneling along the steepest descent path of C sub 1 symmetry which connects two D sub 3 sub d wells via one point on the D sub 3 symmetry saddle trough; for strong coupling, the A symmetry state is dominated by tun...

  14. Airborne field strength monitoring

    Science.gov (United States)

    Bredemeyer, J.; Kleine-Ostmann, T.; Schrader, T.; Münter, K.; Ritter, J.

    2007-06-01

    In civil and military aviation, ground based navigation aids (NAVAIDS) are still crucial for flight guidance even though the acceptance of satellite based systems (GNSS) increases. Part of the calibration process for NAVAIDS (ILS, DME, VOR) is to perform a flight inspection according to specified methods as stated in a document (DOC8071, 2000) by the International Civil Aviation Organization (ICAO). One major task is to determine the coverage, or, in other words, the true signal-in-space field strength of a ground transmitter. This has always been a challenge to flight inspection up to now, since, especially in the L-band (DME, 1GHz), the antenna installed performance was known with an uncertainty of 10 dB or even more. In order to meet ICAO's required accuracy of ±3 dB it is necessary to have a precise 3-D antenna factor of the receiving antenna operating on the airborne platform including all losses and impedance mismatching. Introducing precise, effective antenna factors to flight inspection to achieve the required accuracy is new and not published in relevant papers yet. The authors try to establish a new balanced procedure between simulation and validation by airborne and ground measurements. This involves the interpretation of measured scattering parameters gained both on the ground and airborne in comparison with numerical results obtained by the multilevel fast multipole algorithm (MLFMA) accelerated method of moments (MoM) using a complex geometric model of the aircraft. First results will be presented in this paper.

  15. Strength of Chemical Bonds

    Science.gov (United States)

    Christian, Jerry D.

    1973-01-01

    Students are not generally made aware of the extraordinary magnitude of the strengths of chemical bonds in terms of the forces required to pull them apart. Molecular bonds are usually considered in terms of the energies required to break them, and we are not astonished at the values encountered. For example, the Cl2 bond energy, 57.00 kcal/mole, amounts to only 9.46 x 10(sup -20) cal/molecule, a very small amount of energy, indeed, and impossible to measure directly. However, the forces involved in realizing the energy when breaking the bond operate over a very small distance, only 2.94 A, and, thus, f(sub ave) approx. equals De/(r - r(sub e)) must be very large. The forces involved in dissociating the molecule are discussed in the following. In consideration of average forces, the molecule shall be assumed arbitrarily to be dissociated when the atoms are far enough separated so that the potential, relative to that of the infinitely separated atoms, is reduced by 99.5% from the potential of the molecule at the equilibrium bond length (r(sub e)) for Cl2 of 1.988 A this occurs at 4.928 A.

  16. Band coupling and crossing in nuclei

    Energy Technology Data Exchange (ETDEWEB)

    Nojarov, R. (Bylgarska Akademiya na Naukite, Sofia. Inst. za Yadrena Izsledvaniya i Yadrena Energetika; Sofia Univ. (Bulgaria). Fizicheski Fakultet); Nadjakov, E. (Joint Inst. for Nuclear Research, Dubna (USSR))

    1983-03-28

    A model of coupled rotational bands, including three types of phonons, ..beta.., ..gamma.. and S(Ksup(..pi..) = 1/sup +/ or O/sup +/), is proposed and applied to a number of even-even rare earth back-bending nuclei. It reproduces the most complicated experimentally known multiple-band crossings in /sup 154/Gd, /sup 156/Dy, /sup 164/Er and the clockwise circling of the yrast B(E2) values (versus ..omega../sup 2/) in back-bending nuclei. The direct coupling strengths, derived from a fit to experimental data, are discussed in detail.

  17. Wave spectra of strongly coupled magnetized plasmas

    Science.gov (United States)

    Kaehlert, Hanno; Reynolds, Alexi; Ott, Torben; Bonitz, Michael

    2011-10-01

    Results are presented for the wave propagation in a strongly coupled, magnetized one-component plasma. For different angles of the wave vector with respect to the external magnetic field we discuss the dispersion and polarization based on the quasi-localized charge approximation (QLCA). Further, the results of the QLCA are compared with molecular dynamics simulations, extending previous results for two-dimensional systems, e.g.,. The dependence of the wave spectra on the coupling parameter and the magnetic field strength is examined. Support by the Deutsche Forschungsgemeinschaft via SFB-TR 24 and DAAD via the RISE program is acknowledged.

  18. Cancer Prevention: Distinguishing Strength of Evidence from Strength of Opinion

    Science.gov (United States)

    Barnett S. Kramer, MD, MPH, Associate Director for Disease Prevention and Director of the Office of Medical Applications of Research in the Office of Disease Prevention, Office of the Director, National Institutes of Health, Bethesda, MD, presented "Cancer Prevention: Distinguishing Strength of Evidence from Strength of Opinion".

  19. Transient absorption and lasing without inversion in an artificial molecule via Josephson coupling energy

    Science.gov (United States)

    Hamedi, Hamid Reza

    2015-03-01

    This letter investigates the dynamical behavior of the absorption in a superconducting quantum circuit with a tunable V-type artificial molecule constructed by two superconducting Josephson charge qubits coupled with each other through a superconducting quantum interference device. It is found that the ratio of the Josephson coupling energy to the capacitive coupling strength provides an extra controlling parameter for manipulating transient absorption behaviors. It is also realized that in the presence of an incoherent pumping field, lasing without inversion can be obtained just through the joint effect of the Josephson coupling energy and the capacitive coupling strength. Results may provide some new possibilities for solid-state quantum information science.

  20. Neural Correlates of Stimulus Reportability

    OpenAIRE

    Hulme, Oliver J.; Friston, Karl F.; Zeki, Semir

    2009-01-01

    Most experiments on the “neural correlates of consciousness” employ stimulus reportability as an operational definition of what is consciously perceived. The interpretation of such experiments therefore depends critically on understanding the neural basis of stimulus reportability. Using a high volume of fMRI data, we investigated the neural correlates of stimulus reportability using a partial report object detection paradigm. Subjects were presented with a random array of circularly arranged...

  1. Symbolic processing in neural networks

    OpenAIRE

    Neto, João Pedro; Hava T Siegelmann; Costa,J.Félix

    2003-01-01

    In this paper we show that programming languages can be translated into recurrent (analog, rational weighted) neural nets. Implementation of programming languages in neural nets turns to be not only theoretical exciting, but has also some practical implications in the recent efforts to merge symbolic and sub symbolic computation. To be of some use, it should be carried in a context of bounded resources. Herein, we show how to use resource bounds to speed up computations over neural nets, thro...

  2. Homogenization of heterogeneously coupled bistable ODE's - applied to excitation waves in pancreatic islets of Langerhans

    DEFF Research Database (Denmark)

    Pedersen, Morten Gram

    2004-01-01

    We consider a lattice of coupled identical differential equations. The coupling is between nearest neighbors and of resistance type, but the strength of coupling varies from site to site. Such a lattice can, for example, model an islet of Langerhans, where the sites in the lattice model individua...

  3. AM to PM noise conversion in a cross-coupled quadrature harmonic oscillator

    DEFF Research Database (Denmark)

    Djurhuus, Torsten; Krozer, Viktor; Vidkjær, Jens

    2006-01-01

    We derive the dynamic equations governing the cross-coupled quadrature oscillator, perturbed by noise, leading to an expression for the close-in phase noise. The theory shows that a nonlinear coupling transconductance results in AM-PM noise conversion close to the carrier, which increases...... with the coupling strength. A simple linear time-domain model is employed to illustrate the results...

  4. Dynamical dispersion engineering in coupled vertical cavities employing a high-contrast grating

    DEFF Research Database (Denmark)

    Taghizadeh, Alireza; Chung, Il-Sug

    2017-01-01

    strength. This can be implemented by employing a high-contrast grating (HCG) as the coupling reflector in a system of two coupled vertical cavities, and engineering both the HCG reflection phase and amplitude response. Several examples of HCG-based coupled cavities with novel features are discussed...

  5. A model for the neural control of pineal periodicity

    Science.gov (United States)

    de Oliveira Cruz, Frederico Alan; Soares, Marilia Amavel Gomes; Cortez, Celia Martins

    2016-12-01

    The aim of this work was verify if a computational model associating the synchronization dynamics of coupling oscillators to a set of synaptic transmission equations would be able to simulate the control of pineal by a complex neural pathway that connects the retina to this gland. Results from the simulations showed that the frequency and temporal firing patterns were in the range of values found in literature.

  6. [Artificial neural networks in Neurosciences].

    Science.gov (United States)

    Porras Chavarino, Carmen; Salinas Martínez de Lecea, José María

    2011-11-01

    This article shows that artificial neural networks are used for confirming the relationships between physiological and cognitive changes. Specifically, we explore the influence of a decrease of neurotransmitters on the behaviour of old people in recognition tasks. This artificial neural network recognizes learned patterns. When we change the threshold of activation in some units, the artificial neural network simulates the experimental results of old people in recognition tasks. However, the main contributions of this paper are the design of an artificial neural network and its operation inspired by the nervous system and the way the inputs are coded and the process of orthogonalization of patterns.

  7. Neural Correlates of Face Detection

    National Research Council Canada - National Science Library

    Xu, Xiaokun; Biederman, Irving

    2014-01-01

    Although face detection likely played an essential adaptive role in our evolutionary past and in contemporary social interactions, there have been few rigorous studies investigating its neural correlates...

  8. Coupling social attention to the self forms a network for personal significance.

    Science.gov (United States)

    Sui, Jie; Rotshtein, Pia; Humphreys, Glyn W

    2013-05-07

    Prior social psychological studies show that newly assigned personal significance can modulate high-level cognitive processes, e.g., memory and social evaluation, with self- and other-related information processed in dissociated prefrontal structure: ventral vs. dorsal, respectively. Here, we demonstrate the impact of personal significance on perception and show the neural network that supports this effect. We used an associative learning procedure in which we "tag" a neutral shape with a self-relevant label. Participants were instructed to associate three neutral shapes with labels for themselves, their best friend, or an unfamiliar other. Functional magnetic resonance imaging data were acquired while participants judged whether the shape-label pairs were maintained or swapped. Behaviorally, participants rapidly tagged a neutral stimulus with self-relevance, showing a robust advantage for self-tagged stimuli. Self-tagging responses were associated with enhanced activity in brain regions linked to self-representation [the ventral medial prefrontal cortex (vmPFC)] and to sensory-driven regions associated with social attention [the left posterior superior temporal sulcus (LpSTS)]. In contrast, associations formed with other people recruited a dorsal frontoparietal control network, with the two networks being inversely correlated. Responses in the vmPFC and LpSTS predicted behavioral self-bias effects. Effective connectivity analyses showed that the vmPFC and the LpSTS were functionally coupled, with the strength of coupling associated with behavioral self-biases. The data show that assignment of personal social significance affects perceptual matching by coupling internal self-representations to brain regions modulating attentional responses to external stimuli.

  9. Stimulus-dependent maximum entropy models of neural population codes.

    Directory of Open Access Journals (Sweden)

    Einat Granot-Atedgi

    Full Text Available Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. For large populations, direct sampling of these distributions is impossible, and so we must rely on constructing appropriate models. We show here that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. We introduce the stimulus-dependent maximum entropy (SDME model-a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. We find that the SDME model gives a more accurate account of single cell responses and in particular significantly outperforms uncoupled models in reproducing the distributions of population codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like average surprise and information transmission in a neural population.

  10. Neural networks within multi-core optic fibers.

    Science.gov (United States)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-07-07

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

  11. Neural networks within multi-core optic fibers

    Science.gov (United States)

    Cohen, Eyal; Malka, Dror; Shemer, Amir; Shahmoon, Asaf; Zalevsky, Zeev; London, Michael

    2016-07-01

    Hardware implementation of artificial neural networks facilitates real-time parallel processing of massive data sets. Optical neural networks offer low-volume 3D connectivity together with large bandwidth and minimal heat production in contrast to electronic implementation. Here, we present a conceptual design for in-fiber optical neural networks. Neurons and synapses are realized as individual silica cores in a multi-core fiber. Optical signals are transferred transversely between cores by means of optical coupling. Pump driven amplification in erbium-doped cores mimics synaptic interactions. We simulated three-layered feed-forward neural networks and explored their capabilities. Simulations suggest that networks can differentiate between given inputs depending on specific configurations of amplification; this implies classification and learning capabilities. Finally, we tested experimentally our basic neuronal elements using fibers, couplers, and amplifiers, and demonstrated that this configuration implements a neuron-like function. Therefore, devices similar to our proposed multi-core fiber could potentially serve as building blocks for future large-scale small-volume optical artificial neural networks.

  12. An artifical neural network for detection of simulated dental caries

    Energy Technology Data Exchange (ETDEWEB)

    Kositbowornchai, S. [Khon Kaen Univ. (Thailand). Dept. of Oral Diagnosis; Siriteptawee, S.; Plermkamon, S.; Bureerat, S. [Khon Kaen Univ. (Thailand). Dept. of Mechanical Engineering; Chetchotsak, D. [Khon Kaen Univ. (Thailand). Dept. of Industrial Engineering

    2006-08-15

    Objects: A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard. Materials and methods: The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to 'train' an artificial neural network. After the 'training' process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test. Results: The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively. Conclusions: The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries. (orig.)

  13. Strength Training for Young Athletes.

    Science.gov (United States)

    Kraemer, William J.; Fleck, Steven J.

    This guide is designed to serve as a resource for developing strength training programs for children. Chapter 1 uses research findings to explain why strength training is appropriate for children. Chapter 2 explains some of the important physiological concepts involved in children's growth and development as they apply to developing strength…

  14. Loading Conditions and Longitudinal Strength

    DEFF Research Database (Denmark)

    Sørensen, Herman

    1995-01-01

    Methods for the calculation of the lightweight of the ship.Loading conditions satisfying draught, trim and intact stability requirements and analysis of the corresponding stillwater longitudinal strength.......Methods for the calculation of the lightweight of the ship.Loading conditions satisfying draught, trim and intact stability requirements and analysis of the corresponding stillwater longitudinal strength....

  15. Phase strength and super lattices

    Indian Academy of Sciences (India)

    Unknown

    Abstract. Powder XRD investigations on dotriacontane-decane and dotriacontane-decanol mixtures are made. Phase strength, phase separation and formation of superlattices are discussed. The role of tunnel-like defects is considered. Keywords. Hydrocarbons; mixtures; phase strength; tunnel-like defects; super lattices. 1.

  16. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  17. A laboratory experiment on coupled non-identical pendulums

    Energy Technology Data Exchange (ETDEWEB)

    Li Ang; Zeng Jingyi; Yang Hujiang; Xiao Jinghua, E-mail: yanghj@bupt.edu.cn, E-mail: jhxiao@bupt.edu.cn [School of Science, Beijing University of Posts and Telecommunications, Beijing 100876 (China)

    2011-09-15

    In this paper, coupled pendulums with different lengths are studied. Through steel magnets, each pendulum is coupled with others, and a stepping motor is used to drive the whole system. To record the data automatically, we designed a data acquisition system with a CCD camera connected to a computer. The coupled system shows in-phase, locked-phase and anti-phase synchronizations when the driving frequency and the coupling strength are changed. With background knowledge from general physics and the simplicity of the equipment, this experiment is easy to implement and would be of interest to undergraduate students.

  18. STRENGTH OF NANOMODIFIED HIGH-STRENGTH LIGHTWEIGHT CONCRETES

    Directory of Open Access Journals (Sweden)

    NOZEMTСEV Alexandr Sergeevich

    2013-02-01

    Full Text Available The paper presents the results of research aimed at development of nanomodified high-strength lightweight concrete for construction. The developed concretes are of low average density and high ultimate compressive strength. It is shown that to produce this type of concrete one need to use hollow glass and aluminosilicate microspheres. To increase the durability of adhesion between cement stone and fine filler the authors offer to use complex nanodimensinal modifier based on iron hydroxide sol and silica sol as a surface nanomodifier for hollow microspheres. It is hypothesized that the proposed modifier has complex effect on the activity of the cement hydration and, at the same time increases bond strength between filler and cement-mineral matrix. The compositions for energy-efficient nanomodified high-strength lightweight concrete which density is 1300…1500 kg/m³ and compressive strength is 40…65 MPa have been developed. The approaches to the design of high-strength lightweight concrete with density of less than 2000 kg/m³ are formulated. It is noted that the proposed concretes possess dense homogeneous structure and moderate mobility. Thus, they allow processing by vibration during production. The economic and practical implications for realization of high-strength lightweight concrete in industrial production have been justified.

  19. Strength properties of fly ash based controlled low strength materials.

    Science.gov (United States)

    Türkel, S

    2007-08-25

    Controlled low strength material (CLSM) is a flowable mixture that can be used as a backfill material in place of compacted soils. Flowable fill requires no tamping or compaction to achieve its strength and typically has a load carrying capacity much higher than compacted soils, but it can still be excavated easily. The selection of CLSM type should be based on technical and economical considerations for specific applications. In this study, a mixture of high volume fly ash (FA), crushed limestone powder (filler) and a low percentage of pozzolana cement have been tried in different compositions. The amount of pozzolana cement was kept constant for all mixes as, 5% of fly ash weight. The amount of mixing water was chosen in order to provide optimum pumpability by determining the spreading ratio of CLSM mixtures using flow table method. The shear strength of the material is a measure of the materials ability to support imposed stresses on the material. The shear strength properties of CLSM mixtures have been investigated by a series of laboratory tests. The direct shear test procedure was applied for determining the strength parameters Phi (angle of shearing resistance) and C(h) (cohesion intercept) of the material. The test results indicated that CLSM mixtures have superior shear strength properties compared to compacted soils. Shear strength, cohesion intercept and angle of shearing resistance values of CLSM mixtures exceeded conventional soil materials' similar properties at 7 days. These parameters proved that CLSM mixtures are suitable materials for backfill applications.

  20. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  1. A linear model for characterization of synchronization frequencies of neural networks.

    Science.gov (United States)

    Lv, Peili; Hu, Xintao; Lv, Jinglei; Han, Junwei; Guo, Lei; Liu, Tianming

    2014-02-01

    The synchronization frequency of neural networks and its dynamics have important roles in deciphering the working mechanisms of the brain. It has been widely recognized that the properties of functional network synchronization and its dynamics are jointly determined by network topology, network connection strength, i.e., the connection strength of different edges in the network, and external input signals, among other factors. However, mathematical and computational characterization of the relationships between network synchronization frequency and these three important factors are still lacking. This paper presents a novel computational simulation framework to quantitatively characterize the relationships between neural network synchronization frequency and network attributes and input signals. Specifically, we constructed a series of neural networks including simulated small-world networks, real functional working memory network derived from functional magnetic resonance imaging, and real large-scale structural brain networks derived from diffusion tensor imaging, and performed synchronization simulations on these networks via the Izhikevich neuron spiking model. Our experiments demonstrate that both of the network synchronization strength and synchronization frequency change according to the combination of input signal frequency and network self-synchronization frequency. In particular, our extensive experiments show that the network synchronization frequency can be represented via a linear combination of the network self-synchronization frequency and the input signal frequency. This finding could be attributed to an intrinsically-preserved principle in different types of neural systems, offering novel insights into the working mechanism of neural systems.

  2. Photon strength function of 97Zr

    Science.gov (United States)

    Mosby, Shea; Couture, Aaron; Lee, Hye Young

    2015-10-01

    Some of the major questions in stockpile stewardship require nuclear reaction rates on fission fragments where there are few or no experimental constraints. Theoretical calculations are an alternative, but their reliability is ultimately limited by our incomplete understanding of such physics inputs as the photon strength function. 96Zr lies near the light mass peak for 239Pu fission, and neutron capture on and near this nucleus is of great importance for applications. The DANCE array at LANSCE and the Apollo array coupled to HELIOS at Argonne National Laboratory offer complementary probes into the neutron capture reaction, and an experimental campaign is underway to study 96Zr(n, γ) and 96Zr(d , p) with these instruments. The status of these reaction studies will be presented.

  3. Artificial Neural Networks·

    Indian Academy of Sciences (India)

    differences between biological neural networks (BNNs) of the brain and ANN s. A thorough understanding of ... neurons. Artificial neural models are loosely based on biology since a complete understanding of the .... A learning scheme for updating a neuron's connections (weights) was proposed by Donald Hebb in 1949.

  4. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  5. The Neural Support Vector Machine

    NARCIS (Netherlands)

    Wiering, Marco; van der Ree, Michiel; Embrechts, Mark; Stollenga, Marijn; Meijster, Arnold; Nolte, A; Schomaker, Lambertus

    2013-01-01

    This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a

  6. Knee Pain during Strength Training Shortly following Fast-Track Total Knee Arthroplasty

    DEFF Research Database (Denmark)

    Bandholm, Thomas; Thorborg, Kristian; Lunn, Troels Haxholdt

    2014-01-01

    BACKGROUND: Loading and contraction failure (muscular exhaustion) are strength training variables known to influence neural activation of the exercising muscle in healthy subjects, which may help reduce neural inhibition of the quadriceps muscle following total knee arthroplasty (TKA). It is unkn......BACKGROUND: Loading and contraction failure (muscular exhaustion) are strength training variables known to influence neural activation of the exercising muscle in healthy subjects, which may help reduce neural inhibition of the quadriceps muscle following total knee arthroplasty (TKA......). It is unknown how these exercise variables influence knee pain after TKA. OBJECTIVE: To investigate the effect of loading and contraction failure on knee pain during strength training, shortly following TKA. DESIGN: Cross-sectional study. SETTING: Consecutive sample of patients from the Copenhagen area, Denmark......, receiving a TKA, between November 2012 and April 2013. PARTICIPANTS: Seventeen patients, no more than 3 weeks after their TKA. MAIN OUTCOME MEASURES: In a randomized order, the patients performed 1 set of 4 standardized knee extensions, using relative loads of 8, 14, and 20 repetition maximum (RM...

  7. The Neural Correlates of Race

    Science.gov (United States)

    Ito, Tiffany A.; Bartholow, Bruce D.

    2009-01-01

    Behavioral analyses are a natural choice for understanding the wide-ranging behavioral consequences of racial stereotyping and prejudice. However, neuroimaging and electrophysiological research has recently considered the neural mechanisms that underlie racial categorization and the activation and application of racial stereotypes and prejudice, revealing exciting new insights. Work reviewed here points to the importance of neural structures previously associated with face processing, semantic knowledge activation, evaluation, and self-regulatory behavioral control, allowing for the specification of a neural model of race processing. We show how research on the neural correlates of race can serve to link otherwise disparate lines of evidence on the neural underpinnings of a broad array of social-cognitive phenomena, and consider implications for effecting change in race relations. PMID:19896410

  8. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... examined, and it appears that considering 'normal' neural network models with, say, 500 samples, the problem of over-fitting is neglible, and therefore it is not taken into consideration afterwards. Numerous model types, often met in control applications, are implemented as neural network models...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  9. Electromagnetic clutches and couplings

    CERN Document Server

    Vorob'Yeva, T M; Fry, D W; Higinbotham, W

    2013-01-01

    Electromagnetic Clutches and Couplings contains a detailed description of U.S.S.R. electromagnetic friction clutches, magnetic couplings, and magnetic particle couplings. This book is divided into four chapters. The first chapter discusses the design and construction of magnetic (solenoid-operated) couplings, which are very quick-acting devices and used in low power high-speed servo-systems. Chapter 2 describes the possible fields of application, design, construction, and utilization of magnetic particle couplings. The aspects of construction, design, and utilization of induction clutches (sli

  10. Precipitation Nowcast using Deep Recurrent Neural Network

    Science.gov (United States)

    Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

    2016-12-01

    An accurate precipitation nowcast (0-6 hours) with a fine temporal and spatial resolution has always been an important prerequisite for flood warning, streamflow prediction and risk management. Most of the popular approaches used for forecasting precipitation can be categorized into two groups. One type of precipitation forecast relies on numerical modeling of the physical dynamics of atmosphere and another is based on empirical and statistical regression models derived by local hydrologists or meteorologists. Given the recent advances in artificial intelligence, in this study a powerful Deep Recurrent Neural Network, termed as Long Short-Term Memory (LSTM) model, is creatively used to extract the patterns and forecast the spatial and temporal variability of Cloud Top Brightness Temperature (CTBT) observed from GOES satellite. Then, a 0-6 hours precipitation nowcast is produced using a Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) algorithm, in which the CTBT nowcast is used as the PERSIANN algorithm's raw inputs. Two case studies over the continental U.S. have been conducted that demonstrate the improvement of proposed approach as compared to a classical Feed Forward Neural Network and a couple simple regression models. The advantages and disadvantages of the proposed method are summarized with regard to its capability of pattern recognition through time, handling of vanishing gradient during model learning, and working with sparse data. The studies show that the LSTM model performs better than other methods, and it is able to learn the temporal evolution of the precipitation events through over 1000 time lags. The uniqueness of PERSIANN's algorithm enables an alternative precipitation nowcast approach as demonstrated in this study, in which the CTBT prediction is produced and used as the inputs for generating precipitation nowcast.

  11. Travelling waves in models of neural tissue: from localised structures to periodic waves

    NARCIS (Netherlands)

    Meijer, Hil Gaétan Ellart; Coombes, Stephen

    2014-01-01

    We consider travelling waves (fronts, pulses and periodics) in spatially extended one dimensional neural field models. We demonstrate for an excitatory field with linear adaptation that, in addition to an expected stable pulse solution, a stable anti-pulse can exist. Varying the adaptation strength

  12. Prediction of Torsional Strength for Very High Early Strength Geopolymer

    Directory of Open Access Journals (Sweden)

    Woraphot PRACHASAREE

    2017-11-01

    Full Text Available Very early high strength geopolymers are gaining acceptance as alternative repair materials for highways and other infrastructure. In this study, a very rapid geopolymer binder based on Metakaolin (MK and Parawood ash (PWA, developed by the authors, was experimentally tested and a prediction model for its torsional strength is proposed. The geopolymer samples were subjected to uniaxial compression, flexural beam, and torsion tests. The modulus of rupture and torsional strength in terms of compression strength were found to be well approximated by 0.7(f’c1/2 and 1/7(x2y (f’c1/2, respectively. Also an interaction relation to describe combined bending and torsion was developed in this study. In addition, the effects of aspect ratio (y/x were studied on both torsional strength and combined bending and torsion. It was found that an aspect ratio of y/x = 3 significantly reduced the torsional resistance, to about 50 % of the torsional strength of a square section.DOI: http://dx.doi.org/10.5755/j01.ms.23.4.17280

  13. Rapid communication: Transverse spin with coupled plasmons

    Science.gov (United States)

    Mukherjee, Samyobrata; Gopal, A. V.; Gupta, S. Dutta

    2017-08-01

    We study theoretically the transverse spin associated with the eigenmodes of a thin metal film embedded in a dielectric. We show that the transverse spin has a direct dependence on the nature and strength of the coupling leading to two distinct branches for the long- and short-range modes. We show that the short-range mode exhibits larger extraordinary spin because of its more `structured' nature due to higher decay in propagation. In contrast to some of the earlier studies, calculations are performed retaining the full lossy character of the metal. In the limit of vanishing losses, we present analytical results for the extraordinary spin for both the coupled modes. The results can have direct implications for enhancing the elusive transverse spin exploiting the coupled plasmon structures.

  14. Human neural progenitors express functional lysophospholipid receptors that regulate cell growth and morphology

    Directory of Open Access Journals (Sweden)

    Callihan Phillip

    2008-12-01

    Full Text Available Abstract Background Lysophospholipids regulate the morphology and growth of neurons, neural cell lines, and neural progenitors. A stable human neural progenitor cell line is not currently available in which to study the role of lysophospholipids in human neural development. We recently established a stable, adherent human embryonic stem cell-derived neuroepithelial (hES-NEP cell line which recapitulates morphological and phenotypic features of neural progenitor cells isolated from fetal tissue. The goal of this study was to determine if hES-NEP cells express functional lysophospholipid receptors, and if activation of these receptors mediates cellular responses critical for neural development. Results Our results demonstrate that Lysophosphatidic Acid (LPA and Sphingosine-1-phosphate (S1P receptors are functionally expressed in hES-NEP cells and are coupled to multiple cellular signaling pathways. We have shown that transcript levels for S1P1 receptor increased significantly in the transition from embryonic stem cell to hES-NEP. hES-NEP cells express LPA and S1P receptors coupled to Gi/o G-proteins that inhibit adenylyl cyclase and to Gq-like phospholipase C activity. LPA and S1P also induce p44/42 ERK MAP kinase phosphorylation in these cells and stimulate cell proliferation via Gi/o coupled receptors in an Epidermal Growth Factor Receptor (EGFR- and ERK-dependent pathway. In contrast, LPA and S1P stimulate transient cell rounding and aggregation that is independent of EGFR and ERK, but dependent on the Rho effector p160 ROCK. Conclusion Thus, lysophospholipids regulate neural progenitor growth and morphology through distinct mechanisms. These findings establish human ES cell-derived NEP cells as a model system for studying the role of lysophospholipids in neural progenitors.

  15. Strength training and shoulder proprioception

    National Research Council Canada - National Science Library

    Salles, José Inácio; Velasques, Bruna; Cossich, Victor; Nicoliche, Eduardo; Ribeiro, Pedro; Amaral, Marcus Vinicius; Motta, Geraldo

    2015-01-01

    .... To evaluate the result of an 8-week strength-training program on shoulder JPS and to verify whether using training intensities that are the same or divergent for the shoulder's dynamic-stabilizer...

  16. Muscle Strength and Poststroke Hemiplegia

    DEFF Research Database (Denmark)

    Kristensen, Otto H; Stenager, Egon; Dalgas, Ulrik

    2017-01-01

    OBJECTIVES: To systematically review (1) psychometric properties of criterion isokinetic dynamometry testing of muscle strength in persons with poststroke hemiplegia (PPSH); and (2) literature that compares muscle strength in patients poststroke with that in healthy controls assessed by criterion...... isokinetic dynamometry. DATA SOURCES: A systematic literature search of 7 databases was performed. STUDY SELECTION: Included studies (1) enrolled participants with definite poststroke hemiplegia according to defined criteria; (2) assessed muscle strength or power by criterion isokinetic dynamometry; (3) had...... undergone peer review; and (4) were available in English or Danish. DATA EXTRACTION: The psychometric properties of isokinetic dynamometry were reviewed with respect to reliability, validity, and responsiveness. Furthermore, comparisons of strength between paretic, nonparetic, and comparable healthy muscles...

  17. Strengths, weaknesses, opportunities and threats

    DEFF Research Database (Denmark)

    Bull, Joseph William; Jobstvogt, N.; Böhnke-Henrichs, A.

    2016-01-01

    The ecosystem services concept (ES) is becoming a cornerstone of contemporary sustainability thought. Challenges with this concept and its applications are well documented, but have not yet been systematically assessed alongside strengths and external factors that influence uptake. Such an assess......The ecosystem services concept (ES) is becoming a cornerstone of contemporary sustainability thought. Challenges with this concept and its applications are well documented, but have not yet been systematically assessed alongside strengths and external factors that influence uptake....... Such an assessment could form the basis for improving ES thinking, further embedding it into environmental decisions and management.The Young Ecosystem Services Specialists (YESS) completed a Strengths-Weaknesses-Opportunities-Threats (SWOT) analysis of ES through YESS member surveys. Strengths include the approach...

  18. Particle Strength of Bayer Hydrate

    Science.gov (United States)

    Anjier, J. L.; Marten, D. F. G.

    Because of the proposed use of fluid bed calciners at the Kaiser Aluminum Baton Rouge Works, studies into the strength of alumina and alumina trihydrate from eight different alumina plants were initiated. It was found in the course of these studies that the particle strength of Bayer hydrate depended on the precipitation process conditions under which it was produced. A series of laboratory precipitation tests was conducted to determine the effect on particle strength of process variables such as seed charge, temperature, caustic concentration and seed recycle. It is concluded from these studies that relative particle strength of alumina trihydrate, as measured by a modified Forsythe-Hertwig Apparatus, can be predicted from a knowledge of the precipitation process conditions.

  19. An Optoelectronic Neural Network

    Science.gov (United States)

    Neil, Mark A. A.; White, Ian H.; Carroll, John E.

    1990-02-01

    We describe and present results of an optoelectronic neural network processing system. The system uses an algorithm based on the Hebbian learning rule to memorise a set of associated vector pairs. Recall occurs by the processing of the input vector with these stored associations in an incoherent optical vector multiplier using optical polarisation rotating liquid crystal spatial light modulators to store the vectors and an optical polarisation shadow casting technique to perform multiplications. Results are detected on a photodiode array and thresholded electronically by a controlling microcomputer. The processor is shown to work in autoassociative and heteroassociative modes with up to 10 stored memory vectors of length 64 (equivalent to 64 neurons) and a cycle time of 50ms. We discuss the limiting factors at work in this system, how they affect its scalability and the general applicability of its principles to other systems.

  20. Neural Darwinism and consciousness.

    Science.gov (United States)

    Seth, Anil K; Baars, Bernard J

    2005-03-01

    Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.

  1. Cortical neural prosthetics.

    Science.gov (United States)

    Schwartz, Andrew B

    2004-01-01

    Control of prostheses using cortical signals is based on three elements: chronic microelectrode arrays, extraction algorithms, and prosthetic effectors. Arrays of microelectrodes are permanently implanted in cerebral cortex. These arrays must record populations of single- and multiunit activity indefinitely. Information containing position and velocity correlates of animate movement needs to be extracted continuously in real time from the recorded activity. Prosthetic arms, the current effectors used in this work, need to have the agility and configuration of natural arms. Demonstrations using closed-loop control show that subjects change their neural activity to improve performance with these devices. Adaptive-learning algorithms that capitalize on these improvements show that this technology has the capability of restoring much of the arm movement lost with immobilizing deficits.

  2. Predicting company growth using logistic regression and neural networks

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2016-12-01

    Full Text Available The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre -processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non -parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.

  3. THE USE OF NEURAL NETWORK TECHNOLOGY TO MODEL SWIMMING PERFORMANCE

    Directory of Open Access Journals (Sweden)

    António José Silva

    2007-03-01

    Full Text Available The aims of the present study were: to identify the factors which are able to explain the performance in the 200 meters individual medley and 400 meters front crawl events in young swimmers, to model the performance in those events using non-linear mathematic methods through artificial neural networks (multi-layer perceptrons and to assess the neural network models precision to predict the performance. A sample of 138 young swimmers (65 males and 73 females of national level was submitted to a test battery comprising four different domains: kinanthropometric evaluation, dry land functional evaluation (strength and flexibility, swimming functional evaluation (hydrodynamics, hydrostatic and bioenergetics characteristics and swimming technique evaluation. To establish a profile of the young swimmer non-linear combinations between preponderant variables for each gender and swim performance in the 200 meters medley and 400 meters font crawl events were developed. For this purpose a feed forward neural network was used (Multilayer Perceptron with three neurons in a single hidden layer. The prognosis precision of the model (error lower than 0.8% between true and estimated performances is supported by recent evidence. Therefore, we consider that the neural network tool can be a good approach in the resolution of complex problems such as performance modeling and the talent identification in swimming and, possibly, in a wide variety of sports

  4. Estimation of Effectivty Connectivity via Data-Driven Neural Modeling

    Directory of Open Access Journals (Sweden)

    Dean Robert Freestone

    2014-11-01

    Full Text Available This research introduces a new method for functional brain imaging via a process of model inversion. By estimating parameters of a computational model, we are able to track effective connectivity and mean membrane potential dynamics that cannot be directly measured using electrophysiological measurements alone. The ability to track the hidden aspects of neurophysiology will have a profound impact on the way we understand and treat epilepsy. For example, under the assumption the model captures the key features of the cortical circuits of interest, the framework will provide insights into seizure initiation and termination on a patient-specific basis. It will enable investigation into the effect a particular drug has on specific neural populations and connectivity structures using minimally invasive measurements. The method is based on approximating brain networks using an interconnected neural population model. The neural population model is based on a neural mass model that describes the functional activity of the brain, capturing the mesoscopic biophysics and anatomical structure. The model is made subject-specific by estimating the strength of intra-cortical connections within a region and inter-cortical connections between regions using a novel Kalman filtering method. We demonstrate through simulation how the framework can be used the track the mechanisms involved in seizure initiation and termination.

  5. Hadoop neural network for parallel and distributed feature selection.

    Science.gov (United States)

    Hodge, Victoria J; O'Keefe, Simon; Austin, Jim

    2016-06-01

    In this paper, we introduce a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection in Big Data sets. It is underpinned by an associative memory (binary) neural network which is highly amenable to parallel and distributed processing and fits with the Hadoop paradigm. There are many feature selectors described in the literature which all have various strengths and weaknesses. We present the implementation details of five feature selection algorithms constructed using our artificial neural network framework embedded in Hadoop YARN. Hadoop allows parallel and distributed processing. Each feature selector can be divided into subtasks and the subtasks can then be processed in parallel. Multiple feature selectors can also be processed simultaneously (in parallel) allowing multiple feature selectors to be compared. We identify commonalities among the five features selectors. All can be processed in the framework using a single representation and the overall processing can also be greatly reduced by only processing the common aspects of the feature selectors once and propagating these aspects across all five feature selectors as necessary. This allows the best feature selector and the actual features to select to be identified for large and high dimensional data sets through exploiting the efficiency and flexibility of embedding the binary associative-memory neural network in Hadoop. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  6. Relationship between the edgewise compression strength of ...

    African Journals Online (AJOL)

    The compression strength of a corrugated board box is a direct measure of its stacking strength. The edgewise compression strength of corrugated board is the major contributor to the box stacking strength. This relation can be further extended to the critical strength properties of paper substrates. It was, therefore, the aim of ...

  7. Synchronization transition in gap-junction-coupled leech neurons

    Science.gov (United States)

    Wang, Qingyun; Duan, Zhisheng; Feng, Zhaosheng; Chen, Guanrong; Lu, Qishao

    2008-07-01

    Real neurons can exhibit various types of firings including tonic spiking, bursting as well as silent state, which are frequently observed in neuronal electrophysiological experiments. More interestingly, it is found that neurons can demonstrate the co-existing mode of stable tonic spiking and bursting, which depends on initial conditions. In this paper, synchronization in gap-junction-coupled neurons with co-existing attractors of spiking and bursting firings is investigated as the coupling strength gets increased. Synchronization transitions can be identified by means of the bifurcation diagram and the correlation coefficient. It is illustrated that the coupled neurons can exhibit different types of synchronization transitions between spiking and bursting when the coupling strength increases. In the course of synchronization transitions, an intermittent synchronization can be observed. These results may be instructive to understand synchronization transitions in neuronal systems.

  8. Search for anomalous Wtb couplings and flavour-changing neutral currents in t-channel single top quark production in pp collisions at √{s}=7 and 8 TeV

    Science.gov (United States)

    Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; König, A.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rad, N.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; De Wolf, E. A.; Janssen, X.; Lauwers, J.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Heracleous, N.; Lowette, S.; Moortgat, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Parijs, I.; Brun, H.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Delannoy, H.; Fasanella, G.; Favart, L.; Goldouzian, R.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Luetic, J.; Maerschalk, T.; Marinov, A.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Yonamine, R.; Zenoni, F.; Zhang, F.; Cimmino, A.; Cornelis, T.; Dobur, D.; Fagot, A.; Garcia, G.; Gul, M.; Poyraz, D.; Salva, S.; Schöfbeck, R.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Bakhshiansohi, H.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; Ceard, L.; De Visscher, S.; Delaere, C.; Delcourt, M.; Forthomme, L.; Francois, B.; Giammanco, A.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Magitteri, A.; Mertens, A.; Musich, M.; Nuttens, C.; Piotrzkowski, K.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Wertz, S.; Beliy, N.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; Hensel, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; Da Silveira, G. G.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Huertas Guativa, L. M.; Malbouisson, H.; Matos Figueiredo, D.; Mora Herrera, C.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Ahuja, S.; Bernardes, C. A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Moon, C. S.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Ruiz Vargas, J. C.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Fang, W.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Chen, Y.; Cheng, T.; Jiang, C. H.; Leggat, D.; Liu, Z.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Zhang, H.; Zhao, J.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; González Hernández, C. F.; Ruiz Alvarez, J. D.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Puljak, I.; Ribeiro Cipriano, P. M.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Ferencek, D.; Kadija, K.; Micanovic, S.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Finger, M.; Finger, M.; Carrera Jarrin, E.; Elgammal, S.; Mohamed, A.; Mohammed, Y.; Salama, E.; Calpas, B.; Kadastik, M.; Murumaa, M.; Perrini, L.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Pekkanen, J.; Voutilainen, M.; Härkönen, J.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Peltola, T.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Ghosh, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Kucher, I.; Locci, E.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Zghiche, A.; Abdulsalam, A.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Davignon, O.; Granier de Cassagnac, R.; Jo, M.; Lisniak, S.; Miné, P.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Regnard, S.; Salerno, R.; Sirois, Y.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Coubez, X.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Le Bihan, A.-C.; Merlin, J. A.; Skovpen, K.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Bouvier, E.; Carrillo Montoya, C. A.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Grenier, G.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Popov, A.; Sabes, D.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Toriashvili, T.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Feld, L.; Heister, A.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Ostapchuk, A.; Preuten, M.; Raupach, F.; Schael, S.; Schomakers, C.; Schulte, J. F.; Schulz, J.; Verlage, T.; Weber, H.; Zhukov, V.; Brodski, M.; Dietz-Laursonn, E.; Duchardt, D.; Endres, M.; Erdmann, M.; Erdweg, S.; Esch, T.; Fischer, R.; Güth, A.; Hebbeker, T.; Heidemann, C.; Hoepfner, K.; Knutzen, S.; Merschmeyer, M.; Meyer, A.; Millet, P.; Mukherjee, S.; Olschewski, M.; Padeken, K.; Papacz, P.; Pook, T.; Radziej, M.; Reithler, H.; Rieger, M.; Scheuch, F.; Sonnenschein, L.; Teyssier, D.; Thüer, S.; Cherepanov, V.; Erdogan, Y.; Flügge, G.; Haj Ahmad, W.; Hoehle, F.; Kargoll, B.; Kress, T.; Künsken, A.; Lingemann, J.; Nehrkorn, A.; Nowack, A.; Nugent, I. M.; Pistone, C.; Pooth, O.; Stahl, A.; Aldaya Martin, M.; Asawatangtrakuldee, C.; Asin, I.; Beernaert, K.; Behnke, O.; Behrens, U.; Bin Anuar, A. A.; Borras, K.; Campbell, A.; Connor, P.; Contreras-Campana, C.; Costanza, F.; Diez Pardos, C.; Dolinska, G.; Eckerlin, G.; Eckstein, D.; Gallo, E.; Garay Garcia, J.; Geiser, A.; Gizhko, A.; Grados Luyando, J. M.; Gunnellini, P.; Harb, A.; Hauk, J.; Hempel, M.; Jung, H.; Kalogeropoulos, A.; Karacheban, O.; Kasemann, M.; Keaveney, J.; Kieseler, J.; Kleinwort, C.; Korol, I.; Lange, W.; Lelek, A.; Leonard, J.; Lipka, K.; Lobanov, A.; Lohmann, W.; Mankel, R.; Melzer-Pellmann, I.-A.; Meyer, A. B.; Mittag, G.; Mnich, J.; Mussgiller, A.; Ntomari, E.; Pitzl, D.; Placakyte, R.; Raspereza, A.; Roland, B.; Sahin, M. Ö.; Saxena, P.; Schoerner-Sadenius, T.; Seitz, C.; Spannagel, S.; Stefaniuk, N.; Trippkewitz, K. D.; Van Onsem, G. P.; Walsh, R.; Wissing, C.; Blobel, V.; Centis Vignali, M.; Draeger, A. R.; Dreyer, T.; Garutti, E.; Goebel, K.; Gonzalez, D.; Haller, J.; Hoffmann, M.; Junkes, A.; Klanner, R.; Kogler, R.; Kovalchuk, N.; Lapsien, T.; Lenz, T.; Marchesini, I.; Marconi, D.; Meyer, M.; Niedziela, M.; Nowatschin, D.; Ott, J.; Pantaleo, F.; Peiffer, T.; Perieanu, A.; Poehlsen, J.; Sander, C.; Scharf, C.; Schleper, P.; Schmidt, A.; Schumann, S.; Schwandt, J.; Stadie, H.; Steinbrück, G.; Stober, F. M.; Stöver, M.; Tholen, H.; Troendle, D.; Usai, E.; Vanelderen, L.; Vanhoefer, A.; Vormwald, B.; Barth, C.; Baus, C.; Berger, J.; Butz, E.; Chwalek, T.; Colombo, F.; De Boer, W.; Dierlamm, A.; Fink, S.; Friese, R.; Giffels, M.; Gilbert, A.; Haitz, D.; Hartmann, F.; Heindl, S. M.; Husemann, U.; Katkov, I.; Lobelle Pardo, P.; Maier, B.; Mildner, H.; Mozer, M. U.; Müller, T.; Müller, Th.; Plagge, M.; Quast, G.; Rabbertz, K.; Röcker, S.; Roscher, F.; Schröder, M.; Sieber, G.; Simonis, H. J.; Ulrich, R.; Wagner-Kuhr, J.; Wayand, S.; Weber, M.; Weiler, T.; Williamson, S.; Wöhrmann, C.; Wolf, R.; Anagnostou, G.; Daskalakis, G.; Geralis, T.; Giakoumopoulou, V. A.; Kyriakis, A.; Loukas, D.; Topsis-Giotis, I.; Agapitos, A.; Kesisoglou, S.; Panagiotou, A.; Saoulidou, N.; Tziaferi, E.; Evangelou, I.; Flouris, G.; Foudas, C.; Kokkas, P.; Loukas, N.; Manthos, N.; Papadopoulos, I.; Paradas, E.; Filipovic, N.; Bencze, G.; Hajdu, C.; Hidas, P.; Horvath, D.; Sikler, F.; Veszpremi, V.; Vesztergombi, G.; Zsigmond, A. J.; Beni, N.; Czellar, S.; Karancsi, J.; Makovec, A.; Molnar, J.; Szillasi, Z.; Bartók, M.; Raics, P.; Trocsanyi, Z. L.; Ujvari, B.; Bahinipati, S.; Choudhury, S.; Mal, P.; Mandal, K.; Nayak, A.; Sahoo, D. K.; Sahoo, N.; Swain, S. K.; Bansal, S.; Beri, S. B.; Bhatnagar, V.; Chawla, R.; Bhawandeep, U.; Kalsi, A. K.; Kaur, A.; Kaur, M.; Kumar, R.; Mehta, A.; Mittal, M.; Singh, J. B.; Walia, G.; Kumar, Ashok; Bhardwaj, A.; Choudhary, B. C.; Garg, R. B.; Keshri, S.; Kumar, A.; Malhotra, S.; Naimuddin, M.; Nishu, N.; Ranjan, K.; Sharma, R.; Sharma, V.; Bhattacharya, R.; Bhattacharya, S.; Chatterjee, K.; Dey, S.; Dutt, S.; Dutta, S.; Ghosh, S.; Majumdar, N.; Modak, A.; Mondal, K.; Mukhopadhyay, S.; Nandan, S.; Purohit, A.; Roy, A.; Roy, D.; Roy Chowdhury, S.; Sarkar, S.; Sharan, M.; Thakur, S.; Behera, P. K.; Chudasama, R.; Dutta, D.; Jha, V.; Kumar, V.; Mohanty, A. K.; Netrakanti, P. K.; Pant, L. M.; Shukla, P.; Topkar, A.; Aziz, T.; Dugad, S.; Kole, G.; Mahakud, B.; Mitra, S.; Mohanty, G. B.; Sur, N.; Sutar, B.; Banerjee, S.; Bhowmik, S.; Dewanjee, R. K.; Ganguly, S.; Guchait, M.; Jain, Sa.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Parida, B.; Sarkar, T.; Wickramage, N.; Chauhan, S.; Dube, S.; Kapoor, A.; Kothekar, K.; Rane, A.; Sharma, S.; Behnamian, H.; Chenarani, S.; Eskandari Tadavani, E.; Etesami, S. M.; Fahim, A.; Khakzad, M.; Mohammadi Najafabadi, M.; Naseri, M.; Paktinat Mehdiabadi, S.; Rezaei Hosseinabadi, F.; Safarzadeh, B.; Zeinali, M.; Felcini, M.; Grunewald, M.; Abbrescia, M.; Calabria, C.; Caputo, C.; Colaleo, A.; Creanza, D.; Cristella, L.; De Filippis, N.; De Palma, M.; Fiore, L.; Iaselli, G.; Maggi, G.; Maggi, M.; Miniello, G.; My, S.; Nuzzo, S.; Pompili, A.; Pugliese, G.; Radogna, R.; Ranieri, A.; Selvaggi, G.; Silvestris, L.; Venditti, R.; Verwilligen, P.; Abbiendi, G.; Battilana, C.; Bonacorsi, D.; Braibant-Giacomelli, S.; Brigliadori, L.; Campanini, R.; Capiluppi, P.; Castro, A.; Cavallo, F. R.; Chhibra, S. S.; Codispoti, G.; Cuffiani, M.; Dallavalle, G. M.; Fabbri, F.; Fanfani, A.; Fasanella, D.; Giacomelli, P.; Grandi, C.; Guiducci, L.; Marcellini, S.; Masetti, G.; Montanari, A.; Navarria, F. L.; Perrotta, A.; Rossi, A. M.; Rovelli, T.; Siroli, G. P.; Tosi, N.; Albergo, S.; Chiorboli, M.; Costa, S.; Di Mattia, A.; Giordano, F.; Potenza, R.; Tricomi, A.; Tuve, C.; Barbagli, G.; Ciulli, V.; Civinini, C.; D'Alessandro, R.; Focardi, E.; Gori, V.; Lenzi, P.; Meschini, M.; Paoletti, S.; Sguazzoni, G.; Viliani, L.; Benussi, L.; Bianco, S.; Fabbri, F.; Piccolo, D.; Primavera, F.; Calvelli, V.; Ferro, F.; Lo Vetere, M.; Monge, M. R.; Robutti, E.; Tosi, S.; Brianza, L.; Dinardo, M. E.; Fiorendi, S.; Gennai, S.; Ghezzi, A.; Govoni, P.; Malvezzi, S.; Manzoni, R. A.; Marzocchi, B.; Menasce, D.; Moroni, L.; Paganoni, M.; Pedrini, D.; Pigazzini, S.; Ragazzi, S.; Tabarelli de Fatis, T.; Buontempo, S.; Cavallo, N.; De Nardo, G.; Di Guida, S.; Esposito, M.; Fabozzi, F.; Iorio, A. O. M.; Lanza, G.; Lista, L.; Meola, S.; Paolucci, P.; Sciacca, C.; Thyssen, F.; Azzi, P.; Bacchetta, N.; Benato, L.; Bisello, D.; Boletti, A.; Carlin, R.; Carvalho Antunes De Oliveira, A.; Checchia, P.; Dall'Osso, M.; De Castro Manzano, P.; Dorigo, T.; Dosselli, U.; Gasparini, F.; Gasparini, U.; Gozzelino, A.; Lacaprara, S.; Margoni, M.; Meneguzzo, A. T.; Pazzini, J.; Pozzobon, N.; Ronchese, P.; Simonetto, F.; Torassa, E.; Zanetti, M.; Zotto, P.; Zucchetta, A.; Zumerle, G.; Braghieri, A.; Magnani, A.; Montagna, P.; Ratti, S. P.; Re, V.; Riccardi, C.; Salvini, P.; Vai, I.; Vitulo, P.; Alunni Solestizi, L.; Bilei, G. M.; Ciangottini, D.; Fanò, L.; Lariccia, P.; Leonardi, R.; Mantovani, G.; Menichelli, M.; Saha, A.; Santocchia, A.; Androsov, K.; Azzurri, P.; Bagliesi, G.; Bernardini, J.; Boccali, T.; Castaldi, R.; Ciocci, M. A.; Dell'Orso, R.; Donato, S.; Fedi, G.; Giassi, A.; Grippo, M. T.; Ligabue, F.; Lomtadze, T.; Martini, L.; Messineo, A.; Palla, F.; Rizzi, A.; SavoyNavarro, A.; Spagnolo, P.; Tenchini, R.; Tonelli, G.; Venturi, A.; Verdini, P. G.; Barone, L.; Cavallari, F.; Cipriani, M.; D'imperio, G.; Del Re, D.; Diemoz, M.; Gelli, S.; Jorda, C.; Longo, E.; Margaroli, F.; Meridiani, P.; Organtini, G.; Paramatti, R.; Preiato, F.; Rahatlou, S.; Rovelli, C.; Santanastasio, F.; Amapane, N.; Arcidiacono, R.; Argiro, S.; Arneodo, M.; Bartosik, N.; Bellan, R.; Biino, C.; Cartiglia, N.; Cenna, F.; Costa, M.; Covarelli, R.; Degano, A.; Demaria, N.; Finco, L.; Kiani, B.; Mariotti, C.; Maselli, S.; Migliore, E.; Monaco, V.; Monteil, E.; Obertino, M. M.; Pacher, L.; Pastrone, N.; Pelliccioni, M.; Pinna Angioni, G. L.; Ravera, F.; Romero, A.; Ruspa, M.; Sacchi, R.; Shchelina, K.; Sola, V.; Solano, A.; Staiano, A.; Traczyk, P.; Belforte, S.; Casarsa, M.; Cossutti, F.; Della Ricca, G.; La Licata, C.; Schizzi, A.; Zanetti, A.; Kim, D. H.; Kim, G. N.; Kim, M. S.; Lee, S.; Lee, S. W.; Oh, Y. D.; Sekmen, S.; Son, D. C.; Yang, Y. C.; Lee, A.; Brochero Cifuentes, J. A.; Kim, T. J.; Cho, S.; Choi, S.; Go, Y.; Gyun, D.; Ha, S.; Hong, B.; Jo, Y.; Kim, Y.; Lee, B.; Lee, K.; Lee, K. S.; Lee, S.; Lim, J.; Park, S. K.; Roh, Y.; Almond, J.; Kim, J.; Oh, S. B.; Seo, S. h.; Yang, U. K.; Yoo, H. D.; Yu, G. B.; Choi, M.; Kim, H.; Kim, H.; Kim, J. H.; Lee, J. S. H.; Park, I. C.; Ryu, G.; Ryu, M. S.; Choi, Y.; Goh, J.; Hwang, C.; Lee, J.; Yu, I.; Dudenas, V.; Juodagalvis, A.; Vaitkus, J.; Ahmed, I.; Ibrahim, Z. A.; Komaragiri, J. R.; Md Ali, M. A. B.; Mohamad Idris, F.; Wan Abdullah, W. A. T.; Yusli, M. N.; Zolkapli, Z.; Castilla-Valdez, H.; De La Cruz-Burelo, E.; Heredia-De La Cruz, I.; Hernandez-Almada, A.; Lopez-Fernandez, R.; Mejia Guisao, J.; Sanchez-Hernandez, A.; Carrillo Moreno, S.; Oropeza Barrera, C.; Vazquez Valencia, F.; Carpinteyro, S.; Pedraza, I.; Salazar Ibarguen, H. A.; Uribe Estrada, C.; Morelos Pineda, A.; Krofcheck, D.; Butler, P. H.; Ahmad, A.; Ahmad, M.; Hassan, Q.; Hoorani, H. R.; Khan, W. A.; Shah, M. A.; Shoaib, M.; Waqas, M.; Bialkowska, H.; Bluj, M.; Boimska, B.; Frueboes, T.; Górski, M.; Kazana, M.; Nawrocki, K.; Romanowska-Rybinska, K.; Szleper, M.; Zalewski, P.; Bunkowski, K.; Byszuk, A.; Doroba, K.; Kalinowski, A.; Konecki, M.; Krolikowski, J.; Misiura, M.; Olszewski, M.; Walczak, M.; Bargassa, P.; Beirão Da Cruz E Silva, C.; Di Francesco, A.; Faccioli, P.; Ferreira Parracho, P. G.; Gallinaro, M.; Hollar, J.; Leonardo, N.; Lloret Iglesias, L.; Nemallapudi, M. V.; Rodrigues Antunes, J.; Seixas, J.; Toldaiev, O.; Vadruccio, D.; Varela, J.; Vischia, P.; Golunov, A.; Golutvin, I.; Gorbounov, N.; Kamenev, A.; Karjavin, V.; Korenkov, V.; Lanev, A.; Malakhov, A.; Matveev, V.; Mitsyn, V. V.; Moisenz, P.; Palichik, V.; Perelygin, V.; Shmatov, S.; Shulha, S.; Skatchkov, N.; Smirnov, V.; Tikhonenko, E.; Zarubin, A.; Chtchipounov, L.; Golovtsov, V.; Ivanov, Y.; Kim, V.; Kuznetsova, E.; Murzin, V.; Oreshkin, V.; Sulimov, V.; Vorobyev, A.; Andreev, Yu.; Dermenev, A.; Gninenko, S.; Golubev, N.; Karneyeu, A.; Kirsanov, M.; Krasnikov, N.; Pashenkov, A.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Spiridonov, A.; Toms, M.; Vlasov, E.; Zhokin, A.; Chadeeva, M.; Danilov, M.; Markin, O.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Rusakov, S. V.; Terkulov, A.; Baskakov, A.; Belyaev, A.; Boos, E.; Bunichev, V.; Dubinin, M.; Dudko, L.; Klyukhin, V.; Kodolova, O.; Korneeva, N.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Perfilov, M.; Savrin, V.; Volkov, P.; Vorotnikov, G.; Azhgirey, I.; Bayshev, I.; Bitioukov, S.; Elumakhov, D.; Kachanov, V.; Kalinin, A.; Konstantinov, D.; Krychkine, V.; Petrov, V.; Ryutin, R.; Sobol, A.; Troshin, S.; Tyurin, N.; Uzunian, A.; Volkov, A.; Adzic, P.; Cirkovic, P.; Devetak, D.; Milosevic, J.; Rekovic, V.; Alcaraz Maestre, J.; Calvo, E.; Cerrada, M.; Chamizo Llatas, M.; Colino, N.; De La Cruz, B.; Delgado Peris, A.; Escalante Del Valle, A.; Fernandez Bedoya, C.; Fernández Ramos, J. P.; Flix, J.; Fouz, M. C.; Garcia-Abia, P.; Gonzalez Lopez, O.; Goy Lopez, S.; Hernandez, J. M.; Josa, M. I.; Navarro De Martino, E.; Pérez-Calero Yzquierdo, A.; Puerta Pelayo, J.; Quintario Olmeda, A.; Redondo, I.; Romero, L.; Soares, M. S.; de Trocóniz, J. 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    2017-02-01

    Single top quark events produced in the t channel are used to set limits on anomalous Wtb couplings and to search for top quark flavour-changing neutral current (FCNC) interactions. The data taken with the CMS detector at the LHC in proton-proton collisions at √{s}=7 and 8 TeV correspond to integrated luminosities of 5.0 and 19.7 fb-1, respectively. The analysis is performed using events with one muon and two or three jets. A Bayesian neural network technique is used to discriminate between the signal and backgrounds, which are observed to be consistent with the standard model prediction. The 95% confidence level (CL) exclusion limits on anomalous right-handed vector, and left- and right-handed tensor Wtb couplings are measured to be | f V R | < 0.16, | f T L | < 0.057, and - 0.049 < f T R < 0.048, respectively. For the FCNC couplings κ tug and κ tcg, the 95% CL upper limits on coupling strengths are | κ tug|/ Λ < 4.1 × 10- 3 TeV-1 and | κ tcg|/ Λ < 1.8 × 10- 2 TeV-1, where Λ is the scale for new physics, and correspond to upper limits on the branching fractions of 2 .0 × 10-5 and 4 .1 × 10-4 for the decays t → ug and t → cg, respectively.

  9. Search for anomalous Wtb couplings and flavour-changing neutral currents in $t$-channel single top quark production in pp collisions at $\\sqrt{s}= $ 7 and 8 TeV

    CERN Document Server

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Kaur, Anterpreet; Kaur, Manjit; Kumar, Ramandeep; Mehta, Ankita; Mittal, Monika; Singh, Jasbir; Walia, Genius; Kumar, Ashok; Bhardwaj, Ashutosh; Choudhary, Brajesh C; Garg, Rocky Bala; Keshri, Sumit; Kumar, Ajay; Malhotra, Shivali; Naimuddin, Md; Nishu, Nishu; Ranjan, Kirti; Sharma, Ramkrishna; Sharma, Varun; Bhattacharya, Rajarshi; Bhattacharya, Satyaki; Chatterjee, Kalyanmoy; Dey, Sourav; Dutt, Suneel; Dutta, Suchandra; Ghosh, Shamik; Majumdar, Nayana; Modak, Atanu; Mondal, Kuntal; Mukhopadhyay, Supratik; Nandan, Saswati; Purohit, Arnab; Roy, Ashim; Roy, Debarati; Roy Chowdhury, Suvankar; Sarkar, Subir; Sharan, Manoj; Thakur, Shalini; Behera, Prafulla Kumar; Chudasama, Ruchi; Dutta, Dipanwita; Jha, Vishwajeet; Kumar, Vineet; Mohanty, Ajit Kumar; Netrakanti, Pawan Kumar; Pant, Lalit Mohan; Shukla, Prashant; Topkar, Anita; Aziz, Tariq; Dugad, Shashikant; Kole, Gouranga; Mahakud, Bibhuprasad; Mitra, Soureek; Mohanty, Gagan Bihari; Sur, Nairit; Sutar, Bajrang; Banerjee, Sudeshna; Bhowmik, Sandeep; Dewanjee, Ram Krishna; Ganguly, Sanmay; Guchait, Monoranjan; Jain, Sandhya; Kumar, Sanjeev; Maity, Manas; Majumder, Gobinda; Mazumdar, Kajari; Parida, Bibhuti; Sarkar, Tanmay; Wickramage, Nadeesha; Chauhan, Shubhanshu; Dube, Sourabh; Kapoor, Anshul; Kothekar, Kunal; Rane, Aditee; Sharma, Seema; Behnamian, Hadi; Chenarani, Shirin; Eskandari Tadavani, Esmaeel; Etesami, Seyed Mohsen; Fahim, Ali; Khakzad, Mohsen; Mohammadi Najafabadi, Mojtaba; Naseri, Mohsen; Paktinat Mehdiabadi, Saeid; Rezaei Hosseinabadi, Ferdos; Safarzadeh, Batool; Zeinali, Maryam; Felcini, Marta; Grunewald, Martin; Abbrescia, Marcello; Calabria, Cesare; Caputo, Claudio; Colaleo, Anna; Creanza, Donato; Cristella, Leonardo; De Filippis, Nicola; De Palma, Mauro; Fiore, Luigi; Iaselli, Giuseppe; Maggi, Giorgio; Maggi, Marcello; Miniello, Giorgia; My, Salvatore; Nuzzo, Salvatore; Pompili, Alexis; Pugliese, Gabriella; Radogna, Raffaella; Ranieri, Antonio; Selvaggi, Giovanna; Silvestris, Lucia; Venditti, Rosamaria; Verwilligen, Piet; Abbiendi, Giovanni; Battilana, Carlo; Bonacorsi, Daniele; Braibant-Giacomelli, Sylvie; Brigliadori, Luca; Campanini, Renato; Capiluppi, Paolo; Castro, Andrea; Cavallo, Francesca Romana; Chhibra, Simranjit Singh; Codispoti, Giuseppe; Cuffiani, Marco; Dallavalle, Gaetano-Marco; Fabbri, Fabrizio; Fanfani, Alessandra; Fasanella, Daniele; Giacomelli, Paolo; Grandi, Claudio; Guiducci, Luigi; Marcellini, Stefano; Masetti, Gianni; Montanari, Alessandro; Navarria, Francesco; Perrotta, Andrea; Rossi, Antonio; Rovelli, Tiziano; Siroli, Gian Piero; Tosi, Nicolò; Albergo, Sebastiano; Chiorboli, Massimiliano; Costa, Salvatore; Di Mattia, Alessandro; Giordano, Ferdinando; Potenza, Renato; Tricomi, Alessia; Tuve, Cristina; Barbagli, Giuseppe; Ciulli, Vitaliano; Civinini, Carlo; D'Alessandro, Raffaello; Focardi, Ettore; Gori, Valentina; Lenzi, Piergiulio; Meschini, Marco; Paoletti, Simone; Sguazzoni, Giacomo; Viliani, Lorenzo; Benussi, Luigi; Bianco, Stefano; Fabbri, Franco; Piccolo, Davide; Primavera, Federica; Calvelli, Valerio; Ferro, Fabrizio; Lo Vetere, Maurizio; Monge, Maria Roberta; Robutti, Enrico; Tosi, Silvano; Brianza, Luca; Dinardo, Mauro Emanuele; Fiorendi, Sara; Gennai, Simone; Ghezzi, Alessio; Govoni, Pietro; Malvezzi, Sandra; Manzoni, Riccardo Andrea; Marzocchi, Badder; Menasce, Dario; Moroni, Luigi; Paganoni, Marco; Pedrini, Daniele; Pigazzini, Simone; Ragazzi, Stefano; Tabarelli de Fatis, Tommaso; Buontempo, Salvatore; Cavallo, Nicola; De Nardo, Guglielmo; Di Guida, Salvatore; Esposito, Marco; Fabozzi, Francesco; Iorio, Alberto Orso Maria; Lanza, Giuseppe; Lista, Luca; Meola, Sabino; Paolucci, Pierluigi; Sciacca, Crisostomo; Thyssen, Filip; Azzi, Patrizia; Bacchetta, Nicola; Benato, Lisa; Bisello, Dario; Boletti, Alessio; Carlin, Roberto; Carvalho Antunes De Oliveira, Alexandra; Checchia, Paolo; Dall'Osso, Martino; De Castro Manzano, Pablo; Dorigo, Tommaso; Dosselli, Umberto; Gasparini, Fabrizio; Gasparini, Ugo; Gozzelino, Andrea; Lacaprara, Stefano; Margoni, Martino; Meneguzzo, Anna Teresa; Pazzini, Jacopo; Pozzobon, Nicola; Ronchese, Paolo; Simonetto, Franco; Torassa, Ezio; Zanetti, Marco; Zotto, Pierluigi; Zucchetta, Alberto; Zumerle, Gianni; Braghieri, Alessandro; Magnani, Alice; Montagna, Paolo; Ratti, Sergio P; Re, Valerio; Riccardi, Cristina; Salvini, Paola; Vai, Ilaria; Vitulo, Paolo; Alunni Solestizi, Luisa; Bilei, Gian Mario; Ciangottini, Diego; Fanò, Livio; Lariccia, Paolo; Leonardi, Roberto; Mantovani, Giancarlo; Menichelli, Mauro; Saha, Anirban; Santocchia, Attilio; Androsov, Konstantin; Azzurri, Paolo; Bagliesi, Giuseppe; Bernardini, Jacopo; Boccali, Tommaso; Castaldi, Rino; Ciocci, Maria Agnese; Dell'Orso, Roberto; Donato, Silvio; Fedi, Giacomo; Giassi, Alessandro; Grippo, Maria Teresa; Ligabue, Franco; Lomtadze, Teimuraz; Martini, Luca; Messineo, Alberto; Palla, Fabrizio; Rizzi, Andrea; Savoy-Navarro, Aurore; Spagnolo, Paolo; Tenchini, Roberto; Tonelli, Guido; Venturi, Andrea; Verdini, Piero Giorgio; Barone, Luciano; Cavallari, Francesca; Cipriani, Marco; D'imperio, Giulia; Del Re, Daniele; Diemoz, Marcella; Gelli, Simone; Jorda, Clara; Longo, Egidio; Margaroli, Fabrizio; Meridiani, Paolo; Organtini, Giovanni; Paramatti, Riccardo; Preiato, Federico; Rahatlou, Shahram; Rovelli, Chiara; Santanastasio, Francesco; Amapane, Nicola; Arcidiacono, Roberta; Argiro, Stefano; Arneodo, Michele; Bartosik, Nazar; Bellan, Riccardo; Biino, Cristina; Cartiglia, Nicolo; Cenna, Francesca; Costa, Marco; Covarelli, Roberto; Degano, Alessandro; Demaria, Natale; Finco, Linda; Kiani, Bilal; Mariotti, Chiara; Maselli, Silvia; Migliore, Ernesto; Monaco, Vincenzo; Monteil, Ennio; Obertino, Maria Margherita; Pacher, Luca; Pastrone, Nadia; Pelliccioni, Mario; Pinna Angioni, Gian Luca; Ravera, Fabio; Romero, Alessandra; Ruspa, Marta; Sacchi, Roberto; Shchelina, Ksenia; Sola, Valentina; Solano, Ada; Staiano, Amedeo; Traczyk, Piotr; Belforte, Stefano; Casarsa, Massimo; Cossutti, Fabio; Della Ricca, Giuseppe; La Licata, Chiara; Schizzi, Andrea; Zanetti, Anna; Kim, Dong Hee; Kim, Gui Nyun; Kim, Min Suk; Lee, Sangeun; Lee, Seh Wook; Oh, Young Do; Sekmen, Sezen; Son, Dong-Chul; Yang, Yu Chul; Lee, Ari; Brochero Cifuentes, Javier Andres; Kim, Tae Jeong; Cho, Sungwoong; Choi, Suyong; Go, Yeonju; Gyun, Dooyeon; Ha, Seungkyu; Hong, Byung-Sik; Jo, Youngkwon; Kim, Yongsun; Lee, Byounghoon; Lee, Kisoo; Lee, Kyong Sei; Lee, Songkyo; Lim, Jaehoon; Park, Sung Keun; Roh, Youn; Almond, John; Kim, Junho; Oh, Sung Bin; Seo, Seon-hee; Yang, Unki; Yoo, Hwi Dong; Yu, Geum Bong; Choi, Minkyoo; Kim, Hyunchul; Kim, Hyunyong; Kim, Ji Hyun; Lee, Jason Sang Hun; Park, Inkyu; Ryu, Geonmo; Ryu, Min Sang; Choi, Young-Il; Goh, Junghwan; Hwang, Chanwook; Lee, Jongseok; Yu, Intae; Dudenas, Vytautas; Juodagalvis, Andrius; Vaitkus, Juozas; Ahmed, Ijaz; Ibrahim, Zainol Abidin; Komaragiri, Jyothsna Rani; Md Ali, Mohd Adli Bin; Mohamad Idris, Faridah; Wan Abdullah, Wan Ahmad Tajuddin; Yusli, Mohd Nizam; Zolkapli, Zukhaimira; Castilla-Valdez, Heriberto; De La Cruz-Burelo, Eduard; Heredia-De La Cruz, Ivan; Hernandez-Almada, Alberto; Lopez-Fernandez, Ricardo; Mejia Guisao, Jhovanny; Sánchez Hernández, Alberto; Carrillo Moreno, Salvador; Oropeza Barrera, Cristina; Vazquez Valencia, Fabiola; Carpinteyro, Severiano; Pedraza, Isabel; Salazar Ibarguen, Humberto Antonio; Uribe Estrada, Cecilia; Morelos Pineda, Antonio; Krofcheck, David; Butler, Philip H; Ahmad, Ashfaq; Ahmad, Muhammad; Hassan, Qamar; Hoorani, Hafeez R; Khan, Wajid Ali; Shah, Mehar Ali; Shoaib, Muhammad; Waqas, Muhammad; Bialkowska, Helena; Bluj, Michal; Boimska, Bożena; Frueboes, Tomasz; Górski, Maciej; Kazana, Malgorzata; Nawrocki, Krzysztof; Romanowska-Rybinska, Katarzyna; Szleper, Michal; Zalewski, Piotr; Bunkowski, Karol; Byszuk, Adrian; Doroba, Krzysztof; Kalinowski, Artur; Konecki, Marcin; Krolikowski, Jan; Misiura, Maciej; Olszewski, Michal; Walczak, Marek; Bargassa, Pedrame; Beirão Da Cruz E Silva, Cristóvão; Di Francesco, Agostino; Faccioli, Pietro; Ferreira Parracho, Pedro Guilherme; Gallinaro, Michele; Hollar, Jonathan; Leonardo, Nuno; Lloret Iglesias, Lara; Nemallapudi, Mythra Varun; Rodrigues Antunes, Joao; Seixas, Joao; Toldaiev, Oleksii; Vadruccio, Daniele; Varela, Joao; Vischia, Pietro; Golunov, Alexander; Golutvin, Igor; Gorbounov, Nikolai; Kamenev, Alexey; Karjavin, Vladimir; Korenkov, Vladimir; Lanev, Alexander; Malakhov, Alexander; Matveev, Viktor; Mitsyn, Valeri Valentinovitch; Moisenz, Petr; Palichik, Vladimir; Perelygin, Victor; Shmatov, Sergey; Shulha, Siarhei; Skatchkov, Nikolai; Smirnov, Vitaly; Tikhonenko, Elena; Zarubin, Anatoli; Chtchipounov, Leonid; Golovtsov, Victor; Ivanov, Yury; Kim, Victor; Kuznetsova, Ekaterina; Murzin, Victor; Oreshkin, Vadim; Sulimov, Valentin; Vorobyev, Alexey; Andreev, Yuri; Dermenev, Alexander; Gninenko, Sergei; Golubev, Nikolai; Karneyeu, Anton; Kirsanov, Mikhail; Krasnikov, Nikolai; Pashenkov, Anatoli; Tlisov, Danila; Toropin, Alexander; Epshteyn, Vladimir; Gavrilov, Vladimir; Lychkovskaya, Natalia; Popov, Vladimir; Pozdnyakov, Ivan; Safronov, Grigory; Spiridonov, Alexander; Toms, Maria; Vlasov, Evgueni; Zhokin, Alexander; Chadeeva, Marina; Danilov, Mikhail; Markin, Oleg; Andreev, Vladimir; Azarkin, Maksim; Dremin, Igor; Kirakosyan, Martin; Leonidov, Andrey; Rusakov, Sergey V; Terkulov, Adel; Baskakov, Alexey; Belyaev, Andrey; Boos, Edouard; Bunichev, Viacheslav; Dubinin, Mikhail; Dudko, Lev; Klyukhin, Vyacheslav; Kodolova, Olga; Korneeva, Natalia; Lokhtin, Igor; Miagkov, Igor; Obraztsov, Stepan; Perfilov, Maxim; Savrin, Viktor; Volkov, Petr; Vorotnikov, George; Azhgirey, Igor; Bayshev, Igor; Bitioukov, Sergei; Elumakhov, Dmitry; Kachanov, Vassili; Kalinin, Alexey; Konstantinov, Dmitri; Krychkine, Victor; Petrov, Vladimir; Ryutin, Roman; Sobol, Andrei; Troshin, Sergey; Tyurin, Nikolay; Uzunian, Andrey; Volkov, Alexey; Adzic, Petar; Cirkovic, Predrag; Devetak, Damir; Milosevic, Jovan; Rekovic, Vladimir; Alcaraz Maestre, Juan; Calvo, Enrique; Cerrada, Marcos; Chamizo Llatas, Maria; Colino, Nicanor; De La Cruz, Begona; Delgado Peris, Antonio; Escalante Del Valle, Alberto; Fernandez Bedoya, Cristina; Fernández Ramos, Juan Pablo; Flix, Jose; Fouz, Maria Cruz; Garcia-Abia, Pablo; Gonzalez Lopez, Oscar; Goy Lopez, Silvia; Hernandez, Jose M; Josa, Maria Isabel; Navarro De Martino, Eduardo; Pérez-Calero Yzquierdo, Antonio María; Puerta Pelayo, Jesus; Quintario Olmeda, Adrián; Redondo, Ignacio; Romero, Luciano; Senghi Soares, Mara; de Trocóniz, Jorge F; Missiroli, Marino; Moran, Dermot; Cuevas, Javier; Fernandez Menendez, Javier; Gonzalez Caballero, Isidro; González Fernández, Juan Rodrigo; Palencia Cortezon, Enrique; Sanchez Cruz, Sergio; Suárez Andrés, Ignacio; Vizan Garcia, Jesus Manuel; Cabrillo, Iban Jose; Calderon, Alicia; Castiñeiras De Saa, Juan Ramon; Curras, Esteban; Fernandez, Marcos; Garcia-Ferrero, Juan; Gomez, Gervasio; Lopez Virto, Amparo; Marco, Jesus; Martinez Rivero, Celso; Matorras, Francisco; Piedra Gomez, Jonatan; Rodrigo, Teresa; Ruiz-Jimeno, Alberto; Scodellaro, Luca; Trevisani, Nicolò; Vila, Ivan; Vilar Cortabitarte, Rocio; Abbaneo, Duccio; Auffray, Etiennette; Auzinger, Georg; Bachtis, Michail; Baillon, Paul; Ball, Austin; Barney, David; Bloch, Philippe; Bocci, Andrea; Bonato, Alessio; Botta, Cristina; Camporesi, Tiziano; Castello, Roberto; Cepeda, Maria; Cerminara, Gianluca; D'Alfonso, Mariarosaria; D'Enterria, David; Dabrowski, Anne; Daponte, Vincenzo; David Tinoco Mendes, Andre; De Gruttola, Michele; De Guio, Federico; De Roeck, Albert; Di Marco, Emanuele; Dobson, Marc; Dordevic, Milos; Dorney, Brian; Du Pree, Tristan; Duggan, Daniel; Dünser, Marc; Dupont, Niels; Elliott-Peisert, Anna; Fartoukh, Stephane; Franzoni, Giovanni; Fulcher, Jonathan; Funk, Wolfgang; Gigi, Dominique; Gill, Karl; Girone, Maria; Glege, Frank; Gulhan, Doga; Gundacker, Stefan; Guthoff, Moritz; Hammer, Josef; Harris, Philip; Hegeman, Jeroen; Innocente, Vincenzo; Janot, Patrick; Kirschenmann, Henning; Knünz, Valentin; Kornmayer, Andreas; Kortelainen, Matti J; Kousouris, Konstantinos; Krammer, Manfred; Lecoq, Paul; Lourenco, Carlos; Lucchini, Marco Toliman; Malgeri, Luca; Mannelli, Marcello; Martelli, Arabella; Meijers, Frans; Mersi, Stefano; Meschi, Emilio; Moortgat, Filip; Morovic, Srecko; Mulders, Martijn; Neugebauer, Hannes; Orfanelli, Styliani; Orsini, Luciano; Pape, Luc; Perez, Emmanuelle; Peruzzi, Marco; Petrilli, Achille; Petrucciani, Giovanni; Pfeiffer, Andreas; Pierini, Maurizio; Racz, Attila; Reis, Thomas; Rolandi, Gigi; Rovere, Marco; Ruan, Manqi; Sakulin, Hannes; Sauvan, Jean-Baptiste; Schäfer, Christoph; Schwick, Christoph; Seidel, Markus; Sharma, Archana; Silva, Pedro; Simon, Michal; Sphicas, Paraskevas; Steggemann, Jan; Stoye, Markus; Takahashi, Yuta; Tosi, Mia; Treille, Daniel; Triossi, Andrea; Tsirou, Andromachi; Veckalns, Viesturs; Veres, Gabor Istvan; Wardle, Nicholas; Wöhri, Hermine Katharina; Zagoździńska, Agnieszka; Zeuner, Wolfram Dietrich; Bertl, Willi; Deiters, Konrad; Erdmann, Wolfram; Horisberger, Roland; Ingram, Quentin; Kaestli, Hans-Christian; Kotlinski, Danek; Langenegger, Urs; Rohe, Tilman; Bachmair, Felix; Bäni, Lukas; Bianchini, Lorenzo; Casal, Bruno; Dissertori, Günther; Dittmar, Michael; Donegà, Mauro; Eller, Philipp; Grab, Christoph; Heidegger, Constantin; Hits, Dmitry; Hoss, Jan; Kasieczka, Gregor; Lecomte, Pierre; Lustermann, Werner; Mangano, Boris; Marionneau, Matthieu; Martinez Ruiz del Arbol, Pablo; Masciovecchio, Mario; Meinhard, Maren Tabea; Meister, Daniel; Micheli, Francesco; Musella, Pasquale; Nessi-Tedaldi, Francesca; Pandolfi, Francesco; Pata, Joosep; Pauss, Felicitas; Perrin, Gaël; Perrozzi, Luca; Quittnat, Milena; Rossini, Marco; Schönenberger, Myriam; Starodumov, Andrei; Takahashi, Maiko; Tavolaro, Vittorio Raoul; Theofilatos, Konstantinos; Wallny, Rainer; Aarrestad, Thea Klaeboe; Amsler, Claude; Caminada, Lea; Canelli, Maria Florencia; Chiochia, Vincenzo; De Cosa, Annapaola; Galloni, Camilla; Hinzmann, Andreas; Hreus, Tomas; Kilminster, Benjamin; Lange, Clemens; Ngadiuba, Jennifer; Pinna, Deborah; Rauco, Giorgia; Robmann, Peter; Salerno, Daniel; Yang, Yong; Candelise, Vieri; Doan, Thi Hien; Jain, Shilpi; Khurana, Raman; Konyushikhin, Maxim; Kuo, Chia-Ming; Lin, Willis; Lu, Yun-Ju; Pozdnyakov, Andrey; Yu, Shin-Shan; Kumar, Arun; Chang, Paoti; Chang, You-Hao; Chang, Yu-Wei; Chao, Yuan; Chen, Kai-Feng; Chen, Po-Hsun; Dietz, Charles; Fiori, Francesco; Hou, George Wei-Shu; Hsiung, Yee; Liu, Yueh-Feng; Lu, Rong-Shyang; Miñano Moya, Mercedes; Paganis, Efstathios; Psallidas, Andreas; Tsai, Jui-fa; Tzeng, Yeng-Ming; Asavapibhop, Burin; Singh, Gurpreet; Srimanobhas, Norraphat; Suwonjandee, Narumon; Adiguzel, Aytul; Bakirci, Mustafa Numan; Cerci, Salim; Damarseckin, Serdal; Demiroglu, Zuhal Seyma; Dozen, Candan; Dumanoglu, Isa; Girgis, Semiray; Gokbulut, Gul; Guler, Yalcin; Gurpinar, Emine; Hos, Ilknur; Kangal, Evrim Ersin; Kara, Ozgun; Kayis Topaksu, Aysel; Kiminsu, Ugur; Oglakci, Mehmet; Onengut, Gulsen; Ozdemir, Kadri; Tali, Bayram; Turkcapar, Semra; Zorbakir, Ibrahim Soner; Zorbilmez, Caglar; Bilin, Bugra; Bilmis, Selcuk; Isildak, Bora; Karapinar, Guler; Yalvac, Metin; Zeyrek, Mehmet; Gülmez, Erhan; Kaya, Mithat; Kaya, Ozlem; Yetkin, Elif Asli; Yetkin, Taylan; Cakir, Altan; Cankocak, Kerem; Sen, Sercan; Grynyov, Boris; Levchuk, Leonid; Sorokin, Pavel; Aggleton, Robin; Ball, Fionn; Beck, Lana; Brooke, James John; Burns, Douglas; Clement, Emyr; Cussans, David; Flacher, Henning; Goldstein, Joel; Grimes, Mark; Heath, Greg P; Heath, Helen F; Jacob, Jeson; Kreczko, Lukasz; Lucas, Chris; Newbold, Dave M; Paramesvaran, Sudarshan; Poll, Anthony; Sakuma, Tai; Seif El Nasr-storey, Sarah; Smith, Dominic; Smith, Vincent J; Bell, Ken W; Belyaev, Alexander; Brew, Christopher; Brown, Robert M; Calligaris, Luigi; Cieri, Davide; Cockerill, David JA; Coughlan, John A; Harder, Kristian; Harper, Sam; Olaiya, Emmanuel; Petyt, David; Shepherd-Themistocleous, Claire; Thea, Alessandro; Tomalin, Ian R; Williams, Thomas; Baber, Mark; Bainbridge, Robert; Buchmuller, Oliver; Bundock, Aaron; Burton, Darren; Casasso, Stefano; Citron, Matthew; Colling, David; Corpe, Louie; Dauncey, Paul; Davies, Gavin; De Wit, Adinda; Della Negra, Michel; Dunne, Patrick; Elwood, Adam; Futyan, David; Haddad, Yacine; Hall, Geoffrey; Iles, Gregory; Lane, Rebecca; Laner, Christian; Lucas, Robyn; Lyons, Louis; Magnan, Anne-Marie; Malik, Sarah; Mastrolorenzo, Luca; Nash, Jordan; Nikitenko, Alexander; Pela, Joao; Penning, Bjoern; Pesaresi, Mark; Raymond, David Mark; Richards, Alexander; Rose, Andrew; Seez, Christopher; Tapper, Alexander; Uchida, Kirika; Vazquez Acosta, Monica; Virdee, Tejinder; Zenz, Seth Conrad; Cole, Joanne; Hobson, Peter R; Khan, Akram; Kyberd, Paul; Leslie, Dawn; Reid, Ivan; Symonds, Philip; Teodorescu, Liliana; Turner, Mark; Borzou, Ahmad; Call, Kenneth; Dittmann, Jay; Hatakeyama, Kenichi; Liu, Hongxuan; Pastika, Nathaniel; Charaf, Otman; Cooper, Seth; Henderson, Conor; Rumerio, Paolo; Arcaro, Daniel; Avetisyan, Aram; Bose, Tulika; Gastler, Daniel; Rankin, Dylan; Richardson, Clint; Rohlf, James; Sulak, Lawrence; Zou, David; Benelli, Gabriele; Berry, Edmund; Cutts, David; Garabedian, Alex; Hakala, John; Heintz, Ulrich; Hogan, Julie Managan; Jesus, Orduna; Laird, Edward; Landsberg, Greg; Mao, Zaixing; Narain, Meenakshi; Piperov, Stefan; Sagir, Sinan; Spencer, Eric; Syarif, Rizki; Breedon, Richard; Breto, Guillermo; Burns, Dustin; Calderon De La Barca Sanchez, Manuel; Chauhan, Sushil; Chertok, Maxwell; Conway, John; Conway, Rylan; Cox, Peter Timothy; Erbacher, Robin; Flores, Chad; Funk, Garrett; Gardner, Michael; Ko, Winston; Lander, Richard; Mclean, Christine; Mulhearn, Michael; Pellett, Dave; Pilot, Justin; Ricci-Tam, Francesca; Shalhout, Shalhout; Smith, John; Squires, Michael; Stolp, Dustin; Tripathi, Mani; Wilbur, Scott; Yohay, Rachel; Cousins, Robert; Everaerts, Pieter; Florent, Alice; Hauser, Jay; Ignatenko, Mikhail; Saltzberg, David; Takasugi, Eric; Valuev, Vyacheslav; Weber, Matthias; Burt, Kira; Clare, Robert; Ellison, John Anthony; Gary, J William; Hanson, Gail; Heilman, Jesse; Jandir, Pawandeep; Kennedy, Elizabeth; Lacroix, Florent; Long, Owen Rosser; Malberti, Martina; Olmedo Negrete, Manuel; Paneva, Mirena Ivova; Shrinivas, Amithabh; Wei, Hua; Wimpenny, Stephen; Yates, Brent; Branson, James G; Cerati, Giuseppe Benedetto; Cittolin, Sergio; Derdzinski, Mark; Gerosa, Raffaele; Holzner, André; Klein, Daniel; Krutelyov, Vyacheslav; Letts, James; Macneill, Ian; Olivito, Dominick; Padhi, Sanjay; Pieri, Marco; Sani, Matteo; Sharma, Vivek; Simon, Sean; Tadel, Matevz; Vartak, Adish; Wasserbaech, Steven; Welke, Charles; Wood, John; Würthwein, Frank; Yagil, Avraham; Zevi Della Porta, Giovanni; Bhandari, Rohan; Bradmiller-Feld, John; Campagnari, Claudio; Dishaw, Adam; Dutta, Valentina; Flowers, Kristen; Franco Sevilla, Manuel; Geffert, Paul; George, Christopher; Golf, Frank; Gouskos, Loukas; Gran, Jason; Heller, Ryan; Incandela, Joe; Mccoll, Nickolas; Mullin, Sam Daniel; Ovcharova, Ana; Richman, Jeffrey; Stuart, David; Suarez, Indara; West, Christopher; Yoo, Jaehyeok; Anderson, Dustin; Apresyan, Artur; Bendavid, Joshua; Bornheim, Adolf; Bunn, Julian; Chen, Yi; Duarte, Javier; Mott, Alexander; Newman, Harvey B; Pena, Cristian; Spiropulu, Maria; Vlimant, Jean-Roch; Xie, Si; Zhu, Ren-Yuan; Andrews, Michael Benjamin; Azzolini, Virginia; Carlson, Benjamin; Ferguson, Thomas; Paulini, Manfred; Russ, James; Sun, Menglei; Vogel, Helmut; Vorobiev, Igor; Cumalat, John Perry; Ford, William T; Jensen, Frank; Johnson, Andrew; Krohn, Michael; Mulholland, Troy; Stenson, Kevin; Wagner, Stephen Robert; Alexander, James; Chaves, Jorge; Chu, Jennifer; Dittmer, Susan; Mcdermott, Kevin; Mirman, Nathan; Nicolas Kaufman, Gala; Patterson, Juliet Ritchie; Rinkevicius, Aurelijus; Ryd, Anders; Skinnari, Louise; Soffi, Livia; Tan, Shao Min; Tao, Zhengcheng; Thom, Julia; Tucker, Jordan; Wittich, Peter; Zientek, Margaret; Winn, Dave; Abdullin, Salavat; Albrow, Michael; Apollinari, Giorgio; Banerjee, Sunanda; Bauerdick, Lothar AT; Beretvas, Andrew; Berryhill, Jeffrey; Bhat, Pushpalatha C; Bolla, Gino; Burkett, Kevin; Butler, Joel Nathan; Cheung, Harry; Chlebana, Frank; Cihangir, Selcuk; Cremonesi, Matteo; Elvira, Victor Daniel; Fisk, Ian; Freeman, Jim; Gottschalk, Erik; Gray, Lindsey; Green, Dan; Grünendahl, Stefan; Gutsche, Oliver; Hare, Daryl; Harris, Robert M; Hasegawa, Satoshi; Hirschauer, James; Hu, Zhen; Jayatilaka, Bodhitha; Jindariani, Sergo; Johnson, Marvin; Joshi, Umesh; Klima, Boaz; Kreis, Benjamin; Lammel, Stephan; Linacre, Jacob; Lincoln, Don; Lipton, Ron; Liu, Tiehui; Lopes De Sá, Rafael; Lykken, Joseph; Maeshima, Kaori; Magini, Nicolo; Marraffino, John Michael; Maruyama, Sho; Mason, David; McBride, Patricia; Merkel, Petra; Mrenna, Stephen; Nahn, Steve; Newman-Holmes, Catherine; O'Dell, Vivian; Pedro, Kevin; Prokofyev, Oleg; Rakness, Gregory; Ristori, Luciano; Sexton-Kennedy, Elizabeth; Soha, Aron; Spalding, William J; Spiegel, Leonard; Stoynev, Stoyan; Strobbe, Nadja; Taylor, Lucas; Tkaczyk, Slawek; Tran, Nhan Viet; Uplegger, Lorenzo; Vaandering, Eric Wayne; Vernieri, Caterina; Verzocchi, Marco; Vidal, Richard; Wang, Michael; Weber, Hannsjoerg Artur; Whitbeck, Andrew; Acosta, Darin; Avery, Paul; Bortignon, Pierluigi; Bourilkov, Dimitri; Brinkerhoff, Andrew; Carnes, Andrew; Carver, Matthew; Curry, David; Das, Souvik; Field, Richard D; Furic, Ivan-Kresimir; Konigsberg, Jacobo; Korytov, Andrey; Ma, Peisen; Matchev, Konstantin; Mei, Hualin; Milenovic, Predrag; Mitselmakher, Guenakh; Rank, Douglas; Shchutska, Lesya; Sperka, David; Thomas, Laurent; Wang, Jian; Wang, Sean-Jiun; Yelton, John; Linn, Stephan; Markowitz, Pete; Martinez, German; Rodriguez, Jorge Luis; Ackert, Andrew; Adams, Jordon Rowe; Adams, Todd; Askew, Andrew; Bein, Samuel; Diamond, Brendan; Hagopian, Sharon; Hagopian, Vasken; Johnson, Kurtis F; Khatiwada, Ajeeta; Prosper, Harrison; Santra, Arka; Weinberg, Marc; Baarmand, Marc M; Bhopatkar, Vallary; Colafranceschi, Stefano; Hohlmann, Marcus; Noonan, Daniel; Roy, Titas; Yumiceva, Francisco; Adams, Mark Raymond; Apanasevich, Leonard; Berry, Douglas; Betts, Russell Richard; Bucinskaite, Inga; Cavanaugh, Richard; Evdokimov, Olga; Gauthier, Lucie; Gerber, Cecilia Elena; Hofman, David Jonathan; Kurt, Pelin; O'Brien, Christine; Sandoval Gonzalez, Irving Daniel; Turner, Paul; Varelas, Nikos; Wang, Hui; Wu, Zhenbin; Zakaria, Mohammed; Zhang, Jingyu; Bilki, Burak; Clarida, Warren; Dilsiz, Kamuran; Durgut, Süleyman; Gandrajula, Reddy Pratap; Haytmyradov, Maksat; Khristenko, Viktor; Merlo, Jean-Pierre; Mermerkaya, Hamit; Mestvirishvili, Alexi; Moeller, Anthony; Nachtman, Jane; Ogul, Hasan; Onel, Yasar; Ozok, Ferhat; Penzo, Aldo; Snyder, Christina; Tiras, Emrah; Wetzel, James; Yi, Kai; Anderson, Ian; Blumenfeld, Barry; Cocoros, Alice; Eminizer, Nicholas; Fehling, David; Feng, Lei; Gritsan, Andrei; Maksimovic, Petar; Osherson, Marc; Roskes, Jeffrey; Sarica, Ulascan; Swartz, Morris; Xiao, Meng; Xin, Yongjie; You, Can; Al-bataineh, Ayman; Baringer, Philip; Bean, Alice; Bowen, James; Bruner, Christopher; Castle, James; Kenny III, Raymond Patrick; Kropivnitskaya, Anna; Majumder, Devdatta; Mcbrayer, William; Murray, Michael; Sanders, Stephen; Stringer, Robert; Tapia Takaki, Daniel; Wang, Quan; Ivanov, Andrew; Kaadze, Ketino; Khalil, Sadia; Makouski, Mikhail; Maravin, Yurii; Mohammadi, Abdollah; Saini, Lovedeep Kaur; Skhirtladze, Nikoloz; Toda, Sachiko; Lange, David; Rebassoo, Finn; Wright, Douglas; Anelli, Christopher; Baden, Drew; Baron, Owen; Belloni, Alberto; Calvert, Brian; Eno, Sarah Catherine; Ferraioli, Charles; Gomez, Jaime; Hadley, Nicholas John; Jabeen, Shabnam; Kellogg, Richard G; Kolberg, Ted; Kunkle, Joshua; Lu, Ying; Mignerey, Alice; Shin, Young Ho; Skuja, Andris; Tonjes, Marguerite; Tonwar, Suresh C; Abercrombie, Daniel; Allen, Brandon; Apyan, Aram; Barbieri, Richard; Baty, Austin; Bi, Ran; Bierwagen, Katharina; Brandt, Stephanie; Busza, Wit; Cali, Ivan Amos; Demiragli, Zeynep; Di Matteo, Leonardo; Gomez Ceballos, Guillelmo; Goncharov, Maxim; Hsu, Dylan; Iiyama, Yutaro; Innocenti, Gian Michele; Klute, Markus; Kovalskyi, Dmytro; Krajczar, Krisztian; Lai, Yue Shi; Lee, Yen-Jie; Levin, Andrew; Luckey, Paul David; Marini, Andrea Carlo; Mcginn, Christopher; Mironov, Camelia; Narayanan, Siddharth; Niu, Xinmei; Paus, Christoph; Roland, Christof; Roland, Gunther; Salfeld-Nebgen, Jakob; Stephans, George; Sumorok, Konstanty; Tatar, Kaya; Varma, Mukund; Velicanu, Dragos; Veverka, Jan; Wang, Jing; Wang, Ta-Wei; Wyslouch, Bolek; Yang, Mingming; Zhukova, Victoria; Benvenuti, Alberto; Chatterjee, Rajdeep Mohan; Evans, Andrew; Finkel, Alexey; Gude, Alexander; Hansen, Peter; Kalafut, Sean; Kao, Shih-Chuan; Kubota, Yuichi; Lesko, Zachary; Mans, Jeremy; Nourbakhsh, Shervin; Ruckstuhl, Nicole; Rusack, Roger; Tambe, Norbert; Turkewitz, Jared; Acosta, John Gabriel; Oliveros, Sandra; Avdeeva, Ekaterina; Bartek, Rachel; Bloom, Kenneth; Bose, Suvadeep; Claes, Daniel R; Dominguez, Aaron; Fangmeier, Caleb; Gonzalez Suarez, Rebeca; Kamalieddin, Rami; Knowlton, Dan; Kravchenko, Ilya; Malta Rodrigues, Alan; Meier, Frank; Monroy, Jose; Siado, Joaquin Emilo; Snow, Gregory R; Stieger, Benjamin; Alyari, Maral; Dolen, James; George, Jimin; Godshalk, Andrew; Harrington, Charles; Iashvili, Ia; Kaisen, Josh; Kharchilava, Avto; Kumar, Ashish; Parker, Ashley; Rappoccio, Salvatore; Roozbahani, Bahareh; Alverson, George; Barberis, Emanuela; Baumgartel, Darin; Hortiangtham, Apichart; Massironi, Andrea; Morse, David Michael; Nash, David; Orimoto, Toyoko; Teixeira De Lima, Rafael; Trocino, Daniele; Wang, Ren-Jie; Wood, Darien; Bhattacharya, Saptaparna; Hahn, Kristan Allan; Kubik, Andrew; Low, Jia Fu; Mucia, Nicholas; Odell, Nathaniel; Pollack, Brian; Schmitt, Michael Henry; Sung, Kevin; Trovato, Marco; Velasco, Mayda; Dev, Nabarun; Hildreth, Michael; Hurtado Anampa, Kenyi; Jessop, Colin; Karmgard, Daniel John; Kellams, Nathan; Lannon, Kevin; Marinelli, Nancy; Meng, Fanbo; Mueller, Charles; Musienko, Yuri; Planer, Michael; Reinsvold, Allison; Ruchti, Randy; Smith, Geoffrey; Taroni, Silvia; Valls, Nil; Wayne, Mitchell; Wolf, Matthias; Woodard, Anna; Alimena, Juliette; Antonelli, Louis; Brinson, Jessica; Bylsma, Ben; Durkin, Lloyd Stanley; Flowers, Sean; Francis, Brian; Hart, Andrew; Hill, Christopher; Hughes, Richard; Ji, Weifeng; Liu, Bingxuan; Luo, Wuming; Puigh, Darren; Winer, Brian L; Wulsin, Howard Wells; Cooperstein, Stephane; Driga, Olga; Elmer, Peter; Hardenbrook, Joshua; Hebda, Philip; Luo, Jingyu; Marlow, Daniel; Medvedeva, Tatiana; Mooney, Michael; Olsen, James; Palmer, Christopher; Piroué, Pierre; Stickland, David; Tully, Christopher; Zuranski, Andrzej; Malik, Sudhir; Barker, Anthony; Barnes, Virgil E; Benedetti, Daniele; Folgueras, Santiago; Gutay, Laszlo; Jha, Manoj; Jones, Matthew; Jung, Andreas Werner; Jung, Kurt; Miller, David Harry; Neumeister, Norbert; Radburn-Smith, Benjamin Charles; Shi, Xin; Sun, Jian; Svyatkovskiy, Alexey; Wang, Fuqiang; Xie, Wei; Xu, Lingshan; Parashar, Neeti; Stupak, John; Adair, Antony; Akgun, Bora; Chen, Zhenyu; Ecklund, Karl Matthew; Geurts, Frank JM; Guilbaud, Maxime; Li, Wei; Michlin, Benjamin; Northup, Michael; Padley, Brian Paul; Redjimi, Radia; Roberts, Jay; Rorie, Jamal; Tu, Zhoudunming; Zabel, James; Betchart, Burton; Bodek, Arie; de Barbaro, Pawel; Demina, Regina; Duh, Yi-ting; Ferbel, Thomas; Galanti, Mario; Garcia-Bellido, Aran; Han, Jiyeon; Hindrichs, Otto; Khukhunaishvili, Aleko; Lo, Kin Ho; Tan, Ping; Verzetti, Mauro; Chou, John Paul; Contreras-Campana, Emmanuel; Gershtein, Yuri; Gómez Espinosa, Tirso Alejandro; Halkiadakis, Eva; Heindl, Maximilian; Hidas, Dean; Hughes, Elliot; Kaplan, Steven; Kunnawalkam Elayavalli, Raghav; Kyriacou, Savvas; Lath, Amitabh; Nash, Kevin; Saka, Halil; Salur, Sevil; Schnetzer, Steve; Sheffield, David; Somalwar, Sunil; Stone, Robert; Thomas, Scott; Thomassen, Peter; Walker, Matthew; Foerster, Mark; Heideman, Joseph; Riley, Grant; Rose, Keith; Spanier, Stefan; Thapa, Krishna; Bouhali, Othmane; Celik, Ali; Dalchenko, Mykhailo; De Mattia, Marco; Delgado, Andrea; Dildick, Sven; Eusebi, Ricardo; Gilmore, Jason; Huang, Tao; Juska, Evaldas; Kamon, Teruki; Mueller, Ryan; Pakhotin, Yuriy; Patel, Rishi; Perloff, Alexx; Perniè, Luca; Rathjens, Denis; Rose, Anthony; Safonov, Alexei; Tatarinov, Aysen; Ulmer, Keith; Akchurin, Nural; Cowden, Christopher; Damgov, Jordan; Dragoiu, Cosmin; Dudero, Phillip Russell; Faulkner, James; Kunori, Shuichi; Lamichhane, Kamal; Lee, Sung Won; Libeiro, Terence; Undleeb, Sonaina; Volobouev, Igor; Wang, Zhixing; Delannoy, Andrés G; Greene, Senta; Gurrola, Alfredo; Janjam, Ravi; Johns, Willard; Maguire, Charles; Melo, Andrew; Ni, Hong; Sheldon, Paul; Tuo, Shengquan; Velkovska, Julia; Xu, Qiao; Arenton, Michael Wayne; Barria, Patrizia; Cox, Bradley; Goodell, Joseph; Hirosky, Robert; Ledovskoy, Alexander; Li, Hengne; Neu, Christopher; Sinthuprasith, Tutanon; Sun, Xin; Wang, Yanchu; Wolfe, Evan; Xia, Fan; Clarke, Christopher; Harr, Robert; Karchin, Paul Edmund; Lamichhane, Pramod; Sturdy, Jared; Belknap, Donald; Dasu, Sridhara; Dodd, Laura; Duric, Senka; Gomber, Bhawna; Grothe, Monika; Herndon, Matthew; Hervé, Alain; Klabbers, Pamela; Lanaro, Armando; Levine, Aaron; Long, Kenneth; Loveless, Richard; Ojalvo, Isabel; Perry, Thomas; Pierro, Giuseppe Antonio; Polese, Giovanni; Ruggles, Tyler; Savin, Alexander; Sharma, Archana; Smith, Nicholas; Smith, Wesley H; Taylor, Devin; Woods, Nathaniel

    2017-02-07

    Single top quark events produced in the $t$ channel are used to set limits on anomalous Wtb couplings and to search for top quark flavour-changing neutral current (FCNC) interactions. The data taken with the CMS detector at the LHC in proton-proton collisions at $\\sqrt{s}=$ 7 and 8 TeV correspond to integrated luminosities of 5.0 and 19.7 fb$^{-1}$, respectively. The analysis is performed using events with one muon and two or three jets. A Bayesian neural network technique, used to discriminate between the signal and backgrounds, is found to be consistent with the standard model prediction. The 95% confidence level (CL) exclusion limits on anomalous right-handed vector, and left- and right-handed tensor Wtb couplings are measured to be $|f_{\\rm V}^{\\rm R}|< 0.16$, $|f_{\\rm T}^{\\rm L}|<0.057$, and $-0.049couplings $\\kappa_{\\rm tug}$ and $\\kappa_{\\rm tcg}$, the 95% CL upper limits on coupling strengths are $|\\kappa_{\\rm tug}|/\\Lambda < 4.1 \\ti...

  10. Neural and morphological adaptations of vastus lateralis and vastus medialis muscles to isokinetic eccentric training

    Directory of Open Access Journals (Sweden)

    Rodrigo de Azevedo Franke

    2014-09-01

    Full Text Available Vastus lateralis (VL and vastus medialis (VM are frequently targeted in conditioning/rehabilitation programs due to their role in patellar stabilization during knee extension. This study assessed neural and muscular adaptations in these two muscles after an isokinetic eccentric training program. Twenty healthy men underwent a four-week control period followed by a 12-week period of isokinetic eccentric training. Ultrasound evaluations of VL and VM muscle thickness at rest and electromyographic evaluations during maximal isometric tests were used to assess the morphological and neural properties, respectively. No morphological and neural changes were found throughout the control period, whereas both muscles showed significant increases in thickness (VL = 6.9%; p .05 post-training. Isokinetic eccentric training produces neural and greater morphological adaptations in VM compared to VL, which shows that synergistic muscles respond differently to an eccentric isokinetic strength training program

  11. Status of Higgs couplings after run 1 of the LHC

    Science.gov (United States)

    Bernon, Jérémy; Dumont, Béranger; Kraml, Sabine

    2014-10-01

    We provide an update of the global fits of the couplings of the 125.5 GeV Higgs boson using all publicly available experimental results from run 1 of the LHC as per summer 2014. The fits are done by means of the new public code Lilith 1.0. We present a selection of results given in terms of signal strengths, reduced couplings, and for the two-Higgs-doublet models of type I and II.

  12. Cooperating attackers in neural cryptography.

    Science.gov (United States)

    Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

    2004-06-01

    A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

  13. Cooperating attackers in neural cryptography

    Science.gov (United States)

    Shacham, Lanir N.; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

    2004-06-01

    A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the “majority-flipping attacker,” does not decay with the parameters of the model. This attacker’s outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

  14. Neural Computations in Binaural Hearing

    Science.gov (United States)

    Wagner, Hermann

    Binaural hearing helps humans and animals to localize and unmask sounds. Here, binaural computations in the barn owl's auditory system are discussed. Barn owls use the interaural time difference (ITD) for azimuthal sound localization, and they use the interaural level difference (ELD) for elevational sound localization. ITD and ILD and their precursors are processed in separate neural pathways, the time pathway and the intensity pathway, respectively. Representation of ITD involves four main computational steps, while the representation of ILD is accomplished in three steps. In the discussion neural processing in the owl's auditory system is compared with neural computations present in mammals.

  15. Neural Population Dynamics Modeled by Mean-Field Graphs

    Science.gov (United States)

    Kozma, Robert; Puljic, Marko

    2011-09-01

    In this work we apply random graph theory approach to describe neural population dynamics. There are important advantages of using random graph theory approach in addition to ordinary and partial differential equations. The mathematical theory of large-scale random graphs provides an efficient tool to describe transitions between high- and low-dimensional spaces. Recent advances in studying neural correlates of higher cognition indicate the significance of sudden changes in space-time neurodynamics, which can be efficiently described as phase transitions in the neuropil medium. Phase transitions are rigorously defined mathematically on random graph sequences and they can be naturally generalized to a class of percolation processes called neuropercolation. In this work we employ mean-field graphs with given vertex degree distribution and edge strength distribution. We demonstrate the emergence of collective oscillations in the style of brains.

  16. Recursive Bayesian recurrent neural networks for time-series modeling.

    Science.gov (United States)

    Mirikitani, Derrick T; Nikolaev, Nikolay

    2010-02-01

    This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

  17. Lifting strength in two-person teamwork.

    Science.gov (United States)

    Lee, Tzu-Hsien

    2016-01-01

    This study examined the effects of lifting range, hand-to-toe distance, and lifting direction on single-person lifting strengths and two-person teamwork lifting strengths. Six healthy males and seven healthy females participated in this study. Two-person teamwork lifting strengths were examined in both strength-matched and strength-unmatched groups. Our results showed that lifting strength significantly decreased with increasing lifting range or hand-to-toe distance. However, lifting strengths were not affected by lifting direction. Teamwork lifting strength did not conform to the law of additivity for both strength-matched and strength-unmatched groups. In general, teamwork lifting strength was dictated by the weaker of the two members, implying that weaker members might be exposed to a higher potential danger in teamwork exertions. To avoid such overexertion in teamwork, members with significantly different strength ability should not be assigned to the same team.

  18. International Migration of Couples

    OpenAIRE

    Junge, Martin; Munk, Martin D.; Nikolka, Till; Poutvaara, Panu

    2017-01-01

    We analyze emigration and return decisions of Danish couples. Our main questions are how emigration and return migration decisions depend on education, earnings, and the number and age of children. We use register data on full population from 1982 to 2006, focusing on opposite-gender couples in which the female is aged 23 to 37, and the male 25 to 39. We find that power couples in which both are highly educated are most likely to emigrate, but also most likely to return. Couples in which only...

  19. Neural Manifolds for the Control of Movement.

    Science.gov (United States)

    Gallego, Juan A; Perich, Matthew G; Miller, Lee E; Solla, Sara A

    2017-06-07

    The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the "neural modes." We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement. Copyright © 2017 Elsevier Inc. All rights reserved.

  20. Information-geometric measures estimate neural interactions during oscillatory brain states.

    Science.gov (United States)

    Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

    2014-01-01

    The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

  1. Information-geometric measures estimate neural interactions during oscillatory brain states

    Directory of Open Access Journals (Sweden)

    Yimin eNie

    2014-02-01

    Full Text Available The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG, a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

  2. Neural network-based sliding mode control for atmospheric-actuated spacecraft formation using switching strategy

    Science.gov (United States)

    Sun, Ran; Wang, Jihe; Zhang, Dexin; Shao, Xiaowei

    2018-02-01

    This paper presents an adaptive neural networks-based control method for spacecraft formation with coupled translational and rotational dynamics using only aerodynamic forces. It is assumed that each spacecraft is equipped with several large flat plates. A coupled orbit-attitude dynamic model is considered based on the specific configuration of atmospheric-based actuators. For this model, a neural network-based adaptive sliding mode controller is implemented, accounting for system uncertainties and external perturbations. To avoid invalidation of the neural networks destroying stability of the system, a switching control strategy is proposed which combines an adaptive neural networks controller dominating in its active region and an adaptive sliding mode controller outside the neural active region. An optimal process is developed to determine the control commands for the plates system. The stability of the closed-loop system is proved by a Lyapunov-based method. Comparative results through numerical simulations illustrate the effectiveness of executing attitude control while maintaining the relative motion, and higher control accuracy can be achieved by using the proposed neural-based switching control scheme than using only adaptive sliding mode controller.

  3. Engaged listeners: shared neural processing of powerful political speeches.

    Science.gov (United States)

    Schmälzle, Ralf; Häcker, Frank E K; Honey, Christopher J; Hasson, Uri

    2015-08-01

    Powerful speeches can captivate audiences, whereas weaker speeches fail to engage their listeners. What is happening in the brains of a captivated audience? Here, we assess audience-wide functional brain dynamics during listening to speeches of varying rhetorical quality. The speeches were given by German politicians and evaluated as rhetorically powerful or weak. Listening to each of the speeches induced similar neural response time courses, as measured by inter-subject correlation analysis, in widespread brain regions involved in spoken language processing. Crucially, alignment of the time course across listeners was stronger for rhetorically powerful speeches, especially for bilateral regions of the superior temporal gyri and medial prefrontal cortex. Thus, during powerful speeches, listeners as a group are more coupled to each other, suggesting that powerful speeches are more potent in taking control of the listeners' brain responses. Weaker speeches were processed more heterogeneously, although they still prompted substantially correlated responses. These patterns of coupled neural responses bear resemblance to metaphors of resonance, which are often invoked in discussions of speech impact, and contribute to the literature on auditory attention under natural circumstances. Overall, this approach opens up possibilities for research on the neural mechanisms mediating the reception of entertaining or persuasive messages. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  4. Identifying neural drivers with functional MRI: an electrophysiological validation.

    Directory of Open Access Journals (Sweden)

    Olivier David

    2008-12-01

    Full Text Available Whether functional magnetic resonance imaging (fMRI allows the identification of neural drivers remains an open question of particular importance to refine physiological and neuropsychological models of the brain, and/or to understand neurophysiopathology. Here, in a rat model of absence epilepsy showing spontaneous spike-and-wave discharges originating from the first somatosensory cortex (S1BF, we performed simultaneous electroencephalographic (EEG and fMRI measurements, and subsequent intracerebral EEG (iEEG recordings in regions strongly activated in fMRI (S1BF, thalamus, and striatum. fMRI connectivity was determined from fMRI time series directly and from hidden state variables using a measure of Granger causality and Dynamic Causal Modelling that relates synaptic activity to fMRI. fMRI connectivity was compared to directed functional coupling estimated from iEEG using asymmetry in generalised synchronisation metrics. The neural driver of spike-and-wave discharges was estimated in S1BF from iEEG, and from fMRI only when hemodynamic effects were explicitly removed. Functional connectivity analysis applied directly on fMRI signals failed because hemodynamics varied between regions, rendering temporal precedence irrelevant. This paper provides the first experimental substantiation of the theoretical possibility to improve interregional coupling estimation from hidden neural states of fMRI. As such, it has important implications for future studies on brain connectivity using functional neuroimaging.

  5. Epidemiology of neural tube defects

    National Research Council Canada - National Science Library

    Seidahmed, Mohammed Z; Abdelbasit, Omar B; Shaheed, Meeralebbae M; Alhussein, Khalid A; Miqdad, Abeer M; Khalil, Mohamed I; Al-Enazy, Naif M; Salih, Mustafa A

    2014-01-01

    To find the prevalence of neural tube defects (NTDs), and compare the findings with local and international data, and highlight the important role of folic acid supplementation and flour fortification with folic acid in preventing NTDs...

  6. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  7. Memristor-based neural networks

    Science.gov (United States)

    Thomas, Andy

    2013-03-01

    The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them.

  8. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    simulated process and compared. The closing chapter describes some practical experiments, where the different control concepts and training methods are tested on the same practical process operating in very noisy environments. All tests confirm that neural networks also have the potential to be trained......The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...

  9. Neural components of altruistic punishment

    Directory of Open Access Journals (Sweden)

    Emily eDu

    2015-02-01

    Full Text Available Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

  10. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

    This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

  11. CHARGEd with neural crest defects.

    Science.gov (United States)

    Pauli, Silke; Bajpai, Ruchi; Borchers, Annette

    2017-10-30

    Neural crest cells are highly migratory pluripotent cells that give rise to diverse derivatives including cartilage, bone, smooth muscle, pigment, and endocrine cells as well as neurons and glia. Abnormalities in neural crest-derived tissues contribute to the etiology of CHARGE syndrome, a complex malformation disorder that encompasses clinical symptoms like coloboma, heart defects, atresia of the choanae, retarded growth and development, genital hypoplasia, ear anomalies, and deafness. Mutations in the chromodomain helicase DNA-binding protein 7 (CHD7) gene are causative of CHARGE syndrome and loss-of-function data in different model systems have firmly established a role of CHD7 in neural crest development. Here, we will summarize our current understanding of the function of CHD7 in neural crest development and discuss possible links of CHARGE syndrome to other developmental disorders. © 2017 Wiley Periodicals, Inc.

  12. Neural components of altruistic punishment.

    Science.gov (United States)

    Du, Emily; Chang, Steve W C

    2015-01-01

    Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

  13. Pansharpening by Convolutional Neural Networks

    National Research Council Canada - National Science Library

    Masi, Giuseppe; Cozzolino, Davide; Verdoliva, Luisa; Scarpa, Giuseppe

    2016-01-01

    A new pansharpening method is proposed, based on convolutional neural networks. We adapt a simple and effective three-layer architecture recently proposed for super-resolution to the pansharpening problem...

  14. Synchronization-based computation through networks of coupled oscillators

    Directory of Open Access Journals (Sweden)

    Daniel eMalagarriga

    2015-08-01

    Full Text Available The mesoscopic activity of the brain is strongly dynamical, while at the sametime exhibiting remarkable computational capabilities. In order to examinehow these two features coexist, here we show that the patterns of synchronizedoscillations displayed by networks of neural mass models, representing cortical columns, can be usedas substrates for Boolean computation. Our results reveal that different logicaloperations can be implemented by the same neural mass network at different timesfollowing the dynamics of the input. The results are reproduced experimentallywith electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the oscillators responsible for the functioning of the gates. We also show that theinformation-processing capabilities of coupled oscillations go beyond thesimple juxtaposition of logic gates.

  15. Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

    Directory of Open Access Journals (Sweden)

    Andrea Maesani

    2015-11-01

    Full Text Available The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

  16. Iterative free-energy optimization for recurrent neural networks (INFERNO)

    Science.gov (United States)

    2017-01-01

    The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle. PMID:28282439

  17. Mechanotransduction of Neural Cells Through Cell–Substrate Interactions

    Science.gov (United States)

    Stukel, Jessica M.

    2016-01-01

    Neurons and neural stem cells are sensitive to their mechanical and topographical environment, and cell–substrate binding contributes to this sensitivity to activate signaling pathways for basic cell functions. Many transmembrane proteins transmit signals into and out of the cell, including integrins, growth factor receptors, G-protein-coupled receptors, cadherins, cell adhesion molecules, and ion channels. Specifically, integrins are one of the main transmembrane proteins that transmit force across the cell membrane between a cell and its extracellular matrix, making them critical in the study of cell–material interactions. This review focuses on mechanotransduction, defined as the conversion of force a cell generates through cell–substrate bonds to a chemical signal, of neural cells. The chemical signals relay information via pathways through the cellular cytoplasm to the nucleus, where signaling events can affect gene expression. Pathways and the cellular response initiated by substrate binding are explored to better understand their effect on neural cells mechanotransduction. As the results of mechanotransduction affect cell adhesion, cell shape, and differentiation, knowledge regarding neural mechanotransduction is critical for most regenerative strategies in tissue engineering, where novel environments are developed to improve conduit design for central and peripheral nervous system repair in vivo. PMID:26669274

  18. Mechanotransduction of Neural Cells Through Cell-Substrate Interactions.

    Science.gov (United States)

    Stukel, Jessica M; Willits, Rebecca Kuntz

    2016-06-01

    Neurons and neural stem cells are sensitive to their mechanical and topographical environment, and cell-substrate binding contributes to this sensitivity to activate signaling pathways for basic cell functions. Many transmembrane proteins transmit signals into and out of the cell, including integrins, growth factor receptors, G-protein-coupled receptors, cadherins, cell adhesion molecules, and ion channels. Specifically, integrins are one of the main transmembrane proteins that transmit force across the cell membrane between a cell and its extracellular matrix, making them critical in the study of cell-material interactions. This review focuses on mechanotransduction, defined as the conversion of force a cell generates through cell-substrate bonds to a chemical signal, of neural cells. The chemical signals relay information via pathways through the cellular cytoplasm to the nucleus, where signaling events can affect gene expression. Pathways and the cellular response initiated by substrate binding are explored to better understand their effect on neural cells mechanotransduction. As the results of mechanotransduction affect cell adhesion, cell shape, and differentiation, knowledge regarding neural mechanotransduction is critical for most regenerative strategies in tissue engineering, where novel environments are developed to improve conduit design for central and peripheral nervous system repair in vivo.

  19. Iterative free-energy optimization for recurrent neural networks (INFERNO).

    Science.gov (United States)

    Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias

    2017-01-01

    The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

  20. Iterative free-energy optimization for recurrent neural networks (INFERNO.

    Directory of Open Access Journals (Sweden)

    Alexandre Pitti

    Full Text Available The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.