WorldWideScience

Sample records for refining neural connectivity

  1. Finite connectivity attractor neural networks

    International Nuclear Information System (INIS)

    Wemmenhove, B; Coolen, A C C

    2003-01-01

    We study a family of diluted attractor neural networks with a finite average number of (symmetric) connections per neuron. As in finite connectivity spin glasses, their equilibrium properties are described by order parameter functions, for which we derive an integral equation in replica symmetric approximation. A bifurcation analysis of this equation reveals the locations of the paramagnetic to recall and paramagnetic to spin-glass transition lines in the phase diagram. The line separating the retrieval phase from the spin-glass phase is calculated at zero temperature. All phase transitions are found to be continuous

  2. Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity

    Energy Technology Data Exchange (ETDEWEB)

    Li Xiumin; Small, Michael, E-mail: ensmall@polyu.edu.h, E-mail: 07901216r@eie.polyu.edu.h [Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong)

    2010-08-15

    Long-term synaptic plasticity induced by neural activity is of great importance in informing the formation of neural connectivity and the development of the nervous system. It is reasonable to consider self-organized neural networks instead of prior imposition of a specific topology. In this paper, we propose a novel network evolved from two stages of the learning process, which are respectively guided by two experimentally observed synaptic plasticity rules, i.e. the spike-timing-dependent plasticity (STDP) mechanism and the burst-timing-dependent plasticity (BTDP) mechanism. Due to the existence of heterogeneity in neurons that exhibit different degrees of excitability, a two-level hierarchical structure is obtained after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the small-world properties of small shortest path length and high clustering coefficients. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the network.

  3. Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity

    International Nuclear Information System (INIS)

    Li Xiumin; Small, Michael

    2010-01-01

    Long-term synaptic plasticity induced by neural activity is of great importance in informing the formation of neural connectivity and the development of the nervous system. It is reasonable to consider self-organized neural networks instead of prior imposition of a specific topology. In this paper, we propose a novel network evolved from two stages of the learning process, which are respectively guided by two experimentally observed synaptic plasticity rules, i.e. the spike-timing-dependent plasticity (STDP) mechanism and the burst-timing-dependent plasticity (BTDP) mechanism. Due to the existence of heterogeneity in neurons that exhibit different degrees of excitability, a two-level hierarchical structure is obtained after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the small-world properties of small shortest path length and high clustering coefficients. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the network.

  4. Validating neural-network refinements of nuclear mass models

    Science.gov (United States)

    Utama, R.; Piekarewicz, J.

    2018-01-01

    Background: Nuclear astrophysics centers on the role of nuclear physics in the cosmos. In particular, nuclear masses at the limits of stability are critical in the development of stellar structure and the origin of the elements. Purpose: We aim to test and validate the predictions of recently refined nuclear mass models against the newly published AME2016 compilation. Methods: The basic paradigm underlining the recently refined nuclear mass models is based on existing state-of-the-art models that are subsequently refined through the training of an artificial neural network. Bayesian inference is used to determine the parameters of the neural network so that statistical uncertainties are provided for all model predictions. Results: We observe a significant improvement in the Bayesian neural network (BNN) predictions relative to the corresponding "bare" models when compared to the nearly 50 new masses reported in the AME2016 compilation. Further, AME2016 estimates for the handful of impactful isotopes in the determination of r -process abundances are found to be in fairly good agreement with our theoretical predictions. Indeed, the BNN-improved Duflo-Zuker model predicts a root-mean-square deviation relative to experiment of σrms≃400 keV. Conclusions: Given the excellent performance of the BNN refinement in confronting the recently published AME2016 compilation, we are confident of its critical role in our quest for mass models of the highest quality. Moreover, as uncertainty quantification is at the core of the BNN approach, the improved mass models are in a unique position to identify those nuclei that will have the strongest impact in resolving some of the outstanding questions in nuclear astrophysics.

  5. Knowledge synthesis with maps of neural connectivity.

    Science.gov (United States)

    Tallis, Marcelo; Thompson, Richard; Russ, Thomas A; Burns, Gully A P C

    2011-01-01

    This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called "Knowledge Engineering from Experimental Design" (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

  6. Activity-dependent modulation of neural circuit synaptic connectivity

    Directory of Open Access Journals (Sweden)

    Charles R Tessier

    2009-07-01

    Full Text Available In many nervous systems, the establishment of neural circuits is known to proceed via a two-stage process; 1 early, activity-independent wiring to produce a rough map characterized by excessive synaptic connections, and 2 subsequent, use-dependent pruning to eliminate inappropriate connections and reinforce maintained synapses. In invertebrates, however, evidence of the activity-dependent phase of synaptic refinement has been elusive, and the dogma has long been that invertebrate circuits are “hard-wired” in a purely activity-independent manner. This conclusion has been challenged recently through the use of new transgenic tools employed in the powerful Drosophila system, which have allowed unprecedented temporal control and single neuron imaging resolution. These recent studies reveal that activity-dependent mechanisms are indeed required to refine circuit maps in Drosophila during precise, restricted windows of late-phase development. Such mechanisms of circuit refinement may be key to understanding a number of human neurological diseases, including developmental disorders such as Fragile X syndrome (FXS and autism, which are hypothesized to result from defects in synaptic connectivity and activity-dependent circuit function. This review focuses on our current understanding of activity-dependent synaptic connectivity in Drosophila, primarily through analyzing the role of the fragile X mental retardation protein (FMRP in the Drosophila FXS disease model. The particular emphasis of this review is on the expanding array of new genetically-encoded tools that are allowing cellular events and molecular players to be dissected with ever greater precision and detail.

  7. Knowledge synthesis with maps of neural connectivity

    Directory of Open Access Journals (Sweden)

    Marcelo eTallis

    2011-11-01

    Full Text Available This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets.

  8. Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

    Directory of Open Access Journals (Sweden)

    Tao Ye

    2018-06-01

    Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

  9. Refining mass formulas for astrophysical applications: A Bayesian neural network approach

    Science.gov (United States)

    Utama, R.; Piekarewicz, J.

    2017-10-01

    Background: Exotic nuclei, particularly those near the drip lines, are at the core of one of the fundamental questions driving nuclear structure and astrophysics today: What are the limits of nuclear binding? Exotic nuclei play a critical role in both informing theoretical models as well as in our understanding of the origin of the heavy elements. Purpose: Our aim is to refine existing mass models through the training of an artificial neural network that will mitigate the large model discrepancies far away from stability. Methods: The basic paradigm of our two-pronged approach is an existing mass model that captures as much as possible of the underlying physics followed by the implementation of a Bayesian neural network (BNN) refinement to account for the missing physics. Bayesian inference is employed to determine the parameters of the neural network so that model predictions may be accompanied by theoretical uncertainties. Results: Despite the undeniable quality of the mass models adopted in this work, we observe a significant improvement (of about 40%) after the BNN refinement is implemented. Indeed, in the specific case of the Duflo-Zuker mass formula, we find that the rms deviation relative to experiment is reduced from σrms=0.503 MeV to σrms=0.286 MeV. These newly refined mass tables are used to map the neutron drip lines (or rather "drip bands") and to study a few critical r -process nuclei. Conclusions: The BNN approach is highly successful in refining the predictions of existing mass models. In particular, the large discrepancy displayed by the original "bare" models in regions where experimental data are unavailable is considerably quenched after the BNN refinement. This lends credence to our approach and has motivated us to publish refined mass tables that we trust will be helpful for future astrophysical applications.

  10. Attractor neural networks with resource-efficient synaptic connectivity

    Science.gov (United States)

    Pehlevan, Cengiz; Sengupta, Anirvan

    Memories are thought to be stored in the attractor states of recurrent neural networks. Here we explore how resource constraints interplay with memory storage function to shape synaptic connectivity of attractor networks. We propose that given a set of memories, in the form of population activity patterns, the neural circuit choses a synaptic connectivity configuration that minimizes a resource usage cost. We argue that the total synaptic weight (l1-norm) in the network measures the resource cost because synaptic weight is correlated with synaptic volume, which is a limited resource, and is proportional to neurotransmitter release and post-synaptic current, both of which cost energy. Using numerical simulations and replica theory, we characterize optimal connectivity profiles in resource-efficient attractor networks. Our theory explains several experimental observations on cortical connectivity profiles, 1) connectivity is sparse, because synapses are costly, 2) bidirectional connections are overrepresented and 3) are stronger, because attractor states need strong recurrence.

  11. Neural networks applied to characterize blends containing refined and extra virgin olive oils.

    Science.gov (United States)

    Aroca-Santos, Regina; Cancilla, John C; Pariente, Enrique S; Torrecilla, José S

    2016-12-01

    The identification and quantification of binary blends of refined olive oil with four different extra virgin olive oil (EVOO) varietals (Picual, Cornicabra, Hojiblanca and Arbequina) was carried out with a simple method based on combining visible spectroscopy and non-linear artificial neural networks (ANNs). The data obtained from the spectroscopic analysis was treated and prepared to be used as independent variables for a multilayer perceptron (MLP) model. The model was able to perfectly classify the EVOO varietal (100% identification rate), whereas the error for the quantification of EVOO in the mixtures containing between 0% and 20% of refined olive oil, in terms of the mean prediction error (MPE), was 2.14%. These results turn visible spectroscopy and MLP models into a trustworthy, user-friendly, low-cost technique which can be implemented on-line to characterize olive oil mixtures containing refined olive oil and EVOOs. Copyright © 2016 Elsevier B.V. All rights reserved.

  12. Intranasal oxytocin modulates neural functional connectivity during human social interaction.

    Science.gov (United States)

    Rilling, James K; Chen, Xiangchuan; Chen, Xu; Haroon, Ebrahim

    2018-02-10

    Oxytocin (OT) modulates social behavior in primates and many other vertebrate species. Studies in non-primate animals have demonstrated that, in addition to influencing activity within individual brain areas, OT influences functional connectivity across networks of areas involved in social behavior. Previously, we used fMRI to image brain function in human subjects during a dyadic social interaction task following administration of either intranasal oxytocin (INOT) or placebo, and analyzed the data with a standard general linear model. Here, we conduct an extensive re-analysis of these data to explore how OT modulates functional connectivity across a neural network that animal studies implicate in social behavior. OT induced widespread increases in functional connectivity in response to positive social interactions among men and widespread decreases in functional connectivity in response to negative social interactions among women. Nucleus basalis of Meynert, an important regulator of selective attention and motivation with a particularly high density of OT receptors, had the largest number of OT-modulated connections. Regions known to receive mesolimbic dopamine projections such as the nucleus accumbens and lateral septum were also hubs for OT effects on functional connectivity. Our results suggest that the neural mechanism by which OT influences primate social cognition may include changes in patterns of activity across neural networks that regulate social behavior in other animals. © 2018 Wiley Periodicals, Inc.

  13. Exponential stability of neural networks with asymmetric connection weights

    International Nuclear Information System (INIS)

    Yang Jinxiang; Zhong Shouming

    2007-01-01

    This paper investigates the exponential stability of a class of neural networks with asymmetric connection weights. By dividing the network state variables into various parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Lyapunov function and using the method of the variation of constant. The new conditions are associated with the initial values and are described by some blocks of the interconnection matrix, and do not depend on other blocks. Examples are given to further illustrate the theory

  14. Connected Vehicle Pilot Deployment Program Independent Evaluation: Mobility, Environmental, and Public Agency Efficiency Refined Evaluation Plan - New York City

    Science.gov (United States)

    2018-03-01

    The purpose of this report is to provide a refined evaluation plan detailing the approach to be used by the Texas A&M Transportation Institute Connected Vehicle Pilot Deployment Evaluation Team for evaluating the mobility, environmental, and public a...

  15. Neural network connectivity and response latency modelled by stochastic processes

    DEFF Research Database (Denmark)

    Tamborrino, Massimiliano

    is connected to thousands of other neurons. The rst question is: how to model neural networks through stochastic processes? A multivariate Ornstein-Uhlenbeck process, obtained as a diffusion approximation of a jump process, is the proposed answer. Obviously, dependencies between neurons imply dependencies......Stochastic processes and their rst passage times have been widely used to describe the membrane potential dynamics of single neurons and to reproduce neuronal spikes, respectively.However, cerebral cortex in human brains is estimated to contain 10-20 billions of neurons and each of them...... between their spike times. Therefore, the second question is: how to detect neural network connectivity from simultaneously recorded spike trains? Answering this question corresponds to investigate the joint distribution of sequences of rst passage times. A non-parametric method based on copulas...

  16. Connectivity effects in the dynamic model of neural networks

    International Nuclear Information System (INIS)

    Choi, J; Choi, M Y; Yoon, B-G

    2009-01-01

    We study, via extensive Monte Carlo calculations, the effects of connectivity in the dynamic model of neural networks, to observe that the Mattis-state order parameter increases with the number of coupled neurons. Such effects appear more pronounced when the average number of connections is increased by introducing shortcuts in the network. In particular, the power spectra of the order parameter at stationarity are found to exhibit power-law behavior, depending on how the average number of connections is increased. The cluster size distribution of the 'memory-unmatched' sites also follows a power law and possesses strong correlations with the power spectra. It is further observed that the distribution of waiting times for neuron firing fits roughly to a power law, again depending on how neuronal connections are increased

  17. Identification of neural connectivity signatures of autism using machine learning

    Directory of Open Access Journals (Sweden)

    Gopikrishna eDeshpande

    2013-10-01

    Full Text Available Alterations in neural connectivity have been suggested as a signature of the pathobiology of autism. Although disrupted correlation between cortical regions observed from functional MRI is considered to be an explanatory model for autism, the directional causal influence between brain regions is a vital link missing in these studies. The current study focuses on addressing this in an fMRI study of Theory-of-Mind in 15 high-functioning adolescents and adults with autism (ASD and 15 typically developing (TD controls. Participants viewed a series of comic strip vignettes in the MRI scanner and were asked to choose the most logical end to the story from three alternatives, separately for trials involving physical and intentional causality. Causal brain connectivity obtained from a multivariate autoregressive model, along with assessment scores, functional connectivity values, and fractional anisotropy obtained from DTI data for each participant, were submitted to a recursive cluster elimination based support vector machine classifier to determine the accuracy with which the classifier can predict a novel participant’s group membership (ASD or TD. We found a maximum classification accuracy of 95.9 % with 19 features which had the highest discriminative ability between the groups. All of the 19 features were effective connectivity paths, indicating that causal information may be critical in discriminating between ASD and TD groups. These effective connectivity paths were also found to be significantly greater in controls as compared to ASD participants and consisted predominantly of outputs from the fusiform face area and middle temporal gyrus indicating impaired connectivity in ASD participants, particularly in the social brain areas. These findings collectively point towards the fact that alterations in causal brain connectivity in individuals with ASD could serve as a potential non-invasive neuroimaging signature for autism

  18. Training for Micrographia Alters Neural Connectivity in Parkinson's Disease

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    Evelien Nackaerts

    2018-01-01

    Full Text Available Despite recent advances in clarifying the neural networks underlying rehabilitation in Parkinson's disease (PD, the impact of prolonged motor learning interventions on brain connectivity in people with PD is currently unknown. Therefore, the objective of this study was to compare cortical network changes after 6 weeks of visually cued handwriting training (= experimental with a placebo intervention to address micrographia, a common problem in PD. Twenty seven early Parkinson's patients on dopaminergic medication performed a pre-writing task in both the presence and absence of visual cues during behavioral tests and during fMRI. Subsequently, patients were randomized to the experimental (N = 13 or placebo intervention (N = 14 both lasting 6 weeks, after which they underwent the same testing procedure. We used dynamic causal modeling to compare the neural network dynamics in both groups before and after training. Most importantly, intensive writing training propagated connectivity via the left hemispheric visuomotor stream to an increased coupling with the supplementary motor area, not witnessed in the placebo group. Training enhanced communication in the left visuomotor integration system in line with the learned visually steered training. Notably, this pattern was apparent irrespective of the presence of cues, suggesting transfer from cued to uncued handwriting. We conclude that in early PD intensive motor skill learning, which led to clinical improvement, alters cortical network functioning. We showed for the first time in a placebo-controlled design that it remains possible to enhance the drive to the supplementary motor area through motor learning.

  19. Intrinsic connectivity of neural networks in the awake rabbit.

    Science.gov (United States)

    Schroeder, Matthew P; Weiss, Craig; Procissi, Daniel; Disterhoft, John F; Wang, Lei

    2016-04-01

    The way in which the brain is functionally connected into different networks has emerged as an important research topic in order to understand normal neural processing and signaling. Since some experimental manipulations are difficult or unethical to perform in humans, animal models are better suited to investigate this topic. Rabbits are a species that can undergo MRI scanning in an awake and conscious state with minimal preparation and habituation. In this study, we characterized the intrinsic functional networks of the resting New Zealand White rabbit brain using BOLD fMRI data. Group independent component analysis revealed seven networks similar to those previously found in humans, non-human primates and/or rodents including the hippocampus, default mode, cerebellum, thalamus, and visual, somatosensory, and parietal cortices. For the first time, the intrinsic functional networks of the resting rabbit brain have been elucidated demonstrating the rabbit's applicability as a translational animal model. Without the confounding effects of anesthetics or sedatives, future experiments may employ rabbits to understand changes in neural connectivity and brain functioning as a result of experimental manipulation (e.g., temporary or permanent network disruption, learning-related changes, and drug administration). Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Aberrant Neural Connectivity during Emotional Processing Associated with Posttraumatic Stress.

    Science.gov (United States)

    Sadeh, Naomi; Spielberg, Jeffrey M; Warren, Stacie L; Miller, Gregory A; Heller, Wendy

    2014-11-01

    Given the complexity of the brain, characterizing relations among distributed brain regions is likely essential to describing the neural instantiation of posttraumatic stress symptoms. This study examined patterns of functional connectivity among key brain regions implicated in the pathophysiology of posttraumatic stress disorder (PTSD) in 35 trauma-exposed adults using an emotion-word Stroop task. PTSD symptom severity (particularly hyperarousal symptoms) moderated amygdala-mPFC coupling during the processing of unpleasant words, and this moderation correlated positively with reported real-world impairment and amygdala reactivity. Reexperiencing severity moderated hippocampus-insula coupling during pleasant and unpleasant words. Results provide evidence that PTSD symptoms differentially moderate functional coupling during emotional interference and underscore the importance of examining network connectivity in research on PTSD. They suggest that hyperarousal is associated with negative mPFC-amygdala coupling and that reexperiencing is associated with altered insula-hippocampus function, patterns of connectivity that may represent separable indicators of dysfunctional inhibitory control during affective processing.

  1. Application of neural networks to the petroleum refining industry; Aplicando redes neurais a industria de refino de petroleo

    Energy Technology Data Exchange (ETDEWEB)

    Silva, R M.C.F. da [PETROBRAS, Rio de Janeiro, RJ (Brazil); Chaves, C. [Fundacao Gorceix, Belo Horizonte, MG (Brazil)

    2000-07-01

    Neural Network technology is an approach for describing behavior from process data, using mathematical algorithms and statistical techniques. The use of Neural Network in industrial process modeling and property estimation of feedstocks or products, is increasing in several kinds of chemical industries. This paper comments about critical successful factors, advantages and disadvantages of this methodology. Moreover, it presents some applications in Hydrotreating Process of the petroleum refining industry. In Hydrotreating of feedstocks, knowledge about characteristics of process regarding product property estimation, hydrogen consumption and removal of contaminants (sulfur, nitrogen, aromatics), are very important to process optimization, product specification and environment protection. The Neural Network technique has been used to model the behaviour of the chemical hydrogen consumption, the conversions of the hydrogenation of aromatic hydrocarbons, hydrodesulfurization and hydro denitrogenation reactions and the physical properties of product with operational conditions and feedstock properties. In addition, Neural Networks have been built to predict the cetane number of feedstocks. (author)

  2. Stability of a neural network model with small-world connections

    International Nuclear Information System (INIS)

    Li Chunguang; Chen Guanrong

    2003-01-01

    Small-world networks are highly clustered networks with small distances among the nodes. There are many biological neural networks that present this kind of connection. There are no special weightings in the connections of most existing small-world network models. However, this kind of simply connected model cannot characterize biological neural networks, in which there are different weights in synaptic connections. In this paper, we present a neural network model with weighted small-world connections and further investigate the stability of this model

  3. Refined Analysis of Fatigue Crack Initiation Life of Beam-to-Column Welded Connections of Steel Frame under Strong Earthquake

    Directory of Open Access Journals (Sweden)

    Weilian Qu

    2017-01-01

    Full Text Available This paper presents a refined analysis for evaluating low-cycle fatigue crack initiation life of welded beam-to-column connections of steel frame structures under strong earthquake excitation. To consider different length scales between typical beam and column components as well as a few crucial beam-to-column welded connections, a multiscale finite element (FE model having three different length scales is formulated. The model can accurately analyze the inelastic seismic response of a steel frame and then obtain in detail elastoplastic stress and strain field near the welded zone of the connections. It is found that the welded zone is subjected to multiaxial nonproportional loading during strong ground motion and the elastoplastic stress-strain field of the welded zone is three-dimensional. Then, using the correlation of the Fatemi-Socie (FS parameter versus fatigue life obtained by the experimental crack initiation fatigue data of the structural steel weldment subjected to multiaxial loading, the refined evaluation approach of fatigue crack initiation life is developed based on the equivalent plastic strain at fatigue critical position of beam end seams of crucial welded connections when the steel frame is subjected to the strong earthquake excitation.

  4. Connecting Neural Coding to Number Cognition: A Computational Account

    Science.gov (United States)

    Prather, Richard W.

    2012-01-01

    The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

  5. Neural systems language: a formal modeling language for the systematic description, unambiguous communication, and automated digital curation of neural connectivity.

    Science.gov (United States)

    Brown, Ramsay A; Swanson, Larry W

    2013-09-01

    Systematic description and the unambiguous communication of findings and models remain among the unresolved fundamental challenges in systems neuroscience. No common descriptive frameworks exist to describe systematically the connective architecture of the nervous system, even at the grossest level of observation. Furthermore, the accelerating volume of novel data generated on neural connectivity outpaces the rate at which this data is curated into neuroinformatics databases to synthesize digitally systems-level insights from disjointed reports and observations. To help address these challenges, we propose the Neural Systems Language (NSyL). NSyL is a modeling language to be used by investigators to encode and communicate systematically reports of neural connectivity from neuroanatomy and brain imaging. NSyL engenders systematic description and communication of connectivity irrespective of the animal taxon described, experimental or observational technique implemented, or nomenclature referenced. As a language, NSyL is internally consistent, concise, and comprehensible to both humans and computers. NSyL is a promising development for systematizing the representation of neural architecture, effectively managing the increasing volume of data on neural connectivity and streamlining systems neuroscience research. Here we present similar precedent systems, how NSyL extends existing frameworks, and the reasoning behind NSyL's development. We explore NSyL's potential for balancing robustness and consistency in representation by encoding previously reported assertions of connectivity from the literature as examples. Finally, we propose and discuss the implications of a framework for how NSyL will be digitally implemented in the future to streamline curation of experimental results and bridge the gaps among anatomists, imagers, and neuroinformatics databases. Copyright © 2013 Wiley Periodicals, Inc.

  6. Transcranial Magnetic Stimulation and Connectivity Mapping: Tools for Studying the Neural Bases of Brain Disorders

    OpenAIRE

    Hampson, M.; Hoffman, R. E.

    2010-01-01

    There has been an increasing emphasis on characterizing pathophysiology underlying psychiatric and neurological disorders in terms of altered neural connectivity and network dynamics. Transcranial magnetic stimulation (TMS) provides a unique opportunity for investigating connectivity in the human brain. TMS allows researchers and clinicians to directly stimulate cortical regions accessible to electromagnetic coils positioned on the scalp. The induced activation can then propagate through...

  7. Effect of neural connectivity on autocovariance and cross covariance estimates

    Directory of Open Access Journals (Sweden)

    Stecker Mark M

    2007-01-01

    Full Text Available Abstract Background Measurements of auto and cross covariance functions are frequently used to investigate neural systems. In interpreting this data, it is commonly assumed that the largest contribution to the recordings comes from sources near the electrode. However, the potential recorded at an electrode represents the superimposition of the potentials generated by large numbers of active neural structures. This creates situations under which the measured auto and cross covariance functions are dominated by the activity in structures far from the electrode and in which the distance dependence of the cross-covariance function differs significantly from that describing the activity in the actual neural structures. Methods Direct application of electrostatics to calculate the theoretical auto and cross covariance functions that would be recorded from electrodes immersed in a large volume filled with active neural structures with specific statistical properties. Results It is demonstrated that the potentials recorded from a monopolar electrode surrounded by dipole sources in a uniform medium are predominantly due to activity in neural structures far from the electrode when neuronal correlations drop more slowly than 1/r3 or when the size of the neural system is much smaller than a known correlation distance. Recordings from quadrupolar sources are strongly dependent on distant neurons when correlations drop more slowly than 1/r or the size of the system is much smaller than the correlation distance. Differences between bipolar and monopolar recordings are discussed. It is also demonstrated that the cross covariance of the recorded in two spatially separated electrodes declines as a power-law function of the distance between them even when the electrical activity from different neuronal structures is uncorrelated. Conclusion When extracellular electrophysiologic recordings are made from systems containing large numbers of neural structures, it is

  8. Implementation of neural networks on 'Connection Machine'

    International Nuclear Information System (INIS)

    Belmonte, Ghislain

    1990-12-01

    This report is a first approach to the notion of neural networks and their possible applications within the framework of artificial intelligence activities of the Department of Applied Mathematics of the Limeil-Valenton Research Center. The first part is an introduction to the field of neural networks; the main neural network models are described in this section. The applications of neural networks in the field of classification have mainly been studied because they could more particularly help to solve some of the decision support problems dealt with by the C.E.A. As the neural networks perform a large number of parallel operations, it was therefore logical to use a parallel architecture computer: the Connection Machine (which uses 16384 processors and is located at E.T.C.A. Arcueil). The second part presents some generalities on the parallelism and the Connection Machine, and two implementations of neural networks on Connection Machine. The first of these implementations concerns one of the most used algorithms to realize the learning of neural networks: the Gradient Retro-propagation algorithm. The second one, less common, concerns a network of neurons destined mainly to the recognition of forms: the Fukushima Neocognitron. The latter is studied by the C.E.A. of Bruyeres-le-Chatel in order to realize an embedded system (including hardened circuits) for the fast recognition of forms [fr

  9. An efficient optical architecture for sparsely connected neural networks

    Science.gov (United States)

    Hine, Butler P., III; Downie, John D.; Reid, Max B.

    1990-01-01

    An architecture for general-purpose optical neural network processor is presented in which the interconnections and weights are formed by directing coherent beams holographically, thereby making use of the space-bandwidth products of the recording medium for sparsely interconnected networks more efficiently that the commonly used vector-matrix multiplier, since all of the hologram area is in use. An investigation is made of the use of computer-generated holograms recorded on such updatable media as thermoplastic materials, in order to define the interconnections and weights of a neural network processor; attention is given to limits on interconnection densities, diffraction efficiencies, and weighing accuracies possible with such an updatable thin film holographic device.

  10. Measuring symmetry, asymmetry and randomness in neural network connectivity.

    Directory of Open Access Journals (Sweden)

    Umberto Esposito

    Full Text Available Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.

  11. Measuring symmetry, asymmetry and randomness in neural network connectivity.

    Science.gov (United States)

    Esposito, Umberto; Giugliano, Michele; van Rossum, Mark; Vasilaki, Eleni

    2014-01-01

    Cognitive functions are stored in the connectome, the wiring diagram of the brain, which exhibits non-random features, so-called motifs. In this work, we focus on bidirectional, symmetric motifs, i.e. two neurons that project to each other via connections of equal strength, and unidirectional, non-symmetric motifs, i.e. within a pair of neurons only one neuron projects to the other. We hypothesise that such motifs have been shaped via activity dependent synaptic plasticity processes. As a consequence, learning moves the distribution of the synaptic connections away from randomness. Our aim is to provide a global, macroscopic, single parameter characterisation of the statistical occurrence of bidirectional and unidirectional motifs. To this end we define a symmetry measure that does not require any a priori thresholding of the weights or knowledge of their maximal value. We calculate its mean and variance for random uniform or Gaussian distributions, which allows us to introduce a confidence measure of how significantly symmetric or asymmetric a specific configuration is, i.e. how likely it is that the configuration is the result of chance. We demonstrate the discriminatory power of our symmetry measure by inspecting the eigenvalues of different types of connectivity matrices. We show that a Gaussian weight distribution biases the connectivity motifs to more symmetric configurations than a uniform distribution and that introducing a random synaptic pruning, mimicking developmental regulation in synaptogenesis, biases the connectivity motifs to more asymmetric configurations, regardless of the distribution. We expect that our work will benefit the computational modelling community, by providing a systematic way to characterise symmetry and asymmetry in network structures. Further, our symmetry measure will be of use to electrophysiologists that investigate symmetry of network connectivity.

  12. Multiscale neural connectivity during human sensory processing in the brain

    Science.gov (United States)

    Maksimenko, Vladimir A.; Runnova, Anastasia E.; Frolov, Nikita S.; Makarov, Vladimir V.; Nedaivozov, Vladimir; Koronovskii, Alexey A.; Pisarchik, Alexander; Hramov, Alexander E.

    2018-05-01

    Stimulus-related brain activity is considered using wavelet-based analysis of neural interactions between occipital and parietal brain areas in alpha (8-12 Hz) and beta (15-30 Hz) frequency bands. We show that human sensory processing related to the visual stimuli perception induces brain response resulted in different ways of parieto-occipital interactions in these bands. In the alpha frequency band the parieto-occipital neuronal network is characterized by homogeneous increase of the interaction between all interconnected areas both within occipital and parietal lobes and between them. In the beta frequency band the occipital lobe starts to play a leading role in the dynamics of the occipital-parietal network: The perception of visual stimuli excites the visual center in the occipital area and then, due to the increase of parieto-occipital interactions, such excitation is transferred to the parietal area, where the attentional center takes place. In the case when stimuli are characterized by a high degree of ambiguity, we find greater increase of the interaction between interconnected areas in the parietal lobe due to the increase of human attention. Based on revealed mechanisms, we describe the complex response of the parieto-occipital brain neuronal network during the perception and primary processing of the visual stimuli. The results can serve as an essential complement to the existing theory of neural aspects of visual stimuli processing.

  13. Changes in resting neural connectivity during propofol sedation.

    Directory of Open Access Journals (Sweden)

    Emmanuel A Stamatakis

    2010-12-01

    Full Text Available The default mode network consists of a set of functionally connected brain regions (posterior cingulate, medial prefrontal cortex and bilateral parietal cortex maximally active in functional imaging studies under "no task" conditions. It has been argued that the posterior cingulate is important in consciousness/awareness, but previous investigations of resting interactions between the posterior cingulate cortex and other brain regions during sedation and anesthesia have produced inconsistent results.We examined the connectivity of the posterior cingulate at different levels of consciousness. "No task" fMRI (BOLD data were collected from healthy volunteers while awake and at low and moderate levels of sedation, induced by the anesthetic agent propofol. Our data show that connectivity of the posterior cingulate changes during sedation to include areas that are not traditionally considered to be part of the default mode network, such as the motor/somatosensory cortices, the anterior thalamic nuclei, and the reticular activating system.This neuroanatomical signature resembles that of non-REM sleep, and may be evidence for a system that reduces its discriminable states and switches into more stereotypic patterns of firing under sedation.

  14. Transcranial magnetic stimulation and connectivity mapping: tools for studying the neural bases of brain disorders.

    Science.gov (United States)

    Hampson, M; Hoffman, R E

    2010-01-01

    There has been an increasing emphasis on characterizing pathophysiology underlying psychiatric and neurological disorders in terms of altered neural connectivity and network dynamics. Transcranial magnetic stimulation (TMS) provides a unique opportunity for investigating connectivity in the human brain. TMS allows researchers and clinicians to directly stimulate cortical regions accessible to electromagnetic coils positioned on the scalp. The induced activation can then propagate through long-range connections to other brain areas. Thus, by identifying distal regions activated during TMS, researchers can infer connectivity patterns in the healthy human brain and can examine how those patterns may be disrupted in patients with different brain disorders. Conversely, connectivity maps derived using neuroimaging methods can identify components of a dysfunctional network. Nodes in this dysfunctional network accessible as targets for TMS by virtue of their proximity to the scalp may then permit TMS-induced alterations of components of the network not directly accessible to TMS via propagated effects. Thus TMS can provide a portal for accessing and altering neural dynamics in networks that are widely distributed anatomically. Finally, when long-term modulation of network dynamics is induced by trains of repetitive TMS, changes in functional connectivity patterns can be studied in parallel with changes in patient symptoms. These correlational data can elucidate neural mechanisms underlying illness and recovery. In this review, we focus on the application of these approaches to the study of psychiatric and neurological illnesses.

  15. Transcranial magnetic stimulation and connectivity mapping: tools for studying the neural bases of brain disorders.

    Directory of Open Access Journals (Sweden)

    Michelle Hampson

    2010-08-01

    Full Text Available There has been an increasing emphasis on characterizing pathophysiology underlying psychiatric and neurological disorders in terms of altered neural connectivity and network dynamics. Transcranial magnetic stimulation (TMS provides a unique opportunity for investigating connectivity in the human brain. TMS allows researchers and clinicians to directly stimulate cortical regions accessible to electromagnetic coils positioned on the scalp. The induced activation can then propagate through long-range connections to other brain areas. Thus, by identifying distal regions activated during TMS, researchers can infer connectivity patterns in the healthy human brain and can examine how those patterns may be disrupted in patients with different brain disorders. Conversely, connectivity maps derived using neuroimaging methods can identify components of a dysfunctional network. Nodes in this dysfunctional network accessible as targets for TMS by virtue of their proximity to the scalp may then permit TMS-induced alterations of components of the network not directly accessible to TMS via propagated effects. Thus TMS can provide a portal for accessing and altering neural dynamics in networks that are widely distributed anatomically. Finally, when long-term modulation of network dynamics is induced by trains of repetitive TMS, changes in functional connectivity patterns can be studied in parallel with changes in patient symptoms. These correlational data can elucidate neural mechanisms underlying illness and recovery. In this review, we focus on the application of these approaches to the study of psychiatric and neurological illnesses.

  16. Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

    Directory of Open Access Journals (Sweden)

    Christian Nowke

    2018-06-01

    Full Text Available Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases—the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

  17. Connectivity inference from neural recording data: Challenges, mathematical bases and research directions.

    Science.gov (United States)

    Magrans de Abril, Ildefons; Yoshimoto, Junichiro; Doya, Kenji

    2018-06-01

    This article presents a review of computational methods for connectivity inference from neural activity data derived from multi-electrode recordings or fluorescence imaging. We first identify biophysical and technical challenges in connectivity inference along the data processing pipeline. We then review connectivity inference methods based on two major mathematical foundations, namely, descriptive model-free approaches and generative model-based approaches. We investigate representative studies in both categories and clarify which challenges have been addressed by which method. We further identify critical open issues and possible research directions. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

  18. Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

    Science.gov (United States)

    Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

    2017-01-01

    Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

  19. Neural activity, neural connectivity, and the processing of emotionally valenced information in older adults: links with life satisfaction.

    Science.gov (United States)

    Waldinger, Robert J; Kensinger, Elizabeth A; Schulz, Marc S

    2011-09-01

    This study examines whether differences in late-life well-being are linked to how older adults encode emotionally valenced information. Using fMRI with 39 older adults varying in life satisfaction, we examined how viewing positive and negative images would affect activation and connectivity of an emotion-processing network. Participants engaged most regions within this network more robustly for positive than for negative images, but within the PFC this effect was moderated by life satisfaction, with individuals higher in satisfaction showing lower levels of activity during the processing of positive images. Participants high in satisfaction showed stronger correlations among network regions-particularly between the amygdala and other emotion processing regions-when viewing positive, as compared with negative, images. Participants low in satisfaction showed no valence effect. Findings suggest that late-life satisfaction is linked with how emotion-processing regions are engaged and connected during processing of valenced information. This first demonstration of a link between neural recruitment and late-life well-being suggests that differences in neural network activation and connectivity may account for the preferential encoding of positive information seen in some older adults.

  20. Effect of Explicit Evaluation on Neural Connectivity Related to Listening to Unfamiliar Music

    Science.gov (United States)

    Liu, Chao; Brattico, Elvira; Abu-jamous, Basel; Pereira, Carlos S.; Jacobsen, Thomas; Nandi, Asoke K.

    2017-01-01

    People can experience different emotions when listening to music. A growing number of studies have investigated the brain structures and neural connectivities associated with perceived emotions. However, very little is known about the effect of an explicit act of judgment on the neural processing of emotionally-valenced music. In this study, we adopted the novel consensus clustering paradigm, called binarisation of consensus partition matrices (Bi-CoPaM), to study whether and how the conscious aesthetic evaluation of the music would modulate brain connectivity networks related to emotion and reward processing. Participants listened to music under three conditions – one involving a non-evaluative judgment, one involving an explicit evaluative aesthetic judgment, and one involving no judgment at all (passive listening only). During non-evaluative attentive listening we obtained auditory-limbic connectivity whereas when participants were asked to decide explicitly whether they liked or disliked the music excerpt, only two clusters of intercommunicating brain regions were found: one including areas related to auditory processing and action observation, and the other comprising higher-order structures involved with visual processing. Results indicate that explicit evaluative judgment has an impact on the neural auditory-limbic connectivity during affective processing of music. PMID:29311874

  1. Topological probability and connection strength induced activity in complex neural networks

    International Nuclear Information System (INIS)

    Du-Qu, Wei; Bo, Zhang; Dong-Yuan, Qiu; Xiao-Shu, Luo

    2010-01-01

    Recent experimental evidence suggests that some brain activities can be assigned to small-world networks. In this work, we investigate how the topological probability p and connection strength C affect the activities of discrete neural networks with small-world (SW) connections. Network elements are described by two-dimensional map neurons (2DMNs) with the values of parameters at which no activity occurs. It is found that when the value of p is smaller or larger, there are no active neurons in the network, no matter what the value of connection strength is; for a given appropriate connection strength, there is an intermediate range of topological probability where the activity of 2DMN network is induced and enhanced. On the other hand, for a given intermediate topological probability level, there exists an optimal value of connection strength such that the frequency of activity reaches its maximum. The possible mechanism behind the action of topological probability and connection strength is addressed based on the bifurcation method. Furthermore, the effects of noise and transmission delay on the activity of neural network are also studied. (general)

  2. Multi-Connection Pattern Analysis: Decoding the representational content of neural communication.

    Science.gov (United States)

    Li, Yuanning; Richardson, Robert Mark; Ghuman, Avniel Singh

    2017-11-15

    The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population. Successful MCPA-based decoding indicates the involvement of distributed computational processing and provides a framework for probing the representational structure of the interaction. Simulations demonstrate the efficacy of MCPA in realistic circumstances. In addition, we demonstrate that MCPA can be applied to different signal modalities to evaluate a variety of hypothesis associated with information coding in neural communications. We apply MCPA to fMRI and human intracranial electrophysiological data to provide a proof-of-concept of the utility of this method for decoding individual natural images and faces in functional connectivity data. We further use a MCPA-based representational similarity analysis to illustrate how MCPA may be used to test computational models of information transfer among regions of the visual processing stream. Thus, MCPA can be used to assess the information represented in the coupled activity of interacting neural circuits and probe the underlying principles of information transformation between regions. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. The necessity of connection structures in neural models of variable binding.

    Science.gov (United States)

    van der Velde, Frank; de Kamps, Marc

    2015-08-01

    In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1-11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other ('connectivity based') type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman's analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman's analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.

  4. Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks

    OpenAIRE

    Shuhui, L.; Fu, X.; Jaithwa, I.; Alonso, E.; Fairbank, M.; Wunsch, D. C.

    2015-01-01

    A microgrid consists of a variety of inverter-interfaced distributed energy resources (DERs). A key issue is how to control DERs within the microgrid and how to connect them to or disconnect them from the microgrid quickly. This paper presents a strategy for controlling inverter-interfaced DERs within a microgrid using an artificial neural network, which implements a dynamic programming algorithm and is trained with a new Levenberg-Marquardt backpropagation algorithm. Compared to conventional...

  5. Age-related difference in the effective neural connectivity associated with probabilistic category learning

    International Nuclear Information System (INIS)

    Yoon, Eun Jin; Cho, Sang Soo; Kim, Hee Jung; Bang, Seong Ae; Park, Hyun Soo; Kim, Yu Kyeong; Kim, Sang Eun

    2007-01-01

    Although it is well known that explicit memory is affected by the deleterious changes in brain with aging, but effect of aging in implicit memory such as probabilistic category learning (PCL) is not clear. To identify the effect of aging on the neural interaction for successful PCL, we investigated the neural substrates of PCL and the age-related changes of the neural network between these brain regions. 23 young (age, 252 y; 11 males) and 14 elderly (673 y; 7 males) healthy subjects underwent FDG PET during a resting state and 150-trial weather prediction (WP) task. Correlations between the WP hit rates and regional glucose metabolism were assessed using SPM2 (P diff (37) = 142.47, P<0.005), Systematic comparisons of each path revealed that frontal crosscallosal and the frontal to parahippocampal connection were most responsible for the model differences (P<0.05). For the successful PCL, the elderly recruits the basal ganglia implicit memory system but MTL recruitment differs from the young. The inadequate MTL correlation pattern in the elderly is may be caused by the changes of the neural pathway related with explicit memory. These neural changes can explain the decreased performance of PCL in elderly subjects

  6. An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

    Science.gov (United States)

    2012-01-01

    Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685

  7. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

    Directory of Open Access Journals (Sweden)

    Bota Mihail

    2011-08-01

    Full Text Available Abstract Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871 that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED based on experimental variables and their interdependencies. The software has three parts: (a the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger

  8. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case

    Science.gov (United States)

    2011-01-01

    Background We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. Results The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. Conclusions We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized

  9. Knowledge engineering tools for reasoning with scientific observations and interpretations: a neural connectivity use case.

    Science.gov (United States)

    Russ, Thomas A; Ramakrishnan, Cartic; Hovy, Eduard H; Bota, Mihail; Burns, Gully A P C

    2011-08-22

    We address the goal of curating observations from published experiments in a generalizable form; reasoning over these observations to generate interpretations and then querying this interpreted knowledge to supply the supporting evidence. We present web-application software as part of the 'BioScholar' project (R01-GM083871) that fully instantiates this process for a well-defined domain: using tract-tracing experiments to study the neural connectivity of the rat brain. The main contribution of this work is to provide the first instantiation of a knowledge representation for experimental observations called 'Knowledge Engineering from Experimental Design' (KEfED) based on experimental variables and their interdependencies. The software has three parts: (a) the KEfED model editor - a design editor for creating KEfED models by drawing a flow diagram of an experimental protocol; (b) the KEfED data interface - a spreadsheet-like tool that permits users to enter experimental data pertaining to a specific model; (c) a 'neural connection matrix' interface that presents neural connectivity as a table of ordinal connection strengths representing the interpretations of tract-tracing data. This tool also allows the user to view experimental evidence pertaining to a specific connection. BioScholar is built in Flex 3.5. It uses Persevere (a noSQL database) as a flexible data store and PowerLoom® (a mature First Order Logic reasoning system) to execute queries using spatial reasoning over the BAMS neuroanatomical ontology. We first introduce the KEfED approach as a general approach and describe its possible role as a way of introducing structured reasoning into models of argumentation within new models of scientific publication. We then describe the design and implementation of our example application: the BioScholar software. This is presented as a possible biocuration interface and supplementary reasoning toolkit for a larger, more specialized bioinformatics system: the Brain

  10. Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings

    NARCIS (Netherlands)

    van Rooij, Daan; Hartman, Catharina A.; Mennes, Maarten; Oosterlaan, Jaap; Franke, Barbara; Rommelse, Nanda; Heslenfeld, Dirk; Faraone, Stephen V.; Buitelaar, Jan K.; Hoekstra, Pieter J.

    2015-01-01

    Introduction: Response inhibition is one of the executive functions impaired in attention-deficit/hyperactivity disorder (ADHD). Increasing evidence indicates that altered functional and structural neural connectivity are part of the neurobiological basis of ADHD. Here, we investigated if

  11. Slowly evolving connectivity in recurrent neural networks: I. The extreme dilution regime

    International Nuclear Information System (INIS)

    Wemmenhove, B; Skantzos, N S; Coolen, A C C

    2004-01-01

    We study extremely diluted spin models of neural networks in which the connectivity evolves in time, although adiabatically slowly compared to the neurons, according to stochastic equations which on average aim to reduce frustration. The (fast) neurons and (slow) connectivity variables equilibrate separately, but at different temperatures. Our model is exactly solvable in equilibrium. We obtain phase diagrams upon making the condensed ansatz (i.e. recall of one pattern). These show that, as the connectivity temperature is lowered, the volume of the retrieval phase diverges and the fraction of mis-aligned spins is reduced. Still one always retains a region in the retrieval phase where recall states other than the one corresponding to the 'condensed' pattern are locally stable, so the associative memory character of our model is preserved

  12. Functional Connectivity with Distinct Neural Networks Tracks Fluctuations in Gain/Loss Framing Susceptibility

    Science.gov (United States)

    Smith, David V.; Sip, Kamila E.; Delgado, Mauricio R.

    2016-01-01

    Multiple large-scale neural networks orchestrate a wide range of cognitive processes. For example, interoceptive processes related to self-referential thinking have been linked to the default-mode network (DMN); whereas exteroceptive processes related to cognitive control have been linked to the executive-control network (ECN). Although the DMN and ECN have been postulated to exert opposing effects on cognition, it remains unclear how connectivity with these spatially overlapping networks contribute to fluctuations in behavior. While previous work has suggested the medial prefrontal cortex (MPFC) is involved in behavioral change following feedback, these observations could be linked to interoceptive processes tied to DMN or exteroceptive processes tied to ECN because MPFC is positioned in both networks. To address this problem, we employed independent component analysis combined with dual-regression functional connectivity analysis. Participants made a series of financial decisions framed as monetary gains or losses. In some sessions, participants received feedback from a peer observing their choices; in other sessions, feedback was not provided. Following feedback, framing susceptibility—indexed as the increase in gambling behavior in loss frames compared to gain frames—was heightened in some participants and diminished in others. We examined whether these individual differences were linked to differences in connectivity by contrasting sessions containing feedback against those that did not contain feedback. We found two key results. As framing susceptibility increased, the MPFC increased connectivity with DMN; in contrast, temporal-parietal junction decreased connectivity with the ECN. Our results highlight how functional connectivity patterns with distinct neural networks contribute to idiosyncratic behavioral changes. PMID:25858445

  13. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

    Science.gov (United States)

    Li, Siqi; Jiang, Huiyan; Pang, Wenbo

    2017-05-01

    Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

  14. [A method of recognizing biology surface spectrum using cascade-connection artificial neural nets].

    Science.gov (United States)

    Shi, Wei-Jie; Yao, Yong; Zhang, Tie-Qiang; Meng, Xian-Jiang

    2008-05-01

    A method of recognizing the visible spectrum of micro-areas on the biological surface with cascade-connection artificial neural nets is presented in the present paper. The visible spectra of spots on apples' pericarp, ranging from 500 to 730 nm, were obtained with a fiber-probe spectrometer, and a new spectrum recognition system consisting of three-level cascade-connection neural nets was set up. The experiments show that the spectra of rotten, scar and bumped spot on an apple's pericarp can be recognized by the spectrum recognition system, and the recognition accuracy is higher than 85% even when noise level is 15%. The new recognition system overcomes the disadvantages of poor accuracy and poor anti-noise with the traditional system based on single cascade neural nets. Finally, a new method of expression of recognition results was proved. The method is based on the conception of degree of membership in fuzzing mathematics, and through it the recognition results can be expressed exactly and objectively.

  15. Connectivity strategies for higher-order neural networks applied to pattern recognition

    Science.gov (United States)

    Spirkovska, Lilly; Reid, Max B.

    1990-01-01

    Different strategies for non-fully connected HONNs (higher-order neural networks) are discussed, showing that by using such strategies an input field of 128 x 128 pixels can be attained while still achieving in-plane rotation and translation-invariant recognition. These techniques allow HONNs to be used with the larger input scenes required for practical pattern-recognition applications. The number of interconnections that must be stored has been reduced by a factor of approximately 200,000 in a T/C case and about 2000 in a Space Shuttle/F-18 case by using regional connectivity. Third-order networks have been simulated using several connection strategies. The method found to work best is regional connectivity. The main advantages of this strategy are the following: (1) it considers features of various scales within the image and thus gets a better sample of what the image looks like; (2) it is invariant to shape-preserving geometric transformations, such as translation and rotation; (3) the connections are predetermined so that no extra computations are necessary during run time; and (4) it does not require any extra storage for recording which connections were formed.

  16. On the connection between level of education and the neural circuitry of emotion perception

    Directory of Open Access Journals (Sweden)

    Liliana Ramona Demenescu

    2014-10-01

    Full Text Available Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of the NESDA fMRI study, we compared two groups of fourteen healthy volunteers with intermediate and high educational attainment, matched for age and gender. The data concerned event-related functional magnetic resonance imaging of brain activation during perception of facial emotional expressions. The region of interest analysis showed stronger right amygdala activation to facial expressions in participants with lower relative to higher educational attainment. The psychophysiological interaction analysis revealed that participants with higher educational attainment exhibited stronger right amygdala – right insula connectivity during perception of emotional and neutral facial expressions. This exploratory study suggests the relevance of educational attainment on the neural mechanism of facial expression processing.

  17. Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

    Science.gov (United States)

    Lin, Tiger W; Das, Anup; Krishnan, Giri P; Bazhenov, Maxim; Sejnowski, Terrence J

    2017-10-01

    With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.

  18. Estimation of effective connectivity using multi-layer perceptron artificial neural network.

    Science.gov (United States)

    Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

    2018-02-01

    Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.

  19. Neural activation and functional connectivity during motor imagery of bimanual everyday actions.

    Directory of Open Access Journals (Sweden)

    André J Szameitat

    Full Text Available Bimanual actions impose intermanual coordination demands not present during unimanual actions. We investigated the functional neuroanatomical correlates of these coordination demands in motor imagery (MI of everyday actions using functional magnetic resonance imaging (fMRI. For this, 17 participants imagined unimanual actions with the left and right hand as well as bimanual actions while undergoing fMRI. A univariate fMRI analysis showed no reliable cortical activations specific to bimanual MI, indicating that intermanual coordination demands in MI are not associated with increased neural processing. A functional connectivity analysis based on psychophysiological interactions (PPI, however, revealed marked increases in connectivity between parietal and premotor areas within and between hemispheres. We conclude that in MI of everyday actions intermanual coordination demands are primarily met by changes in connectivity between areas and only moderately, if at all, by changes in the amount of neural activity. These results are the first characterization of the neuroanatomical correlates of bimanual coordination demands in MI. Our findings support the assumed equivalence of overt and imagined actions and highlight the differences between uni- and bimanual actions. The findings extent our understanding of the motor system and may aid the development of clinical neurorehabilitation approaches based on mental practice.

  20. Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

    Science.gov (United States)

    Lin, Tiger W.; Das, Anup; Krishnan, Giri P.; Bazhenov, Maxim; Sejnowski, Terrence J.

    2017-01-01

    With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005; Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. PMID:28777719

  1. Reconstruction of sparse connectivity in neural networks from spike train covariances

    International Nuclear Information System (INIS)

    Pernice, Volker; Rotter, Stefan

    2013-01-01

    The inference of causation from correlation is in general highly problematic. Correspondingly, it is difficult to infer the existence of physical synaptic connections between neurons from correlations in their activity. Covariances in neural spike trains and their relation to network structure have been the subject of intense research, both experimentally and theoretically. The influence of recurrent connections on covariances can be characterized directly in linear models, where connectivity in the network is described by a matrix of linear coupling kernels. However, as indirect connections also give rise to covariances, the inverse problem of inferring network structure from covariances can generally not be solved unambiguously. Here we study to what degree this ambiguity can be resolved if the sparseness of neural networks is taken into account. To reconstruct a sparse network, we determine the minimal set of linear couplings consistent with the measured covariances by minimizing the L 1 norm of the coupling matrix under appropriate constraints. Contrary to intuition, after stochastic optimization of the coupling matrix, the resulting estimate of the underlying network is directed, despite the fact that a symmetric matrix of count covariances is used for inference. The performance of the new method is best if connections are neither exceedingly sparse, nor too dense, and it is easily applicable for networks of a few hundred nodes. Full coupling kernels can be obtained from the matrix of full covariance functions. We apply our method to networks of leaky integrate-and-fire neurons in an asynchronous–irregular state, where spike train covariances are well described by a linear model. (paper)

  2. Neural correlate of resting-state functional connectivity under α2 adrenergic receptor agonist, medetomidine.

    Science.gov (United States)

    Nasrallah, Fatima A; Lew, Si Kang; Low, Amanda Si-Min; Chuang, Kai-Hsiang

    2014-01-01

    Correlative fluctuations in functional MRI (fMRI) signals across the brain at rest have been taken as a measure of functional connectivity, but the neural basis of this resting-state MRI (rsMRI) signal is not clear. Previously, we found that the α2 adrenergic agonist, medetomidine, suppressed the rsMRI correlation dose-dependently but not the stimulus evoked activation. To understand the underlying electrophysiology and neurovascular coupling, which might be altered due to the vasoconstrictive nature of medetomidine, somatosensory evoked potential (SEP) and resting electroencephalography (EEG) were measured and correlated with corresponding BOLD signals in rat brains under three dosages of medetomidine. The SEP elicited by electrical stimulation to both forepaws was unchanged regardless of medetomidine dosage, which was consistent with the BOLD activation. Identical relationship between the SEP and BOLD signal under different medetomidine dosages indicates that the neurovascular coupling was not affected. Under resting state, EEG power was the same but a depression of inter-hemispheric EEG coherence in the gamma band was observed at higher medetomidine dosage. Different from medetomidine, both resting EEG power and BOLD power and coherence were significantly suppressed with increased isoflurane level. Such reduction was likely due to suppressed neural activity as shown by diminished SEP and BOLD activation under isoflurane, suggesting different mechanisms of losing synchrony at resting-state. Even though, similarity between electrophysiology and BOLD under stimulation and resting-state implicates a tight neurovascular coupling in both medetomidine and isoflurane. Our results confirm that medetomidine does not suppress neural activity but dissociates connectivity in the somatosensory cortex. The differential effect of medetomidine and its receptor specific action supports the neuronal origin of functional connectivity and implicates the mechanism of its sedative

  3. Age-related difference in the effective neural connectivity associated with probabilistic category learning

    Energy Technology Data Exchange (ETDEWEB)

    Yoon, Eun Jin; Cho, Sang Soo; Kim, Hee Jung; Bang, Seong Ae; Park, Hyun Soo; Kim, Yu Kyeong; Kim, Sang Eun [Seoul National Univ. College of Medicine, Seoul (Korea, Republic of)

    2007-07-01

    Although it is well known that explicit memory is affected by the deleterious changes in brain with aging, but effect of aging in implicit memory such as probabilistic category learning (PCL) is not clear. To identify the effect of aging on the neural interaction for successful PCL, we investigated the neural substrates of PCL and the age-related changes of the neural network between these brain regions. 23 young (age, 252 y; 11 males) and 14 elderly (673 y; 7 males) healthy subjects underwent FDG PET during a resting state and 150-trial weather prediction (WP) task. Correlations between the WP hit rates and regional glucose metabolism were assessed using SPM2 (P<0.05 uncorrected). For path analysis, seven brain regions (bilateral middle frontal gyri and putamen, left fusiform gyrus, anterior cingulate and right parahippocampal gyri) were selected based on the results of the correlation analysis. Model construction and path analysis processing were done by AMOS 5.0. The elderly had significantly lower total hit rates than the young (P<0.005). In the correlation analysis, both groups showed similar metabolic correlation in frontal and striatal area. But correlation in the medial temporal lobe (MTL) was found differently by group. In path analysis, the functional networks for the constructed model was accepted (X(2) =0.80, P=0.67) and it proved to be significantly different between groups (X{sub diff}(37) = 142.47, P<0.005), Systematic comparisons of each path revealed that frontal crosscallosal and the frontal to parahippocampal connection were most responsible for the model differences (P<0.05). For the successful PCL, the elderly recruits the basal ganglia implicit memory system but MTL recruitment differs from the young. The inadequate MTL correlation pattern in the elderly is may be caused by the changes of the neural pathway related with explicit memory. These neural changes can explain the decreased performance of PCL in elderly subjects.

  4. Motor sequence learning-induced neural efficiency in functional brain connectivity.

    Science.gov (United States)

    Karim, Helmet T; Huppert, Theodore J; Erickson, Kirk I; Wollam, Mariegold E; Sparto, Patrick J; Sejdić, Ervin; VanSwearingen, Jessie M

    2017-02-15

    Previous studies have shown the functional neural circuitry differences before and after an explicitly learned motor sequence task, but have not assessed these changes during the process of motor skill learning. Functional magnetic resonance imaging activity was measured while participants (n=13) were asked to tap their fingers to visually presented sequences in blocks that were either the same sequence repeated (learning block) or random sequences (control block). Motor learning was associated with a decrease in brain activity during learning compared to control. Lower brain activation was noted in the posterior parietal association area and bilateral thalamus during the later periods of learning (not during the control). Compared to the control condition, we found the task-related motor learning was associated with decreased connectivity between the putamen and left inferior frontal gyrus and left middle cingulate brain regions. Motor learning was associated with changes in network activity, spatial extent, and connectivity. Copyright © 2016 Elsevier B.V. All rights reserved.

  5. Chimera states in brain networks: Empirical neural vs. modular fractal connectivity

    Science.gov (United States)

    Chouzouris, Teresa; Omelchenko, Iryna; Zakharova, Anna; Hlinka, Jaroslav; Jiruska, Premysl; Schöll, Eckehard

    2018-04-01

    Complex spatiotemporal patterns, called chimera states, consist of coexisting coherent and incoherent domains and can be observed in networks of coupled oscillators. The interplay of synchrony and asynchrony in complex brain networks is an important aspect in studies of both the brain function and disease. We analyse the collective dynamics of FitzHugh-Nagumo neurons in complex networks motivated by its potential application to epileptology and epilepsy surgery. We compare two topologies: an empirical structural neural connectivity derived from diffusion-weighted magnetic resonance imaging and a mathematically constructed network with modular fractal connectivity. We analyse the properties of chimeras and partially synchronized states and obtain regions of their stability in the parameter planes. Furthermore, we qualitatively simulate the dynamics of epileptic seizures and study the influence of the removal of nodes on the network synchronizability, which can be useful for applications to epileptic surgery.

  6. Dynamic Changes in Amygdala Psychophysiological Connectivity Reveal Distinct Neural Networks for Facial Expressions of Basic Emotions.

    Science.gov (United States)

    Diano, Matteo; Tamietto, Marco; Celeghin, Alessia; Weiskrantz, Lawrence; Tatu, Mona-Karina; Bagnis, Arianna; Duca, Sergio; Geminiani, Giuliano; Cauda, Franco; Costa, Tommaso

    2017-03-27

    The quest to characterize the neural signature distinctive of different basic emotions has recently come under renewed scrutiny. Here we investigated whether facial expressions of different basic emotions modulate the functional connectivity of the amygdala with the rest of the brain. To this end, we presented seventeen healthy participants (8 females) with facial expressions of anger, disgust, fear, happiness, sadness and emotional neutrality and analyzed amygdala's psychophysiological interaction (PPI). In fact, PPI can reveal how inter-regional amygdala communications change dynamically depending on perception of various emotional expressions to recruit different brain networks, compared to the functional interactions it entertains during perception of neutral expressions. We found that for each emotion the amygdala recruited a distinctive and spatially distributed set of structures to interact with. These changes in amygdala connectional patters characterize the dynamic signature prototypical of individual emotion processing, and seemingly represent a neural mechanism that serves to implement the distinctive influence that each emotion exerts on perceptual, cognitive, and motor responses. Besides these differences, all emotions enhanced amygdala functional integration with premotor cortices compared to neutral faces. The present findings thus concur to reconceptualise the structure-function relation between brain-emotion from the traditional one-to-one mapping toward a network-based and dynamic perspective.

  7. Functional connectivity and information flow of the respiratory neural network in chronic obstructive pulmonary disease.

    Science.gov (United States)

    Yu, Lianchun; De Mazancourt, Marine; Hess, Agathe; Ashadi, Fakhrul R; Klein, Isabelle; Mal, Hervé; Courbage, Maurice; Mangin, Laurence

    2016-08-01

    Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736-2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

  8. Neural field theory of perceptual echo and implications for estimating brain connectivity

    Science.gov (United States)

    Robinson, P. A.; Pagès, J. C.; Gabay, N. C.; Babaie, T.; Mukta, K. N.

    2018-04-01

    Neural field theory is used to predict and analyze the phenomenon of perceptual echo in which random input stimuli at one location are correlated with electroencephalographic responses at other locations. It is shown that this echo correlation (EC) yields an estimate of the transfer function from the stimulated point to other locations. Modal analysis then explains the observed spatiotemporal structure of visually driven EC and the dominance of the alpha frequency; two eigenmodes of similar amplitude dominate the response, leading to temporal beating and a line of low correlation that runs from the crown of the head toward the ears. These effects result from mode splitting and symmetry breaking caused by interhemispheric coupling and cortical folding. It is shown how eigenmodes obtained from functional magnetic resonance imaging experiments can be combined with temporal dynamics from EC or other evoked responses to estimate the spatiotemporal transfer function between any two points and hence their effective connectivity.

  9. Tracting the neural basis of music: Deficient structural connectivity underlying acquired amusia.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Särkämö, Teppo; Leo, Vera; Rodríguez-Fornells, Antoni; Saunavaara, Jani; Parkkola, Riitta; Soinila, Seppo

    2017-12-01

    Acquired amusia provides a unique opportunity to investigate the fundamental neural architectures of musical processing due to the transition from a functioning to defective music processing system. Yet, the white matter (WM) deficits in amusia remain systematically unexplored. To evaluate which WM structures form the neural basis for acquired amusia and its recovery, we studied 42 stroke patients longitudinally at acute, 3-month, and 6-month post-stroke stages using DTI [tract-based spatial statistics (TBSS) and deterministic tractography (DT)] and the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Non-recovered amusia was associated with structural damage and subsequent degeneration in multiple WM tracts including the right inferior fronto-occipital fasciculus (IFOF), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and frontal aslant tract (FAT), as well as in the corpus callosum (CC) and its posterior part (tapetum). In a linear regression analysis, the volume of the right IFOF was the main predictor of MBEA performance across time. Overall, our results provide a comprehensive picture of the large-scale deficits in intra- and interhemispheric structural connectivity underlying amusia, and conversely highlight which pathways are crucial for normal music perception. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Computational neuroanatomy: ontology-based representation of neural components and connectivity.

    Science.gov (United States)

    Rubin, Daniel L; Talos, Ion-Florin; Halle, Michael; Musen, Mark A; Kikinis, Ron

    2009-02-05

    A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.

  11. Participant and Public Involvement in Refining a Peer-Volunteering Active Aging Intervention: Project ACE (Active, Connected, Engaged).

    Science.gov (United States)

    Withall, Janet; Thompson, Janice L; Fox, Kenneth R; Davis, Mark; Gray, Selena; de Koning, Jolanthe; Lloyd, Liz; Parkhurst, Graham; Stathi, Afroditi

    2018-03-19

    Evidence for the health benefits of a physically active lifestyle among older adults is strong, yet only a small proportion of older people meet physical activity recommendations. A synthesis of evidence identified "best bet" approaches, and this study sought guidance from end-user representatives and stakeholders to refine one of these, a peer-volunteering active aging intervention. Focus groups with 28 older adults and four professional volunteer managers were conducted. Semi-structured interviews were conducted with 9 older volunteers. Framework analysis was used to gauge participants' views on the ACE intervention. Motives for engaging in community groups and activities were almost entirely social. Barriers to participation were lack of someone to attend with, lack of confidence, fear of exclusion or "cliquiness" in established groups, bad weather, transport issues, inaccessibility of activities, ambivalence, and older adults being "set in their ways". Motives for volunteering included "something to do," avoiding loneliness, the need to feel needed, enjoyment, and altruism. Challenges included negative events between volunteer and recipient of volunteering support, childcare commitments, and high volunteering workload. Peer-volunteering approaches have great potential for promotion of active aging. The systematic multistakeholder approach adopted in this study led to important refinements of the original ACE intervention. The findings provide guidance for active aging community initiatives highlighting the importance of effective recruitment strategies and of tackling major barriers including lack of motivation, confidence, and readiness to change; transport issues; security concerns and cost; activity availability; and lack of social support.

  12. Visual working memory load-related changes in neural activity and functional connectivity.

    Directory of Open Access Journals (Sweden)

    Ling Li

    Full Text Available BACKGROUND: Visual working memory (VWM helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we recorded electroencephalography (EEG from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4-8 Hz, alpha- (8-12 Hz, beta- (12-32 Hz, and gamma- (32-40 Hz frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. CONCLUSIONS/SIGNIFICANCE: We suggest that the differences in theta- and alpha- bands between LVF and RVF

  13. Visual Working Memory Load-Related Changes in Neural Activity and Functional Connectivity

    Science.gov (United States)

    Li, Ling; Zhang, Jin-Xiang; Jiang, Tao

    2011-01-01

    Background Visual working memory (VWM) helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. Methodology/Principal Findings In this study, we recorded electroencephalography (EEG) from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF) memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP) at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4–8 Hz), alpha- (8–12 Hz), beta- (12–32 Hz), and gamma- (32–40 Hz) frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF) WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. Conclusions/Significance We suggest that the differences in theta- and alpha- bands between LVF and RVF conditions in

  14. Lasting modulation effects of rTMS on neural activity and connectivity as revealed by resting-state EEG.

    Science.gov (United States)

    Ding, Lei; Shou, Guofa; Yuan, Han; Urbano, Diamond; Cha, Yoon-Hee

    2014-07-01

    The long-lasting neuromodulatory effects of repetitive transcranial magnetic stimulation (rTMS) are of great interest for therapeutic applications in various neurological and psychiatric disorders, due to which functional connectivity among brain regions is profoundly disturbed. Classic TMS studies selectively alter neural activity in specific brain regions and observe neural activity changes on nonperturbed areas to infer underlying connectivity and its changes. Less has been indicated in direct measures of functional connectivity and/or neural network and on how connectivity/network alterations occur. Here, we developed a novel analysis framework to directly investigate both neural activity and connectivity changes induced by rTMS from resting-state EEG (rsEEG) acquired in a group of subjects with a chronic disorder of imbalance, known as the mal de debarquement syndrome (MdDS). Resting-state activity in multiple functional brain areas was identified through a data-driven blind source separation analysis on rsEEG data, and the connectivity among them was characterized using a phase synchronization measure. Our study revealed that there were significant long-lasting changes in resting-state neural activity, in theta, low alpha, and high alpha bands and neural networks in theta, low alpha, high alpha and beta bands, over broad cortical areas 4 to 5 h after the last application of rTMS in a consecutive five-day protocol. Our results of rsEEG connectivity further indicated that the changes, mainly in the alpha band, over the parietal and occipital cortices from pre- to post-TMS sessions were significantly correlated, in both magnitude and direction, to symptom changes in this group of subjects with MdDS. This connectivity measure not only suggested that rTMS can generate positive treatment effects in MdDS patients, but also revealed new potential targets for future therapeutic trials to improve treatment effects. It is promising that the new connectivity measure

  15. Polygenic risk for five psychiatric disorders and cross-disorder and disorder-specific neural connectivity in two independent populations.

    Science.gov (United States)

    Wang, Tianqi; Zhang, Xiaolong; Li, Ang; Zhu, Meifang; Liu, Shu; Qin, Wen; Li, Jin; Yu, Chunshui; Jiang, Tianzi; Liu, Bing

    2017-01-01

    Major psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), autism (AUT), bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SZ), are highly heritable and polygenic. Evidence suggests that these five disorders have both shared and distinct genetic risks and neural connectivity abnormalities. To measure aggregate genetic risks, the polygenic risk score (PGRS) was computed. Two independent general populations (N = 360 and N = 323) were separately examined to investigate whether the cross-disorder PGRS and PGRS for a specific disorder were associated with individual variability in functional connectivity. Consistent altered functional connectivity was found with the bilateral insula: for the left supplementary motor area and the left superior temporal gyrus with the cross-disorder PGRS, for the left insula and right middle and superior temporal lobe associated with the PGRS for autism, for the bilateral midbrain, posterior cingulate, cuneus, and precuneus associated with the PGRS for BD, and for the left angular gyrus and the left dorsolateral prefrontal cortex associated with the PGRS for schizophrenia. No significant functional connectivity was found associated with the PGRS for ADHD and MDD. Our findings indicated that genetic effects on the cross-disorder and disorder-specific neural connectivity of common genetic risk loci are detectable in the general population. Our findings also indicated that polygenic risk contributes to the main neurobiological phenotypes of psychiatric disorders and that identifying cross-disorder and specific functional connectivity related to polygenic risks may elucidate the neural pathways for these disorders.

  16. Altered Immune Function Associated with Disordered Neural Connectivity and Executive Dysfunctions: A Neurophysiological Study on Children with Autism Spectrum Disorders

    Science.gov (United States)

    Han, Yvonne M. Y.; Chan, Agnes S.; Sze, Sophia L.; Cheung, Mei-Chun; Wong, Chun-kwok; Lam, Joseph M. K.; Poon, Priscilla M. K.

    2013-01-01

    Previous studies have shown that children with autism spectrum disorders (ASDs) have impaired executive function, disordered neural connectivity, and abnormal immunologic function. The present study examined whether these abnormalities were associated. Seventeen high-functioning (HFA) and 17 low-functioning (LFA) children with ASD, aged 8-17…

  17. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. Zebrafish msxB, msxC and msxE function together to refine the neural-nonneural border and regulate cranial placodes and neural crest development.

    Science.gov (United States)

    Phillips, Bryan T; Kwon, Hye-Joo; Melton, Colt; Houghtaling, Paul; Fritz, Andreas; Riley, Bruce B

    2006-06-15

    The zebrafish muscle segment homeobox genes msxB, msxC and msxE are expressed in partially overlapping domains in the neural crest and preplacodal ectoderm. We examined the roles of these msx genes in early development. Disrupting individual msx genes causes modest variable defects, whereas disrupting all three produces a reproducible severe phenotype, suggesting functional redundancy. Neural crest differentiation is blocked at an early stage. Preplacodal development begins normally, but placodes arising from the msx expression domain later show elevated apoptosis and are reduced in size. Cell proliferation is normal in these tissues. Unexpectedly, Msx-deficient embryos become ventralized by late gastrulation whereas misexpression of msxB dorsalizes the embryo. These effects appear to involve Distal-less (Dlx) protein activity, as loss of dlx3b and dlx4b suppresses ventralization in Msx-depleted embryos. At the same time, Msx-depletion restores normal preplacodal gene expression to dlx3b-dlx4b mutants. These data suggest that mutual antagonism between Msx and Dlx proteins achieves a balance of function required for normal preplacodal differentiation and placement of the neural-nonneural border.

  19. The relationship between structural and functional connectivity: graph theoretical analysis of an EEG neural mass model

    NARCIS (Netherlands)

    Ponten, S.C.; Daffertshofer, A.; Hillebrand, A.; Stam, C.J.

    2010-01-01

    We investigated the relationship between structural network properties and both synchronization strength and functional characteristics in a combined neural mass and graph theoretical model of the electroencephalogram (EEG). Thirty-two neural mass models (NMMs), each representing the lump activity

  20. Llgl1 Connects Cell Polarity with Cell-Cell Adhesion in Embryonic Neural Stem Cells.

    Science.gov (United States)

    Jossin, Yves; Lee, Minhui; Klezovitch, Olga; Kon, Elif; Cossard, Alexia; Lien, Wen-Hui; Fernandez, Tania E; Cooper, Jonathan A; Vasioukhin, Valera

    2017-06-05

    Malformations of the cerebral cortex (MCCs) are devastating developmental disorders. We report here that mice with embryonic neural stem-cell-specific deletion of Llgl1 (Nestin-Cre/Llgl1 fl/fl ), a mammalian ortholog of the Drosophila cell polarity gene lgl, exhibit MCCs resembling severe periventricular heterotopia (PH). Immunohistochemical analyses and live cortical imaging of PH formation revealed that disruption of apical junctional complexes (AJCs) was responsible for PH in Nestin-Cre/Llgl1 fl/fl brains. While it is well known that cell polarity proteins govern the formation of AJCs, the exact mechanisms remain unclear. We show that LLGL1 directly binds to and promotes internalization of N-cadherin, and N-cadherin/LLGL1 interaction is inhibited by atypical protein kinase C-mediated phosphorylation of LLGL1, restricting the accumulation of AJCs to the basolateral-apical boundary. Disruption of the N-cadherin-LLGL1 interaction during cortical development in vivo is sufficient for PH. These findings reveal a mechanism responsible for the physical and functional connection between cell polarity and cell-cell adhesion machineries in mammalian cells. Copyright © 2017 Elsevier Inc. All rights reserved.

  1. Fetoscopic Open Neural Tube Defect Repair: Development and Refinement of a Two-Port, Carbon Dioxide Insufflation Technique.

    Science.gov (United States)

    Belfort, Michael A; Whitehead, William E; Shamshirsaz, Alireza A; Bateni, Zhoobin H; Olutoye, Oluyinka O; Olutoye, Olutoyin A; Mann, David G; Espinoza, Jimmy; Williams, Erin; Lee, Timothy C; Keswani, Sundeep G; Ayres, Nancy; Cassady, Christopher I; Mehollin-Ray, Amy R; Sanz Cortes, Magdalena; Carreras, Elena; Peiro, Jose L; Ruano, Rodrigo; Cass, Darrell L

    2017-04-01

    To describe development of a two-port fetoscopic technique for spina bifida repair in the exteriorized, carbon dioxide-filled uterus and report early results of two cohorts of patients: the first 15 treated with an iterative technique and the latter 13 with a standardized technique. This was a retrospective cohort study (2014-2016). All patients met Management of Myelomeningocele Study selection criteria. The intraoperative approach was iterative in the first 15 patients and was then standardized. Obstetric, maternal, fetal, and early neonatal outcomes were compared. Standard parametric and nonparametric tests were used as appropriate. Data for 28 patients (22 endoscopic only, four hybrid, two abandoned) are reported, but only those with a complete fetoscopic repair were analyzed (iterative technique [n=10] compared with standardized technique [n=12]). Maternal demographics and gestational age (median [range]) at fetal surgery (25.4 [22.9-25.9] compared with 24.8 [24-25.6] weeks) were similar, but delivery occurred at 35.9 (26-39) weeks of gestation with the iterative technique compared with 39 (35.9-40) weeks of gestation with the standardized technique (Pmet in 9 of 12 (75%) and 3 of 10 (30%), respectively, and 7 of 12 (58%) compared with 2 of 10 (20%) have been treated for hydrocephalus to date. These latter differences were not statistically significant. Fetoscopic open neural tube defect repair does not appear to increase maternal-fetal complications as compared with repair by hysterotomy, allows for vaginal delivery, and may reduce long-term maternal risks. ClinicalTrials.gov, https://clinicaltrials.gov, NCT02230072.

  2. A Novel Neural Network Vector Control for Single-Phase Grid-Connected Converters with L, LC and LCL Filters

    Directory of Open Access Journals (Sweden)

    Xingang Fu

    2016-04-01

    Full Text Available This paper investigates a novel recurrent neural network (NN-based vector control approach for single-phase grid-connected converters (GCCs with L (inductor, LC (inductor-capacitor and LCL (inductor-capacitor-inductor filters and provides their comparison study with the conventional standard vector control method. A single neural network controller replaces two current-loop PI controllers, and the NN training approximates the optimal control for the single-phase GCC system. The Levenberg–Marquardt (LM algorithm was used to train the NN controller based on the complete system equations without any decoupling policies. The proposed NN approach can solve the decoupling problem associated with the conventional vector control methods for L, LC and LCL-filter-based single-phase GCCs. Both simulation study and hardware experiments demonstrate that the neural network vector controller shows much more improved performance than that of conventional vector controllers, including faster response speed and lower overshoot. Especially, NN vector control could achieve very good performance using low switch frequency. More importantly, the neural network vector controller is a damping free controller, which is generally required by a conventional vector controller for an LCL-filter-based single-phase grid-connected converter and, therefore, can overcome the inefficiency problem caused by damping policies.

  3. Application of neural networks to connectional expert system for identification of transients in nuclear power plants

    International Nuclear Information System (INIS)

    Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

    1991-01-01

    The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

  4. Connectivity in the yeast cell cycle transcription network: inferences from neural networks.

    Directory of Open Access Journals (Sweden)

    Christopher E Hart

    2006-12-01

    Full Text Available A current challenge is to develop computational approaches to infer gene network regulatory relationships based on multiple types of large-scale functional genomic data. We find that single-layer feed-forward artificial neural network (ANN models can effectively discover gene network structure by integrating global in vivo protein:DNA interaction data (ChIP/Array with genome-wide microarray RNA data. We test this on the yeast cell cycle transcription network, which is composed of several hundred genes with phase-specific RNA outputs. These ANNs were robust to noise in data and to a variety of perturbations. They reliably identified and ranked 10 of 12 known major cell cycle factors at the top of a set of 204, based on a sum-of-squared weights metric. Comparative analysis of motif occurrences among multiple yeast species independently confirmed relationships inferred from ANN weights analysis. ANN models can capitalize on properties of biological gene networks that other kinds of models do not. ANNs naturally take advantage of patterns of absence, as well as presence, of factor binding associated with specific expression output; they are easily subjected to in silico "mutation" to uncover biological redundancies; and they can use the full range of factor binding values. A prominent feature of cell cycle ANNs suggested an analogous property might exist in the biological network. This postulated that "network-local discrimination" occurs when regulatory connections (here between MBF and target genes are explicitly disfavored in one network module (G2, relative to others and to the class of genes outside the mitotic network. If correct, this predicts that MBF motifs will be significantly depleted from the discriminated class and that the discrimination will persist through evolution. Analysis of distantly related Schizosaccharomyces pombe confirmed this, suggesting that network-local discrimination is real and complements well-known enrichment of

  5. Altered neural connectivity during response inhibition in adolescents with attention-deficit/hyperactivity disorder and their unaffected siblings.

    Science.gov (United States)

    van Rooij, Daan; Hartman, Catharina A; Mennes, Maarten; Oosterlaan, Jaap; Franke, Barbara; Rommelse, Nanda; Heslenfeld, Dirk; Faraone, Stephen V; Buitelaar, Jan K; Hoekstra, Pieter J

    2015-01-01

    Response inhibition is one of the executive functions impaired in attention-deficit/hyperactivity disorder (ADHD). Increasing evidence indicates that altered functional and structural neural connectivity are part of the neurobiological basis of ADHD. Here, we investigated if adolescents with ADHD show altered functional connectivity during response inhibition compared to their unaffected siblings and healthy controls. Response inhibition was assessed using the stop signal paradigm. Functional connectivity was assessed using psycho-physiological interaction analyses applied to BOLD time courses from seed regions within inferior- and superior frontal nodes of the response inhibition network. Resulting networks were compared between adolescents with ADHD (N = 185), their unaffected siblings (N = 111), and controls (N = 125). Control subjects showed stronger functional connectivity than the other two groups within the response inhibition network, while subjects with ADHD showed relatively stronger connectivity between default mode network (DMN) nodes. Stronger connectivity within the response inhibition network was correlated with lower ADHD severity, while stronger connectivity with the DMN was correlated with increased ADHD severity. Siblings showed connectivity patterns similar to controls during successful inhibition and to ADHD subjects during failed inhibition. Additionally, siblings showed decreased connectivity with the primary motor areas as compared to both participants with ADHD and controls. Subjects with ADHD fail to integrate activation within the response inhibition network and to inhibit connectivity with task-irrelevant regions. Unaffected siblings show similar alterations only during failed stop trials, as well as unique suppression of motor areas, suggesting compensatory strategies. These findings support the role of altered functional connectivity in understanding the neurobiology and familial transmission of ADHD.

  6. Neural correlates and network connectivity underlying narrative production and comprehension: a combined fMRI and PET study.

    Science.gov (United States)

    AbdulSabur, Nuria Y; Xu, Yisheng; Liu, Siyuan; Chow, Ho Ming; Baxter, Miranda; Carson, Jessica; Braun, Allen R

    2014-08-01

    The neural correlates of narrative production and comprehension remain poorly understood. Here, using positron emission tomography (PET), functional magnetic resonance imaging (fMRI), contrast and functional network connectivity analyses we comprehensively characterize the neural mechanisms underlying these complex behaviors. Eighteen healthy subjects told and listened to fictional stories during scanning. In addition to traditional language areas (e.g., left inferior frontal and posterior middle temporal gyri), both narrative production and comprehension engaged regions associated with mentalizing and situation model construction (e.g., dorsomedial prefrontal cortex, precuneus and inferior parietal lobules) as well as neocortical premotor areas, such as the pre-supplementary motor area and left dorsal premotor cortex. Narrative comprehension alone showed marked bilaterality, activating right hemisphere homologs of perisylvian language areas. Narrative production remained predominantly left lateralized, uniquely activating executive and motor-related regions essential to language formulation and articulation. Connectivity analyses revealed strong associations between language areas and the superior and middle temporal gyri during both tasks. However, only during storytelling were these same language-related regions connected to cortical and subcortical motor regions. In contrast, during story comprehension alone, they were strongly linked to regions supporting mentalizing. Thus, when employed in a more complex, ecologically-valid context, language production and comprehension show both overlapping and idiosyncratic patterns of activation and functional connectivity. Importantly, in each case the language system is integrated with regions that support other cognitive and sensorimotor domains. Copyright © 2014. Published by Elsevier Ltd.

  7. Mutual Connectivity Analysis (MCA) Using Generalized Radial Basis Function Neural Networks for Nonlinear Functional Connectivity Network Recovery in Resting-State Functional MRI.

    Science.gov (United States)

    DSouza, Adora M; Abidin, Anas Zainul; Nagarajan, Mahesh B; Wismüller, Axel

    2016-03-29

    We investigate the applicability of a computational framework, called mutual connectivity analysis (MCA), for directed functional connectivity analysis in both synthetic and resting-state functional MRI data. This framework comprises of first evaluating non-linear cross-predictability between every pair of time series prior to recovering the underlying network structure using community detection algorithms. We obtain the non-linear cross-prediction score between time series using Generalized Radial Basis Functions (GRBF) neural networks. These cross-prediction scores characterize the underlying functionally connected networks within the resting brain, which can be extracted using non-metric clustering approaches, such as the Louvain method. We first test our approach on synthetic models with known directional influence and network structure. Our method is able to capture the directional relationships between time series (with an area under the ROC curve = 0.92 ± 0.037) as well as the underlying network structure (Rand index = 0.87 ± 0.063) with high accuracy. Furthermore, we test this method for network recovery on resting-state fMRI data, where results are compared to the motor cortex network recovered from a motor stimulation sequence, resulting in a strong agreement between the two (Dice coefficient = 0.45). We conclude that our MCA approach is effective in analyzing non-linear directed functional connectivity and in revealing underlying functional network structure in complex systems.

  8. On the Nature of the Intrinsic Connectivity of the Cat Motor Cortex: Evidence for a Recurrent Neural Network Topology

    DEFF Research Database (Denmark)

    Capaday, Charles; Ethier, C; Brizzi, L

    2009-01-01

    and functional significance of the intrinsic horizontal connections between neurons in the motor cortex (MCx) remain to be clarified. To further elucidate the nature of this intracortical connectivity pattern, experiments were done on the MCx of three cats. The anterograde tracer biocytin was ejected......Capaday C, Ethier C, Brizzi L, Sik A, van Vreeswijk C, Gingras D. On the nature of the intrinsic connectivity of the cat motor cortex: evidence for a recurrent neural network topology. J Neurophysiol 102: 2131-2141, 2009. First published July 22, 2009; doi: 10.1152/jn.91319.2008. The details...... iontophoretically in layers II, III, and V. Some 30-50 neurons within a radius of similar to 250 mu m were thus stained. The functional output of the motor cortical point at which biocytin was injected, and of the surrounding points, was identified by microstimulation and electromyographic recordings. The axonal...

  9. Refining revolution

    Energy Technology Data Exchange (ETDEWEB)

    Fesharaki, F.; Isaak, D.

    1984-01-01

    A review of changes in the oil refining industry since 1973 examines the drop in capacity use and its effect on profits of the Organization of Economic Cooperation and Development (OECD) countries compared to world refining. OPEC countries used their new oil revenues to expand Gulf refineries, which put additional pressure on OECD refiners. OPEC involvement in global marketing, however, could help to secure supplies. Scrapping some older OECD refineries could improve the percentage of capacity in use if new construction is kept to a minimum. Other issues facing refiners are the changes in oil demand patterns and government responses to the market. 2 tables.

  10. Global robust stability of delayed neural networks: Estimating upper limit of norm of delayed connection weight matrix

    International Nuclear Information System (INIS)

    Singh, Vimal

    2007-01-01

    The question of estimating the upper limit of -parallel B -parallel 2 , which is a key step in some recently reported global robust stability criteria for delayed neural networks, is revisited ( B denotes the delayed connection weight matrix). Recently, Cao, Huang, and Qu have given an estimate of the upper limit of -parallel B -parallel 2 . In the present paper, an alternative estimate of the upper limit of -parallel B -parallel 2 is highlighted. It is shown that the alternative estimate may yield some new global robust stability results

  11. A realistic neural mass model of the cortex with laminar-specific connections and synaptic plasticity - evaluation with auditory habituation.

    Directory of Open Access Journals (Sweden)

    Peng Wang

    Full Text Available In this work we propose a biologically realistic local cortical circuit model (LCCM, based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1 activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2 realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1 besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6, there exists a parallel "short-cut" pathway (layer 4 to layer 5/6, (2 the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3 the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3 are more strongly habituated than backward connections (from Layer 5/6 to layer 4. Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG, which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.

  12. Effects of small-world connectivity on noise-induced temporal and spatial order in neural media

    International Nuclear Information System (INIS)

    Perc, Matjaz

    2007-01-01

    We present an overview of possible effects of small-world connectivity on noise-induced temporal and spatial order in a two-dimensional network of excitable neural media with FitzHugh-Nagumo local dynamics. Small-world networks are characterized by a given fraction of so-called long-range couplings or shortcut links that connect distant units of the system, while all other units are coupled in a diffusive-like manner. Interestingly, already a small fraction of these long-range couplings can have wide-ranging effects on the temporal as well as spatial noise-induced dynamics of the system. Here we present two main effects. First, we show that the temporal order, characterized by the autocorrelation of a firing-rate function, can be greatly enhanced by the introduction of small-world connectivity, whereby the effect increases with the increasing fraction of introduced shortcut links. Second, we show that the introduction of long-range couplings induces disorder of otherwise ordered, spiral-wave-like, noise-induced patterns that can be observed by exclusive diffusive connectivity of spatial units. Thereby, already a small fraction of shortcut links is sufficient to destroy coherent pattern formation in the media. Although the two results seem contradictive, we provide an explanation considering the inherent scale-free nature of small-world networks, which on one hand, facilitates signal transduction and thus temporal order in the system, whilst on the other hand, disrupts the internal spatial scale of the media thereby hindering the existence of coherent wave-like patterns. Additionally, the importance of spatially versus temporally ordered neural network functioning is discussed

  13. Neural reuse leads to associative connections between concrete (physical) and abstract (social) concepts and motives.

    Science.gov (United States)

    Wang, Yimeng; Bargh, John A

    2016-01-01

    Consistent with neural reuse theory, empirical tests of the related "scaffolding" principle of abstract concept development show that higher-level concepts "reuse" and are built upon fundamental motives such as survival, safety, and consumption. This produces mutual influence between the two levels, with far-ranging impacts from consumer behavior to political attitudes.

  14. On the connection between level of education and the neural circuitry of emotion perception

    NARCIS (Netherlands)

    Demenescu, Liliana R.; Stan, Adrian; Kortekaas, Rudie; van der Wee, Nic J. A.; Veltman, Dick J.; Aleman, Andre

    2014-01-01

    Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of The

  15. Neural ECM in laminar organization and connectivity development in healthy and diseased human brain

    NARCIS (Netherlands)

    Jovanov Milošević, Nataša; Judaš, Miloš; Aronica, Eleonora; Kostovic, Ivica

    2014-01-01

    The neural extracellular matrix (ECM) provides a supportive framework for differentiating cells and their processes and regulates morphogenetic events by spatially and temporally relevant localization of signaling molecules and by direct signaling via receptor and/or coreceptor-mediated action. The

  16. Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

    Science.gov (United States)

    Rich, Scott; Zochowski, Michal; Booth, Victoria

    2018-01-01

    Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

  17. Mode of effective connectivity within a putative neural network differentiates moral cognitions related to care and justice ethics.

    Science.gov (United States)

    Cáceda, Ricardo; James, G Andrew; Ely, Timothy D; Snarey, John; Kilts, Clinton D

    2011-02-25

    Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC) and posterior (PCC) cingulate cortex, posterior superior temporal sulcus (pSTS), insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses.

  18. Modeling, control, and simulation of grid connected intelligent hybrid battery/photovoltaic system using new hybrid fuzzy-neural method.

    Science.gov (United States)

    Rezvani, Alireza; Khalili, Abbas; Mazareie, Alireza; Gandomkar, Majid

    2016-07-01

    Nowadays, photovoltaic (PV) generation is growing increasingly fast as a renewable energy source. Nevertheless, the drawback of the PV system is its dependence on weather conditions. Therefore, battery energy storage (BES) can be considered to assist for a stable and reliable output from PV generation system for loads and improve the dynamic performance of the whole generation system in grid connected mode. In this paper, a novel topology of intelligent hybrid generation systems with PV and BES in a DC-coupled structure is presented. Each photovoltaic cell has a specific point named maximum power point on its operational curve (i.e. current-voltage or power-voltage curve) in which it can generate maximum power. Irradiance and temperature changes affect these operational curves. Therefore, the nonlinear characteristic of maximum power point to environment has caused to development of different maximum power point tracking techniques. In order to capture the maximum power point (MPP), a hybrid fuzzy-neural maximum power point tracking (MPPT) method is applied in the PV system. Obtained results represent the effectiveness and superiority of the proposed method, and the average tracking efficiency of the hybrid fuzzy-neural is incremented by approximately two percentage points in comparison to the conventional methods. It has the advantages of robustness, fast response and good performance. A detailed mathematical model and a control approach of a three-phase grid-connected intelligent hybrid system have been proposed using Matlab/Simulink. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Mode of Effective Connectivity within a Putative Neural Network Differentiates Moral Cognitions Related to Care and Justice Ethics

    Science.gov (United States)

    Cáceda, Ricardo; James, G. Andrew; Ely, Timothy D.; Snarey, John; Kilts, Clinton D.

    2011-01-01

    Background Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC) and posterior (PCC) cingulate cortex, posterior superior temporal sulcus (pSTS), insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. Methodology/Principal Findings Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. Conclusions/Significance These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses. PMID:21364916

  20. Mode of effective connectivity within a putative neural network differentiates moral cognitions related to care and justice ethics.

    Directory of Open Access Journals (Sweden)

    Ricardo Cáceda

    Full Text Available BACKGROUND: Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC and posterior (PCC cingulate cortex, posterior superior temporal sulcus (pSTS, insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. METHODOLOGY/PRINCIPAL FINDINGS: Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. CONCLUSIONS/SIGNIFICANCE: These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses.

  1. Fully Connected Cascade Artificial Neural Network Architecture for Attention Deficit Hyperactivity Disorder Classification From Functional Magnetic Resonance Imaging Data.

    Science.gov (United States)

    Deshpande, Gopikrishna; Wang, Peng; Rangaprakash, D; Wilamowski, Bogdan

    2015-12-01

    Automated recognition and classification of brain diseases are of tremendous value to society. Attention deficit hyperactivity disorder (ADHD) is a diverse spectrum disorder whose diagnosis is based on behavior and hence will benefit from classification utilizing objective neuroimaging measures. Toward this end, an international competition was conducted for classifying ADHD using functional magnetic resonance imaging data acquired from multiple sites worldwide. Here, we consider the data from this competition as an example to illustrate the utility of fully connected cascade (FCC) artificial neural network (ANN) architecture for performing classification. We employed various directional and nondirectional brain connectivity-based methods to extract discriminative features which gave better classification accuracy compared to raw data. Our accuracy for distinguishing ADHD from healthy subjects was close to 90% and between the ADHD subtypes was close to 95%. Further, we show that, if properly used, FCC ANN performs very well compared to other classifiers such as support vector machines in terms of accuracy, irrespective of the feature used. Finally, the most discriminative connectivity features provided insights about the pathophysiology of ADHD and showed reduced and altered connectivity involving the left orbitofrontal cortex and various cerebellar regions in ADHD.

  2. Spatial working memory in neurofibromatosis 1: Altered neural activity and functional connectivity

    Directory of Open Access Journals (Sweden)

    Amira F.A. Ibrahim

    2017-01-01

    Conclusions: Dysfunctional engagement of WM circuitry, and aberrant functional connectivity of ‘task-negative’ regions in NF1 patients may underlie spatial WM difficulties characteristic of the disorder.

  3. Changes in Neural Connectivity and Memory Following a Yoga Intervention for Older Adults: A Pilot Study.

    Science.gov (United States)

    Eyre, Harris A; Acevedo, Bianca; Yang, Hongyu; Siddarth, Prabha; Van Dyk, Kathleen; Ercoli, Linda; Leaver, Amber M; Cyr, Natalie St; Narr, Katherine; Baune, Bernhard T; Khalsa, Dharma S; Lavretsky, Helen

    2016-01-01

    No study has explored the effect of yoga on cognitive decline and resting-state functional connectivity. This study explored the relationship between performance on memory tests and resting-state functional connectivity before and after a yoga intervention versus active control for subjects with mild cognitive impairment (MCI). Participants ( ≥ 55 y) with MCI were randomized to receive a yoga intervention or active "gold-standard" control (i.e., memory enhancement training (MET)) for 12 weeks. Resting-state functional magnetic resonance imaging was used to map correlations between brain networks and memory performance changes over time. Default mode networks (DMN), language and superior parietal networks were chosen as networks of interest to analyze the association with changes in verbal and visuospatial memory performance. Fourteen yoga and 11 MET participants completed the study. The yoga group demonstrated a statistically significant improvement in depression and visuospatial memory. We observed improved verbal memory performance correlated with increased connectivity between the DMN and frontal medial cortex, pregenual anterior cingulate cortex, right middle frontal cortex, posterior cingulate cortex, and left lateral occipital cortex. Improved verbal memory performance positively correlated with increased connectivity between the language processing network and the left inferior frontal gyrus. Improved visuospatial memory performance correlated inversely with connectivity between the superior parietal network and the medial parietal cortex. Yoga may be as effective as MET in improving functional connectivity in relation to verbal memory performance. These findings should be confirmed in larger prospective studies.

  4. Distinct Neural Signatures Detected for ADHD Subtypes After Controlling for Micro-Movements in Resting State Functional Connectivity MRI Data

    Directory of Open Access Journals (Sweden)

    Damien eFair

    2013-02-01

    Full Text Available In recent years, there has been growing enthusiasm that functional MRI could achieve clinical utility for a broad range of neuropsychiatric disorders. However, several barriers remain. For example, the acquisition of large-scale datasets capable of clarifying the marked heterogeneity that exists in psychiatric illnesses will need to be realized. In addition, there continues to be a need for the development of image processing and analysis methods capable of separating signal from artifact. As a prototypical hyperkinetic disorder, and movement related artifact being a significant confound in functional imaging studies, ADHD offers a unique challenge. As part of the ADHD-200 Global Competition and this special edition of Frontiers, the ADHD-200 Consortium demonstrates the utility of an aggregate dataset pooled across five institutions in addressing these challenges. The work aimed to A examine the impact of emerging techniques for controlling for micro-movements, and B provide novel insights into the neural correlates of ADHD subtypes. Using SVM based MVPA we show that functional connectivity patterns in individuals are capable of differentiating the two most prominent ADHD subtypes. The application of graph-theory revealed that the Combined (ADHD-C and Inattentive (ADHD-I subtypes demonstrated some overlapping (particularly sensorimotor systems, but unique patterns of atypical connectivity. For ADHD-C, atypical connectivity was prominent in midline default network components, as well as insular cortex; in contrast, the ADHD-I group exhibited atypical patterns within the dlPFC regions and cerebellum. Systematic motion-related artifact was noted, and highlighted the need for stringent motion correction. Findings reported were robust to the specific motion correction strategy employed. These data suggest that rs-fcMRI data can be used to characterize individual patients with ADHD and to identify neural distinctions underlying the clinical

  5. Neural correlates of verbal creativity: Differences in resting-state functional connectivity associated with expertise in creative writing

    Directory of Open Access Journals (Sweden)

    Martin eLotze

    2014-07-01

    Full Text Available Neural characteristics of verbal creativity as assessed by word generation tasks have been recently identified, but differences in resting-state functional connectivity (rFC between experts and non-experts in creative writing have not been reported yet. Previous electroencephalography (EEG coherence measures during rest demonstrated a decreased cooperation between brain areas in association with creative thinking ability. Here, we used resting-state functional magnetic resonance imaging to compare 20 experts in creative writing and 23 age-matched non-experts with respect to rFC strengths within a brain network previously found to be associated with creative writing. Decreased rFC for experts was found between areas 44 of both hemispheres. Increased rFC for experts was observed between right hemispheric caudate and intraparietal sulcus. Correlation analysis of verbal creativity indices with rFC values in the expert group revealed predominantly negative associations, particularly of rFC between left area 44 and left temporal pole. Overall, our data support previous findings on reduced connectivity between interhemispheric areas and increased right-hemispheric connectivity during rest in highly verbally creative individuals.

  6. Neural Connectivity and Immunocytochemical Studies of Anatomical Sites Related to Nauseogenic and Emetic Reflexes

    Science.gov (United States)

    Fox, Robert A. (Principal Investigator)

    1992-01-01

    The studies conducted in this research project examined several aspects of neuroanatomical structures and neurochemical processes related to motion sickness in animal models. A principle objective of these studies was to investigate neurochemical changes in the central nervous system that are related to motion sickness with the objective of defining neural mechanisms important to this malady. For purposes of exposition, the studies and research finding have been classified into five categories. These are: immunoreactivity in the brainstem, vasopressin effects, lesion studies of area postrema, role of the vagus nerve, and central nervous system structure related to adaptation to microgravity.

  7. Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

    Directory of Open Access Journals (Sweden)

    A. Novellino

    2007-01-01

    Full Text Available One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason x201C;embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA, to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.

  8. Connecting Neurons to a Mobile Robot: An In Vitro Bidirectional Neural Interface

    Science.gov (United States)

    Novellino, A.; D'Angelo, P.; Cozzi, L.; Chiappalone, M.; Sanguineti, V.; Martinoia, S.

    2007-01-01

    One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses. PMID:18350128

  9. Effects of aging on neural connectivity underlying selective memory for emotional scenes.

    Science.gov (United States)

    Waring, Jill D; Addis, Donna Rose; Kensinger, Elizabeth A

    2013-02-01

    Older adults show age-related reductions in memory for neutral items within complex visual scenes, but just like young adults, older adults exhibit a memory advantage for emotional items within scenes compared with the background scene information. The present study examined young and older adults' encoding-stage effective connectivity for selective memory of emotional items versus memory for both the emotional item and its background. In a functional magnetic resonance imaging (fMRI) study, participants viewed scenes containing either positive or negative items within neutral backgrounds. Outside the scanner, participants completed a memory test for items and backgrounds. Irrespective of scene content being emotionally positive or negative, older adults had stronger positive connections among frontal regions and from frontal regions to medial temporal lobe structures than did young adults, especially when items and backgrounds were subsequently remembered. These results suggest there are differences between young and older adults' connectivity accompanying the encoding of emotional scenes. Older adults may require more frontal connectivity to encode all elements of a scene rather than just encoding the emotional item. Published by Elsevier Inc.

  10. Neural intrinsic connectivity networks associated with risk aversion in old age.

    Science.gov (United States)

    Han, S Duke; Boyle, Patricia A; Arfanakis, Konstantinos; Fleischman, Debra A; Yu, Lei; Edmonds, Emily C; Bennett, David A

    2012-02-01

    Risk aversion is associated with several important real world outcomes. Although the neurobiological correlates of risk aversion have been studied in young persons, little is known of the neurobiological correlates of risk aversion among older persons. Resting-state functional MRI data were collected on 134 non-demented participants of the Rush Memory and Aging Project, a community-based cohort study of aging. Risk aversion was measured using a series of standard questions in which participants were asked to choose between a certain monetary payment ($15) versus a gamble in which they could gain more than $15 or gain nothing, with potential gains varied across questions. Participants determined to be "high" (n=27) and "low" (n=27) in risk aversion were grouped accordingly. Using a spherical seed region of interest in the anterior cingulate cortex, voxel-wise functional connectivity network similarities were observed in bilateral frontal, anterior and posterior cingulate, insula, basal ganglia, temporal, parietal, and thalamic regions. Differences in functional connectivity were observed such that those low in risk aversion had greater connectivity to clusters in the superior, middle, and medial frontal regions, as well as cerebellar, parietal, occipital, and inferior temporal regions. Those high in risk aversion had greater connectivity to clusters in the inferior and orbital frontal, parahippocampal, and insula regions, as well as thalamic, parietal, precentral gyrus, postcentral gyrus, and middle temporal regions. Similarities and differences in functional connectivity patterns may reflect the historical recruitment of specific brain regions as a network in the active processing of risk in older adults. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. Neural substrates underlying balanced time perspective: A combined voxel-based morphometry and resting-state functional connectivity study.

    Science.gov (United States)

    Guo, Yiqun; Chen, Zhiyi; Feng, Tingyong

    2017-08-14

    Balanced time perspective (BTP), which is defined as a mental ability to switch flexibly among different time perspectives Zimbardo and Boyd (1999), has been suggested to be a central component of positive psychology Boniwell and Zimbardo (2004). BTP reflects individual's cognitive flexibility towards different time frames, which leads to many positive outcomes, including positive mood, subjective wellbeing, emotional intelligence, fluid intelligence, and executive control. However, the neural basis of BTP is still unclear. To address this question, we quantified individual's deviation from the BTP (DBTP), and investigated the neural substrates of DBTP using both voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) methods VBM analysis found that DBTP scores were positively correlated with gray matter volume (GMV) in the ventral precuneus. We further found that DBTP scores were negatively associated with RSFCs between the ventral precuneus seed region and medial prefrontal cortex (mPFC), bilateral temporoparietal junction (TPJ), parahippocampa gyrus (PHG), and middle frontal gyrus (MFG). These brain regions found in both VBM and RSFC analyses are commonly considered as core nodes of the default mode network (DMN) that is known to be involved in many functions, including episodic and autobiographical memory, self-related processing, theory of mind, and imagining the future. These functions of the DMN are also essential to individuals with BTP. Taken together, we provide the first evidence for the structural and functional neural basis of BTP, and highlight the crucial role of the DMN in cultivating an individual's BTP. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Bifurcation analysis for a discrete-time Hopfield neural network of two neurons with two delays and self-connections

    International Nuclear Information System (INIS)

    Kaslik, E.; Balint, St.

    2009-01-01

    In this paper, a bifurcation analysis is undertaken for a discrete-time Hopfield neural network of two neurons with two different delays and self-connections. Conditions ensuring the asymptotic stability of the null solution are found, with respect to two characteristic parameters of the system. It is shown that for certain values of these parameters, Fold or Neimark-Sacker bifurcations occur, but Flip and codimension 2 (Fold-Neimark-Sacker, double Neimark-Sacker, resonance 1:1 and Flip-Neimark-Sacker) bifurcations may also be present. The direction and the stability of the Neimark-Sacker bifurcations are investigated by applying the center manifold theorem and the normal form theory

  13. Theory of Mind and the Whole Brain Functional Connectivity: Behavioral and Neural Evidences with the Amsterdam Resting State Questionnaire.

    Science.gov (United States)

    Marchetti, Antonella; Baglio, Francesca; Costantini, Isa; Dipasquale, Ottavia; Savazzi, Federica; Nemni, Raffaello; Sangiuliano Intra, Francesca; Tagliabue, Semira; Valle, Annalisa; Massaro, Davide; Castelli, Ilaria

    2015-01-01

    A topic of common interest to psychologists and philosophers is the spontaneous flow of thoughts when the individual is awake but not involved in cognitive demands. This argument, classically referred to as the "stream of consciousness" of James, is now known in the psychological literature as "Mind-Wandering." Although of great interest, this construct has been scarcely investigated so far. Diaz et al. (2013) created the Amsterdam Resting State Questionnaire (ARSQ), composed of 27 items, distributed in seven factors: discontinuity of mind, theory of mind (ToM), self, planning, sleepiness, comfort, and somatic awareness. The present study aims at: testing psychometric properties of the ARSQ in a sample of 670 Italian subjects; exploring the neural correlates of a subsample of participants (N = 28) divided into two groups on the basis of the scores obtained in the ToM factor. Results show a satisfactory reliability of the original factional structure in the Italian sample. In the subjects with a high mean in the ToM factor compared to low mean subjects, functional MRI revealed: a network (48 nodes) with higher functional connectivity (FC) with a dominance of the left hemisphere; an increased within-lobe FC in frontal and insular lobes. In both neural and behavioral terms, our results support the idea that the mind, which does not rest even when explicitly asked to do so, has various and interesting mentalistic-like contents.

  14. Neural signature of coma revealed by posteromedial cortex connection density analysis

    Directory of Open Access Journals (Sweden)

    Briguita Malagurski

    2017-01-01

    A complex pattern of decreased and increased connections was observed, suggesting a network imbalance between internal/external processing systems, within PMC during coma. The number of PMC voxels with hypo-CD positive correlation showed a significant negative association with the CRS-R score, notwithstanding aetiology. Traumatic injury specifically appeared to be associated with a greater prevalence of hyper-connected (negative correlation voxels, which was inversely associated with patient neurological outcome. A logistic regression model using the number of hypo-CD positive and hyper-CD negative correlations, accurately permitted patient's outcome prediction (AUC = 0.906, 95%IC = 0.795–1. These points might reflect adaptive plasticity mechanism and pave the way for innovative prognosis and therapeutics methods.

  15. Analysis of nonlocal neural fields for both general and gamma-distributed connectivities

    Science.gov (United States)

    Hutt, Axel; Atay, Fatihcan M.

    2005-04-01

    This work studies the stability of equilibria in spatially extended neuronal ensembles. We first derive the model equation from statistical properties of the neuron population. The obtained integro-differential equation includes synaptic and space-dependent transmission delay for both general and gamma-distributed synaptic connectivities. The latter connectivity type reveals infinite, finite, and vanishing self-connectivities. The work derives conditions for stationary and nonstationary instabilities for both kernel types. In addition, a nonlinear analysis for general kernels yields the order parameter equation of the Turing instability. To compare the results to findings for partial differential equations (PDEs), two typical PDE-types are derived from the examined model equation, namely the general reaction-diffusion equation and the Swift-Hohenberg equation. Hence, the discussed integro-differential equation generalizes these PDEs. In the case of the gamma-distributed kernels, the stability conditions are formulated in terms of the mean excitatory and inhibitory interaction ranges. As a novel finding, we obtain Turing instabilities in fields with local inhibition-lateral excitation, while wave instabilities occur in fields with local excitation and lateral inhibition. Numerical simulations support the analytical results.

  16. Action Refinement

    NARCIS (Netherlands)

    Gorrieri, R.; Rensink, Arend; Bergstra, J.A.; Ponse, A.; Smolka, S.A.

    2001-01-01

    In this chapter, we give a comprehensive overview of the research results in the field of action refinement during the past 12 years. The different approaches that have been followed are outlined in detail and contrasted to each other in a uniform framework. We use two running examples to discuss

  17. Artificial neural networks for control of a grid-connected rectifier/inverter under disturbance, dynamic and power converter switching conditions.

    Science.gov (United States)

    Li, Shuhui; Fairbank, Michael; Johnson, Cameron; Wunsch, Donald C; Alonso, Eduardo; Proaño, Julio L

    2014-04-01

    Three-phase grid-connected converters are widely used in renewable and electric power system applications. Traditionally, grid-connected converters are controlled with standard decoupled d-q vector control mechanisms. However, recent studies indicate that such mechanisms show limitations in their applicability to dynamic systems. This paper investigates how to mitigate such restrictions using a neural network to control a grid-connected rectifier/inverter. The neural network implements a dynamic programming algorithm and is trained by using back-propagation through time. To enhance performance and stability under disturbance, additional strategies are adopted, including the use of integrals of error signals to the network inputs and the introduction of grid disturbance voltage to the outputs of a well-trained network. The performance of the neural-network controller is studied under typical vector control conditions and compared against conventional vector control methods, which demonstrates that the neural vector control strategy proposed in this paper is effective. Even in dynamic and power converter switching environments, the neural vector controller shows strong ability to trace rapidly changing reference commands, tolerate system disturbances, and satisfy control requirements for a faulted power system.

  18. Neural signature of coma revealed by posteromedial cortex connection density analysis.

    Science.gov (United States)

    Malagurski, Briguita; Péran, Patrice; Sarton, Benjamine; Riu, Beatrice; Gonzalez, Leslie; Vardon-Bounes, Fanny; Seguin, Thierry; Geeraerts, Thomas; Fourcade, Olivier; de Pasquale, Francesco; Silva, Stein

    2017-01-01

    Posteromedial cortex (PMC) is a highly segregated and dynamic core, which appears to play a critical role in internally/externally directed cognitive processes, including conscious awareness. Nevertheless, neuroimaging studies on acquired disorders of consciousness, have traditionally explored PMC as a homogenous and indivisible structure. We suggest that a fine-grained description of intrinsic PMC topology during coma, could expand our understanding about how this cortical hub contributes to consciousness generation and maintain, and could permit the identification of specific markers related to brain injury mechanism and useful for neurological prognostication. To explore this, we used a recently developed voxel-based unbiased approach, named functional connectivity density (CD). We compared 27 comatose patients (15 traumatic and 12 anoxic), to 14 age-matched healthy controls. The patients' outcome was assessed 3 months later using Coma Recovery Scale-Revised (CRS-R). A complex pattern of decreased and increased connections was observed, suggesting a network imbalance between internal/external processing systems, within PMC during coma. The number of PMC voxels with hypo-CD positive correlation showed a significant negative association with the CRS-R score, notwithstanding aetiology. Traumatic injury specifically appeared to be associated with a greater prevalence of hyper-connected (negative correlation) voxels, which was inversely associated with patient neurological outcome. A logistic regression model using the number of hypo-CD positive and hyper-CD negative correlations, accurately permitted patient's outcome prediction (AUC = 0.906, 95%IC = 0.795-1). These points might reflect adaptive plasticity mechanism and pave the way for innovative prognosis and therapeutics methods.

  19. Symmetries of a generic utricular projection: neural connectivity and the distribution of utricular information.

    Science.gov (United States)

    Chartrand, Thomas; McCollum, Gin; Hanes, Douglas A; Boyle, Richard D

    2016-02-01

    Sensory contribution to perception and action depends on both sensory receptors and the organization of pathways (or projections) reaching the central nervous system. Unlike the semicircular canals that are divided into three discrete sensitivity directions, the utricle has a relatively complicated anatomical structure, including sensitivity directions over essentially 360° of a curved, two-dimensional disk. The utricle is not flat, and we do not assume it to be. Directional sensitivity of individual utricular afferents decreases in a cosine-like fashion from peak excitation for movement in one direction to a null or near null response for a movement in an orthogonal direction. Directional sensitivity varies slowly between neighboring cells except within the striolar region that separates the medial from the lateral zone, where the directional selectivity abruptly reverses along the reversal line. Utricular primary afferent pathways reach the vestibular nuclei and cerebellum and, in many cases, converge on target cells with semicircular canal primary afferents and afference from other sources. Mathematically, some canal pathways are known to be characterized by symmetry groups related to physical space. These groups structure rotational information and movement. They divide the target neural center into distinct populations according to the innervation patterns they receive. Like canal pathways, utricular pathways combine symmetries from the utricle with those from target neural centers. This study presents a generic set of transformations drawn from the known structure of the utricle and therefore likely to be found in utricular pathways, but not exhaustive of utricular pathway symmetries. This generic set of transformations forms a 32-element group that is a semi-direct product of two simple abelian groups. Subgroups of the group include order-four elements corresponding to discrete rotations. Evaluation of subgroups allows us to functionally identify the

  20. Chimeras in leaky integrate-and-fire neural networks: effects of reflecting connectivities

    Science.gov (United States)

    Tsigkri-DeSmedt, Nefeli Dimitra; Hizanidis, Johanne; Schöll, Eckehard; Hövel, Philipp; Provata, Astero

    2017-07-01

    The effects of attracting-nonlocal and reflecting connectivity are investigated in coupled Leaky Integrate-and-Fire (LIF) elements, which model the exchange of electrical signals between neurons. Earlier investigations have demonstrated that repulsive-nonlocal and hierarchical network connectivity can induce complex synchronization patterns and chimera states in systems of coupled oscillators. In the LIF system we show that if the elements are nonlocally linked with positive diffusive coupling on a ring network, the system splits into a number of alternating domains. Half of these domains contain elements whose potential stays near the threshold and they are interrupted by active domains where the elements perform regular LIF oscillations. The active domains travel along the ring with constant velocity, depending on the system parameters. When we introduce reflecting coupling in LIF networks unexpected complex spatio-temporal structures arise. For relatively extensive ranges of parameter values, the system splits into two coexisting domains: one where all elements stay near the threshold and one where incoherent states develop, characterized by multi-leveled mean phase velocity profiles.

  1. Spanish Refining

    International Nuclear Information System (INIS)

    Lores, F.R.

    2001-01-01

    An overview of petroleum refining in Spain is presented (by Repsol YPF) and some views on future trends are discussed. Spain depends heavily on imports. Sub-headings in the article cover: sources of crude imports, investments and logistics and marketing, -detailed data for each are shown diagrammatically. Tables show: (1) economic indicators (e.g. total GDP, vehicle numbers and inflation) for 1998-200; (2) crude oil imports for 1995-2000; (3) oil products balance for 1995-2000; (4) commodities demand, by product; (5) refining in Spain in terms of capacity per region; (6) outlets in Spain and other European countries in 2002 and (7) sales distribution channel by product

  2. Effects of gratitude meditation on neural network functional connectivity and brain-heart coupling.

    Science.gov (United States)

    Kyeong, Sunghyon; Kim, Joohan; Kim, Dae Jin; Kim, Hesun Erin; Kim, Jae-Jin

    2017-07-11

    A sense of gratitude is a powerful and positive experience that can promote a happier life, whereas resentment is associated with life dissatisfaction. To explore the effects of gratitude and resentment on mental well-being, we acquired functional magnetic resonance imaging and heart rate (HR) data before, during, and after the gratitude and resentment interventions. Functional connectivity (FC) analysis was conducted to identify the modulatory effects of gratitude on the default mode, emotion, and reward-motivation networks. The average HR was significantly lower during the gratitude intervention than during the resentment intervention. Temporostriatal FC showed a positive correlation with HR during the gratitude intervention, but not during the resentment intervention. Temporostriatal resting-state FC was significantly decreased after the gratitude intervention compared to the resentment intervention. After the gratitude intervention, resting-state FC of the amygdala with the right dorsomedial prefrontal cortex and left dorsal anterior cingulate cortex were positively correlated with anxiety scale and depression scale, respectively. Taken together, our findings shed light on the effect of gratitude meditation on an individual's mental well-being, and indicate that it may be a means of improving both emotion regulation and self-motivation by modulating resting-state FC in emotion and motivation-related brain regions.

  3. Neural Signatures of the Reading-Writing Connection: Greater Involvement of Writing in Chinese Reading than English Reading.

    Directory of Open Access Journals (Sweden)

    Fan Cao

    Full Text Available Research on cross-linguistic comparisons of the neural correlates of reading has consistently found that the left middle frontal gyrus (MFG is more involved in Chinese than in English. However, there is a lack of consensus on the interpretation of the language difference. Because this region has been found to be involved in writing, we hypothesize that reading Chinese characters involves this writing region to a greater degree because Chinese speakers learn to read by repeatedly writing the characters. To test this hypothesis, we recruited English L1 learners of Chinese, who performed a reading task and a writing task in each language. The English L1 sample had learned some Chinese characters through character-writing and others through phonological learning, allowing a test of writing-on-reading effect. We found that the left MFG was more activated in Chinese than English regardless of task, and more activated in writing than in reading regardless of language. Furthermore, we found that this region was more activated for reading Chinese characters learned by character-writing than those learned by phonological learning. A major conclusion is that writing regions are also activated in reading, and that this reading-writing connection is modulated by the learning experience. We replicated the main findings in a group of native Chinese speakers, which excluded the possibility that the language differences observed in the English L1 participants were due to different language proficiency level.

  4. Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Suliang Ma

    2016-11-01

    Full Text Available Photovoltaic (PV systems have non-linear characteristics that generate maximum power at one particular operating point. Environmental factors such as irradiance and temperature variations greatly affect the maximum power point (MPP. Diverse offline and online techniques have been introduced for tracking the MPP. Here, to track the MPP, an augmented-state feedback linearized (AFL non-linear controller combined with an artificial neural network (ANN is proposed. This approach linearizes the non-linear characteristics in PV systems and DC/DC converters, for tracking and optimizing the PV system operation. It also reduces the dependency of the designed controller on linearized models, to provide global stability. A complete model of the PV system is simulated. The existing maximum power-point tracking (MPPT and DC/DC boost-converter controller techniques are compared with the proposed ANN method. Two case studies, which simulate realistic circumstances, are presented to demonstrate the effectiveness and superiority of the proposed method. The AFL with ANN controller can provide good dynamic operation, faster convergence speed, and fewer operating-point oscillations around the MPP. It also tracks the global maxima under different conditions, especially irradiance-mutating situations, more effectively than the conventional methods. Detailed mathematical models and a control approach for a three-phase grid-connected intelligent hybrid system are proposed using MATLAB/Simulink.

  5. Neural Signatures of the Reading-Writing Connection: Greater Involvement of Writing in Chinese Reading than English Reading.

    Science.gov (United States)

    Cao, Fan; Perfetti, Charles A

    2016-01-01

    Research on cross-linguistic comparisons of the neural correlates of reading has consistently found that the left middle frontal gyrus (MFG) is more involved in Chinese than in English. However, there is a lack of consensus on the interpretation of the language difference. Because this region has been found to be involved in writing, we hypothesize that reading Chinese characters involves this writing region to a greater degree because Chinese speakers learn to read by repeatedly writing the characters. To test this hypothesis, we recruited English L1 learners of Chinese, who performed a reading task and a writing task in each language. The English L1 sample had learned some Chinese characters through character-writing and others through phonological learning, allowing a test of writing-on-reading effect. We found that the left MFG was more activated in Chinese than English regardless of task, and more activated in writing than in reading regardless of language. Furthermore, we found that this region was more activated for reading Chinese characters learned by character-writing than those learned by phonological learning. A major conclusion is that writing regions are also activated in reading, and that this reading-writing connection is modulated by the learning experience. We replicated the main findings in a group of native Chinese speakers, which excluded the possibility that the language differences observed in the English L1 participants were due to different language proficiency level.

  6. Self-consistent determination of the spike-train power spectrum in a neural network with sparse connectivity

    Directory of Open Access Journals (Sweden)

    Benjamin eDummer

    2014-09-01

    Full Text Available A major source of random variability in cortical networks is the quasi-random arrival of presynaptic action potentials from many other cells. In network studies as well as in the study of the response properties of single cells embedded in a network, synaptic background input is often approximated by Poissonian spike trains. However, the output statistics of the cells is in most cases far from being Poisson. This is inconsistent with the assumption of similar spike-train statistics for pre- and postsynaptic cells in a recurrent network. Here we tackle this problem for the popular class of integrate-and-fire neurons and study a self-consistent statistics of input and output spectra of neural spike trains. Instead of actually using a large network, we use an iterative scheme, in which we simulate a single neuron over several generations. In each of these generations, the neuron is stimulated with surrogate stochastic input that has a similar statistics as the output of the previous generation. For the surrogate input, we employ two distinct approximations: (i a superposition of renewal spike trains with the same interspike interval density as observed in the previous generation and (ii a Gaussian current with a power spectrum proportional to that observed in the previous generation. For input parameters that correspond to balanced input in the network, both the renewal and the Gaussian iteration procedure converge quickly and yield comparable results for the self-consistent spike-train power spectrum. We compare our results to large-scale simulations of a random sparsely connected network of leaky integrate-and-fire neurons (Brunel, J. Comp. Neurosci. 2000 and show that in the asynchronous regime close to a state of balanced synaptic input from the network, our iterative schemes provide excellent approximations to the autocorrelation of spike trains in the recurrent network.

  7. Perceived social isolation is associated with altered functional connectivity in neural networks associated with tonic alertness and executive control.

    Science.gov (United States)

    Layden, Elliot A; Cacioppo, John T; Cacioppo, Stephanie; Cappa, Stefano F; Dodich, Alessandra; Falini, Andrea; Canessa, Nicola

    2017-01-15

    Perceived social isolation (PSI), colloquially known as loneliness, is associated with selectively altered attentional, cognitive, and affective processes in humans, but the neural mechanisms underlying these adjustments remain largely unexplored. Behavioral, eye tracking, and neuroimaging research has identified associations between PSI and implicit hypervigilance for social threats. Additionally, selective executive dysfunction has been evidenced by reduced prepotent response inhibition in social Stroop and dichotic listening tasks. Given that PSI is associated with pre-attentional processes, PSI may also be related to altered resting-state functional connectivity (FC) in the brain. Therefore, we conducted the first resting-state fMRI FC study of PSI in healthy young adults. Five-minute resting-state scans were obtained from 55 participants (31 females). Analyses revealed robust associations between PSI and increased brain-wide FC in areas encompassing the right central operculum and right supramarginal gyrus, and these associations were not explained by depressive symptomatology, objective isolation, or demographics. Further analyses revealed that PSI was associated with increased FC between several nodes of the cingulo-opercular network, a network known to underlie the maintenance of tonic alertness. These regions encompassed the bilateral insula/frontoparietal opercula and ACC/pre-SMA. In contrast, FC between the cingulo-opercular network and right middle/superior frontal gyrus was reduced, a finding associated with diminished executive function in prior literature. We suggest that, in PSI, increased within-network cingulo-opercular FC may be associated with hypervigilance to social threat, whereas reduced right middle/superior frontal gyrus FC to the cingulo-opercular network may be associated with diminished impulse control. Copyright © 2016 Elsevier Inc. All rights reserved.

  8. Conservatism and the neural circuitry of threat: economic conservatism predicts greater amygdala–BNST connectivity during periods of threat vs safety

    Science.gov (United States)

    Muftuler, L Tugan; Larson, Christine L

    2018-01-01

    Abstract Political conservatism is associated with an increased negativity bias, including increased attention and reactivity toward negative and threatening stimuli. Although the human amygdala has been implicated in the response to threatening stimuli, no studies to date have investigated whether conservatism is associated with altered amygdala function toward threat. Furthermore, although an influential theory posits that connectivity between the amygdala and bed nucleus of the stria terminalis (BNST) is important in initiating the response to sustained or uncertain threat, whether individual differences in conservatism modulate this connectivity is unknown. To test whether conservatism is associated with increased reactivity in neural threat circuitry, we measured participants’ self-reported social and economic conservatism and asked them to complete high-resolution fMRI scans while under threat of an unpredictable shock and while safe. We found that economic conservatism predicted greater connectivity between the BNST and a cluster of voxels in the left amygdala during threat vs safety. These results suggest that increased amygdala–BNST connectivity during threat may be a key neural correlate of the enhanced negativity bias found in conservatism. PMID:29126127

  9. Conservatism and the neural circuitry of threat: economic conservatism predicts greater amygdala-BNST connectivity during periods of threat vs safety.

    Science.gov (United States)

    Pedersen, Walker S; Muftuler, L Tugan; Larson, Christine L

    2018-01-01

    Political conservatism is associated with an increased negativity bias, including increased attention and reactivity toward negative and threatening stimuli. Although the human amygdala has been implicated in the response to threatening stimuli, no studies to date have investigated whether conservatism is associated with altered amygdala function toward threat. Furthermore, although an influential theory posits that connectivity between the amygdala and bed nucleus of the stria terminalis (BNST) is important in initiating the response to sustained or uncertain threat, whether individual differences in conservatism modulate this connectivity is unknown. To test whether conservatism is associated with increased reactivity in neural threat circuitry, we measured participants' self-reported social and economic conservatism and asked them to complete high-resolution fMRI scans while under threat of an unpredictable shock and while safe. We found that economic conservatism predicted greater connectivity between the BNST and a cluster of voxels in the left amygdala during threat vs safety. These results suggest that increased amygdala-BNST connectivity during threat may be a key neural correlate of the enhanced negativity bias found in conservatism. © The Author (2017). Published by Oxford University Press.

  10. Connective-Tissue Growth Factor (CTGF/CCN2 Induces Astrogenesis and Fibronectin Expression of Embryonic Neural Cells In Vitro.

    Directory of Open Access Journals (Sweden)

    Fabio A Mendes

    Full Text Available Connective-tissue growth factor (CTGF is a modular secreted protein implicated in multiple cellular events such as chondrogenesis, skeletogenesis, angiogenesis and wound healing. CTGF contains four different structural modules. This modular organization is characteristic of members of the CCN family. The acronym was derived from the first three members discovered, cysteine-rich 61 (CYR61, CTGF and nephroblastoma overexpressed (NOV. CTGF is implicated as a mediator of important cell processes such as adhesion, migration, proliferation and differentiation. Extensive data have shown that CTGF interacts particularly with the TGFβ, WNT and MAPK signaling pathways. The capacity of CTGF to interact with different growth factors lends it an important role during early and late development, especially in the anterior region of the embryo. ctgf knockout mice have several cranio-facial defects, and the skeletal system is also greatly affected due to an impairment of the vascular-system development during chondrogenesis. This study, for the first time, indicated that CTGF is a potent inductor of gliogenesis during development. Our results showed that in vitro addition of recombinant CTGF protein to an embryonic mouse neural precursor cell culture increased the number of GFAP- and GFAP/Nestin-positive cells. Surprisingly, CTGF also increased the number of Sox2-positive cells. Moreover, this induction seemed not to involve cell proliferation. In addition, exogenous CTGF activated p44/42 but not p38 or JNK MAPK signaling, and increased the expression and deposition of the fibronectin extracellular matrix protein. Finally, CTGF was also able to induce GFAP as well as Nestin expression in a human malignant glioma stem cell line, suggesting a possible role in the differentiation process of gliomas. These results implicate ctgf as a key gene for astrogenesis during development, and suggest that its mechanism may involve activation of p44/42 MAPK signaling

  11. Brief Report: Anomalous Neural Deactivations and Functional Connectivity during Receptive Language in Autism Spectrum Disorder--A Functional MRI Study

    Science.gov (United States)

    Karten, Ariel; Hirsch, Joy

    2015-01-01

    Neural mechanisms that underlie language disability in autism spectrum disorder (ASD) have been associated with reduced excitatory processes observed as positive blood oxygen level dependent (BOLD) responses. However, negative BOLD responses (NBR) associated with language and inhibitory processes have been less studied in ASD. In this study,…

  12. Neural mechanism of activity spread in the cat motor cortex and its relation to the intrinsic connectivity

    DEFF Research Database (Denmark)

    Capaday, Charles; van Vreeswijk, Carl; Ethier, Christian

    2011-01-01

    NON TECHNICAL SUMMARY{NBSP}: The motor cortex (MCx) is an important brain region that initiates and controls voluntary movements. Neurons in MCx are anatomically connected by recurrent (feedback) networks. This connectivity pattern allows neurons to communicate reciprocally with each other potent...

  13. The neural basis of trait self-esteem revealed by the amplitude of low-frequency fluctuations and resting state functional connectivity.

    Science.gov (United States)

    Pan, Weigang; Liu, Congcong; Yang, Qian; Gu, Yan; Yin, Shouhang; Chen, Antao

    2016-03-01

    Self-esteem is an affective, self-evaluation of oneself and has a significant effect on mental and behavioral health. Although research has focused on the neural substrates of self-esteem, little is known about the spontaneous brain activity that is associated with trait self-esteem (TSE) during the resting state. In this study, we used the resting-state functional magnetic resonance imaging (fMRI) signal of the amplitude of low-frequency fluctuations (ALFFs) and resting state functional connectivity (RSFC) to identify TSE-related regions and networks. We found that a higher level of TSE was associated with higher ALFFs in the left ventral medial prefrontal cortex (vmPFC) and lower ALFFs in the left cuneus/lingual gyrus and right lingual gyrus. RSFC analyses revealed that the strengths of functional connectivity between the left vmPFC and bilateral hippocampus were positively correlated with TSE; however, the connections between the left vmPFC and right inferior frontal gyrus and posterior superior temporal sulcus were negatively associated with TSE. Furthermore, the strengths of functional connectivity between the left cuneus/lingual gyrus and right dorsolateral prefrontal cortex and anterior cingulate cortex were positively related to TSE. These findings indicate that TSE is linked to core regions in the default mode network and social cognition network, which is involved in self-referential processing, autobiographical memory and social cognition. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. On the refinement calculus

    CERN Document Server

    Vickers, Trevor

    1992-01-01

    On the Refinement Calculus gives one view of the development of the refinement calculus and its attempt to bring together - among other things - Z specifications and Dijkstra's programming language. It is an excellent source of reference material for all those seeking the background and mathematical underpinnings of the refinement calculus.

  15. Determination of relevant neuron-neuron connections for neural prosthetics using time-delayed mutual information: tutorial and preliminary results.

    Science.gov (United States)

    Taghva, Alexander; Song, Dong; Hampson, Robert E; Deadwyler, Sam A; Berger, Theodore W

    2012-12-01

    Identification of functional dependence among neurons is a necessary component in both the rational design of neural prostheses as well as in the characterization of network physiology. The objective of this article is to provide a tutorial for neurosurgeons regarding information theory, specifically time-delayed mutual information, and to compare time-delayed mutual information, an information theoretic quantity based on statistical dependence, with cross-correlation, a commonly used metric for this task in a preliminary analysis of rat hippocampal neurons. Spike trains were recorded from rats performing delayed nonmatch-to-sample task using an array of electrodes surgically implanted into the hippocampus of each hemisphere of the brain. In addition, spike train simulations of positively correlated neurons, negatively correlated neurons, and neurons correlated by nonlinear functions were generated. These were evaluated by time-delayed mutual information (MI) and cross-correlation. Application of time-delayed MI to experimental data indicated the optimal bin size for information capture in the CA3-CA1 system was 40 ms, which may provide some insight into the spatiotemporal nature of encoding in the rat hippocampus. On simulated data, time-delayed MI showed peak values at appropriate time lags in positively correlated, negatively correlated, and complexly correlated data. Cross-correlation showed peak and troughs with positively correlated and negatively correlated data, but failed to capture some higher order correlations. Comparison of time-delayed MI to cross-correlation in identification of functionally dependent neurons indicates that the methods are not equivalent. Time-delayed MI appeared to capture some interactions between CA3-CA1 neurons at physiologically plausible time delays missed by cross-correlation. It should be considered as a method for identification of functional dependence between neurons and may be useful in the development of neural

  16. Decreased functional connectivity and disrupted neural network in the prefrontal cortex of affective disorders: A resting-state fNIRS study.

    Science.gov (United States)

    Zhu, Huilin; Xu, Jie; Li, Jiangxue; Peng, Hongjun; Cai, Tingting; Li, Xinge; Wu, Shijing; Cao, Wei; He, Sailing

    2017-10-15

    Affective disorders (AD) have been conceptualized as neural network-level diseases. In this study, we utilized functional near infrared spectroscopy (fNIRS) to investigate the spontaneous hemodynamic activities in the prefrontal cortex (PFC) of the AD patients with or without medications. 42 optical channels were applied to cover the superior frontal gyrus (SFG), middle frontal gyrus (MFG), and inferior frontal gyrus (IFG), which constitute one of the most important affective networks of the brain. We performed resting-state measurements on 28 patients who were diagnosed as having AD and 30 healthy controls (HC). Raw fNIRS data were preprocessed with independent component analysis (ICA) and a band-pass filter to remove artifacts and physiological noise. By systematically analyzing the intra-regional, intrahemispheric, and interhemispheric connectivities based on the spontaneous oscillations of Δ[HbO], our results indicated that patients with AD exhibited significantly reduced intra-regional and symmetrically interhemispheric connectivities in the PFC when compared to HC. More specifically, relative to HC, AD patients showed significantly lower locally functional connectivity in the right IFG, and poor long-distance connectivity between bilateral IFG. In addition, AD patients without medication presented more disrupted cortical organizations in the PFC, and the severity of self-reported symptoms of depression was negatively correlated with the strength of intra-regional and symmetrically interhemispheric connectivity in the PFC. Regarding the measuring technique, fNIRS has restricted measurement depth and spatial resolution. During the study, the subgroups of AD, such as major depressive disorder, bipolar, comorbidity, or non-comorbidity, dosage of psychotropic drugs, as well as different types of pharmacological responses were not distinguished and systematically compared. Furthermore, due to the limitation of the research design, it was still not very clear how

  17. Innervation of Extrahepatic Biliary Tract, With Special Reference to the Direct Bidirectional Neural Connections of the Gall Bladder, Sphincter of Oddi and Duodenum in Suncus murinus, in Whole-Mount Immunohistochemical Study.

    Science.gov (United States)

    Yi, S-Q; Ren, K; Kinoshita, M; Takano, N; Itoh, M; Ozaki, N

    2016-06-01

    Sphincter of Oddi dysfunction is one of the most important symptoms in post-cholecystectomy syndrome. Using either electrical or mechanical stimulation and retrogradely transported neuronal dyes, it has been demonstrated that there are direct neural pathways connecting gall bladder and the sphincter of Oddi in the Australian opossum and the golden hamster. In the present study, we employed whole-mount immunohistochemistry staining to observe and verify that there are two different plexuses of the extrahepatic biliary tract in Suncus murinus. One, named Pathway One, showed a fine, irregular but dense network plexus that ran adhesively and resided on/in the extrahepatic biliary tract wall, and the plexus extended into the intrahepatic area. On the other hand, named Pathway Two, exhibiting simple, thicker and straight neural bundles, ran parallel to the surface of the extrahepatic biliary tract and passed between the gall bladder and duodenum, but did not give off any branches to the liver. Pathway Two was considered to involve direct bidirectional neural connections between the duodenum and the biliary tract system. For the first time, morphologically, we demonstrated direct neural connections between gall bladder and duodenum in S. murinus. Malfunction of the sphincter of Oddi may be caused by injury of the direct neural pathways between gall bladder and duodenum by cholecystectomy. From the viewpoint of preserving the function of the major duodenal papilla and common bile duct, we emphasize the importance of avoiding kocherization of the common bile duct so as to preserve the direct neural connections between gall bladder and sphincter of Oddi. © 2015 Blackwell Verlag GmbH.

  18. Creating value in refining

    International Nuclear Information System (INIS)

    Cobb, C.B.

    2001-01-01

    This article focuses on recent developments in the US refining industry and presents a model for improving the performance of refineries based on the analysis of the refining industry by Cap Gemini Ernst and Young. The identification of refineries in risk of failing, the construction of pipelines for refinery products from Gulf State refineries, mergers and acquisitions, and poor financial performance are discussed. Current challenges concerning the stagnant demand for refinery products, environmental regulations, and shareholder value are highlighted. The structure of the industry, the creation of value in refining, and the search for business models are examined. The top 25 US companies and US refining business groups are listed

  19. Neural activation patterns and connectivity in visual attention during Number and Non-number processing: An ERP study using the Ishihara pseudoisochromatic plates.

    Science.gov (United States)

    Al-Marri, Faraj; Reza, Faruque; Begum, Tahamina; Hitam, Wan Hazabbah Wan; Jin, Goh Khean; Xiang, Jing

    2017-10-25

    Visual cognitive function is important to build up executive function in daily life. Perception of visual Number form (e.g., Arabic digit) and numerosity (magnitude of the Number) is of interest to cognitive neuroscientists. Neural correlates and the functional measurement of Number representations are complex occurrences when their semantic categories are assimilated with other concepts of shape and colour. Colour perception can be processed further to modulate visual cognition. The Ishihara pseudoisochromatic plates are one of the best and most common screening tools for basic red-green colour vision testing. However, there is a lack of study of visual cognitive function assessment using these pseudoisochromatic plates. We recruited 25 healthy normal trichromat volunteers and extended these studies using a 128-sensor net to record event-related EEG. Subjects were asked to respond by pressing Numbered buttons when they saw the Number and Non-number plates of the Ishihara colour vision test. Amplitudes and latencies of N100 and P300 event related potential (ERP) components were analysed from 19 electrode sites in the international 10-20 system. A brain topographic map, cortical activation patterns and Granger causation (effective connectivity) were analysed from 128 electrode sites. No major significant differences between N100 ERP components in either stimulus indicate early selective attention processing was similar for Number and Non-number plate stimuli, but Non-number plate stimuli evoked significantly higher amplitudes, longer latencies of the P300 ERP component with a slower reaction time compared to Number plate stimuli imply the allocation of attentional load was more in Non-number plate processing. A different pattern of asymmetric scalp voltage map was noticed for P300 components with a higher intensity in the left hemisphere for Number plate tasks and higher intensity in the right hemisphere for Non-number plate tasks. Asymmetric cortical activation

  20. Rodent Zic Genes in Neural Network Wiring.

    Science.gov (United States)

    Herrera, Eloísa

    2018-01-01

    The formation of the nervous system is a multistep process that yields a mature brain. Failure in any of the steps of this process may cause brain malfunction. In the early stages of embryonic development, neural progenitors quickly proliferate and then, at a specific moment, differentiate into neurons or glia. Once they become postmitotic neurons, they migrate to their final destinations and begin to extend their axons to connect with other neurons, sometimes located in quite distant regions, to establish different neural circuits. During the last decade, it has become evident that Zic genes, in addition to playing important roles in early development (e.g., gastrulation and neural tube closure), are involved in different processes of late brain development, such as neuronal migration, axon guidance, and refinement of axon terminals. ZIC proteins are therefore essential for the proper wiring and connectivity of the brain. In this chapter, we review our current knowledge of the role of Zic genes in the late stages of neural circuit formation.

  1. Relational Demonic Fuzzy Refinement

    Directory of Open Access Journals (Sweden)

    Fairouz Tchier

    2014-01-01

    Full Text Available We use relational algebra to define a refinement fuzzy order called demonic fuzzy refinement and also the associated fuzzy operators which are fuzzy demonic join (⊔fuz, fuzzy demonic meet (⊓fuz, and fuzzy demonic composition (□fuz. Our definitions and properties are illustrated by some examples using mathematica software (fuzzy logic.

  2. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

    Science.gov (United States)

    Zhang, Zhen; Ma, Cheng; Zhu, Rong

    2016-10-14

    High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

  3. Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules

    Directory of Open Access Journals (Sweden)

    Zhen Zhang

    2016-10-01

    Full Text Available High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial–temporal complexity. This paper presents a multi-input multi-output (MIMO self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional–integral–derivative (PID neural network (FCPIDNN and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

  4. Concurrent tACS-fMRI Reveals Causal Influence of Power Synchronized Neural Activity on Resting State fMRI Connectivity.

    Science.gov (United States)

    Bächinger, Marc; Zerbi, Valerio; Moisa, Marius; Polania, Rafael; Liu, Quanying; Mantini, Dante; Ruff, Christian; Wenderoth, Nicole

    2017-05-03

    Resting state fMRI (rs-fMRI) is commonly used to study the brain's intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSNs). It has been hypothesized that slow rs-fMRI oscillations (5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices. The two driving tACS signals were tailored to the individual's α rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either α oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS). Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared with phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brain's intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity. SIGNIFICANCE STATEMENT Resting state fMRI (rs-fMRI) has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes. It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to

  5. Refining margins and prospects

    International Nuclear Information System (INIS)

    Baudouin, C.; Favennec, J.P.

    1997-01-01

    Refining margins throughout the world have remained low in 1996. In Europe, in spite of an improvement, particularly during the last few weeks, they are still not high enough to finance new investments. Although the demand for petroleum products is increasing, experts are still sceptical about any rapid recovery due to prevailing overcapacity and to continuing capacity growth. After a historical review of margins and an analysis of margins by regions, we analyse refining over-capacities in Europe and the unbalances between production and demand. Then we discuss the current situation concerning barriers to the rationalization, agreements between oil companies, and the consequences on the future of refining capacities and margins. (author)

  6. North American refining

    International Nuclear Information System (INIS)

    Osten, James; Haltmaier, Susan

    2000-01-01

    This article examines the current status of the North American refining industry, and considers the North American economy and the growth in demand in the petroleum industry, petroleum product demand and quality, crude oil upgrading to meet product standards, and changes in crude oil feedstocks such as the use of heavier crudes and bitumens. Refining expansion, the declining profits in refining, and changes due to environmental standards are discussed. The Gross Domestic Product and oil demand for the USA, Canada, Mexico, and Venezuela for the years 1995-2020 are tabulated

  7. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

    Science.gov (United States)

    Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

    2017-01-01

    The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre

  8. Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

    Directory of Open Access Journals (Sweden)

    Xinyu Guo

    2017-08-01

    Full Text Available The whole-brain functional connectivity (FC pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes. Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150. Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross

  9. Linearly Refined Session Types

    Directory of Open Access Journals (Sweden)

    Pedro Baltazar

    2012-11-01

    Full Text Available Session types capture precise protocol structure in concurrent programming, but do not specify properties of the exchanged values beyond their basic type. Refinement types are a form of dependent types that can address this limitation, combining types with logical formulae that may refer to program values and can constrain types using arbitrary predicates. We present a pi calculus with assume and assert operations, typed using a session discipline that incorporates refinement formulae written in a fragment of Multiplicative Linear Logic. Our original combination of session and refinement types, together with the well established benefits of linearity, allows very fine-grained specifications of communication protocols in which refinement formulae are treated as logical resources rather than persistent truths.

  10. Refinement by interface instantiation

    DEFF Research Database (Denmark)

    Hallerstede, Stefan; Hoang, Thai Son

    2012-01-01

    be easily refined. Our first contribution hence is a proposal for a new construct called interface that encapsulates the external variables, along with a mechanism for interface instantiation. Using the new construct and mechanism, external variables can be refined consistently. Our second contribution...... is an approach for verifying the correctness of Event-B extensions using the supporting Rodin tool. We illustrate our approach by proving the correctness of interface instantiation....

  11. Relational Demonic Fuzzy Refinement

    OpenAIRE

    Tchier, Fairouz

    2014-01-01

    We use relational algebra to define a refinement fuzzy order called demonic fuzzy refinement and also the associated fuzzy operators which are fuzzy demonic join $({\\bigsqcup }_{\\mathrm{\\text{f}}\\mathrm{\\text{u}}\\mathrm{\\text{z}}})$ , fuzzy demonic meet $({\\sqcap }_{\\mathrm{\\text{f}}\\mathrm{\\text{u}}\\mathrm{\\text{z}}})$ , and fuzzy demonic composition $({\\square }_{\\mathrm{\\text{f}}\\mathrm{\\text{u}}\\mathrm{\\text{z}}})$ . Our definitions and properties are illustrated by some examples using ma...

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

    Science.gov (United States)

    Bowman, Caitlin R; Dennis, Nancy A

    2016-08-01

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

  13. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

    Energy Technology Data Exchange (ETDEWEB)

    Mellit, Adel [Department of Electronics, Faculty of Sciences and Technology, LAMEL, Jijel University, Ouled-aissa, P.O. Box 98, Jijel 18000 (Algeria); Pavan, Alessandro Massi [Department of Materials and Natural Resources, University of Trieste Via A. Valerio, 2 - 34127 Trieste (Italy)

    2010-05-15

    Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became - with reference to the Grid Connected Photovoltaic Plants (GCPV) - fundamental in making power dispatching plans and - with reference to stand alone and hybrid systems - also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45 40'N, longitude 13 46'E), Italy. In order to check the generalization capability of the MLP-forecaster, a K-fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98-99% for sunny days and 94-96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model. (author)

  14. Refining margins: recent trends

    International Nuclear Information System (INIS)

    Baudoin, C.; Favennec, J.P.

    1999-01-01

    Despite a business environment that was globally mediocre due primarily to the Asian crisis and to a mild winter in the northern hemisphere, the signs of improvement noted in the refining activity in 1996 were borne out in 1997. But the situation is not yet satisfactory in this sector: the low return on invested capital and the financing of environmental protection expenditure are giving cause for concern. In 1998, the drop in crude oil prices and the concomitant fall in petroleum product prices was ultimately rather favorable to margins. Two elements tended to put a damper on this relative optimism. First of all, margins continue to be extremely volatile and, secondly, the worsening of the economic and financial crisis observed during the summer made for a sharp decline in margins in all geographic regions, especially Asia. Since the beginning of 1999, refining margins are weak and utilization rates of refining capacities have decreased. (authors)

  15. Refining and petrochemicals

    Energy Technology Data Exchange (ETDEWEB)

    Constancio, Silva

    2006-07-01

    In 2004, refining margins showed a clear improvement that persisted throughout the first three quarters of 2005. This enabled oil companies to post significantly higher earnings for their refining activity in 2004 compared to 2003, with the results of the first half of 2005 confirming this trend. As for petrochemicals, despite a steady rise in the naphtha price, higher cash margins enabled a turnaround in 2004 as well as a clear improvement in oil company financial performance that should continue in 2005, judging by the net income figures reported for the first half-year. Despite this favorable business environment, capital expenditure in refining and petrochemicals remained at a low level, especially investment in new capacity, but a number of projects are being planned for the next five years. (author)

  16. Refining and petrochemicals

    International Nuclear Information System (INIS)

    Constancio, Silva

    2006-01-01

    In 2004, refining margins showed a clear improvement that persisted throughout the first three quarters of 2005. This enabled oil companies to post significantly higher earnings for their refining activity in 2004 compared to 2003, with the results of the first half of 2005 confirming this trend. As for petrochemicals, despite a steady rise in the naphtha price, higher cash margins enabled a turnaround in 2004 as well as a clear improvement in oil company financial performance that should continue in 2005, judging by the net income figures reported for the first half-year. Despite this favorable business environment, capital expenditure in refining and petrochemicals remained at a low level, especially investment in new capacity, but a number of projects are being planned for the next five years. (author)

  17. Indian refining industry

    International Nuclear Information System (INIS)

    Singh, I.J.

    2002-01-01

    The author discusses the history of the Indian refining industry and ongoing developments under the headings: the present state; refinery configuration; Indian capabilities for refinery projects; and reforms in the refining industry. Tables lists India's petroleum refineries giving location and capacity; new refinery projects together with location and capacity; and expansion projects of Indian petroleum refineries. The Indian refinery industry has undergone substantial expansion as well as technological changes over the past years. There has been progressive technology upgrading, energy efficiency, better environmental control and improved capacity utilisation. Major reform processes have been set in motion by the government of India: converting the refining industry from a centrally controlled public sector dominated industry to a delicensed regime in a competitive market economy with the introduction of a liberal exploration policy; dismantling the administered price mechanism; and a 25 year hydrocarbon vision. (UK)

  18. Refining - Panorama 2008

    International Nuclear Information System (INIS)

    2008-01-01

    Investment rallied in 2007, and many distillation and conversion projects likely to reach the industrial stage were announced. With economic growth sustained in 2006 and still pronounced in 2007, oil demand remained strong - especially in emerging countries - and refining margins stayed high. Despite these favorable business conditions, tensions persisted in the refining sector, which has fallen far behind in terms of investing in refinery capacity. It will take renewed efforts over a long period to catch up. Looking at recent events that have affected the economy in many countries (e.g. the sub-prime crisis), prudence remains advisable

  19. Panorama 2012 - Refining 2030

    International Nuclear Information System (INIS)

    Marion, Pierre; Saint-Antonin, Valerie

    2011-11-01

    The major uncertainty characterizing the global energy landscape impacts particularly on transport, which remains the virtually-exclusive bastion of the oil industry. The industry must therefore respond to increasing demand for mobility against a background marked by the emergence of alternatives to oil-based fuels and the need to reduce emissions of pollutants and greenhouse gases (GHG). It is in this context that the 'Refining 2030' study conducted by IFP Energies Nouvelles (IFPEN) forecasts what the global supply and demand balance for oil products could be, and highlights the type and geographical location of the refinery investment required. Our study shows that the bulk of the refining investment will be concentrated in the emerging countries (mainly those in Asia), whilst the areas historically strong in refining (Europe and North America) face reductions in capacity. In this context, the drastic reduction in the sulphur specification of bunker oil emerges as a structural issue for European refining, in the same way as increasingly restrictive regulation of refinery CO 2 emissions (quotas/taxation) and the persistent imbalance between gasoline and diesel fuels. (authors)

  20. US refining reviewed

    International Nuclear Information System (INIS)

    Yamaguchi, N.D.

    1998-01-01

    The paper reviews the history, present position and future prospects of the petroleum industry in the USA. The main focus is on supply and demand, the high quality of the products, refinery capacity and product trade balances. Diagrams show historical trends in output, product demand, demand for transport fuels and oil, refinery capacity, refinery closures, and imports and exports. Some particularly salient points brought out were (i) production of US crude shows a marked downward trend but imports of crude will continue to increase, (ii) product demand will continue to grow even though the levels are already high, (iii) the demand is dominated by those products that typically yield the highest income for the refiner, (i.e. high quality transport fuels for environmental compliance), (iv) refinery capacity has decreased since 1980 and (v) refining will continue to have financial problems but will still be profitable. (UK)

  1. Outlook for Canadian refining

    International Nuclear Information System (INIS)

    Boje, G.

    1998-01-01

    The petroleum supply and demand balance was discussed and a comparison between Canadian and U.S. refineries was provided. The impact of changing product specifications on the petroleum industry was also discussed. The major changes include sulphur reductions in gasoline, benzene and MMT additives. These changes have been made in an effort to satisfy environmental needs. Geographic margin variations in refineries between east and west were reviewed. An overview of findings from the Solomon Refining Study of Canadian and American refineries, which has been very complimentary of the Canadian refining industry, was provided. From this writer's point of view refinery utilization has improved but there is a threat from increasing efficiency of US competitors. Environmental issues will continue to impact upon the industry and while the chances for making economic returns on investment are good for the years ahead, it will be a challenge to maintain profitability

  2. Future of French refining

    International Nuclear Information System (INIS)

    Calvet, B.

    1993-01-01

    Over recent years, the refining industry has had to grapple with a growing burden of environmental and safety regulations concerning not only its plants and other facilities, but also its end products. At the same time, it has had to bear the effects of the reduction of the special status that used to apply to petroleum, and the consequences of economic freedom, to which we should add, as specifically concerns the French market, the impact of energy policy and the pro-nuclear option. The result is a drop in heavy fuel oil from 36 million tonnes per year in 1973 to 6.3 million in 1992, and in home-heating fuel from 37 to 18 million per year. This fast-moving market is highly competitive. The French market in particular is wide open to imports, but the refining companies are still heavy exporters for those products with high added-value, like lubricants, jet fuel, and lead-free gasolines. The competition has led the refining companies to commit themselves to quality, and to publicize their efforts in this direction. This is why the long-term perspectives for petroleum fuels are still wide open. This is supported by the probable expectation that the goal of economic efficiency is likely to soften the effects of the energy policy, which penalizes petroleum products, in that they have now become competitive again. In the European context, with the challenge of environmental protection and the decline in heavy fuel outlets, French refining has to keep on improving the quality of its products and plants, which means major investments. The industry absolutely must return to a more normal level of profitability, in order to sustain this financial effort, and generate the prosperity of its high-performance plants and equipment. 1 fig., 5 tabs

  3. Process for refining hydrocarbons

    Energy Technology Data Exchange (ETDEWEB)

    Risenfeld, E H

    1924-11-26

    A process is disclosed for the refining of hydrocarbons or other mixtures through treatment in vapor form with metal catalysts, characterized by such metals being used as catalysts, which are obtained by reduction of the oxide of minerals containing the iron group, and by the vapors of the hydrocarbons, in the presence of the water vapor, being led over these catalysts at temperatures from 200 to 300/sup 0/C.

  4. Panorama 2009 - refining

    International Nuclear Information System (INIS)

    2008-01-01

    For oil companies to invest in new refining and conversion capacity, favorable conditions over time are required. In other words, refining margins must remain high and demand sustained over a long period. That was the situation prevailing before the onset of the financial crisis in the second half of 2008. The economic conjuncture has taken a substantial turn for the worse since then and the forecasts for 2009 do not look bright. Oil demand is expected to decrease in the OECD countries and to grow much more slowly in the emerging countries. It is anticipated that refining margins will fall in 2009 - in 2008, they slipped significantly in the United States - as a result of increasingly sluggish demand, especially for light products. The next few months will probably be unfavorable to investment. In addition to a gloomy business outlook, there may also be a problem of access to sources of financing. As for investment projects, a mainstream trend has emerged in the last few years: a shift away from the regions that have historically been most active (the OECD countries) towards certain emerging countries, mostly in Asia or the Middle East. The new conjuncture will probably not change this trend

  5. Refining discordant gene trees.

    Science.gov (United States)

    Górecki, Pawel; Eulenstein, Oliver

    2014-01-01

    Evolutionary studies are complicated by discordance between gene trees and the species tree in which they evolved. Dealing with discordant trees often relies on comparison costs between gene and species trees, including the well-established Robinson-Foulds, gene duplication, and deep coalescence costs. While these costs have provided credible results for binary rooted gene trees, corresponding cost definitions for non-binary unrooted gene trees, which are frequently occurring in practice, are challenged by biological realism. We propose a natural extension of the well-established costs for comparing unrooted and non-binary gene trees with rooted binary species trees using a binary refinement model. For the duplication cost we describe an efficient algorithm that is based on a linear time reduction and also computes an optimal rooted binary refinement of the given gene tree. Finally, we show that similar reductions lead to solutions for computing the deep coalescence and the Robinson-Foulds costs. Our binary refinement of Robinson-Foulds, gene duplication, and deep coalescence costs for unrooted and non-binary gene trees together with the linear time reductions provided here for computing these costs significantly extends the range of trees that can be incorporated into approaches dealing with discordance.

  6. Towards automated crystallographic structure refinement with phenix.refine

    OpenAIRE

    Afonine, Pavel V.; Grosse-Kunstleve, Ralf W.; Echols, Nathaniel; Headd, Jeffrey J.; Moriarty, Nigel W.; Mustyakimov, Marat; Terwilliger, Thomas C.; Urzhumtsev, Alexandre; Zwart, Peter H.; Adams, Paul D.

    2012-01-01

    phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. It has several automation features and is also highly flexible. Several hundred parameters enable extensive customizations for complex use cases. Multiple user-defined refinement strategies can be applied to specific parts of the model in a single refinement run. An i...

  7. Towards automated crystallographic structure refinement with phenix.refine

    Energy Technology Data Exchange (ETDEWEB)

    Afonine, Pavel V., E-mail: pafonine@lbl.gov; Grosse-Kunstleve, Ralf W.; Echols, Nathaniel; Headd, Jeffrey J.; Moriarty, Nigel W. [Lawrence Berkeley National Laboratory, One Cyclotron Road, MS64R0121, Berkeley, CA 94720 (United States); Mustyakimov, Marat; Terwilliger, Thomas C. [Los Alamos National Laboratory, M888, Los Alamos, NM 87545 (United States); Urzhumtsev, Alexandre [CNRS–INSERM–UdS, 1 Rue Laurent Fries, BP 10142, 67404 Illkirch (France); Université Henri Poincaré, Nancy 1, BP 239, 54506 Vandoeuvre-lès-Nancy (France); Zwart, Peter H. [Lawrence Berkeley National Laboratory, One Cyclotron Road, MS64R0121, Berkeley, CA 94720 (United States); Adams, Paul D. [Lawrence Berkeley National Laboratory, One Cyclotron Road, MS64R0121, Berkeley, CA 94720 (United States); University of California Berkeley, Berkeley, CA 94720 (United States)

    2012-04-01

    phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods. phenix.refine is a program within the PHENIX package that supports crystallographic structure refinement against experimental data with a wide range of upper resolution limits using a large repertoire of model parameterizations. It has several automation features and is also highly flexible. Several hundred parameters enable extensive customizations for complex use cases. Multiple user-defined refinement strategies can be applied to specific parts of the model in a single refinement run. An intuitive graphical user interface is available to guide novice users and to assist advanced users in managing refinement projects. X-ray or neutron diffraction data can be used separately or jointly in refinement. phenix.refine is tightly integrated into the PHENIX suite, where it serves as a critical component in automated model building, final structure refinement, structure validation and deposition to the wwPDB. This paper presents an overview of the major phenix.refine features, with extensive literature references for readers interested in more detailed discussions of the methods.

  8. 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.

  9. Hirshfeld atom refinement.

    Science.gov (United States)

    Capelli, Silvia C; Bürgi, Hans-Beat; Dittrich, Birger; Grabowsky, Simon; Jayatilaka, Dylan

    2014-09-01

    Hirshfeld atom refinement (HAR) is a method which determines structural parameters from single-crystal X-ray diffraction data by using an aspherical atom partitioning of tailor-made ab initio quantum mechanical molecular electron densities without any further approximation. Here the original HAR method is extended by implementing an iterative procedure of successive cycles of electron density calculations, Hirshfeld atom scattering factor calculations and structural least-squares refinements, repeated until convergence. The importance of this iterative procedure is illustrated via the example of crystalline ammonia. The new HAR method is then applied to X-ray diffraction data of the dipeptide Gly-l-Ala measured at 12, 50, 100, 150, 220 and 295 K, using Hartree-Fock and BLYP density functional theory electron densities and three different basis sets. All positions and anisotropic displacement parameters (ADPs) are freely refined without constraints or restraints - even those for hydrogen atoms. The results are systematically compared with those from neutron diffraction experiments at the temperatures 12, 50, 150 and 295 K. Although non-hydrogen-atom ADPs differ by up to three combined standard uncertainties (csu's), all other structural parameters agree within less than 2 csu's. Using our best calculations (BLYP/cc-pVTZ, recommended for organic molecules), the accuracy of determining bond lengths involving hydrogen atoms from HAR is better than 0.009 Å for temperatures of 150 K or below; for hydrogen-atom ADPs it is better than 0.006 Å(2) as judged from the mean absolute X-ray minus neutron differences. These results are among the best ever obtained. Remarkably, the precision of determining bond lengths and ADPs for the hydrogen atoms from the HAR procedure is comparable with that from the neutron measurements - an outcome which is obtained with a routinely achievable resolution of the X-ray data of 0.65 Å.

  10. Developmental Changes in Brain Network Hub Connectivity in Late Adolescence.

    Science.gov (United States)

    Baker, Simon T E; Lubman, Dan I; Yücel, Murat; Allen, Nicholas B; Whittle, Sarah; Fulcher, Ben D; Zalesky, Andrew; Fornito, Alex

    2015-06-17

    The human brain undergoes substantial development throughout adolescence and into early adulthood. This maturational process is thought to include the refinement of connectivity between putative connectivity hub regions of the brain, which collectively form a dense core that enhances the functional integration of anatomically distributed, and functionally specialized, neural systems. Here, we used longitudinal diffusion magnetic resonance imaging to characterize changes in connectivity between 80 cortical and subcortical anatomical regions over a 2 year period in 31 adolescents between the ages of 15 and 19 years. Connectome-wide analysis indicated that only a small subset of connections showed evidence of statistically significant developmental change over the study period, with 8% and 6% of connections demonstrating decreased and increased structural connectivity, respectively. Nonetheless, these connections linked 93% and 90% of the 80 regions, respectively, pointing to a selective, yet anatomically distributed pattern of developmental changes that involves most of the brain. Hub regions showed a distinct tendency to be highly connected to each other, indicating robust "rich-club" organization. Moreover, connectivity between hubs was disproportionately influenced by development, such that connectivity between subcortical hubs decreased over time, whereas frontal-subcortical and frontal-parietal hub-hub connectivity increased over time. These findings suggest that late adolescence is characterized by selective, yet significant remodeling of hub-hub connectivity, with the topological organization of hubs shifting emphasis from subcortical hubs in favor of an increasingly prominent role for frontal hub regions. Copyright © 2015 the authors 0270-6474/15/359078-10$15.00/0.

  11. Refining and petrochemicals

    International Nuclear Information System (INIS)

    Benazzi, E.

    2003-01-01

    Down sharply in 2002, refining margins showed a clear improvement in the first half-year of 2003. As a result, the earnings reported by oil companies for financial year 2002 were significantly lower than in 2001, but the prospects are brighter for 2003. In the petrochemicals sector, slow demand and higher feedstock prices eroded margins in 2002, especially in Europe and the United States. The financial results for the first part of 2003 seem to indicate that sector profitability will not improve before 2004. (author)

  12. Refining and petrochemicals

    International Nuclear Information System (INIS)

    Benazzi, E.; Alario, F.

    2004-01-01

    In 2003, refining margins showed a clear improvement that continued throughout the first three quarters of 2004. Oil companies posted significantly higher earnings in 2003 compared to 2002, with the results of first quarter 2004 confirming this trend. Due to higher feedstock prices, the implementation of new capacity and more intense competition, the petrochemicals industry was not able to boost margins in 2003. In such difficult business conditions, aggravated by soaring crude prices, the petrochemicals industry is not likely to see any improvement in profitability before the second half of 2004. (author)

  13. Refining mineral oils

    Energy Technology Data Exchange (ETDEWEB)

    1946-07-05

    A process is described refining raw oils such as mineral oils, shale oils, tar, their fractions and derivatives, by extraction with a selected solvent or a mixture of solvents containing water, forming a solvent more favorable for the hydrocarbons poor in hydrogen than for hydrocarbons rich in hydrogen, this process is characterized by the addition of an aiding solvent for the water which can be mixed or dissolved in the water and the solvent or in the dissolving mixture and increasing in this way the solubility of the water in the solvent or the dissolving mixture.

  14. Atlantic Basin refining profitability

    International Nuclear Information System (INIS)

    Jones, R.J.

    1998-01-01

    A review of the profitability margins of oil refining in the Atlantic Basin was presented. Petroleum refiners face the continuous challenge of balancing supply with demand. It would appear that the profitability margins in the Atlantic Basin will increase significantly in the near future because of shrinking supply surpluses. Refinery capacity utilization has reached higher levels than ever before. The American Petroleum Institute reported that in August 1997, U.S. refineries used 99 per cent of their capacity for several weeks in a row. U.S. gasoline inventories have also declined as the industry has focused on reducing capital costs. This is further evidence that supply and demand are tightly balanced. Some of the reasons for tightening supplies were reviewed. It was predicted that U.S. gasoline demand will continue to grow in the near future. Gasoline demand has not declined as expected because new vehicles are not any more fuel efficient today than they were a decade ago. Although federally-mandated fuel efficiency standards were designed to lower gasoline consumption, they may actually have prevented consumption from falling. Atlantic margins were predicted to continue moving up because of the supply and demand evidence: high capacity utilization rates, low operating inventories, limited capacity addition resulting from lower capital spending, continued U.S. gasoline demand growth, and steady total oil demand growth. 11 figs

  15. Petroleum refining industry in China

    International Nuclear Information System (INIS)

    Walls, W.D.

    2010-01-01

    The oil refining industry in China has faced rapid growth in oil imports of increasingly sour grades of crude with which to satisfy growing domestic demand for a slate of lighter and cleaner finished products sold at subsidized prices. At the same time, the world petroleum refining industry has been moving from one that serves primarily local and regional markets to one that serves global markets for finished products, as world refining capacity utilization has increased. Globally, refined product markets are likely to experience continued globalization until refining investments significantly expand capacity in key demand regions. We survey the oil refining industry in China in the context of the world market for heterogeneous crude oils and growing world trade in refined petroleum products. (author)

  16. Evidence of adaptations of locomotor neural drive in response to enhanced intermuscular connectivity between the triceps surae muscles of the rat.

    Science.gov (United States)

    Bernabei, Michel; van Dieën, Jaap H; Maas, Huub

    2017-09-01

    The aims of this study were to investigate changes 1 ) in the coordination of activation of the triceps surae muscle group, and 2 ) in muscle belly length of soleus (SO) and lateral gastrocnemius (LG) during locomotion (trotting) in response to increased stiffness of intermuscular connective tissues in the rat. We measured muscle activation and muscle belly lengths, as well as hindlimb kinematics, before and after an artificial enhancement of the connectivity between SO and LG muscles obtained by implanting a tissue-integrating surgical mesh at the muscles' interface. We found that SO muscle activation decreased to 62%, while activation of LG and medial gastrocnemius muscles increased to 134 and 125%, respectively, compared with the levels measured preintervention. Although secondary additional or amplified activation bursts were observed with enhanced connectivity, the primary pattern of activation over the stride and the burst duration were not affected by the intervention. Similar muscle length changes after manipulation were observed, suggesting that length feedback from spindle receptors within SO and LG was not affected by the connectivity enhancement. We conclude that peripheral mechanical constraints given by morphological (re)organization of connective tissues linking synergists are taken into account by the central nervous system. The observed shift in activity toward the gastrocnemius muscles after the intervention suggests that these larger muscles are preferentially recruited when the soleus has a similar mechanical disadvantage in that it produces an unwanted flexion moment around the knee. NEW & NOTEWORTHY Connective tissue linkages between muscle-tendon units may act as an additional mechanical constraint on the musculoskeletal system, thereby reducing the spectrum of solutions for performing a motor task. We found that intermuscular coordination changes following intermuscular connectivity enhancement. Besides showing that the extent of such

  17. Comparing Refinements for Failure and Bisimulation Semantics

    NARCIS (Netherlands)

    Eshuis, H.; Fokkinga, M.M.

    2002-01-01

    Refinement in bisimulation semantics is defined differently from refinement in failure semantics: in bisimulation semantics refinement is based on simulations between labelled transition systems, whereas in failure semantics refinement is based on inclusions between failure systems. There exist

  18. Commercial refining in the Mediterranean

    International Nuclear Information System (INIS)

    Packer, P.

    1999-01-01

    About 9% of the world's oil refining capacity is on the Mediterranean: some of the world's biggest and most advanced refineries are on Sicily and Sardinia. The Mediterranean refineries are important suppliers to southern Europe and N. Africa. The article discusses commercial refining in the Mediterranean under the headings of (i) historic development, (ii) product demand, (iii) refinery configurations, (iv) refined product trade, (v) financial performance and (vi) future outlook. Although some difficulties are foreseen, refining in the Mediterranean is likely to continue to be important well into the 21st century. (UK)

  19. New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

    Science.gov (United States)

    Riera-Fernández, Pablo; Munteanu, Cristian R; Escobar, Manuel; Prado-Prado, Francisco; Martín-Romalde, Raquel; Pereira, David; Villalba, Karen; Duardo-Sánchez, Aliuska; González-Díaz, Humberto

    2012-01-21

    Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction

  20. On Modal Refinement and Consistency

    DEFF Research Database (Denmark)

    Nyman, Ulrik; Larsen, Kim Guldstrand; Wasowski, Andrzej

    2007-01-01

    Almost 20 years after the original conception, we revisit several fundamental question about modal transition systems. First, we demonstrate the incompleteness of the standard modal refinement using a counterexample due to Hüttel. Deciding any refinement, complete with respect to the standard...

  1. Crystal structure refinement with SHELXL

    Energy Technology Data Exchange (ETDEWEB)

    Sheldrick, George M., E-mail: gsheldr@shelx.uni-ac.gwdg.de [Department of Structural Chemistry, Georg-August Universität Göttingen, Tammannstraße 4, Göttingen 37077 (Germany)

    2015-01-01

    New features added to the refinement program SHELXL since 2008 are described and explained. The improvements in the crystal structure refinement program SHELXL have been closely coupled with the development and increasing importance of the CIF (Crystallographic Information Framework) format for validating and archiving crystal structures. An important simplification is that now only one file in CIF format (for convenience, referred to simply as ‘a CIF’) containing embedded reflection data and SHELXL instructions is needed for a complete structure archive; the program SHREDCIF can be used to extract the .hkl and .ins files required for further refinement with SHELXL. Recent developments in SHELXL facilitate refinement against neutron diffraction data, the treatment of H atoms, the determination of absolute structure, the input of partial structure factors and the refinement of twinned and disordered structures. SHELXL is available free to academics for the Windows, Linux and Mac OS X operating systems, and is particularly suitable for multiple-core processors.

  2. Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method.

    Science.gov (United States)

    Zou, Ling; Guo, Qian; Xu, Yi; Yang, Biao; Jiao, Zhuqing; Xiang, Jianbo

    2016-04-29

    Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity.

  3. Natural language acquisition in large scale neural semantic networks

    Science.gov (United States)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

  4. South Korea - oil refining overview

    International Nuclear Information System (INIS)

    Hayes, D.

    1999-01-01

    Following the economic problems of the 1990s, the petroleum refining industry of South Korea underwent much involuntary restructuring in 1999 with respect to takeovers and mergers and these are discussed. The demand for petroleum has now pretty well recovered. The reasons for fluctuating prices in the 1990s, how the new structure should be cushioned against changes in the future, and the potential for South Korea to export refined petroleum, are all discussed

  5. Adaptive Mesh Refinement in CTH

    International Nuclear Information System (INIS)

    Crawford, David

    1999-01-01

    This paper reports progress on implementing a new capability of adaptive mesh refinement into the Eulerian multimaterial shock- physics code CTH. The adaptivity is block-based with refinement and unrefinement occurring in an isotropic 2:1 manner. The code is designed to run on serial, multiprocessor and massive parallel platforms. An approximate factor of three in memory and performance improvements over comparable resolution non-adaptive calculations has-been demonstrated for a number of problems

  6. Steel refining possibilities in LF

    Science.gov (United States)

    Dumitru, M. G.; Ioana, A.; Constantin, N.; Ciobanu, F.; Pollifroni, M.

    2018-01-01

    This article presents the main possibilities for steel refining in Ladle Furnace (LF). These, are presented: steelmaking stages, steel refining through argon bottom stirring, online control of the bottom stirring, bottom stirring diagram during LF treatment of a heat, porous plug influence over the argon stirring, bottom stirring porous plug, analysis of porous plugs disposal on ladle bottom surface, bottom stirring simulation with ANSYS, bottom stirring simulation with Autodesk CFD.

  7. High serotonin levels during brain development alter the structural input-output connectivity of neural networks in the rat somatosensory layer IV

    Directory of Open Access Journals (Sweden)

    Stéphanie eMiceli

    2013-06-01

    Full Text Available Homeostatic regulation of serotonin (5-HT concentration is critical for normal topographical organization and development of thalamocortical (TC afferent circuits. Down-regulation of the serotonin transporter (SERT and the consequent impaired reuptake of 5-HT at the synapse, results in a reduced terminal branching of developing TC afferents within the primary somatosensory cortex (S1. Despite the presence of multiple genetic models, the effect of high extracellular 5-HT levels on the structure and function of developing intracortical neural networks is far from being understood. Here, using juvenile SERT knockout (SERT-/- rats we investigated, in vitro, the effect of increased 5-HT levels on the structural organization of (i the thalamocortical projections of the ventroposteromedial thalamic nucleus towards S1, (ii the general barrel-field pattern and (iii the electrophysiological and morphological properties of the excitatory cell population in layer IV of S1 (spiny stellate and pyramidal cells. Our results confirmed previous findings that high levels of 5-HT during development lead to a reduction of the topographical precision of TCA projections towards the barrel cortex. Also, the barrel pattern was altered but not abolished in SERT-/- rats. In layer IV, both excitatory spiny stellate and pyramidal cells showed a significantly reduced intracolumnar organization of their axonal projections. In addition, the layer IV spiny stellate cells gave rise to a prominent projection towards the infragranular layer Vb. Our findings point to a structural and functional reorganization, of TCAs, as well as early stage intracortical microcircuitry, following the disruption of 5-HT reuptake during critical developmental periods. The increased projection pattern of the layer IV neurons suggests that the intracortical network changes are not limited to the main entry layer IV but may also affect the subsequent stages of the canonical circuits of the barrel

  8. Handbook of Brain Connectivity

    CERN Document Server

    Jirsa, Viktor K

    2007-01-01

    Our contemporary understanding of brain function is deeply rooted in the ideas of the nonlinear dynamics of distributed networks. Cognition and motor coordination seem to arise from the interactions of local neuronal networks, which themselves are connected in large scales across the entire brain. The spatial architectures between various scales inevitably influence the dynamics of the brain and thereby its function. But how can we integrate brain connectivity amongst these structural and functional domains? Our Handbook provides an account of the current knowledge on the measurement, analysis and theory of the anatomical and functional connectivity of the brain. All contributors are leading experts in various fields concerning structural and functional brain connectivity. In the first part of the Handbook, the chapters focus on an introduction and discussion of the principles underlying connected neural systems. The second part introduces the currently available non-invasive technologies for measuring struct...

  9. Refinement of Parallel and Reactive Programs

    OpenAIRE

    Back, R. J. R.

    1992-01-01

    We show how to apply the refinement calculus to stepwise refinement of parallel and reactive programs. We use action systems as our basic program model. Action systems are sequential programs which can be implemented in a parallel fashion. Hence refinement calculus methods, originally developed for sequential programs, carry over to the derivation of parallel programs. Refinement of reactive programs is handled by data refinement techniques originally developed for the sequential refinement c...

  10. The equilibrium of neural firing: A mathematical theory

    Energy Technology Data Exchange (ETDEWEB)

    Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

    2014-12-15

    Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

  11. Explicit logic circuits discriminate neural states.

    Directory of Open Access Journals (Sweden)

    Lane Yoder

    Full Text Available The magnitude and apparent complexity of the brain's connectivity have left explicit networks largely unexplored. As a result, the relationship between the organization of synaptic connections and how the brain processes information is poorly understood. A recently proposed retinal network that produces neural correlates of color vision is refined and extended here to a family of general logic circuits. For any combination of high and low activity in any set of neurons, one of the logic circuits can receive input from the neurons and activate a single output neuron whenever the input neurons have the given activity state. The strength of the output neuron's response is a measure of the difference between the smallest of the high inputs and the largest of the low inputs. The networks generate correlates of known psychophysical phenomena. These results follow directly from the most cost-effective architectures for specific logic circuits and the minimal cellular capabilities of excitation and inhibition. The networks function dynamically, making their operation consistent with the speed of most brain functions. The networks show that well-known psychophysical phenomena do not require extraordinarily complex brain structures, and that a single network architecture can produce apparently disparate phenomena in different sensory systems.

  12. Romanian refining industry assesses restructuring

    International Nuclear Information System (INIS)

    Tanasescu, D.G.

    1991-01-01

    The Romanian crude oil refining industry, as all the other economic sectors, faces the problems accompanying the transition from a centrally planned economy to a market economy. At present, all refineries have registered as joint-stock companies and all are coordinated and assisted by Rafirom S.A., from both a legal and a production point of view. Rafirom S.A. is a joint-stock company that holds shares in refineries and other stock companies with activities related to oil refining. Such activities include technological research, development, design, transportation, storage, and domestic and foreign marketing. This article outlines the market forces that are expected to: drive rationalization and restructuring of refining operations and define the targets toward which the reconfigured refineries should strive

  13. Data refinement for true concurrency

    Directory of Open Access Journals (Sweden)

    Brijesh Dongol

    2013-05-01

    Full Text Available The majority of modern systems exhibit sophisticated concurrent behaviour, where several system components modify and observe the system state with fine-grained atomicity. Many systems (e.g., multi-core processors, real-time controllers also exhibit truly concurrent behaviour, where multiple events can occur simultaneously. This paper presents data refinement defined in terms of an interval-based framework, which includes high-level operators that capture non-deterministic expression evaluation. By modifying the type of an interval, our theory may be specialised to cover data refinement of both discrete and continuous systems. We present an interval-based encoding of forward simulation, then prove that our forward simulation rule is sound with respect to our data refinement definition. A number of rules for decomposing forward simulation proofs over both sequential and parallel composition are developed.

  14. Bauxite Mining and Alumina Refining

    Science.gov (United States)

    Frisch, Neale; Olney, David

    2014-01-01

    Objective: To describe bauxite mining and alumina refining processes and to outline the relevant physical, chemical, biological, ergonomic, and psychosocial health risks. Methods: Review article. Results: The most important risks relate to noise, ergonomics, trauma, and caustic soda splashes of the skin/eyes. Other risks of note relate to fatigue, heat, and solar ultraviolet and for some operations tropical diseases, venomous/dangerous animals, and remote locations. Exposures to bauxite dust, alumina dust, and caustic mist in contemporary best-practice bauxite mining and alumina refining operations have not been demonstrated to be associated with clinically significant decrements in lung function. Exposures to bauxite dust and alumina dust at such operations are also not associated with the incidence of cancer. Conclusions: A range of occupational health risks in bauxite mining and alumina refining require the maintenance of effective control measures. PMID:24806720

  15. Analysis of connectivity in NeuCube spiking neural network models trained on EEG data for the understanding of functional changes in the brain: A case study on opiate dependence treatment.

    Science.gov (United States)

    Capecci, Elisa; Kasabov, Nikola; Wang, Grace Y

    2015-08-01

    The paper presents a methodology for the analysis of functional changes in brain activity across different conditions and different groups of subjects. This analysis is based on the recently proposed NeuCube spiking neural network (SNN) framework and more specifically on the analysis of the connectivity of a NeuCube model trained with electroencephalography (EEG) data. The case study data used to illustrate this method is EEG data collected from three groups-subjects with opiate addiction, patients undertaking methadone maintenance treatment, and non-drug users/healthy control group. The proposed method classifies more accurately the EEG data than traditional statistical and artificial intelligence (AI) methods and can be used to predict response to treatment and dose-related drug effect. But more importantly, the method can be used to compare functional brain activities of different subjects and the changes of these activities as a result of treatment, which is a step towards a better understanding of both the EEG data and the brain processes that generated it. The method can also be used for a wide range of applications, such as a better understanding of disease progression or aging. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Refining Nodes and Edges of State Machines

    DEFF Research Database (Denmark)

    Hallerstede, Stefan; Snook, Colin

    2011-01-01

    State machines are hierarchical automata that are widely used to structure complex behavioural specifications. We develop two notions of refinement of state machines, node refinement and edge refinement. We compare the two notions by means of examples and argue that, by adopting simple conventions...... refinement theory and UML-B state machine refinement influences the style of node refinement. Hence we propose a method with direct proof of state machine refinement avoiding the detour via Event-B that is needed by UML-B....

  17. Process for refining shale bitumen

    Energy Technology Data Exchange (ETDEWEB)

    Plauson, H

    1920-09-19

    A process is disclosed for refining shale bitumen for use as heavy mineral oil, characterized by mixtures of blown hard shale pitch and heavy mineral oil being blown with hot air at temperatures of 120 to 150/sup 0/ with 1 to 3 percent sulfur, and if necessary with 0.5 to 3 percent of an aldehyde.

  18. Panorama 2007: Refining and Petrochemicals

    International Nuclear Information System (INIS)

    Silva, C.

    2007-01-01

    The year 2005 saw a new improvement in refining margins that continued during the first three quarters of 2006. The restoration of margins in the last three years has allowed the refining sector to regain its profitability. In this context, the oil companies reported earnings for fiscal year 2005 that were up significantly compared to 2004, and the figures for the first half-year 2006 confirm this trend. Despite this favorable business environment, investments only saw a minimal increase in 2005 and the improvement expected for 2006 should remain fairly limited. Looking to 2010-2015, it would appear that the planned investment projects with the highest probability of reaching completion will be barely adequate to cover the increase in demand. Refining sector should continue to find itself under pressure. As for petrochemicals, despite a steady up-trend in the naphtha price, the restoration of margins consolidated a comeback that started in 2005. All in all, capital expenditure remained fairly low in both the refining and petrochemicals sectors, but many projects are planned for the next ten years. (author)

  19. Multigrid for refined triangle meshes

    Energy Technology Data Exchange (ETDEWEB)

    Shapira, Yair

    1997-02-01

    A two-level preconditioning method for the solution of (locally) refined finite element schemes using triangle meshes is introduced. In the isotropic SPD case, it is shown that the condition number of the preconditioned stiffness matrix is bounded uniformly for all sufficiently regular triangulations. This is also verified numerically for an isotropic diffusion problem with highly discontinuous coefficients.

  20. European refining: evolution or revolution?

    International Nuclear Information System (INIS)

    Cuthbert, N.

    1999-01-01

    A recent detailed analysis of the refining business in Europe (by Purvin and Gurtz) was used to highlight some key issues facing the industry. The article was written under five sub-sections: (i) economic environment (assessment of the economic prospects for Europe), (ii) energy efficiency and global warming (lists the four points of the EU car makers' voluntary agreement), (iii) fuel quality and refinery investment (iv) refinery capacity and utilisation and (v) industry structure and development. Diagrams show GDP per capita for East and West, European road fuel demand to 2015 and European net trade and European refinery ownership by crude capacity. It was concluded that the future of refining in Europe is 'exciting and challenging' and there are likely to be more large joint venture refineries. (UK)

  1. Neural Network Control for Variable Pitch Angle in Grid Connected Wind Turbine%并网风力机中基于变桨距角的神经网络控制方法

    Institute of Scientific and Technical Information of China (English)

    王凌云; 张涛; 孟娟

    2012-01-01

    针对并网风力机的运行特性,在其传动系统和发电机的动态模型基础上设计控制器.当外界风速较大,提出采用基于神经网络的风力机叶片桨距角控制器抑制多余的风能进入发电系统,维持风力发电机馈送到电网的功率稳定;当风速较低时,风力机转速需要跟随风速变化,调整叶片桨距角处于捕捉最大风能位置处,保证风力机的风能转换效率最优,提高其运行效率.仿真结果验证了该控制方法的有效性.%For the operation characteristics of a grid connected wind turbine, two controllers are designed based on the dynamical model of the wind turbine drive system and generator. When the wind speed is higher, the neural network controller of the turbine blades pitch angle is proposed to restrict the excess wind energy entering the generation system in order to keep the power injected into the grid stable. Meanwhile, when the wind speed is lower, the turbine speed is changed with the variation of wind speed by adjusting the blades angle at the value of capturing maximum wind power, then the optimal wind energy conversion efficiency is guaranteed. The simulation results verify this control method is highly effective.

  2. Uranium refining by solvent extraction

    International Nuclear Information System (INIS)

    Kraikaew, J.

    1996-01-01

    The yellow cake refining was studied in both laboratory and semi-pilot scales. The process units mainly consist of dissolution and filtration, solvent extraction, and precipitation and filtration. Effect of flow ratio (organic flow rate/ aqueous flow rate) on working efficiencies of solvent extraction process was studied. Detailed studies were carried out on extraction, scrubbing and stripping processes. Purity of yellow cake product obtained is high as 90.32% U 3 O 8

  3. Process for refining naphthalene, etc

    Energy Technology Data Exchange (ETDEWEB)

    Petroff, G

    1922-05-13

    A process is described for the refining of naphthalene, its distillates, and mineral oils by the use of dilute sulfuric acid, characterized in that the oils are oxidized with oxygen of the air and thereafter are treated with 65 to 75 percent sulfuric acid to separate the unsaturated hydrocarbons in the form of polymerized products whereby, if necessary, heating and application of usual or higher pressure can take place.

  4. Preparation of refined oils, etc

    Energy Technology Data Exchange (ETDEWEB)

    1931-02-03

    A process is disclosed for the preparation of refined sulfur-containing oils from sulfur-containing crude oils obtained by distillation of bituminous limestone, characterized by this crude oil being first subjected to a purification by distillation with steam in the known way, then treated with lime and chloride of lime and distilled preferably in the presence of zinc powder, whereby in this purification a rectification can be added for the purpose of recovering definite fractions.

  5. Bauxite Mining and Alumina Refining

    OpenAIRE

    Donoghue, A. Michael; Frisch, Neale; Olney, David

    2014-01-01

    Objective: To describe bauxite mining and alumina refining processes and to outline the relevant physical, chemical, biological, ergonomic, and psychosocial health risks. Methods: Review article. Results: The most important risks relate to noise, ergonomics, trauma, and caustic soda splashes of the skin/eyes. Other risks of note relate to fatigue, heat, and solar ultraviolet and for some operations tropical diseases, venomous/dangerous animals, and remote locations. Exposures to bauxite dust,...

  6. The Charfuel coal refining process

    International Nuclear Information System (INIS)

    Meyer, L.G.

    1991-01-01

    The patented Charfuel coal refining process employs fluidized hydrocracking to produce char and liquid products from virtually all types of volatile-containing coals, including low rank coal and lignite. It is not gasification or liquefaction which require the addition of expensive oxygen or hydrogen or the use of extreme heat or pressure. It is not the German pyrolysis process that merely 'cooks' the coal, producing coke and tar-like liquids. Rather, the Charfuel coal refining process involves thermal hydrocracking which results in the rearrangement of hydrogen within the coal molecule to produce a slate of co-products. In the Charfuel process, pulverized coal is rapidly heated in a reducing atmosphere in the presence of internally generated process hydrogen. This hydrogen rearrangement allows refinement of various ranks of coals to produce a pipeline transportable, slurry-type, environmentally clean boiler fuel and a slate of value-added traditional fuel and chemical feedstock co-products. Using coal and oxygen as the only feedstocks, the Charfuel hydrocracking technology economically removes much of the fuel nitrogen, sulfur, and potential air toxics (such as chlorine, mercury, beryllium, etc.) from the coal, resulting in a high heating value, clean burning fuel which can increase power plant efficiency while reducing operating costs. The paper describes the process, its thermal efficiency, its use in power plants, its pipeline transport, co-products, environmental and energy benefits, and economics

  7. A Macdonald refined topological vertex

    Science.gov (United States)

    Foda, Omar; Wu, Jian-Feng

    2017-07-01

    We consider the refined topological vertex of Iqbal et al (2009 J. High Energy Phys. JHEP10(2009)069), as a function of two parameters ≤ft\\lgroup x, y \\right\\rgroup , and deform it by introducing the Macdonald parameters ≤ft\\lgroup q, t \\right\\rgroup , as in the work of Vuletić on plane partitions (Vuletić M 2009 Trans. Am. Math. Soc. 361 2789-804), to obtain ‘a Macdonald refined topological vertex’. In the limit q → t , we recover the refined topological vertex of Iqbal et al and in the limit x → y , we obtain a qt-deformation of the original topological vertex of Aganagic et al (2005 Commun. Math. Phys. 25 425-78). Copies of the vertex can be glued to obtain qt-deformed 5D instanton partition functions that have well-defined 4D limits and, for generic values of ≤ft\\lgroup q, t\\right\\rgroup , contain infinite-towers of poles for every pole present in the limit q → t .

  8. Refining's-clean new jingle

    International Nuclear Information System (INIS)

    Anon.

    1992-01-01

    This paper reports that at a time when profit margins are slim and gasoline demand is down, the U.S. petroleum-refining industry is facing one of its greatest challenges; How to meet new federal and state laws for reformulated gasoline, oxygenated fuels, low-sulfur diesel and other measures to improve the environment. The American Petroleum Institute (API) estimates that industry will spend between $15 and $23 billion by the end of the decade to meet the U.S. Clean Air Act Amendments (CAAA) of 1990, and other legislation. ENSR Consulting and Engineering's capital-spending figure runs to between $70 and 100 billion this decade, including $24 billion to produce reformulated fuels and $10-12 billion to reduce refinery emissions. M.W. Kellogg Co. estimates that refiners may have to spend up to $30 billion this decade to meet the demand for reformulated gasoline. The estimates are wide-ranging because refiners are still studying their options and delaying final decisions as long as they can, to try to ensure they are the best and least-costly decisions. Oxygenated fuels will be required next winter, but federal regulations for reformulated gasoline won't go into effect until 1995, while California's tougher reformulated-fuels law will kick in the following year

  9. Neural network regulation driven by autonomous neural firings

    Science.gov (United States)

    Cho, Myoung Won

    2016-07-01

    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  10. Southeast Asian oil markets and refining

    Energy Technology Data Exchange (ETDEWEB)

    Yamaguchi, N.D. [FACTS, Inc., Honolulu, Hawaii (United States)

    1999-09-01

    An overview of the Southeast Asian oil markets and refining is presented concentrating on Brunei, Malaysia, the Philippines, Singapore and Thailand refiners. Key statistics of the refiners in this region are tabulated. The demand and the quality of Indonesian, Malaysian, Philippine, Singapore and Thai petroleum products are analysed. Crude distillation unit capacity trends in the Southeastern Asian refining industry are discussed along with cracking to distillation ratios, refining in these countries, and the impact of changes in demand and refining on the product trade.

  11. Southeast Asian oil markets and refining

    International Nuclear Information System (INIS)

    Yamaguchi, N.D.

    1999-01-01

    An overview of the Southeast Asian oil markets and refining is presented concentrating on Brunei, Malaysia, the Philippines, Singapore and Thailand refiners. Key statistics of the refiners in this region are tabulated. The demand and the quality of Indonesian, Malaysian, Philippine, Singapore and Thai petroleum products are analysed. Crude distillation unit capacity trends in the Southeastern Asian refining industry are discussed along with cracking to distillation ratios, refining in these countries, and the impact of changes in demand and refining on the product trade

  12. Neural connections between antrum and duodenum

    DEFF Research Database (Denmark)

    Kraglund, K; Schrøder, H D; Stødkilde-Jørgensen, H

    1983-01-01

    Postprandial coordination of antroduodenal motility partly takes place via intrinsic mural pathways. The nature and origin of these nerve fibers have not yet been clarified. In this investigation using fluorochromic substances injected into the antrum and duodenum it was demonstrated that common ...... central neurons for the antroduodenal area exist in the vagal nucleus....

  13. Neural Networks and Micromechanics

    Science.gov (United States)

    Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

    The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

  14. Deep Neural Yodelling

    OpenAIRE

    Pfäffli, Daniel (Autor/in)

    2018-01-01

    Yodel music differs from most other genres by exercising the transition from chest voice to falsetto with an audible glottal stop which is recognised even by laymen. Yodel often consists of a yodeller with a choir accompaniment. In Switzerland, it is differentiated between the natural yodel and yodel songs. Today's approaches to music generation with machine learning algorithms are based on neural networks, which are best described by stacked layers of neurons which are connected with neurons...

  15. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  16. Connecting Grammaticalisation

    DEFF Research Database (Denmark)

    Nørgård-Sørensen, Jens; Heltoft, Lars; Schøsler, Lene

    morphological, topological and constructional paradigms often connect to form complex paradigms. The book introduces the concept of connecting grammaticalisation to describe the formation, restructuring and dismantling of such complex paradigms. Drawing primarily on data from Germanic, Romance and Slavic...

  17. Latin American oil markets and refining

    International Nuclear Information System (INIS)

    Yamaguchi, N.D.; Obadia, C.

    1999-01-01

    This paper provides an overview of the oil markets and refining in Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru and Venezuela, and examines the production of crude oil in these countries. Details are given of Latin American refiners highlighting trends in crude distillation unit capacity, cracking to distillation ratios, and refining in the different countries. Latin American oil trade is discussed, and charts are presented illustrating crude production, oil consumption, crude refining capacity, cracking to distillation ratios, and oil imports and exports

  18. From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

    Science.gov (United States)

    Ross, Muriel D.

    1991-01-01

    The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

  19. Grain refinement of aluminum and its alloys

    International Nuclear Information System (INIS)

    Zaid, A.I.O.

    2001-01-01

    Grain refinement of aluminum and its alloys by the binary Al-Ti and Ternary Al-Ti-B master alloys is reviewed and discussed. The importance of grain refining to the cast industry and the parameters affecting it are presented and discussed. These include parameters related to the cast, parameters related to the grain refining alloy and parameters related to the process. The different mechanisms, suggested in the literature for the process of grain refining are presented and discussed, from which it is found that although the mechanism of refining by the binary Al-Ti is well established the mechanism of grain refining by the ternary Al-Ti-B is still a controversial matter and some research work is still needed in this area. The effect of the addition of other alloying elements in the presence of the grain refiner on the grain refining efficiency is also reviewed and discussed. It is found that some elements e.g. V, Mo, C improves the grain refining efficiency, whereas other elements e.g. Cr, Zr, Ta poisons the grain refinement. Based on the parameters affecting the grain refinement and its mechanism, a criterion for selection of the optimum grain refiner is forwarded and discussed. (author)

  20. Neutron Powder Diffraction and Constrained Refinement

    DEFF Research Database (Denmark)

    Pawley, G. S.; Mackenzie, Gordon A.; Dietrich, O. W.

    1977-01-01

    The first use of a new program, EDINP, is reported. This program allows the constrained refinement of molecules in a crystal structure with neutron diffraction powder data. The structures of p-C6F4Br2 and p-C6F4I2 are determined by packing considerations and then refined with EDINP. Refinement is...

  1. Niobium-base grain refiner for aluminium

    International Nuclear Information System (INIS)

    Silva Pontes, P. da; Robert, M.H.; Cupini, N.L.

    1980-01-01

    A new chemical grain refiner for aluminium has been developed, using inoculation of a niobium-base compound. When a bath of molten aluminium is inoculated whith this refiner, an intermetallic aluminium-niobium compound is formed which acts as a powerful nucleant, producing extremely fine structure comparable to those obtained by means of the traditional grain refiner based on titanium and boron. It was found that the refinement of the structure depends upon the weight percentage of the new refiner inoculated as well as the time of holding the bath after inoculation and before pouring, but mainly on the inoculating temperature. (Author) [pt

  2. Front Propagation in Stochastic Neural Fields

    KAUST Repository

    Bressloff, Paul C.; Webber, Matthew A.

    2012-01-01

    We analyze the effects of extrinsic multiplicative noise on front propagation in a scalar neural field with excitatory connections. Using a separation of time scales, we represent the fluctuating front in terms of a diffusive-like displacement

  3. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

    Science.gov (United States)

    Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

    2016-01-01

    Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was

  4. Morse Set Classification and Hierarchical Refinement Using Conley Index

    KAUST Repository

    Guoning Chen,; Qingqing Deng,; Szymczak, A.; Laramee, R. S.; Zhang, E.

    2012-01-01

    Morse decomposition provides a numerically stable topological representation of vector fields that is crucial for their rigorous interpretation. However, Morse decomposition is not unique, and its granularity directly impacts its computational cost. In this paper, we propose an automatic refinement scheme to construct the Morse Connection Graph (MCG) of a given vector field in a hierarchical fashion. Our framework allows a Morse set to be refined through a local update of the flow combinatorialization graph, as well as the connection regions between Morse sets. The computation is fast because the most expensive computation is concentrated on a small portion of the domain. Furthermore, the present work allows the generation of a topologically consistent hierarchy of MCGs, which cannot be obtained using a global method. The classification of the extracted Morse sets is a crucial step for the construction of the MCG, for which the Poincar index is inadequate. We make use of an upper bound for the Conley index, provided by the Betti numbers of an index pair for a translation along the flow, to classify the Morse sets. This upper bound is sufficiently accurate for Morse set classification and provides supportive information for the automatic refinement process. An improved visualization technique for MCG is developed to incorporate the Conley indices. Finally, we apply the proposed techniques to a number of synthetic and real-world simulation data to demonstrate their utility. © 2006 IEEE.

  5. Morse Set Classification and Hierarchical Refinement Using Conley Index

    KAUST Repository

    Guoning Chen,

    2012-05-01

    Morse decomposition provides a numerically stable topological representation of vector fields that is crucial for their rigorous interpretation. However, Morse decomposition is not unique, and its granularity directly impacts its computational cost. In this paper, we propose an automatic refinement scheme to construct the Morse Connection Graph (MCG) of a given vector field in a hierarchical fashion. Our framework allows a Morse set to be refined through a local update of the flow combinatorialization graph, as well as the connection regions between Morse sets. The computation is fast because the most expensive computation is concentrated on a small portion of the domain. Furthermore, the present work allows the generation of a topologically consistent hierarchy of MCGs, which cannot be obtained using a global method. The classification of the extracted Morse sets is a crucial step for the construction of the MCG, for which the Poincar index is inadequate. We make use of an upper bound for the Conley index, provided by the Betti numbers of an index pair for a translation along the flow, to classify the Morse sets. This upper bound is sufficiently accurate for Morse set classification and provides supportive information for the automatic refinement process. An improved visualization technique for MCG is developed to incorporate the Conley indices. Finally, we apply the proposed techniques to a number of synthetic and real-world simulation data to demonstrate their utility. © 2006 IEEE.

  6. Nonequilibrium landscape theory of neural networks.

    Science.gov (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-11-05

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

  7. Nonequilibrium landscape theory of neural networks

    Science.gov (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-01-01

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

  8. Materials refining on the Moon

    Science.gov (United States)

    Landis, Geoffrey A.

    2007-05-01

    Oxygen, metals, silicon, and glass are raw materials that will be required for long-term habitation and production of structural materials and solar arrays on the Moon. A process sequence is proposed for refining these materials from lunar regolith, consisting of separating the required materials from lunar rock with fluorine. The fluorine is brought to the Moon in the form of potassium fluoride, and is liberated from the salt by electrolysis in a eutectic salt melt. Tetrafluorosilane produced by this process is reduced to silicon by a plasma reduction stage; the fluorine salts are reduced to metals by reaction with metallic potassium. Fluorine is recovered from residual MgF and CaF2 by reaction with K2O.

  9. Adaptive mesh refinement in titanium

    Energy Technology Data Exchange (ETDEWEB)

    Colella, Phillip; Wen, Tong

    2005-01-21

    In this paper, we evaluate Titanium's usability as a high-level parallel programming language through a case study, where we implement a subset of Chombo's functionality in Titanium. Chombo is a software package applying the Adaptive Mesh Refinement methodology to numerical Partial Differential Equations at the production level. In Chombo, the library approach is used to parallel programming (C++ and Fortran, with MPI), whereas Titanium is a Java dialect designed for high-performance scientific computing. The performance of our implementation is studied and compared with that of Chombo in solving Poisson's equation based on two grid configurations from a real application. Also provided are the counts of lines of code from both sides.

  10. Refining shale-oil distillates

    Energy Technology Data Exchange (ETDEWEB)

    Altpeter, J

    1952-03-17

    A process is described for refining distillates from shale oil, brown coal, tar, and other tar products by extraction with selective solvents, such as lower alcohols, halogen-hydrins, dichlorodiethyl ether, liquid sulfur dioxide, and so forth, as well as treating with alkali solution, characterized in that the distillate is first treated with completely or almost completely recovered phenol or cresotate solution, the oil is separated from the phenolate with solvent, for example concentrated or adjusted to a determined water content of lower alcohol, furfural, halogen-hydrin, dichlorodiethyl ether, liquid sulfur dioxide, or the like, extracted, and the raffinate separated from the extract layer, if necessary after distillation or washing out of solvent, and freeing with alkali solution from residual phenol or creosol.

  11. Novel roles for immune molecules in neural development: Implications for neurodevelopmental disoders

    Directory of Open Access Journals (Sweden)

    Paula A Garay

    2010-09-01

    Full Text Available Although the brain has classically been considered "immune-privileged," current research suggests extensive communication between the nervous and the immune systems in both health and disease. Recent studies demonstrate that immune molecules are present at the right place and time to modulate the development and function of the healthy and diseased CNS. Indeed, immune molecules play integral roles in the CNS throughout neural development, including affecting neurogenesis, neuronal migration, axon guidance, synapse formation, activity-dependent refinement of circuits, and synaptic plasticity. Moreover, the roles of individual immune molecules in the nervous system may change over development. This review focuses on the effects of immune molecules on neuronal connections in the mammalian central nervous system—specifically the roles for MHCI and its receptors, complement, and cytokines on the function, refinement, and plasticity of cortical and hippocampal synapses and their relationship to neurodevelopmental disorders. These functions for immune molecules during neural development suggest that they could also mediate pathological responses to chronic elevations of cytokines in neurodevelopmental disorders, including autism spectrum disorders (ASD and schizophrenia.

  12. Making Connections

    Science.gov (United States)

    Pien, Cheng Lu; Dongsheng, Zhao

    2011-01-01

    Effective teaching includes enabling learners to make connections within mathematics. It is easy to accord with this statement, but how often is it a reality in the mathematics classroom? This article describes an approach in "connecting equivalent" fractions and whole number operations. The authors illustrate how a teacher can combine a common…

  13. Memristor-based neural networks

    International Nuclear Information System (INIS)

    Thomas, Andy

    2013-01-01

    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. (topical review)

  14. Refined geometric transition and qq-characters

    Science.gov (United States)

    Kimura, Taro; Mori, Hironori; Sugimoto, Yuji

    2018-01-01

    We show the refinement of the prescription for the geometric transition in the refined topological string theory and, as its application, discuss a possibility to describe qq-characters from the string theory point of view. Though the suggested way to operate the refined geometric transition has passed through several checks, it is additionally found in this paper that the presence of the preferred direction brings a nontrivial effect. We provide the modified formula involving this point. We then apply our prescription of the refined geometric transition to proposing the stringy description of doubly quantized Seiberg-Witten curves called qq-characters in certain cases.

  15. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  16. 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.

  17. About Connections

    Directory of Open Access Journals (Sweden)

    Kathleen S Rockland

    2015-05-01

    Full Text Available Despite the attention attracted by connectomics, one can lose sight of the very real questions concerning What are connections? In the neuroimaging community, structural connectivity is ground truth and underlying constraint on functional or effective connectivity. It is referenced to underlying anatomy; but, as increasingly remarked, there is a large gap between the wealth of human brain mapping and the relatively scant data on actual anatomical connectivity. Moreover, connections have typically been discussed as pairwise, point x projecting to point y (or: to points y and z, or more recently, in graph theoretical terms, as nodes or regions and the interconnecting edges. This is a convenient shorthand, but tends not to capture the richness and nuance of basic anatomical properties as identified in the classic tradition of tracer studies. The present short review accordingly revisits connectional weights, heterogeneity, reciprocity, topography, and hierarchical organization, drawing on concrete examples. The emphasis is on presynaptic long-distance connections, motivated by the intention to probe current assumptions and promote discussions about further progress and synthesis.

  18. Automated knowledge-base refinement

    Science.gov (United States)

    Mooney, Raymond J.

    1994-01-01

    Over the last several years, we have developed several systems for automatically refining incomplete and incorrect knowledge bases. These systems are given an imperfect rule base and a set of training examples and minimally modify the knowledge base to make it consistent with the examples. One of our most recent systems, FORTE, revises first-order Horn-clause knowledge bases. This system can be viewed as automatically debugging Prolog programs based on examples of correct and incorrect I/O pairs. In fact, we have already used the system to debug simple Prolog programs written by students in a programming language course. FORTE has also been used to automatically induce and revise qualitative models of several continuous dynamic devices from qualitative behavior traces. For example, it has been used to induce and revise a qualitative model of a portion of the Reaction Control System (RCS) of the NASA Space Shuttle. By fitting a correct model of this portion of the RCS to simulated qualitative data from a faulty system, FORTE was also able to correctly diagnose simple faults in this system.

  19. A method for refining oil

    Energy Technology Data Exchange (ETDEWEB)

    Bruskin, Yu.A.; Gorokhov, V.V.; Kotler, L.D.; Kovalenko, N.F.; Spasskiy, Yu.B.; Titov, A.M.; Vlasenko, V.Ye.; Vytnov, V.A.

    1983-01-01

    In the method for refining oil through its distillation with the isolation of directly distilled gases and a benzine fraction (BS) with the use of a benzine fraction pyrolysis, in order to increase the output of the lower olefines and to reduce the energy expenditures, the distillation is conducted with the isolation of 10 to 40 percent of the benzine fraction from its potential content along with the directly distilled gases. The obtained mixture of the remaining part of the benzine fraction is absorbed at a pressure of 1.5 to 6 atmospheres with the feeding of the obtained saturated absorbent to pyrolysis and subsequent mixing of the obtained pyrolysis gas with the unabsorbed product and their joint gas division. As compared to the known method, the proposed method makes it possible to reduce the energy expenditures which is achieved through a reduction in the volume of irrigation in the tower, and to increase the output of the olefines through processing of the steam and gas mixture of the benzine and the directly distilled gases.

  20. The effect of the neural activity on topological properties of growing neural networks.

    Science.gov (United States)

    Gafarov, F M; Gafarova, V R

    2016-09-01

    The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

  1. Internet Connectivity

    Indian Academy of Sciences (India)

    First page Back Continue Last page Overview Graphics. Internet Connectivity. BSNL, SIFY, HCL in Guwahati; only BSNL elsewhere in NE (local player in Shillong). Service poor; All vendors lease BW from BSNL.

  2. Mathematics Connection

    African Journals Online (AJOL)

    MATHEMATICS CONNECTION aims at providing a forum topromote the development of Mathematics Education in Ghana. Articles that seekto enhance the teaching and/or learning of mathematics at all levels of theeducational system are welcome.

  3. HR Connect

    Data.gov (United States)

    US Agency for International Development — HR Connect is the USAID HR personnel system which allows HR professionals to process HR actions related to employee's personal and position information. This system...

  4. Integration with U.S. refining

    International Nuclear Information System (INIS)

    Boslett, T.

    2007-01-01

    The production of crude in western Canada is expected to increase significantly in the next 10 to 15 years as the development of oil sands leases continues. In particular, large-scale investments by major oil sands producers have focused on upgrading bitumen to light synthetic crude oil. Major refinery projects in the United States will take advantage of substantially lower capital costs to increase bitumen processing capabilities. In addition, by 2008, new pipeline projects will provide western Canada with more export capacity. The BP Energy Company is investing US$3 billion to increase the refining capacity of Canadian heavy crude at its refinery in Whiting, Indiana, which already has pipeline connectivity and long-term product placement capabilities. The project, which is scheduled to begin in 2011, will increase heavy blend processing capability to 260 thousand barrels per day and produce more than 1.7 million gallons of motor fuel per day. BP's investment has focused on the hydrogen unit, gas oil hydrotreater, hydrotreater revamps, a coker, a modular sulfur recovery unit, a sour water stripper complex, and 62 miles of piping. The refinery in Indiana has an experienced workforce and a greater availability of labor than in western Canada. Production forecasts indicate varying levels of heavy and light production, depending on the amount of condensate imports available. The project team will ensure that the refinery can handle the various levels of total acid number (TAN), sulfur and solids contained in Canadian heavy oils. The project risks are associated with uncertainties regarding production growth and quality; inflation risk as developments costs continue to rise for projects in Alberta; and regulatory risk that can impact bitumen cost. tabs., figs

  5. Refinement Checking on Parametric Modal Transition Systems

    DEFF Research Database (Denmark)

    Benes, Nikola; Kretínsky, Jan; Larsen, Kim Guldstrand

    2015-01-01

    Modal transition systems (MTS) is a well-studied specification formalism of reactive systems supporting a step-wise refinement methodology. Despite its many advantages, the formalism as well as its currently known extensions are incapable of expressing some practically needed aspects in the refin...

  6. Comparing Syntactic and Semantics Action Refinement

    NARCIS (Netherlands)

    Goltz, Ursula; Gorrieri, Roberto; Rensink, Arend

    The semantic definition of action refinement on labelled configuration structures is compared with the notion of syntactic substitution, which can be used as another notion of action refinement in a process algebraic setting. The comparison is done by studying a process algebra equipped with

  7. On Syntactic and Semantic Action Refinement

    NARCIS (Netherlands)

    Hagiya, M.; Goltz, U.; Mitchell, J.C.; Gorrieri, R.; Rensink, Arend

    1994-01-01

    The semantic definition of action refinement on labelled event structures is compared with the notion of syntactic substitution, which can be used as another notion of action refinement in a process algebraic setting. This is done by studying a process algebra equipped with the ACP sequential

  8. Anomalies in the refinement of isoleucine

    Energy Technology Data Exchange (ETDEWEB)

    Berntsen, Karen R. M.; Vriend, Gert, E-mail: gerrit.vriend@radboudumc.nl [Radboud University Medical Center, Geert Grooteplein 26-28, 6525 GA Nijmegen (Netherlands)

    2014-04-01

    The side-chain torsion angles of isoleucines in X-ray protein structures are a function of resolution, secondary structure and refinement software. Detailing the standard torsion angles used in refinement software can improve protein structure refinement. A study of isoleucines in protein structures solved using X-ray crystallography revealed a series of systematic trends for the two side-chain torsion angles χ{sub 1} and χ{sub 2} dependent on the resolution, secondary structure and refinement software used. The average torsion angles for the nine rotamers were similar in high-resolution structures solved using either the REFMAC, CNS or PHENIX software. However, at low resolution these programs often refine towards somewhat different χ{sub 1} and χ{sub 2} values. Small systematic differences can be observed between refinement software that uses molecular dynamics-type energy terms (for example CNS) and software that does not use these terms (for example REFMAC). Detailing the standard torsion angles used in refinement software can improve the refinement of protein structures. The target values in the molecular dynamics-type energy functions can also be improved.

  9. Refined large N duality for knots

    DEFF Research Database (Denmark)

    Kameyama, Masaya; Nawata, Satoshi

    We formulate large N duality of U(N) refined Chern-Simons theory with a torus knot/link in S³. By studying refined BPS states in M-theory, we provide the explicit form of low-energy effective actions of Type IIA string theory with D4-branes on the Ω-background. This form enables us to relate...

  10. Anomalies in the refinement of isoleucine

    International Nuclear Information System (INIS)

    Berntsen, Karen R. M.; Vriend, Gert

    2014-01-01

    The side-chain torsion angles of isoleucines in X-ray protein structures are a function of resolution, secondary structure and refinement software. Detailing the standard torsion angles used in refinement software can improve protein structure refinement. A study of isoleucines in protein structures solved using X-ray crystallography revealed a series of systematic trends for the two side-chain torsion angles χ 1 and χ 2 dependent on the resolution, secondary structure and refinement software used. The average torsion angles for the nine rotamers were similar in high-resolution structures solved using either the REFMAC, CNS or PHENIX software. However, at low resolution these programs often refine towards somewhat different χ 1 and χ 2 values. Small systematic differences can be observed between refinement software that uses molecular dynamics-type energy terms (for example CNS) and software that does not use these terms (for example REFMAC). Detailing the standard torsion angles used in refinement software can improve the refinement of protein structures. The target values in the molecular dynamics-type energy functions can also be improved

  11. Grain refinement of zinc-aluminium alloys

    International Nuclear Information System (INIS)

    Zaid, A.I.O.

    2006-01-01

    It is now well-established that the structure of the zinc-aluminum die casting alloys can be modified by the binary Al-Ti or the ternary Al-Ti-B master alloys. in this paper, grain refinement of zinc-aluminum alloys by rare earth materials is reviewed and discussed. The importance of grain refining of these alloys and parameters affecting it are presented and discussed. These include parameters related to the Zn-Al alloys cast, parameters related to the grain refining elements or alloys and parameters related to the process. The effect of addition of other alloying elements e.g. Zr either alone or in the presence of the main grain refiners Ti or Ti + B on the grain refining efficiency is also reviewed and discussed. Furthermore, based on the grain refinement and the parameters affecting it, a criterion for selection of the optimum grain refiner is suggested. Finally, the recent research work on the effect of grain refiners on the mechanical behaviour, impact strength, wear resistance, and fatigue life of these alloys are presented and discussed. (author)

  12. Refined Phenotyping of Modic Changes

    Science.gov (United States)

    Määttä, Juhani H.; Karppinen, Jaro; Paananen, Markus; Bow, Cora; Luk, Keith D.K.; Cheung, Kenneth M.C.; Samartzis, Dino

    2016-01-01

    Abstract Low back pain (LBP) is the world's most disabling condition. Modic changes (MC) are vertebral bone marrow changes adjacent to the endplates as noted on magnetic resonance imaging. The associations of specific MC types and patterns with prolonged, severe LBP and disability remain speculative. This study assessed the relationship of prolonged, severe LBP and back-related disability, with the presence and morphology of lumbar MC in a large cross-sectional population-based study of Southern Chinese. We addressed the topographical and morphological dimensions of MC along with other magnetic resonance imaging phenotypes (eg, disc degeneration and displacement) on the basis of axial T1 and sagittal T2-weighted imaging of L1-S1. Prolonged severe LBP was defined as LBP lasting ≥30 days during the past year, and a visual analog scale severest pain intensity of at least 6/10. An Oswestry Disability Index score of 15% was regarded as significant disability. We also assessed subject demographics, occupation, and lifestyle factors. In total, 1142 subjects (63% females, mean age 53 years) were assessed. Of these, 282 (24.7%) had MC (7.1% type I, 17.6% type II). MC subjects were older (P = 0.003), had more frequent disc displacements (P disability. The strength of the associations increased with the number of MC. This large-scale study is the first to definitively note MC types and specific morphologies to be independently associated with prolonged severe LBP and back-related disability. This proposed refined MC phenotype may have direct implications in clinical decision-making as to the development and management of LBP. Understanding of these imaging biomarkers can lead to new preventative and personalized therapeutics related to LBP. PMID:27258491

  13. Refinement of boards' role required.

    Science.gov (United States)

    Umbdenstock, R J

    1987-01-01

    The governing board's role in health care is not changing, but new competitive forces necessitate a refinement of the board's approach to fulfilling its role. In a free-standing, community, not-for-profit hospital, the board functions as though it were the "owner." Although it does not truly own the facility in the legal sense, the board does have legal, fiduciary, and financial responsibilities conferred on it by the state. In a religious-sponsored facility, the board fulfills these same obligations on behalf of the sponsoring institute, subject to the institute's reserved powers. In multi-institutional systems, the hospital board's power and authority depend on the role granted it by the system. Boards in all types of facilities are currently faced with the following challenges: Fulfilling their basic responsibilities, such as legal requirements, financial duties, and obligations for the quality of care. Encouraging management and the board itself to "think strategically" in attacking new competitive market forces while protecting the organization's traditional mission and values. Assessing recommended strategies in light of consequences if constituencies think the organization is abandoning its commitments. Boards can take several steps to match their mode of operation with the challenges of the new environment. Boards must rededicate themselves to the hospital's mission. Trustees must expand their understanding of health care trends and issues and their effect on the organization. Boards must evaluate and help strengthen management's performance, rather than acting as a "watchdog" in an adversarial position. Boards must think strategically, rather than focusing solely on operational details. Boards must evaluate the methods they use for conducting business.

  14. North Dakota Refining Capacity Study

    Energy Technology Data Exchange (ETDEWEB)

    Dennis Hill; Kurt Swenson; Carl Tuura; Jim Simon; Robert Vermette; Gilberto Marcha; Steve Kelly; David Wells; Ed Palmer; Kuo Yu; Tram Nguyen; Juliam Migliavacca

    2011-01-05

    According to a 2008 report issued by the United States Geological Survey, North Dakota and Montana have an estimated 3.0 to 4.3 billion barrels of undiscovered, technically recoverable oil in an area known as the Bakken Formation. With the size and remoteness of the discovery, the question became 'can a business case be made for increasing refining capacity in North Dakota?' And, if so what is the impact to existing players in the region. To answer the question, a study committee comprised of leaders in the region's petroleum industry were brought together to define the scope of the study, hire a consulting firm and oversee the study. The study committee met frequently to provide input on the findings and modify the course of the study, as needed. The study concluded that the Petroleum Area Defense District II (PADD II) has an oversupply of gasoline. With that in mind, a niche market, naphtha, was identified. Naphtha is used as a diluent used for pipelining the bitumen (heavy crude) from Canada to crude markets. The study predicted there will continue to be an increase in the demand for naphtha through 2030. The study estimated the optimal configuration for the refinery at 34,000 barrels per day (BPD) producing 15,000 BPD of naphtha and a 52 percent refinery charge for jet and diesel yield. The financial modeling assumed the sponsor of a refinery would invest its own capital to pay for construction costs. With this assumption, the internal rate of return is 9.2 percent which is not sufficient to attract traditional investment given the risk factor of the project. With that in mind, those interested in pursuing this niche market will need to identify incentives to improve the rate of return.

  15. Uranium refining by solvent extraction

    International Nuclear Information System (INIS)

    Kraikaew, J.; Srinuttrakul, W.

    2014-01-01

    The solvent extraction process to produce higher purity uranium from yellowcake was studied in laboratory scale. Yellowcake, which the uranium purity is around 70% and the main impurity is thorium, was obtained from monazite processing pilot plant of Rare Earth Research and Development Center in Thailand. For uranium re-extraction process, the extractant chosen was Tributylphosphate (TBP) in kerosene. It was found that the optimum concentration of TBP was 10% in kerosene and the optimum nitric acid concentration in uranyl nitrate feed solution was 4 N. An increase in concentrations of uranium and thorium in feed solution resulted in a decrease in the distribution of both components in the extractant. However, the distribution of uranium into the extractant was found to be more than that of thorium. The equilibration study of the extraction system, UO_2(NO_3)/4N HNO_3 – 10%TBP/Kerosene, was also investigated. Two extraction stages were calculated graphically from 100,000 ppm uranium concentration in feed solution input with 90% extraction efficiency and the flow ratio of aqueous phase to organic phase was adjusted to 1.0. For thorium impurity scrubbing process, 10% TBP in kerosene was loaded with uranium and minor thorium from uranyl nitrate solution prepared from yellowcake and was scrubbed with different low concentration nitric acid. The results showed that at nitric acid normality was lower than 1 N, uranium distributed well to aqueous phase. As conclusion, optimum nitric acid concentration for scrubbing process should not less than 1 N and diluted nitric acid or de-ionized water should be applied to strip uranium from organic phase in the final refining process. (author)

  16. A new perspective on behavioral inconsistency and neural noise in aging: Compensatory speeding of neural communication

    Directory of Open Access Journals (Sweden)

    S. Lee Hong

    2012-09-01

    Full Text Available This paper seeks to present a new perspective on the aging brain. Here, we make connections between two key phenomena of brain aging: 1 increased neural noise or random background activity; and 2 slowing of brain activity. Our perspective proposes the possibility that the slowing of neural processing due to decreasing nerve conduction velocities leads to a compensatory speeding of neuron firing rates. These increased firing rates lead to a broader distribution of power in the frequency spectrum of neural oscillations, which we propose, can just as easily be interpreted as neural noise. Compensatory speeding of neural activity, as we present, is constrained by the: A availability of metabolic energy sources; and B competition for frequency bandwidth needed for neural communication. We propose that these constraints lead to the eventual inability to compensate for age-related declines in neural function that are manifested clinically as deficits in cognition, affect, and motor behavior.

  17. Stereotyped Synaptic Connectivity Is Restored during Circuit Repair in the Adult Mammalian Retina.

    Science.gov (United States)

    Beier, Corinne; Palanker, Daniel; Sher, Alexander

    2018-06-04

    Proper function of the central nervous system (CNS) depends on the specificity of synaptic connections between cells of various types. Cellular and molecular mechanisms responsible for the establishment and refinement of these connections during development are the subject of an active area of research [1-6]. However, it is unknown if the adult mammalian CNS can form new type-selective synapses following neural injury or disease. Here, we assess whether selective synaptic connections can be reestablished after circuit disruption in the adult mammalian retina. The stereotyped circuitry at the first synapse in the retina, as well as the relatively short distances new neurites must travel compared to other areas of the CNS, make the retina well suited to probing for synaptic specificity during circuit reassembly. Selective connections between short-wavelength sensitive cone photoreceptors (S-cones) and S-cone bipolar cells provides the foundation of the primordial blue-yellow vision, common to all mammals [7-18]. We take advantage of the ground squirrel retina, which has a one-to-one S-cone-to-S-cone-bipolar-cell connection, to test if this connectivity can be reestablished following local photoreceptor loss [8, 19]. We find that after in vivo selective photoreceptor ablation, deafferented S-cone bipolar cells expand their dendritic trees. The new dendrites randomly explore the proper synaptic layer, bypass medium-wavelength sensitive cone photoreceptors (M-cones), and selectively synapse with S-cones. However, non-connected dendrites are not pruned back to resemble unperturbed S-cone bipolar cells. We show, for the first time, that circuit repair in the adult mammalian retina can recreate stereotypic selective wiring. Copyright © 2018 Elsevier Ltd. All rights reserved.

  18. Studies on the phase diagram of boron employing a neural network potential

    Energy Technology Data Exchange (ETDEWEB)

    Morawietz, Tobias; Behler, Joerg [Lehrstuhl fuer Theoretische Chemie, Ruhr-Universitaet Bochum (Germany); Parrinello, Michele [Department of Chemistry and Applied Biosciences, ETH Zuerich (Switzerland)

    2009-07-01

    The crystalline phases of elemental boron have a structural complexity unique in the periodic table. The complex connection pattern of the icosahedral building blocks forms a formidable challenge for the construction of accurate but efficient potentials. We present a high-dimensional neural network potential for boron, which is based on first-principles calculations and can be systematically improved. The potential is several orders of magnitude faster to evaluate than the underlying density-functional theory calculations and allows to perform long molecular dynamics and metadynamics simulations of large system. By a stepwise refinement of the potential and an application of the potential in metadynamics simulations we show that starting from random atomic positions the structure of {alpha}-boron is predicted in agreement with experiment. Further, pressure-induced phase transitions of {alpha}-boron are discussed.

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

    Directory of Open Access Journals (Sweden)

    Rui Guo

    2018-03-01

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

  20. Establishing Connectivity

    DEFF Research Database (Denmark)

    Kjær, Poul F.

    Global law settings are characterised by a structural pre-eminence of connectivity norms, a type of norm which differs from coherency or possibility norms. The centrality of connectivity norms emerges from the function of global law, which is to increase the probability of transfers of condensed ...... and human rights can be understood as serving a constitutionalising function aimed at stabilising and facilitating connectivity. This allows for an understanding of colonialism and contemporary global governance as functional, but not as normative, equivalents.......Global law settings are characterised by a structural pre-eminence of connectivity norms, a type of norm which differs from coherency or possibility norms. The centrality of connectivity norms emerges from the function of global law, which is to increase the probability of transfers of condensed...... social components, such as economic capital and products, religious doctrines and scientific knowledge, from one legally structured context to another within world society. This was the case from colonialism and colonial law to contemporary global supply chains and human rights. Both colonial law...

  1. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Berezin, V; Bock, E; Poulsen, F M

    2000-01-01

    During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...... between two identical fragments of the NCAM. Also during the past year, a link between homophilic cell adhesion and several signal transduction pathways has been proposed, connecting the event of cell surface adhesion to cellular responses such as neurite outgrowth. Finally, the stimulation of neurite...

  2. Connected Traveler

    Energy Technology Data Exchange (ETDEWEB)

    2016-06-01

    The Connected Traveler framework seeks to boost the energy efficiency of personal travel and the overall transportation system by maximizing the accuracy of predicted traveler behavior in response to real-time feedback and incentives. It is anticipated that this approach will establish a feedback loop that 'learns' traveler preferences and customizes incentives to meet or exceed energy efficiency targets by empowering individual travelers with information needed to make energy-efficient choices and reducing the complexity required to validate transportation system energy savings. This handout provides an overview of NREL's Connected Traveler project, including graphics, milestones, and contact information.

  3. Optimizing refiner operation with statistical modelling

    Energy Technology Data Exchange (ETDEWEB)

    Broderick, G [Noranda Research Centre, Pointe Claire, PQ (Canada)

    1997-02-01

    The impact of refining conditions on the energy efficiency of the process and on the handsheet quality of a chemi-mechanical pulp was studied as part of a series of pilot scale refining trials. Statistical models of refiner performance were constructed from these results and non-linear optimization of process conditions were conducted. Optimization results indicated that increasing the ratio of specific energy applied in the first stage led to a reduction of some 15 per cent in the total energy requirement. The strategy can also be used to obtain significant increases in pulp quality for a given energy input. 20 refs., 6 tabs.

  4. Refinement Types for TypeScript

    OpenAIRE

    Vekris, Panagiotis; Cosman, Benjamin; Jhala, Ranjit

    2016-01-01

    We present Refined TypeScript (RSC), a lightweight refinement type system for TypeScript, that enables static verification of higher-order, imperative programs. We develop a formal core of RSC that delineates the interaction between refinement types and mutability. Next, we extend the core to account for the imperative and dynamic features of TypeScript. Finally, we evaluate RSC on a set of real world benchmarks, including parts of the Octane benchmarks, D3, Transducers, and the TypeScript co...

  5. Price implications for Russia's oil refining

    International Nuclear Information System (INIS)

    Khartukov, Eugene M.

    1998-01-01

    Over the past several years, Russia's oil industry has undergone its radical transformation from a wholly state-run and generously subsidized oil distribution system toward a substantially privatized, cash-strapped, and quasi-market ''petropreneurship''. This fully applies to the industry's downstream sector. Still unlike more dynamic E and C operations, the country's refining has turned out better fenced off competitive market forces and is less capable to respond to market imperatives. Consequently, jammed between depressed product prices and persistent feedstock costs, Russian refiners were badly hit by the world oil glut - which has made a radical modernization of the obsolete refining sector clearly a must. (author)

  6. Method of cleansing and refining of liquid hydrocarbons and derivatives of hydrocarbons

    Energy Technology Data Exchange (ETDEWEB)

    Hahn, C A; Nielsen, H

    1934-10-11

    A process is described for cleaning and refining liquid hydrocarbons and derivatives by utilization of acids, followed by washing partly with a basic solution, partly with clean water. The process is characterized by using, in connection with the acid solutions mentioned, a strong solution of a mixture of sulfuric acid and phosphoric acid.

  7. Making connections

    NARCIS (Netherlands)

    Marion Duimel

    2007-01-01

    Original title: Verbinding maken; senioren en internet. More and more older people are finding their way to the Internet. Many people aged over 50 who have only recently gone online say that a new world has opened up for them. By connecting to the Internet they have the feeling that they

  8. CMS Connect

    Science.gov (United States)

    Balcas, J.; Bockelman, B.; Gardner, R., Jr.; Hurtado Anampa, K.; Jayatilaka, B.; Aftab Khan, F.; Lannon, K.; Larson, K.; Letts, J.; Marra Da Silva, J.; Mascheroni, M.; Mason, D.; Perez-Calero Yzquierdo, A.; Tiradani, A.

    2017-10-01

    The CMS experiment collects and analyzes large amounts of data coming from high energy particle collisions produced by the Large Hadron Collider (LHC) at CERN. This involves a huge amount of real and simulated data processing that needs to be handled in batch-oriented platforms. The CMS Global Pool of computing resources provide +100K dedicated CPU cores and another 50K to 100K CPU cores from opportunistic resources for these kind of tasks and even though production and event processing analysis workflows are already managed by existing tools, there is still a lack of support to submit final stage condor-like analysis jobs familiar to Tier-3 or local Computing Facilities users into these distributed resources in an integrated (with other CMS services) and friendly way. CMS Connect is a set of computing tools and services designed to augment existing services in the CMS Physics community focusing on these kind of condor analysis jobs. It is based on the CI-Connect platform developed by the Open Science Grid and uses the CMS GlideInWMS infrastructure to transparently plug CMS global grid resources into a virtual pool accessed via a single submission machine. This paper describes the specific developments and deployment of CMS Connect beyond the CI-Connect platform in order to integrate the service with CMS specific needs, including specific Site submission, accounting of jobs and automated reporting to standard CMS monitoring resources in an effortless way to their users.

  9. Cognitive control network connectivity in adolescent women with and without a parental history of depression

    Directory of Open Access Journals (Sweden)

    Peter C. Clasen

    2014-01-01

    Conclusions: Depressed parents may transmit depression vulnerability to their adolescent daughters via alterations in functional connectivity within neural circuits that underlie cognitive control of emotional information.

  10. Refinement for Transition Systems with Responses

    Directory of Open Access Journals (Sweden)

    Marco Carbone

    2012-07-01

    Full Text Available Motivated by the response pattern for property specifications and applications within flexible workflow management systems, we report upon an initial study of modal and mixed transition systems in which the must transitions are interpreted as must eventually, and in which implementations can contain may behaviors that are resolved at run-time. We propose Transition Systems with Responses (TSRs as a suitable model for this study. We prove that TSRs correspond to a restricted class of mixed transition systems, which we refer to as the action-deterministic mixed transition systems. We show that TSRs allow for a natural definition of deadlocked and accepting states. We then transfer the standard definition of refinement for mixed transition systems to TSRs and prove that refinement does not preserve deadlock freedom. This leads to the proposal of safe refinements, which are those that preserve deadlock freedom. We exemplify the use of TSRs and (safe refinements on a small medication workflow.

  11. Taiwan: refined need for consuming population

    International Nuclear Information System (INIS)

    Hayes, David.

    1995-01-01

    A brief discussion is given of the oil and gas industry in Taiwan. Topics covered include the possibility of privatization, refineries and refining contracts overseas, plans for a new petrochemical complex and an offshore submarine transmission pipeline. (UK)

  12. 1991 worldwide refining and gas processing directory

    International Nuclear Information System (INIS)

    Anon.

    1990-01-01

    This book ia an authority for immediate information on the industry. You can use it to find new business, analyze market trends, and to stay in touch with existing contacts while making new ones. The possibilities for business applications are numerous. Arranged by country, all listings in the directory include address, phone, fax and telex numbers, a description of the company's activities, names of key personnel and their titles, corporate headquarters, branch offices and plant sites. This newly revised edition lists more than 2000 companies and nearly 3000 branch offices and plant locations. This east-to-use reference also includes several of the most vital and informative surveys of the industry, including the U.S. Refining Survey, the Worldwide Construction Survey in Refining, Sulfur, Gas Processing and Related Fuels, the Worldwide Refining and Gas Processing Survey, the Worldwide Catalyst Report, and the U.S. and Canadian Lube and Wax Capacities Report from the National Petroleum Refiner's Association

  13. Development of a Refined Staff Group Trainer

    National Research Council Canada - National Science Library

    Quensel, Susan

    1999-01-01

    .... As a follow-on effort to the previous SGT project, the goal was to refine a brigade-level staff training program to more effectively and efficiently coordinate the activities within and between the...

  14. Oil refining in South Asia and Australasia

    International Nuclear Information System (INIS)

    Yamaguchi, N.D.

    2000-01-01

    An overview of the oil markets of Southeast Asia and Australasia is presented focussing on oil refining. Key statistics of both areas are tabulated, and figures providing information on GDP/capita, crude production, comparison of demand barrels, and product demand are provided. Crude oil production and supply, oil product demand, and the refining industries are examined with details given of evolution of capacity and cracking to distillation ratios

  15. The present state of refining in France

    International Nuclear Information System (INIS)

    1996-01-01

    The european refining industry suffers from a production over-capacity and closures are inevitable; the situation is even worse in France due to the imbalance between gas oil and gasoline prices and the weak margin for distributors. The French refining industry is however an important and essential link for its strategic fuel and petroleum product supply, and represent 17000 jobs. Several measures are introduced by the French Industry department towards restructuring, capacity reduction and fuel price harmonization

  16. Design of Grain Refiners for Aluminium Alloys

    Science.gov (United States)

    Tronche, A.; Greer, A. L.

    The efficiency of a grain refiner can be quantified as the number of grains per nucleant particle in the solidified product. Even for effective refiners in aluminium, such as Al-5Ti-1B, it is known from experiments that efficiencies are very low, at best 10-3 to 102. It is of interest to explore the reasons for such low values, and to assess the prospects for increased efficiency though design of refiners. Recently it has been shown [1] that a simple recalescence-based model can make quantitative predictions of grain size as a function of refiner addition level, cooling rate and solute content. In the model, the initiation of grains is limited by the free growth from nucleant particles, the size distribution of which is very important. The present work uses this model as the basis for discussing the effect of particle size distribution on grain refiner performance. Larger particles (of TiB2 in the case of present interest) promote greater efficiency, as do narrower size distributions. It is shown that even if the size distribution could be exactly specified, compromises would have to be made to balance efficiency (defined as above) with other desirable characteristics of a refiner.

  17. Hopfield neural network in HEP track reconstruction

    International Nuclear Information System (INIS)

    Muresan, R.; Pentia, M.

    1997-01-01

    In experimental particle physics, pattern recognition problems, specifically for neural network methods, occur frequently in track finding or feature extraction. Track finding is a combinatorial optimization problem. Given a set of points in Euclidean space, one tries the reconstruction of particle trajectories, subject to smoothness constraints.The basic ingredients in a neural network are the N binary neurons and the synaptic strengths connecting them. In our case the neurons are the segments connecting all possible point pairs.The dynamics of the neural network is given by a local updating rule wich evaluates for each neuron the sign of the 'upstream activity'. An updating rule in the form of sigmoid function is given. The synaptic strengths are defined in terms of angle between the segments and the lengths of the segments implied in the track reconstruction. An algorithm based on Hopfield neural network has been developed and tested on the track coordinates measured by silicon microstrip tracking system

  18. 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.

  19. Gendered Connections

    DEFF Research Database (Denmark)

    Jensen, Steffen Bo

    2009-01-01

    This article explores the gendered nature of urban politics in Cape Town by focusing on a group of female, township politicians. Employing the Deleuzian concept of `wild connectivity', it argues that these politically entrepreneurial women were able to negotiate a highly volatile urban landscape...... by drawing on and operationalizing violent, male networks — from struggle activists' networks, to vigilante groups and gangs, to the police. The fact that they were women helped them to tap into and exploit these networks. At the same time, they were restricted by their sex, as their ability to navigate...... space also drew on quite traditional notions of female respectability. Furthermore, the article argues, the form of wild connectivity to an extent was a function of the political transition, which destabilized formal structures of gendered authority. It remains a question whether this form...

  20. Adaptive Graph Convolutional Neural Networks

    OpenAIRE

    Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou

    2018-01-01

    Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...

  1. Cosmic Connections

    CERN Document Server

    Ellis, Jonathan Richard

    2003-01-01

    A National Research Council study on connecting quarks with the cosmos has recently posed a number of the more important open questions at the interface between particle physics and cosmology. These questions include the nature of dark matter and dark energy, how the Universe began, modifications to gravity, the effects of neutrinos on the Universe, how cosmic accelerators work, and whether there are new states of matter at high density and pressure. These questions are discussed in the context of the talks presented at this Summer Institute.

  2. Chinese refining capacity for Canadian heavy oil

    International Nuclear Information System (INIS)

    Bruce, G.W.

    2006-01-01

    This paper discussed China's refining capacity in relation to exports of Canadian heavy oil. Demand for oil is increasing throughout the world, and China is expected to consume 25 per cent of the projected yearly oil supplies. Alberta currently has an estimated 174 billion barrels of recoverable bitumen, and produces 1.06 million barrels per day. Production is expected to increase to 4.5 million barrels per day by the year 2020. Currently bitumen blends are refined and diluted with naphtha and sweet synthetic crude oil. Bitumen is a challenging feedstock for refineries, and requires thermal production methods or gasification processes. Primary conversion into sour synthetic crude is typically followed by hydrocracking and further refining into finished petroleum products. There are currently 50 refineries in China with a 7.4 million barrel per day capacity. Coastal refineries using imported crude oil have a 4 million barrel per day capacity. New facilities are being constructed and existing plants are being upgraded in order to process heavier and more sour crude oils. However, current refining capabilities in Chinese refineries have a limited ability for resid conversion. It was concluded that while China has a refining infrastructure, only refineries on the coast will use oil sands-derived feedstocks. However, there are currently opportunities to design refineries to match future feedstocks. tabs., figs

  3. The evolution of oil refining in Europe

    Energy Technology Data Exchange (ETDEWEB)

    Reid, A. [CONCAWE, Brussels (Belgium)

    2013-04-01

    Back in 1963 when CONCAWE was founded, the world looked very different from what it is today, and so did the global and European refining industry. Oil product markets were expanding fast and new refineries were being built at a steady rate. The oil crisis of the 1970s brought an abrupt end to this, heralding a long era of consolidation and stepwise adaptation. At the same time the nature of the global oil business shifted from fully integrated companies producing, transporting and refining their own oil to a much more diversified situation where oil production ('upstream') and refining/distribution ('downstream') gradually became two essentially separate businesses. From being purely a 'cost centre' in an integrated chain, refining has become a separate activity in its own right, operating as a 'profit centre' between two global markets - crude oil and products - which, although not entirely independent, have their own dynamics and influences. In addition demand gradually shifted towards lighter products while the quality requirements on all products were considerably tightened. This article explores the new challenges that these changes have imposed on EU refiners, and describes CONCAWE's contributions to understanding their impact on refinery production and investments.

  4. Grain refinement mechanism in A3003 alloy

    International Nuclear Information System (INIS)

    Cho, Hoon; Shin, Je-Sik; Lee, Byoung-Soo; Jo, Hyung-Ho

    2009-01-01

    In the present study, in order to find out an grain refinement mechanism, 0.1wt.% Al-10wt.%Ti master alloy was added into A3003 alloy melt contained in graphite crucible and in alumina crucible, and then the melt holding time at 750 deg. C was systematically changed from 1 min up to 120 min. It is interesting to note that the grain refinement and fading phenomena remarkably depend on the crucible material. The fading effect in the specimens using alumina crucible can be explained as the result of TiAl 3 phase dissolution into molten aluminium matrix. In the specimens using graphite crucible, the grain refinement was occurred gradually with increasing holding time. It was suggest that the continuous grain refinement is due to transition of refinement mechanism from TiAl 3 phase to TiC phase. It can be mentioned that the TiC formed from titanium and carbon solute in the aluminium melt, which came from the Al-10Ti alloy and the graphite crucible.

  5. A Neural Basis for the Acquired Capability for Suicide

    Directory of Open Access Journals (Sweden)

    Gopikrishna Deshpande

    2016-08-01

    Full Text Available The high rate of fatal suicidal behavior in men is an urgent issue as highlighted in the public eye via news sources and media outlets. In this study, we have attempted to address this issue and understand the neural substrates underlying the gender differences in the rate of fatal suicidal behavior. The Interpersonal-Psychological Theory of Suicide (IPTS has proposed an explanation for the seemingly paradoxical relationship between gender and suicidal behavior, i.e. greater non-fatal suicide attempts by women but higher number of deaths by suicide in men. This theory states that possessing suicidal desire (due to conditions such as depression alone is not sufficient for a lethal suicide attempt. It is imperative for an individual to have acquired the capability for suicide (ACS along with suicidal desire in order to die by suicide. Therefore, higher levels of ACS in men may explain why men are more likely to die by suicide than women, despite being less likely to experience suicidal ideation or depression. In this study, we used activation likelihood estimation meta-analysis to investigate a potential ACS network that involves neural substrates underlying emotional stoicism, sensation seeking, pain tolerance, and fearlessness of death along with a potential depression network that involves neural substrates that underlie clinical depression. Brain regions commonly found in ACS and depression networks for males and females were further used as seeds to obtain regions functionally and structurally connected to them. We found that the male-specific networks were more widespread and diverse than the female-specific ones. Also, while the former involved motor regions such as the premotor cortex and cerebellum, the latter was dominated by limbic regions. This may support the fact that suicidal desire generally leads to fatal/decisive action in males while in females, it manifests as depression, ideation and generally non-fatal actions. The proposed

  6. India's refining prospects linked to economic growth

    International Nuclear Information System (INIS)

    Lewis, E.

    1996-01-01

    International investors assess refining ventures in India the same way they do comparable projects elsewhere in the world: according to their expectations about investment returns. By that standard, India's appeal is mixed, although its need for some measure of additional refining capacity seems certain. The success of future refinery investments will depend heavily on the government's commitment to policies allowing the economy to grow faster than the population. Unless accompanied by economic growth, expected increases in the population will not automatically raise demand for petroleum products. Decisions about investments in India's refining sector, therefore, must carefully weigh market fundamentals, the business environment, and likely investment performance. This paper reviews the market for the various products and predicts new economic trends

  7. Refining processes of selected copper alloys

    Directory of Open Access Journals (Sweden)

    S. Rzadkosz

    2009-04-01

    Full Text Available The analysis of the refining effectiveness of the liquid copper and selected copper alloys by various micro additions and special refiningsubstances – was performed. Examinations of an influence of purifying, modifying and deoxidation operations performed in a metal bath on the properties of certain selected alloys based on copper matrix - were made. Refining substances, protecting-purifying slag, deoxidation and modifying substances containing micro additions of such elements as: zirconium, boron, phosphor, sodium, lithium, or their compounds introduced in order to change micro structures and properties of alloys, were applied in examinations. A special attention was directed to macro and micro structures of alloys, their tensile and elongation strength and hot-cracks sensitivity. Refining effects were estimated by comparing the effectiveness of micro structure changes with property changes of copper and its selected alloys from the group of tin bronzes.

  8. Refining intra-protein contact prediction by graph analysis

    Directory of Open Access Journals (Sweden)

    Eyal Eran

    2007-05-01

    Full Text Available Abstract Background Accurate prediction of intra-protein residue contacts from sequence information will allow the prediction of protein structures. Basic predictions of such specific contacts can be further refined by jointly analyzing predicted contacts, and by adding information on the relative positions of contacts in the protein primary sequence. Results We introduce a method for graph analysis refinement of intra-protein contacts, termed GARP. Our previously presented intra-contact prediction method by means of pair-to-pair substitution matrix (P2PConPred was used to test the GARP method. In our approach, the top contact predictions obtained by a basic prediction method were used as edges to create a weighted graph. The edges were scored by a mutual clustering coefficient that identifies highly connected graph regions, and by the density of edges between the sequence regions of the edge nodes. A test set of 57 proteins with known structures was used to determine contacts. GARP improves the accuracy of the P2PConPred basic prediction method in whole proteins from 12% to 18%. Conclusion Using a simple approach we increased the contact prediction accuracy of a basic method by 1.5 times. Our graph approach is simple to implement, can be used with various basic prediction methods, and can provide input for further downstream analyses.

  9. Low-resolution refinement tools in REFMAC5

    International Nuclear Information System (INIS)

    Nicholls, Robert A.; Long, Fei; Murshudov, Garib N.

    2012-01-01

    Low-resolution refinement tools implemented in REFMAC5 are described, including the use of external structural restraints, helical restraints and regularized anisotropic map sharpening. Two aspects of low-resolution macromolecular crystal structure analysis are considered: (i) the use of reference structures and structural units for provision of structural prior information and (ii) map sharpening in the presence of noise and the effects of Fourier series termination. The generation of interatomic distance restraints by ProSMART and their subsequent application in REFMAC5 is described. It is shown that the use of such external structural information can enhance the reliability of derived atomic models and stabilize refinement. The problem of map sharpening is considered as an inverse deblurring problem and is solved using Tikhonov regularizers. It is demonstrated that this type of map sharpening can automatically produce a map with more structural features whilst maintaining connectivity. Tests show that both of these directions are promising, although more work needs to be performed in order to further exploit structural information and to address the problem of reliable electron-density calculation

  10. Zone refining high-purity germanium

    International Nuclear Information System (INIS)

    Hubbard, G.S.; Haller, E.E.; Hansen, W.L.

    1977-10-01

    The effects of various parameters on germanium purification by zone refining have been examined. These parameters include the germanium container and container coatings, ambient gas and other operating conditions. Four methods of refining are presented which reproducibly yield 3.5 kg germanium ingots from which high purity (vertical barN/sub A/ - N/sub D/vertical bar less than or equal to2 x 10 10 cm -3 ) single crystals can be grown. A qualitative model involving binary and ternary complexes of Si, O, B, and Al is shown to account for the behavior of impurities at these low concentrations

  11. Adaptive mesh refinement for storm surge

    KAUST Repository

    Mandli, Kyle T.; Dawson, Clint N.

    2014-01-01

    An approach to utilizing adaptive mesh refinement algorithms for storm surge modeling is proposed. Currently numerical models exist that can resolve the details of coastal regions but are often too costly to be run in an ensemble forecasting framework without significant computing resources. The application of adaptive mesh refinement algorithms substantially lowers the computational cost of a storm surge model run while retaining much of the desired coastal resolution. The approach presented is implemented in the GeoClaw framework and compared to ADCIRC for Hurricane Ike along with observed tide gauge data and the computational cost of each model run. © 2014 Elsevier Ltd.

  12. Refining - Panorama 2008; Raffinage - Panorama 2008

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    2008-07-01

    Investment rallied in 2007, and many distillation and conversion projects likely to reach the industrial stage were announced. With economic growth sustained in 2006 and still pronounced in 2007, oil demand remained strong - especially in emerging countries - and refining margins stayed high. Despite these favorable business conditions, tensions persisted in the refining sector, which has fallen far behind in terms of investing in refinery capacity. It will take renewed efforts over a long period to catch up. Looking at recent events that have affected the economy in many countries (e.g. the sub-prime crisis), prudence remains advisable.

  13. India beckons participants in burgeoning refining sector

    International Nuclear Information System (INIS)

    Anon.

    1992-01-01

    This paper reports that India has opened its refining sector to full private investment for the first time in more than 2 decades. The government again gave a green light to construction of three 120,000 b/d grassroots refineries in East, West, and Central India. The projects had won various governmental approvals in the past few years, but never moved off high center for a variety of economic and regulatory reasons. The difference this time is that the government is offering interests in the projects to private foreign and domestic investors. It's part of India's push to boost overall refining capacity by more than 80% this century

  14. Adaptive mesh refinement for storm surge

    KAUST Repository

    Mandli, Kyle T.

    2014-03-01

    An approach to utilizing adaptive mesh refinement algorithms for storm surge modeling is proposed. Currently numerical models exist that can resolve the details of coastal regions but are often too costly to be run in an ensemble forecasting framework without significant computing resources. The application of adaptive mesh refinement algorithms substantially lowers the computational cost of a storm surge model run while retaining much of the desired coastal resolution. The approach presented is implemented in the GeoClaw framework and compared to ADCIRC for Hurricane Ike along with observed tide gauge data and the computational cost of each model run. © 2014 Elsevier Ltd.

  15. Contextual Distance Refining for Image Retrieval

    KAUST Repository

    Islam, Almasri

    2014-01-01

    Recently, a number of methods have been proposed to improve image retrieval accuracy by capturing context information. These methods try to compensate for the fact that a visually less similar image might be more relevant because it depicts the same object. We propose a new quick method for refining any pairwise distance metric, it works by iteratively discovering the object in the image from the most similar images, and then refine the distance metric accordingly. Test show that our technique improves over the state of art in terms of accuracy over the MPEG7 dataset.

  16. Russian refining - an industry in transition

    Energy Technology Data Exchange (ETDEWEB)

    Barrett, E [CentreInvest, Moscow (Russian Federation)

    1999-02-01

    In the old Soviet Union (now called the CIS), the refining industry is undergoing much modernisation, although the process is far from complete. Eventually, the CIS is expected to have a market-responsive competitive refining business. The expected transformation is discussed according to a five-stage plan. The stages are (i) the change from horizontally integrated entity to vertically integrated global concerns, (ii) the change from over-manned dinosaurs to modern efficient businesses, (iii) the move towards smaller, more advanced market-orientated processes, (iv) improving the transport and storage infrastructures and (v) improving accountability and profitability. The predictions for 2005 onwards are for sustained profitability. (UK)

  17. Contextual Distance Refining for Image Retrieval

    KAUST Repository

    Islam, Almasri

    2014-09-16

    Recently, a number of methods have been proposed to improve image retrieval accuracy by capturing context information. These methods try to compensate for the fact that a visually less similar image might be more relevant because it depicts the same object. We propose a new quick method for refining any pairwise distance metric, it works by iteratively discovering the object in the image from the most similar images, and then refine the distance metric accordingly. Test show that our technique improves over the state of art in terms of accuracy over the MPEG7 dataset.

  18. Classification of behavior using unsupervised temporal neural networks

    International Nuclear Information System (INIS)

    Adair, K.L.

    1998-03-01

    Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem

  19. Places Connected:

    DEFF Research Database (Denmark)

    Hansen, Annette Skovsted

    This paper argues that development assistance contributed to the globalization of the 20th century by financing truly global networks of people. By focusing on the networks financed by development assistance bound by the national histories of Denmark and Japan, I illustrate how the people who...... experiences of place, however, when it is often the same people who experience many different places? Along with many other so-called donors in the 1950s, Denmark and Japan chose to invest in the education of own and other nationals involved in development and thereby financed personal connections between...... individuals throughout the world. Development assistance , where there are two or three links only between a Bangladeshi farmer, a street child in Sao Paolo and the President of the United States, the Queen of Denmark, or a suburban house wife in Japan, who has never left the Osaka area, but mothered a United...

  20. Differential regulation of polarized synaptic vesicle trafficking and synapse stability in neural circuit rewiring in Caenorhabditis elegans.

    Directory of Open Access Journals (Sweden)

    Naina Kurup

    2017-06-01

    Full Text Available Neural circuits are dynamic, with activity-dependent changes in synapse density and connectivity peaking during different phases of animal development. In C. elegans, young larvae form mature motor circuits through a dramatic switch in GABAergic neuron connectivity, by concomitant elimination of existing synapses and formation of new synapses that are maintained throughout adulthood. We have previously shown that an increase in microtubule dynamics during motor circuit rewiring facilitates new synapse formation. Here, we further investigate cellular control of circuit rewiring through the analysis of mutants obtained in a forward genetic screen. Using live imaging, we characterize novel mutations that alter cargo binding in the dynein motor complex and enhance anterograde synaptic vesicle movement during remodeling, providing in vivo evidence for the tug-of-war between kinesin and dynein in fast axonal transport. We also find that a casein kinase homolog, TTBK-3, inhibits stabilization of nascent synapses in their new locations, a previously unexplored facet of structural plasticity of synapses. Our study delineates temporally distinct signaling pathways that are required for effective neural circuit refinement.

  1. Pilot scale refinning of crude soybean oil | Mensah | Journal of ...

    African Journals Online (AJOL)

    Pilot scale refinning of crude soybean oil. ... Abstract. A laboratory process for refining soybean has been scaled up to a 145 tonne per annum pilot plant to refine crude soybean oil. ... The quality of the refined oil was found to be within national and codex standard specifications for edible oil from vegetable sources.

  2. Self-organized critical neural networks

    International Nuclear Information System (INIS)

    Bornholdt, Stefan; Roehl, Torsten

    2003-01-01

    A mechanism for self-organization of the degree of connectivity in model neural networks is studied. Network connectivity is regulated locally on the basis of an order parameter of the global dynamics, which is estimated from an observable at the single synapse level. This principle is studied in a two-dimensional neural network with randomly wired asymmetric weights. In this class of networks, network connectivity is closely related to a phase transition between ordered and disordered dynamics. A slow topology change is imposed on the network through a local rewiring rule motivated by activity-dependent synaptic development: Neighbor neurons whose activity is correlated, on average develop a new connection while uncorrelated neighbors tend to disconnect. As a result, robust self-organization of the network towards the order disorder transition occurs. Convergence is independent of initial conditions, robust against thermal noise, and does not require fine tuning of parameters

  3. Cortical connective field estimates from resting state fMRI activity

    NARCIS (Netherlands)

    Gravel, Nicolas; Harvey, Ben; Nordhjem, Barbara; Haak, Koen V.; Dumoulin, Serge O.; Renken, Remco; Curcic-Blake, Branisalava; Cornelissen, Frans W.

    2014-01-01

    One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective

  4. Refining the Eye: Dermatology and Visual Literacy

    Science.gov (United States)

    Zimmermann, Corinne; Huang, Jennifer T.; Buzney, Elizabeth A.

    2016-01-01

    In 2014 the Museum of Fine Arts Boston and Harvard Medical School began a partnership focused on building visual literacy skills for dermatology residents in the Harvard Combined Dermatology Residency Program. "Refining the Eye: Art and Dermatology", a four session workshop, took place in the museum's galleries and utilized the Visual…

  5. Structural refinement and coarsening in deformed metals

    DEFF Research Database (Denmark)

    Hansen, N.; Huang, X.; Xing, Q.

    2005-01-01

    The microstructural refinement by plastic deformation is analysed in terms of key parameters, the spacing between and the misorientation angle across the boundaries subdividing the structure. Coarsening of such structures by annealing is also characterised. For both deformed and annealed structur...

  6. Structure refinement of polycrystalline orthorhombic yttrium ...

    Indian Academy of Sciences (India)

    The perovskite ceramic phases with composition Ca1−YTiO3+ (where = 0.1, 0.2 and 0.3; hereafter CYT-10, CYT-20 and CYT-30) have been synthesized by solid state reaction at 1050°C. The structure refinement using general structure analysis system (GSAS) software converges to satisfactory profile indicators ...

  7. Oil refining expansion criteria for Brazil

    International Nuclear Information System (INIS)

    Tavares, M.E.E.; Szklo, A.S.; Machado, G.V.; Schaeffer, R.; Mariano, J.B.; Sala, J.F.

    2006-01-01

    This paper assesses different strategies for the expansion of Brazil's oil refining segment, using criteria that range from energy security (reducing imports and vulnerability for key products) through to maximizing the profitability of this sector (boosting the output of higher value oil products) and adding value to Brazil's oil production (reducing exports of heavy acid oil). The development prospects are analyzed for conventional fuel production technology routes, sketching out three possible refining schemes for Brazilian oil and a GTL plant for producing gasoil from natural gas. Market scenario simulations indicate that investments will be required in Brazil's oil refining segment over and above those allocated to planned modifications in its current facilities, reducing the nation's vulnerability in terms of gasoil and petrochemical naphtha imports. Although not economically attractive, oil refining is a key activity that is crucial to oil company strategies. The decision to invest in this segment depends on local infrastructure conditions, environmental constraints and fuel specifications, in addition to oil company strategies, steady growth in demand and the definition of a government policy that eases institutional risks. (author)

  8. Oil refining expansion criteria for Brazil

    International Nuclear Information System (INIS)

    Tavares, Marina Elisabete Espinho; Szklo, Alexandre Salem; Machado, Giovani Vitoria; Schaeffer, Roberto; Mariano, Jacqueline Barboza; Sala, Janaina Francisco

    2006-01-01

    This paper assesses different strategies for the expansion of Brazil's oil refining segment, using criteria that range from energy security (reducing imports and vulnerability for key products) through to maximizing the profitability of this sector (boosting the output of higher value oil products) and adding value to Brazil's oil production (reducing exports of heavy acid oil). The development prospects are analyzed for conventional fuel production technology routes, sketching out three possible refining schemes for Brazilian oil and a GTL plant for producing gasoil from natural gas. Market scenario simulations indicate that investments will be required in Brazil's oil refining segment over and above those allocated to planned modifications in its current facilities, reducing the nation's vulnerability in terms of gasoil and petrochemical naphtha imports. Although not economically attractive, oil refining is a key activity that is crucial to oil company strategies. The decision to invest in this segment depends on local infrastructure conditions, environmental constraints and fuel specifications, in addition to oil company strategies, steady growth in demand and the definition of a government policy that eases institutional risks

  9. On Syntactic and Semantic Action Refinement

    NARCIS (Netherlands)

    Goltz, Ursula; Gorrieri, Roberto; Rensink, Arend

    1992-01-01

    The semantic definition of action refinement on labelled event structures is compared with the notion of syntactic substitution,which can be used as another notion of action refiment in a process algebraic setting. This is done by studying a process algebra equipped with the ACP sequential

  10. Uranium refining process using ion exchange membrane

    International Nuclear Information System (INIS)

    Yamaguchi, Akira

    1977-01-01

    As for the method of refining uranium ore being carried out in Europe and America at present, uranium ore is roughly refined at the mine sites to yellow cake, then this is transported to refineries and refined by dry method. This method has the following faults, namely the number of processes is large, it requires expensive corrosion-resistant materials because of high temperature treatment, and the impurities in uranium tend to increase. On the other hand, in case of EXCER method, treatment is carried out at low temperature, and high purity uranium can be obtained, but the efficiency of electrolytic reduction process is extremely low, and economically infeasible. In the wet refining method called PNC process, uranium tetrafluoride is produced from uranium ore without making yellow cake, therefore the process is rationalized largely, and highly economical. The electrolytic reduction process in this method was developed by Asahi Chemical Industry Co., Ltd. by constructing the pilot plant in Ningyotoge Mine. The ion exchange membrane, the electrodes, and the problems concerning the process and the engineering for commercial plants were investigated. The electrolytic reduction process, the pilot plant, the development of the elements of electrolytic cells, the establishment of analytical process, the measurement of the electrolytic characteristics, the demonstration operation, and the life time of the electrolytic diaphragm are reported. (Kako, I.)

  11. Anomalies in the refinement of isoleucine

    NARCIS (Netherlands)

    Berntsen, K.R.M.; Vriend, G.

    2014-01-01

    A study of isoleucines in protein structures solved using X-ray crystallography revealed a series of systematic trends for the two side-chain torsion angles chi1 and chi2 dependent on the resolution, secondary structure and refinement software used. The average torsion angles for the nine rotamers

  12. Refining crude oils and gasolines, etc

    Energy Technology Data Exchange (ETDEWEB)

    1931-11-23

    A process of refining crude oils and gasolines distilled from shale and the like is described, consisting of submitting them to a prewash with soda, an oxidation preferably with hypochlorite solution, a hydrogenation with nascent hydrogen, and finally rectification and neutralization.

  13. Refinement from a control problem to program

    DEFF Research Database (Denmark)

    Schenke, Michael; Ravn, Anders P.

    1996-01-01

    The distinguishing feature of the presented refinement approach is that it links formalisms from a top level requirements notation down to programs together in a mathematically coherent development trajectory. The approach uses Duration Calculus, a real-time interval logic, to specifyrequirements...

  14. Robust Refinement as Implemented in TOPAS

    Energy Technology Data Exchange (ETDEWEB)

    Stone, K.; Stephens, P

    2010-01-01

    A robust refinement procedure is implemented in the program TOPAS through an iterative reweighting of the data. Examples are given of the procedure as applied to fitting partially overlapped peaks by full and partial models and also of the structures of ibuprofen and acetaminophen in the presence of unmodeled impurity contributions

  15. Refinement of the concept of uncertainty.

    Science.gov (United States)

    Penrod, J

    2001-04-01

    To analyse the conceptual maturity of uncertainty; to develop an expanded theoretical definition of uncertainty; to advance the concept using methods of concept refinement; and to analyse congruency with the conceptualization of uncertainty presented in the theory of hope, enduring, and suffering. Uncertainty is of concern in nursing as people experience complex life events surrounding health. In an earlier nursing study that linked the concepts of hope, enduring, and suffering into a single theoretical scheme, a state best described as 'uncertainty' arose. This study was undertaken to explore how this conceptualization fit with the scientific literature on uncertainty and to refine the concept. Initially, a concept analysis using advanced methods described by Morse, Hupcey, Mitcham and colleagues was completed. The concept was determined to be partially mature. A theoretical definition was derived and techniques of concept refinement using the literature as data were applied. The refined concept was found to be congruent with the concept of uncertainty that had emerged in the model of hope, enduring and suffering. Further investigation is needed to explore the extent of probabilistic reasoning and the effects of confidence and control on feelings of uncertainty and certainty.

  16. Quantum Geometry of Refined Topological Strings

    NARCIS (Netherlands)

    Aganagic, M.; Cheng, M.C.N.; Dijkgraaf, R.; Kreft, D.; Vafa, C.

    2012-01-01

    We consider branes in refined topological strings. We argue that their wavefunctions satisfy a Schrödinger equation depending on multiple times and prove this in the case where the topological string has a dual matrix model description. Furthermore, in the limit where one of the equivariant

  17. Red refinements of simplices into congruent subsimplices

    Czech Academy of Sciences Publication Activity Database

    Korotov, S.; Křížek, Michal

    2014-01-01

    Roč. 67, č. 12 (2014), s. 2199-2204 ISSN 0898-1221 R&D Projects: GA ČR GA14-02067S Institutional support: RVO:67985840 Keywords : sommerville tetrahedron * red refinement * higher-dimensional simplex Subject RIV: BA - General Mathematics Impact factor: 1.697, year: 2014 http://www.sciencedirect.com/science/article/pii/S0898122114000662

  18. Challenges and choices : a U.S. refiner's perspective on Western Canadian crude

    International Nuclear Information System (INIS)

    Cook, C.

    2004-01-01

    This presentation included a map depicting refineries, pipelines, terminals, coastal terminals and inland terminals in the eastern United States. Major oil trade movements between the United States, Canada, Mexico, South America, Central America, Europe, Middle East, Africa, and Asia-Pacific were also illustrated. A graph depicting Western Canada's long range crude supply forecast shows a decrease in conventional heavy oil supply but an increase in supply of diluent bitumen blend and synthetic bitumen blends. The presentation focused on this shift in refining and how product quality is viewed differently by producers and refiners in terms of gravity, sulfur content, boiling range distribution, distillate cetane, total acid number (TAN), asphalt properties, nitrogen and particulates. Refiners are looking for affordability, quality, consistency, yields and unit balance. Canadian marketing connections to PADD 1, 2, 4 and 5 were outlined. It was noted that more than 45 refineries in over 20 regions in the United States use Western Canadian crude oil. 22 figs

  19. Model for neural signaling leap statistics

    International Nuclear Information System (INIS)

    Chevrollier, Martine; Oria, Marcos

    2011-01-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5 0 C, awaken regime) and Levy statistics (T = 35.5 0 C, sleeping period), characterized by rare events of long range connections.

  20. Model for neural signaling leap statistics

    Science.gov (United States)

    Chevrollier, Martine; Oriá, Marcos

    2011-03-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T = 37.5°C, awaken regime) and Lévy statistics (T = 35.5°C, sleeping period), characterized by rare events of long range connections.

  1. Model for neural signaling leap statistics

    Energy Technology Data Exchange (ETDEWEB)

    Chevrollier, Martine; Oria, Marcos, E-mail: oria@otica.ufpb.br [Laboratorio de Fisica Atomica e Lasers Departamento de Fisica, Universidade Federal da ParaIba Caixa Postal 5086 58051-900 Joao Pessoa, Paraiba (Brazil)

    2011-03-01

    We present a simple model for neural signaling leaps in the brain considering only the thermodynamic (Nernst) potential in neuron cells and brain temperature. We numerically simulated connections between arbitrarily localized neurons and analyzed the frequency distribution of the distances reached. We observed qualitative change between Normal statistics (with T 37.5{sup 0}C, awaken regime) and Levy statistics (T = 35.5{sup 0}C, sleeping period), characterized by rare events of long range connections.

  2. The relationship between viscosity and refinement efficiency of pure aluminum by Al-Ti-B refiner

    Energy Technology Data Exchange (ETDEWEB)

    Yu Lina [Key Laboratory of Liquid Structure and Heredity of Materials, Ministry of Education, Shandong University, 73 Jingshi Road, Jinan 250061 (China); Liu Xiangfa [Key Laboratory of Liquid Structure and Heredity of Materials, Ministry of Education, Shandong University, 73 Jingshi Road, Jinan 250061 (China)]. E-mail: xfliu@sdu.edu.cn

    2006-11-30

    The relationship between viscosity and refinement efficiency of pure aluminum with the addition of Al-Ti-B master alloy was studied in this paper. The experimental results show that when the grain size of solidified sample is finer the viscosity of the melt is higher after the addition of different Al-Ti-B master alloys. This indicates that viscosity can be used to approximately estimate the refinement efficiency of Al-Ti-B refiners in production to a certain extent. The main reason was also discussed in this paper by using transmission electron microscopy (TEM) analysis and differential scanning calorimetry (DSC) experiment.

  3. Maturation of Cerebellar Purkinje Cell Population Activity during Postnatal Refinement of Climbing Fiber Network

    Directory of Open Access Journals (Sweden)

    Jean-Marc Good

    2017-11-01

    Full Text Available Neural circuits undergo massive refinements during postnatal development. In the developing cerebellum, the climbing fiber (CF to Purkinje cell (PC network is drastically reshaped by eliminating early-formed redundant CF to PC synapses. To investigate the impact of CF network refinement on PC population activity during postnatal development, we monitored spontaneous CF responses in neighboring PCs and the activity of populations of nearby CF terminals using in vivo two-photon calcium imaging. Population activity is highly synchronized in newborn mice, and the degree of synchrony gradually declines during the first postnatal week in PCs and, to a lesser extent, in CF terminals. Knockout mice lacking P/Q-type voltage-gated calcium channel or glutamate receptor δ2, in which CF network refinement is severely impaired, exhibit an abnormally high level of synchrony in PC population activity. These results suggest that CF network refinement is a structural basis for developmental desynchronization and maturation of PC population activity.

  4. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  5. Preparation of Al-Ti-B grain refiner by SHS technology

    International Nuclear Information System (INIS)

    Nikitin, V.I.; Wanqi, J.I.E.; Kandalova, E.G.; Makarenko, A.G.; Yong, L.

    2000-01-01

    Since the discovery of the grain refinement effect of aluminum by titanium, especially with the existence of B or C in 1950, grain refiners are widely accepted in industry for microstructure control of aluminum alloys. Research on this topic is to obtain the highest grain refinement efficiency with the lowest possible addition of master alloy. It is widely accepted that the morphology and size of TiAl 3 particles, which are known as heterogeneous nucleation centers, are important factors deterring the grain refinement efficiency. Fine TiAl 3 particles are favorable. The grain refinement process shows a heredity phenomenon, which means that structural information from initial materials transfers through a melt to the final product. It is important to find the connection between microstructural parameters of the master alloy and the final product. To improve the quality of Al-Ti-B master alloys for the use as a grain refiner, a new method based on SHS (self-propagating high-temperature synthesis) technology has been developed in Samara State Technical University to produce the master alloys. SHS, as a new method for preparation of materials, was first utilized by Merzhanov in 1967. This method uses the energy from highly exothermic reactions to sustain the chemical reaction in a combustion wave. The advantages of SHS include simplicity, low energy requirement, and higher product purity. Because SHS reactions can take place between elemental reactants, it is easy to control product composition. The purposes of this investigation were to fabricate an SHS Al-5%Ti-1%B master alloy, to analyze its structure and to test its grain refining performance

  6. Local Dynamics in Trained Recurrent Neural Networks.

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-23

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  7. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  8. Hardware Acceleration of Adaptive Neural Algorithms.

    Energy Technology Data Exchange (ETDEWEB)

    James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

  9. Local Dynamics in Trained Recurrent Neural Networks

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-01

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  10. Automata Learning through Counterexample Guided Abstraction Refinement

    DEFF Research Database (Denmark)

    Aarts, Fides; Heidarian, Faranak; Kuppens, Harco

    2012-01-01

    to a small set of abstract events that can be handled by automata learning tools. In this article, we show how such abstractions can be constructed fully automatically for a restricted class of extended finite state machines in which one can test for equality of data parameters, but no operations on data...... are allowed. Our approach uses counterexample-guided abstraction refinement: whenever the current abstraction is too coarse and induces nondeterministic behavior, the abstraction is refined automatically. Using Tomte, a prototype tool implementing our algorithm, we have succeeded to learn – fully......Abstraction is the key when learning behavioral models of realistic systems. Hence, in most practical applications where automata learning is used to construct models of software components, researchers manually define abstractions which, depending on the history, map a large set of concrete events...

  11. Multivariate refined composite multiscale entropy analysis

    International Nuclear Information System (INIS)

    Humeau-Heurtier, Anne

    2016-01-01

    Multiscale entropy (MSE) has become a prevailing method to quantify signals complexity. MSE relies on sample entropy. However, MSE may yield imprecise complexity estimation at large scales, because sample entropy does not give precise estimation of entropy when short signals are processed. A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. Nevertheless, RCMSE is for univariate signals only. The simultaneous analysis of multi-channel (multivariate) data often over-performs studies based on univariate signals. We therefore introduce an extension of RCMSE to multivariate data. Applications of multivariate RCMSE to simulated processes reveal its better performances over the standard multivariate MSE. - Highlights: • Multiscale entropy quantifies data complexity but may be inaccurate at large scale. • A refined composite multiscale entropy (RCMSE) has therefore recently been proposed. • Nevertheless, RCMSE is adapted to univariate time series only. • We herein introduce an extension of RCMSE to multivariate data. • It shows better performances than the standard multivariate multiscale entropy.

  12. Refined reservoir description to maximize oil recovery

    International Nuclear Information System (INIS)

    Flewitt, W.E.

    1975-01-01

    To assure maximized oil recovery from older pools, reservoir description has been advanced by fully integrating original open-hole logs and the recently introduced interpretive techniques made available through cased-hole wireline saturation logs. A refined reservoir description utilizing normalized original wireline porosity logs has been completed in the Judy Creek Beaverhill Lake ''A'' Pool, a reefal carbonate pool with current potential productivity of 100,000 BOPD and 188 active wells. Continuous porosity was documented within a reef rim and cap while discontinuous porous lenses characterized an interior lagoon. With the use of pulsed neutron logs and production data a separate water front and pressure response was recognized within discrete environmental units. The refined reservoir description aided in reservoir simulation model studies and quantifying pool performance. A pattern water flood has now replaced the original peripheral bottom water drive to maximize oil recovery

  13. Cleaning Data with OpenRefine

    Directory of Open Access Journals (Sweden)

    Seth van Hooland

    2013-08-01

    Full Text Available Duplicate records, empty values and inconsistent formats are phenomena we should be prepared to deal with when using historical data sets. This lesson will teach you how to discover inconsistencies in data contained within a spreadsheet or a database. As we increasingly share, aggregate and reuse data on the web, historians will need to respond to data quality issues which inevitably pop up. Using a program called OpenRefine, you will be able to easily identify systematic errors such as blank cells, duplicates, spelling inconsistencies, etc. OpenRefine not only allows you to quickly diagnose the accuracy of your data, but also to act upon certain errors in an automated manner.

  14. Refined 3d-3d correspondence

    Energy Technology Data Exchange (ETDEWEB)

    Alday, Luis F.; Genolini, Pietro Benetti; Bullimore, Mathew; Loon, Mark van [Mathematical Institute, University of Oxford, Andrew Wiles Building,Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG (United Kingdom)

    2017-04-28

    We explore aspects of the correspondence between Seifert 3-manifolds and 3d N=2 supersymmetric theories with a distinguished abelian flavour symmetry. We give a prescription for computing the squashed three-sphere partition functions of such 3d N=2 theories constructed from boundary conditions and interfaces in a 4d N=2{sup ∗} theory, mirroring the construction of Seifert manifold invariants via Dehn surgery. This is extended to include links in the Seifert manifold by the insertion of supersymmetric Wilson-’t Hooft loops in the 4d N=2{sup ∗} theory. In the presence of a mass parameter for the distinguished flavour symmetry, we recover aspects of refined Chern-Simons theory with complex gauge group, and in particular construct an analytic continuation of the S-matrix of refined Chern-Simons theory.

  15. Government will shape China's refining boom

    International Nuclear Information System (INIS)

    Wang, H.

    1995-01-01

    China's refining system is undergoing a major overhaul. New refineries are being built as existing ones are upgraded and expanded. The success of refineries funded completely or partially by non-chinese companies will depend in part on Chinese government policy. There will be demand for products from third-party processing facilities, but hard current is necessary for the investors to repatriate profits and for china Petrochemical Corp. (Sinopec) to bid on the products from such facilities. The limited convertibility of chinese current constitutes a major central control over the country's entire economy. This control can be affected by limiting product exchange participants and the volumes to be traded. Such a limitation, however, will reduce access of non-Chinese companies to China's markets, and is not likely to occur in the next 10 years. The paper discusses the current situation in capacity and in refining, capacity, expansion, refinery planning, construction projects, third-party processing, and the prospects for change

  16. Computing Refined Buneman Trees in Cubic Time

    DEFF Research Database (Denmark)

    Brodal, G.S.; Fagerberg, R.; Östlin, A.

    2003-01-01

    Reconstructing the evolutionary tree for a set of n species based on pairwise distances between the species is a fundamental problem in bioinformatics. Neighbor joining is a popular distance based tree reconstruction method. It always proposes fully resolved binary trees despite missing evidence...... in the underlying distance data. Distance based methods based on the theory of Buneman trees and refined Buneman trees avoid this problem by only proposing evolutionary trees whose edges satisfy a number of constraints. These trees might not be fully resolved but there is strong combinatorial evidence for each...... proposed edge. The currently best algorithm for computing the refined Buneman tree from a given distance measure has a running time of O(n 5) and a space consumption of O(n 4). In this paper, we present an algorithm with running time O(n 3) and space consumption O(n 2). The improved complexity of our...

  17. Panorama 2016 - Refining outlook for 2035

    International Nuclear Information System (INIS)

    Marion, Pierre; Saint-Antonin, Valerie

    2015-12-01

    The rising influence of objectives intended to address the energy transition in global industry helps to perpetuate a high degree of uncertainty about changes in the transportation sector, currently a bastion of the oil industry. How can the growing need for individual mobility be met while reducing Greenhouse Gas (GHG) emissions in a world of open international competition? The refining sector is gaining strength in Asia and the Middle East to the detriment of Europe and North America, reflecting demand and the intrinsic competitiveness of various geographic regions. The 2025 worldwide roll-out (2020 in Europe) of a bunker fuel grade below 0.5 wt% (percentage by weight) in sulphur could experience delays, given the number of installations to be completed. Finally, the reversal of the 'all diesel' trend in the European transport market is a positive change for the European refining industry. (authors)

  18. The big shedding of the European refining

    International Nuclear Information System (INIS)

    Lepetit, V.

    2007-01-01

    Everywhere in Europe the oil companies are selling their refineries. Even if they work at full capacity, the interest of the European market is far below the one of Asia where demand is in full expansion and Middle-East where the raw matter is abundant. The world refining capacity is of 86 million barrel per day and should reach 106 million barrel per day in 2020. (J.S.)

  19. Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

    OpenAIRE

    Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S.; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

    2017-01-01

    Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality i...

  20. Cleaning Data with OpenRefine

    OpenAIRE

    Seth van Hooland; Ruben Verborgh; Max De Wilde

    2013-01-01

    Duplicate records, empty values and inconsistent formats are phenomena we should be prepared to deal with when using historical data sets. This lesson will teach you how to discover inconsistencies in data contained within a spreadsheet or a database. As we increasingly share, aggregate and reuse data on the web, historians will need to respond to data quality issues which inevitably pop up. Using a program called OpenRefine, you will be able to easily identify systematic errors such as blank...

  1. Rare earths refining by vacuum sublimation method

    International Nuclear Information System (INIS)

    Rytus, N.N.

    1983-01-01

    The process of rare earths refining by the sUblimation; method in high and superhigh oil-free vacuum, is investigated. The method is effective for rare earths obtaining and permits to prepare metal samples with a high value of electric resistance ratio γ=RsUb(298 K)/Rsub(4.2 K). The estimation of general purity is performed for Sm, Eu, Yb, Tm, Dy, Ho, Er and Se

  2. Refining of biodiesel by ceramic membrane separation

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Yong; Ou, Shiyi; Tan, Yanlai; Tang, Shuze [Department of Food Science and Engineering, Jinan University, Guangzhou 510632 (China); Wang, Xingguo; Liu, Yuanfa [School of Food Science and Technology, Jiangnan University, Wuxi 214112 (China)

    2009-03-15

    A ceramic membrane separation process for biodiesel refining was developed to reduce the considerable usage of water needed in the conventional water washing process. Crude biodiesel produced by refined palm oil was micro-filtered by ceramic membranes of the pore size of 0.6, 0.2 and 0.1 {mu}m to remove the residual soap and free glycerol, at the transmembrane pressure of 0.15 MPa and temperature of 60 C. The flux through membrane maintained at 300 L m{sup -} {sup 2} h{sup -} {sup 1} when the volumetric concentrated ratio reached 4. The content of potassium, sodium, calcium and magnesium in the whole permeate was 1.40, 1.78, 0.81 and 0.20 mg/kg respectively, as determined by inductively coupled plasma-atomic emission spectroscopy. These values are lower than the EN 14538 specifications. The residual free glycerol in the permeate was estimated by water extraction, its value was 0.0108 wt.%. This ceramic membrane technology was a potential environmental process for the refining of biodiesel. (author)

  3. A Canadian refiner's perspective of synthetic crudes

    International Nuclear Information System (INIS)

    Halford, T.L.; McIntosh, A.P.; Rasmussen

    1997-01-01

    Some of the factors affecting a refiner's choice of crude oil include refinery hardware, particularly gas oil crackers, products slate and product specifications, crude availability, relative crude price and crude quality. An overview of synthetic crude, the use of synthetic crude combined with other crudes and a comparison of synthetic crude with conventional crude oil was given. The two main users of synthetic crude are basically two groups of refiners, those large groups who use synthetic crude combined with other crudes, and a smaller group who run synthetic crude on specially designed units as a sole feed. The effects of changes in fuel legislation were reviewed. It was predicted that the changes will have a mixed impact on the value of synthetic crude, but low sulphur diesel regulations and gasoline sulphur regulations will make current synthetic crudes attractive. The big future change with a negative impact will be diesel cetane increases to reduce engine emissions. This will reduce synthetic crude attractiveness due to distillate yields and quality and high gas oil yields. Similarly, any legislation limiting aromatics in diesel fuel will also make synthetic crudes less attractive. Problems experienced by refiners with hardware dedicated to synthetic crude (salt, naphthenic acid, fouling, quality variations) were also reviewed. 3 tabs

  4. Oil price scenarios and refining profitability

    International Nuclear Information System (INIS)

    Sweeney, B.

    1993-01-01

    Currently refining profitability is low because there has been an overbuilding of conversion capacity in Western Europe in the last round. Oil marketing, the chemicals business and the fundamental economy itself are at low points in their cycles which have not coincided, at least in the UK, since 1975. Against that gloomy background, it is predicted that downstream profitability will recover in the mid-1990s. Crude oil prices will remain low until the call on OPEC crude increases again and takes up the capacity which has been brought on stream in response to the Gulf War. When this happens, it is likely to trigger another price spike and another round of investment in production capacity. Environmentally driven investments in desulphurisation or emissions reduction will be poorly remunerated all the way through the value chain. Refining margins will recover when white oil demand growth tightens up the need for conversion capacity. Marketing will need to reduce the retail network overcapacity in the mature markets if it is to improve its profitability. In this period of low profitability, even with the light at the end of the tunnel for refiners in the middle of the decade, the industry structure is under threat. There is a strong argument for new modes of competitive behaviour which are backed by strong elements of cooperation. (author)

  5. Problems persist for French refining sector

    International Nuclear Information System (INIS)

    Beck, R.J.

    1992-01-01

    This paper reports that France's refiners face a continuing shortfall of middle distillate capacity and a persistent surplus of heavy fuel oil. That's the main conclusion of the official Hydrocarbon Directorate's report on how France's refining sector performed in 1991. Imports up---The directorate noted that although net production of refined products in French refineries rose to 1.534 million b/d in 1991 from 1.48 million b/d in 1990, products imports jumped 9.7% to 602,000 b/d in the period. The glut of heavy fuel oil eased to some extent last year because French nuclear power capacity, heavily dependent on ample water supplies, was crimped by drought. That spawned fuel switching. The most note worthy increase in imports was for motor diesel, climbing to 176,000 b/d from 148,000 b/d in 1990. Tax credits are spurring French consumption of that fuel. For the first time, consumption of motor diesel in 1991 outstripped that of gasoline at 374,000 b/d and 356,000 b/d respectively

  6. Refining glass structure in two dimensions

    Science.gov (United States)

    Sadjadi, Mahdi; Bhattarai, Bishal; Drabold, D. A.; Thorpe, M. F.; Wilson, Mark

    2017-11-01

    Recently determined atomistic scale structures of near-two dimensional bilayers of vitreous silica (using scanning probe and electron microscopy) allow us to refine the experimentally determined coordinates to incorporate the known local chemistry more precisely. Further refinement is achieved by using classical potentials of varying complexity: one using harmonic potentials and the second employing an electrostatic description incorporating polarization effects. These are benchmarked against density functional calculations. Our main findings are that (a) there is a symmetry plane between the two disordered layers, a nice example of an emergent phenomena, (b) the layers are slightly tilted so that the Si-O-Si angle between the two layers is not 180∘ as originally thought but rather 175 ±2∘ , and (c) while interior areas that are not completely imagined can be reliably reconstructed, surface areas are more problematic. It is shown that small crystallites that appear are just as expected statistically in a continuous random network. This provides a good example of the value that can be added to disordered structures imaged at the atomic level by implementing computer refinement.

  7. Formal refinement of extended state machines

    Directory of Open Access Journals (Sweden)

    Thomas Fayolle

    2016-06-01

    Full Text Available In a traditional formal development process, e.g. using the B method, the informal user requirements are (manually translated into a global abstract formal specification. This translation is especially difficult to achieve. The Event-B method was developed to incrementally and formally construct such a specification using stepwise refinement. Each increment takes into account new properties and system aspects. In this paper, we propose to couple a graphical notation called Algebraic State-Transition Diagrams (ASTD with an Event-B specification in order to provide a better understanding of the software behaviour. The dynamic behaviour is captured by the ASTD, which is based on automata and process algebra operators, while the data model is described by means of an Event-B specification. We propose a methodology to incrementally refine such specification couplings, taking into account new refinement relations and consistency conditions between the control specification and the data specification. We compare the specifications obtained using each approach for readability and proof complexity. The advantages and drawbacks of the traditional approach and of our methodology are discussed. The whole process is illustrated by a railway CBTC-like case study. Our approach is supported by tools for translating ASTD's into B and Event-B into B.

  8. Refining The Grain: Using Resident-Based Walkability Audits To Better Understand Walkable Urban Form.

    Science.gov (United States)

    Schlossberg, Marc; Johnson-Shelton, Deb; Evers, Cody; Moreno, Geraldine

    Researchers use measures of street connectivity to assess neighborhood walkability and many studies show a relationship between neighborhood design and walking activity. Yet, the core of those connectivity measures are based on constructs designed for analyzing automobile mobility - the street network - not pedestrian movement. This paper examines the effect of a finer grained characterization of street connectivity and illustrates the idea using parent ratings of street and intersection walkability for children throughout a suburban school district in Oregon. Several policy and practice recommendations are presented, including a discussion that extends Michael Southworth's (1993; 2005) foundational representation of streets and the walkable city using a refined, more pedestrian-centered approach to visualizing connectivity and walkable urban form.

  9. Refining the classification of irreps of the 1D N-extended supersymmetry

    International Nuclear Information System (INIS)

    Kuznetsova, Zhanna; Toppan, Francesco.

    2007-01-01

    In hep-th/0511274 the classification of the fields content of the linear finite irreducible representations of the algebra of the 1D N-Extended Supersymmetric Quantum Mechanics was given. In hep-th/0611060 it was pointed out that certain irreps with the same fields content can be regarded as inequivalent. This result can be understood in terms of the 'connectivity' properties of the graphs associated to the irreps. We present here a classification of the connectivity of the irreps, refining the hep-th/0511274 classification based on fields content. As a byproduct, we find a counterexample to the hep-th/0611060 claim that the connectivity is uniquely specified by the sources and targets of an irrep graph. We produce one pair of N=5 irreps and three pairs of N=6 irreps with the same number of sources and targets which, nevertheless, differ in connectivity. (author)

  10. Recurrent connectivity can account for the dynamics of disparity processing in V1

    Science.gov (United States)

    Samonds, Jason M.; Potetz, Brian R.; Tyler, Christopher W.; Lee, Tai Sing

    2013-01-01

    Disparity tuning measured in the primary visual cortex (V1) is described well by the disparity energy model, but not all aspects of disparity tuning are fully explained by the model. Such deviations from the disparity energy model provide us with insight into how network interactions may play a role in disparity processing and help to solve the stereo correspondence problem. Here, we propose a neuronal circuit model with recurrent connections that provides a simple account of the observed deviations. The model is based on recurrent connections inferred from neurophysiological observations on spike timing correlations, and is in good accord with existing data on disparity tuning dynamics. We further performed two additional experiments to test predictions of the model. First, we increased the size of stimuli to drive more neurons and provide a stronger recurrent input. Our model predicted sharper disparity tuning for larger stimuli. Second, we displayed anti-correlated stereograms, where dots of opposite luminance polarity are matched between the left- and right-eye images and result in inverted disparity tuning in the disparity energy model. In this case, our model predicted reduced sharpening and strength of inverted disparity tuning. For both experiments, the dynamics of disparity tuning observed from the neurophysiological recordings in macaque V1 matched model simulation predictions. Overall, the results of this study support the notion that, while the disparity energy model provides a primary account of disparity tuning in V1 neurons, neural disparity processing in V1 neurons is refined by recurrent interactions among elements in the neural circuit. PMID:23407952

  11. Pulsed neural networks consisting of single-flux-quantum spiking neurons

    International Nuclear Information System (INIS)

    Hirose, T.; Asai, T.; Amemiya, Y.

    2007-01-01

    An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

  12. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

    Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  13. Neural system prediction and identification challenge.

    Science.gov (United States)

    Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

    2013-01-01

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  14. Developmental changes in brain connectivity assessed using the sleep EEG.

    OpenAIRE

    Tarokh L; Carskadon M A; Achermann P

    2010-01-01

    Adolescence represents a time of significant cortical restructuring. Current theories posit that during this period connections between frequently utilized neural networks are strengthened while underutilized synaptic connections are discarded. The aim of the present study was to examine the developmental evolution of connectivity between brain regions using the sleep EEG. All night sleep EEG recordings in two longitudinal cohorts (children and teens) followed at 1.5 3 year intervals and one ...

  15. Region-of-interest volumetric visual hull refinement

    KAUST Repository

    Knoblauch, Daniel; Kuester, Falko

    2010-01-01

    This paper introduces a region-of-interest visual hull refinement technique, based on flexible voxel grids for volumetric visual hull reconstructions. Region-of-interest refinement is based on a multipass process, beginning with a focussed visual

  16. Grain Refinement of Permanent Mold Cast Copper Base Alloys

    Energy Technology Data Exchange (ETDEWEB)

    M.Sadayappan; J.P.Thomson; M.Elboujdaini; G.Ping Gu; M. Sahoo

    2005-04-01

    Grain refinement is a well established process for many cast and wrought alloys. The mechanical properties of various alloys could be enhanced by reducing the grain size. Refinement is also known to improve casting characteristics such as fluidity and hot tearing. Grain refinement of copper-base alloys is not widely used, especially in sand casting process. However, in permanent mold casting of copper alloys it is now common to use grain refinement to counteract the problem of severe hot tearing which also improves the pressure tightness of plumbing components. The mechanism of grain refinement in copper-base alloys is not well understood. The issues to be studied include the effect of minor alloy additions on the microstructure, their interaction with the grain refiner, effect of cooling rate, and loss of grain refinement (fading). In this investigation, efforts were made to explore and understand grain refinement of copper alloys, especially in permanent mold casting conditions.

  17. An Overview of Bayesian Methods for Neural Spike Train Analysis

    Directory of Open Access Journals (Sweden)

    Zhe Chen

    2013-01-01

    Full Text Available Neural spike train analysis is an important task in computational neuroscience which aims to understand neural mechanisms and gain insights into neural circuits. With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity. Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels. On the theoretical side, we focus on various approximate Bayesian inference techniques as applied to latent state and parameter estimation. On the application side, the topics include spike sorting, tuning curve estimation, neural encoding and decoding, deconvolution of spike trains from calcium imaging signals, and inference of neuronal functional connectivity and synchrony. Some research challenges and opportunities for neural spike train analysis are discussed.

  18. Optimal algebraic multilevel preconditioning for local refinement along a line

    NARCIS (Netherlands)

    Margenov, S.D.; Maubach, J.M.L.

    1995-01-01

    The application of some recently proposed algebraic multilevel methods for the solution of two-dimensional finite element problems on nonuniform meshes is studied. The locally refined meshes are created by the newest vertex mesh refinement method. After the introduction of this refinement technique

  19. Grain refinement of AZ31 magnesium alloy by electromagnetic ...

    Indian Academy of Sciences (India)

    Low-frequency electromagnetic field; AZ31 magnesium alloy; Al4C3; grain refinement. Abstract. The effects of electromagnetic stirring and Al4C3 grain refiner on the grain refinement of semicontinuously cast AZ31 magnesium alloy were discussed in this investigation. The results indicate that electromagnetic stirring has an ...

  20. Neutrosophic Refined Similarity Measure Based on Cosine Function

    Directory of Open Access Journals (Sweden)

    Said Broumi

    2014-12-01

    Full Text Available In this paper, the cosine similarity measure of neutrosophic refined (multi- sets is proposed and its properties are studied. The concept of this cosine similarity measure of neutrosophic refined sets is the extension of improved cosine similarity measure of single valued neutrosophic. Finally, using this cosine similarity measure of neutrosophic refined set, the application of medical diagnosis is presented.

  1. Formal language theory: refining the Chomsky hierarchy.

    Science.gov (United States)

    Jäger, Gerhard; Rogers, James

    2012-07-19

    The first part of this article gives a brief overview of the four levels of the Chomsky hierarchy, with a special emphasis on context-free and regular languages. It then recapitulates the arguments why neither regular nor context-free grammar is sufficiently expressive to capture all phenomena in the natural language syntax. In the second part, two refinements of the Chomsky hierarchy are reviewed, which are both relevant to the extant research in cognitive science: the mildly context-sensitive languages (which are located between context-free and context-sensitive languages), and the sub-regular hierarchy (which distinguishes several levels of complexity within the class of regular languages).

  2. Local adaptive mesh refinement for shock hydrodynamics

    International Nuclear Information System (INIS)

    Berger, M.J.; Colella, P.; Lawrence Livermore Laboratory, Livermore, 94550 California)

    1989-01-01

    The aim of this work is the development of an automatic, adaptive mesh refinement strategy for solving hyperbolic conservation laws in two dimensions. There are two main difficulties in doing this. The first problem is due to the presence of discontinuities in the solution and the effect on them of discontinuities in the mesh. The second problem is how to organize the algorithm to minimize memory and CPU overhead. This is an important consideration and will continue to be important as more sophisticated algorithms that use data structures other than arrays are developed for use on vector and parallel computers. copyright 1989 Academic Press, Inc

  3. Minimum cost connection networks

    DEFF Research Database (Denmark)

    Hougaard, Jens Leth; Tvede, Mich

    In the present paper we consider the allocation of cost in connection networks. Agents have connection demands in form of pairs of locations they want to be connected. Connections between locations are costly to build. The problem is to allocate costs of networks satisfying all connection demands...

  4. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

    Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

  5. 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.

  6. Neural Tube Defects

    Science.gov (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  7. An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

    Science.gov (United States)

    Cabessa, Jérémie; Villa, Alessandro E. P.

    2014-01-01

    We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

  8. Associative memory model with spontaneous neural activity

    Science.gov (United States)

    Kurikawa, Tomoki; Kaneko, Kunihiko

    2012-05-01

    We propose a novel associative memory model wherein the neural activity without an input (i.e., spontaneous activity) is modified by an input to generate a target response that is memorized for recall upon the same input. Suitable design of synaptic connections enables the model to memorize input/output (I/O) mappings equaling 70% of the total number of neurons, where the evoked activity distinguishes a target pattern from others. Spontaneous neural activity without an input shows chaotic dynamics but keeps some similarity with evoked activities, as reported in recent experimental studies.

  9. Neural network tagging in a toy model

    International Nuclear Information System (INIS)

    Milek, Marko; Patel, Popat

    1999-01-01

    The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed

  10. Target recognition based on convolutional neural network

    Science.gov (United States)

    Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

    2017-11-01

    One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

  11. Refining Automatically Extracted Knowledge Bases Using Crowdsourcing

    Directory of Open Access Journals (Sweden)

    Chunhua Li

    2017-01-01

    Full Text Available Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

  12. Adaptive temporal refinement in injection molding

    Science.gov (United States)

    Karyofylli, Violeta; Schmitz, Mauritius; Hopmann, Christian; Behr, Marek

    2018-05-01

    Mold filling is an injection molding stage of great significance, because many defects of the plastic components (e.g. weld lines, burrs or insufficient filling) can occur during this process step. Therefore, it plays an important role in determining the quality of the produced parts. Our goal is the temporal refinement in the vicinity of the evolving melt front, in the context of 4D simplex-type space-time grids [1, 2]. This novel discretization method has an inherent flexibility to employ completely unstructured meshes with varying levels of resolution both in spatial dimensions and in the time dimension, thus allowing the use of local time-stepping during the simulations. This can lead to a higher simulation precision, while preserving calculation efficiency. A 3D benchmark case, which concerns the filling of a plate-shaped geometry, is used for verifying our numerical approach [3]. The simulation results obtained with the fully unstructured space-time discretization are compared to those obtained with the standard space-time method and to Moldflow simulation results. This example also serves for providing reliable timing measurements and the efficiency aspects of the filling simulation of complex 3D molds while applying adaptive temporal refinement.

  13. Refining Automatically Extracted Knowledge Bases Using Crowdsourcing.

    Science.gov (United States)

    Li, Chunhua; Zhao, Pengpeng; Sheng, Victor S; Xian, Xuefeng; Wu, Jian; Cui, Zhiming

    2017-01-01

    Machine-constructed knowledge bases often contain noisy and inaccurate facts. There exists significant work in developing automated algorithms for knowledge base refinement. Automated approaches improve the quality of knowledge bases but are far from perfect. In this paper, we leverage crowdsourcing to improve the quality of automatically extracted knowledge bases. As human labelling is costly, an important research challenge is how we can use limited human resources to maximize the quality improvement for a knowledge base. To address this problem, we first introduce a concept of semantic constraints that can be used to detect potential errors and do inference among candidate facts. Then, based on semantic constraints, we propose rank-based and graph-based algorithms for crowdsourced knowledge refining, which judiciously select the most beneficial candidate facts to conduct crowdsourcing and prune unnecessary questions. Our experiments show that our method improves the quality of knowledge bases significantly and outperforms state-of-the-art automatic methods under a reasonable crowdsourcing cost.

  14. Adaptive hybrid mesh refinement for multiphysics applications

    International Nuclear Information System (INIS)

    Khamayseh, Ahmed; Almeida, Valmor de

    2007-01-01

    The accuracy and convergence of computational solutions of mesh-based methods is strongly dependent on the quality of the mesh used. We have developed methods for optimizing meshes that are comprised of elements of arbitrary polygonal and polyhedral type. We present in this research the development of r-h hybrid adaptive meshing technology tailored to application areas relevant to multi-physics modeling and simulation. Solution-based adaptation methods are used to reposition mesh nodes (r-adaptation) or to refine the mesh cells (h-adaptation) to minimize solution error. The numerical methods perform either the r-adaptive mesh optimization or the h-adaptive mesh refinement method on the initial isotropic or anisotropic meshes to equidistribute weighted geometric and/or solution error function. We have successfully introduced r-h adaptivity to a least-squares method with spherical harmonics basis functions for the solution of the spherical shallow atmosphere model used in climate modeling. In addition, application of this technology also covers a wide range of disciplines in computational sciences, most notably, time-dependent multi-physics, multi-scale modeling and simulation

  15. Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network

    NARCIS (Netherlands)

    Pande, Sandeep; Morgan, Fearghal; Cawley, Seamus; Bruintjes, Tom; Smit, Gerardus Johannes Maria; McGinley, Brian; Carrillo, Snaider; Harkin, Jim; McDaid, Liam

    2013-01-01

    Biologically-inspired packet switched network on chip (NoC) based hardware spiking neural network (SNN) architectures have been proposed as an embedded computing platform for classification, estimation and control applications. Storage of large synaptic connectivity (SNN topology) information in

  16. DISSECTING HABITAT CONNECTIVITY

    Science.gov (United States)

    abstractConnectivity is increasingly recognized as an important element of a successful reserve design. Connectivity matters in reserve design to the extent that it promotes or hinders the viability of target populations. While conceptually straightforward, connectivity i...

  17. Mixed Connective Tissue Disease

    Science.gov (United States)

    Mixed connective tissue disease Overview Mixed connective tissue disease has signs and symptoms of a combination of disorders — primarily lupus, scleroderma and polymyositis. For this reason, mixed connective tissue disease ...

  18. Undifferentiated Connective Tissue Disease

    Science.gov (United States)

    ... Home Conditions Undifferentiated Connective Tissue Disease (UCTD) Undifferentiated Connective Tissue Disease (UCTD) Make an Appointment Find a Doctor ... by Barbara Goldstein, MD (February 01, 2016) Undifferentiated connective tissue disease (UCTD) is a systemic autoimmune disease. This ...

  19. Pacific Basin Heavy Oil Refining Capacity

    Directory of Open Access Journals (Sweden)

    David Hackett

    2013-02-01

    Full Text Available The United States today is Canada’s largest customer for oil and refined oil products. However, this relationship may be strained due to physical, economic and political influences. Pipeline capacity is approaching its limits; Canadian oil is selling at substantive discounts to world market prices; and U.S. demand for crude oil and finished products (such as gasoline, has begun to flatten significantly relative to historical rates. Lower demand, combined with increased shale oil production, means U.S. demand for Canadian oil is expected to continue to decline. Under these circumstances, gaining access to new markets such as those in the Asia-Pacific region is becoming more and more important for the Canadian economy. However, expanding pipeline capacity to the Pacific via the proposed Northern Gateway pipeline and the planned Trans Mountain pipeline expansion is only feasible when there is sufficient demand and processing capacity to support Canadian crude blends. Canadian heavy oil requires more refining and produces less valuable end products than other lighter and sweeter blends. Canadian producers must compete with lighter, sweeter oils from the Middle East, and elsewhere, for a place in the Pacific Basin refineries built to handle heavy crude blends. Canadian oil sands producers are currently expanding production capacity. Once complete, the Northern Gateway pipeline and the Trans Mountain expansion are expected to deliver an additional 500,000 to 1.1 million barrels a day to tankers on the Pacific coast. Through this survey of the capacity of Pacific Basin refineries, including existing and proposed facilities, we have concluded that there is sufficient technical capacity in the Pacific Basin to refine the additional Canadian volume; however, there may be some modifications required to certain refineries to allow them to process Western Canadian crude. Any additional capacity for Canadian oil would require refinery modifications or

  20. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  1. Modular representation of layered neural networks.

    Science.gov (United States)

    Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

    2018-01-01

    Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  2. The Laplacian spectrum of neural networks

    Science.gov (United States)

    de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

    2014-01-01

    The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

  3. The European refining and distribution industry at the 2010 vista

    International Nuclear Information System (INIS)

    Lacour, J.J.; Tessmer, G.; Ward, I.

    1998-01-01

    Oil company chairmen belonging to the AFTP, DGMK and IP associations met together to debate about the future of the European refining industry. The following topics were discussed: is it the end of the refining crisis? Which uncertainties will have to be met? What is the situation of petroleum products supply and demand? What are the consumers' expectations? How to face the environmental constraints? Which future for the refining activities in Europe? Seven round-tables took place with the following themes: the factors of uncertainty in the future of refining activities, the petroleum products supply and demand (automotive fuels, fuel oils, lubricants), the refining activities and the supply of consumers (service stations and supermarkets), the situation of the European petroleum policy, the European refining industry and the public regulations (development of more efficient environmental approaches), the impact of environmental constraints and the technical solutions, and the future of the refining industry. (J.S.)

  4. Technological studies on uranium refining at nuclear materials authority, Egypt

    International Nuclear Information System (INIS)

    Mohammed, H.S.

    1997-01-01

    In 1992 nuclear materials authority (NMA) took a decision to establish yellow cake refining. Unit so as to study refining of El-Atshan yellow cake which recently produced by ion-exchange pilot plant, production sector. The research studies followed the conventional refining rout to produce nuclear grade UO 3 . This implies investigations on some common solvents to refine the cake viz. tri alkyl phosphates, tri alkyl phosphine oxides, dialkyl phosphoric acid as well as high-molecular weight long-chain tertiary amines. Moreover, non-conventional refining process has also been presented depending on the selectivity of uranyl ion to be dissolved by carbonate and to be precipitated by hydrogen peroxide. Most of the proposed processes were found feasible to refine El-Atshan yellow cake. however, the non- conventional refining process appears to be the most promising, owing to its superior performance and economy

  5. Intermodal Passenger Connectivity Database -

    Data.gov (United States)

    Department of Transportation — The Intermodal Passenger Connectivity Database (IPCD) is a nationwide data table of passenger transportation terminals, with data on the availability of connections...

  6. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  7. A Model of Trusted Connection Architecture

    Directory of Open Access Journals (Sweden)

    Zhang Xun

    2017-01-01

    Full Text Available According to that traditional trusted network connection architecture (TNC has limitations on dynamic network environment and the user behavior support, we develop TCA to propose a trusted connection architecture supporting behavior measurement (TCA-SBM, besides, the structure diagram of network architecture is given. Through introducing user behavior measure elements, TCA-SBM can conduct measurement on the whole network in time dimension periodically, and refine the measurement on network behavior in measure dimension to conduct fine-grained dynamic trusted measurement. As a result, TCA-SBM enhances the TCA’s ability to adapt to the dynamic change of network and makes up the deficiency of trusted computing framework in the network connection.

  8. Effect of strontium on the grain refining efficiency of Mg-3Al alloy refined by carbon inoculation

    International Nuclear Information System (INIS)

    Du Jun; Yang Jian; Kuwabara, Mamoru; Li Wenfang; Peng Jihua

    2009-01-01

    The effect of Sr on the grain refining efficiency of the Mg-3Al alloy refined by carbon inoculation has been investigated in the present study. A significant grain refinement was obtained for the Mg-3Al alloy treated with either 0.2% C or 0.2% Sr. The Al-C-O particles were found in the sample refined by 0.2% C, and the element O should come from reaction between Al 4 C 3 nuclei of Mg grains and water during the process of sample preparation. The grain size of the sample refined by carbon inoculation was further decreased after the combined addition of Sr. The grain size decreased with increasing Sr content. Much higher refining efficiency was obtained when the Sr addition was increased to 0.5%. Sr is an effective element to improve the grain refining efficiency for the Mg-Al alloys refined by carbon inoculation. The number of Al 4 C 3 particles in the sample refined by the combination of carbon and Sr was more than that in the sample refined by only carbon. No Al-C-O-Sr-rich particles were obviously found in the sample refined by the combination of carbon and a little (<0.5%) Sr addition

  9. Refining the ideas of "ethnic" skin.

    Science.gov (United States)

    Torres, Vicente; Herane, Maria Isabel; Costa, Adilson; Martin, Jaime Piquero; Troielli, Patricia

    2017-01-01

    Skin disease occur worldwide, affecting people of all nationalities and all skin types. These diseases may have a genetic component and may manifest differently in specific population groups; however, there has been little study on this aspect. If population-based differences exist, it is reasonable to assume that understanding these differences may optimize treatment. While there is a relative paucity of information about similarities and differences in skin diseases around the world, the knowledge-base is expanding. One challenge in understanding population-based variations is posed by terminology used in the literature: including ethnic skin, Hispanic skin, Asian skin, and skin of color. As will be discussed in this article, we recommend that the first three descriptors are no longer used in dermatology because they refer to nonspecific groups of people. In contrast, "skin of color" may be used - perhaps with further refinements in the future - as a term that relates to skin biology and provides relevant information to dermatologists.

  10. China's oil market and refining sector

    International Nuclear Information System (INIS)

    Yamaguchi, N.D.; Fridley, D.G.

    2000-01-01

    The article assesses the future for China's oil industry as the country makes the transition from a command economy to an international market. China has one of the world's biggest oil industries and recent years have seen much growth in exploration and development, refining capacity and trade. China is more and more dependent on oil imports and is now a major international player; it has attracted much outside investment. Diagrams show (i) how coal dominates other sources of energy in China; (ii) crude production 1977-1998; (iii) how Middle East crudes now dominate Chinese crude imports; (iv) the growth of petroleum demand in China; (v) the Chinese demand for petroleum products; (vi) the growth in transport fuels; (vii) Chinese product imports: import ban targeted diesel; (viii) crude imports favoured over product imports and (ix) refinery capacity and throughput. The changes are expected to result in further integration into international markets, increased transparency and a healthier oil business

  11. Japan's oil market and refining sector

    International Nuclear Information System (INIS)

    Yamaguchi, N.D.

    2002-01-01

    The present economic situation in Japan is discussed. In particular, the focus is on fluctuations in oil product demand, imports of crude oil, and the refining industry. Throughout the 1990s, Japan was plagued by a volatile economy and the new millennium has shown no improvement. A prolonged recession means that the country now has little confidence in its leaders and its institutions, consumer confidence is low and asset values have deflated. Due to a low birth rate and long life expectancy, the population is aging and this means lower savings rates. The contrast between the present situation and the so-called economic miracle once enjoyed by the Japanese is hard to accept, but despite all this, the Japanese lifestyle and economy are to be envied

  12. Formal language theory: refining the Chomsky hierarchy

    Science.gov (United States)

    Jäger, Gerhard; Rogers, James

    2012-01-01

    The first part of this article gives a brief overview of the four levels of the Chomsky hierarchy, with a special emphasis on context-free and regular languages. It then recapitulates the arguments why neither regular nor context-free grammar is sufficiently expressive to capture all phenomena in the natural language syntax. In the second part, two refinements of the Chomsky hierarchy are reviewed, which are both relevant to the extant research in cognitive science: the mildly context-sensitive languages (which are located between context-free and context-sensitive languages), and the sub-regular hierarchy (which distinguishes several levels of complexity within the class of regular languages). PMID:22688632

  13. Refining Grasp Affordance Models by Experience

    DEFF Research Database (Denmark)

    Detry, Renaud; Kraft, Dirk; Buch, Anders Glent

    2010-01-01

    We present a method for learning object grasp affordance models in 3D from experience, and demonstrate its applicability through extensive testing and evaluation on a realistic and largely autonomous platform. Grasp affordance refers here to relative object-gripper configurations that yield stable...... with a visual model of the object they characterize. We explore a batch-oriented, experience-based learning paradigm where grasps sampled randomly from a density are performed, and an importance-sampling algorithm learns a refined density from the outcomes of these experiences. The first such learning cycle...... is bootstrapped with a grasp density formed from visual cues. We show that the robot effectively applies its experience by downweighting poor grasp solutions, which results in increased success rates at subsequent learning cycles. We also present success rates in a practical scenario where a robot needs...

  14. Biomaterials Evaluation: Conceptual Refinements and Practical Reforms.

    Science.gov (United States)

    Masaeli, Reza; Zandsalimi, Kavosh; Tayebi, Lobat

    2018-01-01

    Regarding the widespread and ever-increasing applications of biomaterials in different medical fields, their accurate assessment is of great importance. Hence the safety and efficacy of biomaterials is confirmed only through the evaluation process, the way it is done has direct effects on public health. Although every biomaterial undergoes rigorous premarket evaluation, the regulatory agencies receive a considerable number of complications and adverse event reports annually. The main factors that challenge the process of biomaterials evaluation are dissimilar regulations, asynchrony of biomaterials evaluation and biomaterials development, inherent biases of postmarketing data, and cost and timing issues. Several pieces of evidence indicate that current medical device regulations need to be improved so that they can be used more effectively in the evaluation of biomaterials. This article provides suggested conceptual refinements and practical reforms to increase the efficiency and effectiveness of the existing regulations. The main focus of the article is on strategies for evaluating biomaterials in US, and then in EU.

  15. The evolution and refinements of varicocele surgery

    Directory of Open Access Journals (Sweden)

    Joel L Marmar

    2016-01-01

    Full Text Available Varicoceles had been recognized in clinical practice for over a century. Originally, these procedures were utilized for the management of pain but, since 1952, the repairs had been mostly for the treatment of male infertility. However, the diagnosis and treatment of varicoceles were controversial, because the pathophysiology was not clear, the entry criteria of the studies varied among centers, and there were few randomized clinical trials. Nevertheless, clinicians continued developing techniques for the correction of varicoceles, basic scientists continued investigations on the pathophysiology of varicoceles, and new outcome data from prospective randomized trials have appeared in the world′s literature. Therefore, this special edition of the Asian Journal of Andrology was proposed to report much of the new information related to varicoceles and, as a specific part of this project, the present article was developed as a comprehensive review of the evolution and refinements of the corrective procedures.

  16. COSMOLOGICAL ADAPTIVE MESH REFINEMENT MAGNETOHYDRODYNAMICS WITH ENZO

    International Nuclear Information System (INIS)

    Collins, David C.; Xu Hao; Norman, Michael L.; Li Hui; Li Shengtai

    2010-01-01

    In this work, we present EnzoMHD, the extension of the cosmological code Enzo to include the effects of magnetic fields through the ideal magnetohydrodynamics approximation. We use a higher order Godunov method for the computation of interface fluxes. We use two constrained transport methods to compute the electric field from those interface fluxes, which simultaneously advances the induction equation and maintains the divergence of the magnetic field. A second-order divergence-free reconstruction technique is used to interpolate the magnetic fields in the block-structured adaptive mesh refinement framework already extant in Enzo. This reconstruction also preserves the divergence of the magnetic field to machine precision. We use operator splitting to include gravity and cosmological expansion. We then present a series of cosmological and non-cosmological test problems to demonstrate the quality of solution resulting from this combination of solvers.

  17. Panorama 2011: Refining: varying conditions by region

    International Nuclear Information System (INIS)

    Silva, C.

    2011-01-01

    The economic crisis has further weakened a sector that was already facing difficulties, if we look beyond the flush period (2005-2008) when, buoyed by strong demand, margins remained high and refiners could generate profits while maintaining a healthy level of activity. Falling demand and increased over-capacity in some regions - the immediate consequences of the deteriorating economic conditions over the past two years - have led to declining margins and to financial accounts being in the red. The adoption of increasingly stringent emissions standards and product specifications, burdensome regulatory requirements for refineries (for combating local pollution and reducing greenhouse gas emissions), stiffer competition from new fuels: all of these structural factors are weakening the sector, especially in industrialized nations with their more rigorous regulatory compliance. In this generally gloomy climate, numerous new projects are still being envisaged - although many have recently been postponed and tend to be concentrated in developing countries. (author)

  18. Refining prices and margins in 1998

    International Nuclear Information System (INIS)

    Favennec, J.P.; Baudoin, C.

    1999-01-01

    Despite a business environment that was globally mediocre due primarily to the Asian crisis and to a mild winter in the northern hemisphere, the signs of improvement noted in the refining activity in 1996 were borne out in 1997. But the situation is not yet satisfactory in this sector: the low return on invested capital and the financing of environmental protection expenditure are giving cause for concern. In 1998, the drop in crude oil prices and the concomitant fall in petroleum product prices was ultimately rather favorable to margins. Two elements tended to put a damper on this relative optimism. First of all, margins continue to be extremely volatile and, secondly, the worsening of the economic and financial crisis observed during the summer made for a sharp decline in margins in all geographic regions, especially Asia

  19. Structure-function relationships during segregated and integrated network states of human brain functional connectivity.

    Science.gov (United States)

    Fukushima, Makoto; Betzel, Richard F; He, Ye; van den Heuvel, Martijn P; Zuo, Xi-Nian; Sporns, Olaf

    2018-04-01

    Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior.

  20. Modelling collective cell migration of neural crest.

    Science.gov (United States)

    Szabó, András; Mayor, Roberto

    2016-10-01

    Collective cell migration has emerged in the recent decade as an important phenomenon in cell and developmental biology and can be defined as the coordinated and cooperative movement of groups of cells. Most studies concentrate on tightly connected epithelial tissues, even though collective migration does not require a constant physical contact. Movement of mesenchymal cells is more independent, making their emergent collective behaviour less intuitive and therefore lending importance to computational modelling. Here we focus on such modelling efforts that aim to understand the collective migration of neural crest cells, a mesenchymal embryonic population that migrates large distances as a group during early vertebrate development. By comparing different models of neural crest migration, we emphasize the similarity and complementary nature of these approaches and suggest a future direction for the field. The principles derived from neural crest modelling could aid understanding the collective migration of other mesenchymal cell types. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Spatial-temporal patterns of retinal waves underlying activity-dependent refinement of retinofugal projections.

    Science.gov (United States)

    Stafford, Ben K; Sher, Alexander; Litke, Alan M; Feldheim, David A

    2009-10-29

    During development, retinal axons project coarsely within their visual targets before refining to form organized synaptic connections. Spontaneous retinal activity, in the form of acetylcholine-driven retinal waves, is proposed to be necessary for establishing these projection patterns. In particular, both axonal terminations of retinal ganglion cells (RGCs) and the size of receptive fields of target neurons are larger in mice that lack the beta2 subunit of the nicotinic acetylcholine receptor (beta2KO). Here, using a large-scale, high-density multielectrode array to record activity from hundreds of RGCs simultaneously, we present analysis of early postnatal retinal activity from both wild-type (WT) and beta2KO retinas. We find that beta2KO retinas have correlated patterns of activity, but many aspects of these patterns differ from those of WT retina. Quantitative analysis suggests that wave directionality, coupled with short-range correlated bursting patterns of RGCs, work together to refine retinofugal projections.

  2. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  3. BDNF genotype modulates resting functional connectivity in children

    Directory of Open Access Journals (Sweden)

    Moriah E Thomason

    2009-11-01

    Full Text Available A specific polymorphism of the brain-derived neurotrophic factor (BDNF gene is associated with alterations in brain anatomy and memory; its relevance to the functional connectivity of brain networks, however, is unclear. Given that altered hippocampal function and structure has been found in adults who carry the methionine (met allele of the BDNF gene and the molecular studies elucidating the role of BDNF in neurogenesis and synapse formation, we examined in the association between BDNF gene variants and neural resting connectivity in children and adolescents. We observed a reduction in hippocampal and parahippocampal to cortical connectivity in met-allele carriers within each of three resting networks: the default-mode, executive, and paralimbic networks. In contrast, we observed increased connectivity to amygdala, insula and striatal regions in met-carriers, within the paralimbic network. Because the BDNF met-allele has been linked to increased susceptibility to neuropsychiatric disorders, this latter finding of greater connectivity in circuits important for emotion processing may indicate a new neural mechanism through which these gene-related psychiatric differences are manifest. Here we show that the BDNF gene, known to regulate synaptic plasticity and connectivity in the brain, affects functional connectivity at the neural systems level. Additionally, we provide the first demonstration that the spatial topography of multiple high-level resting state networks in healthy children and adolescents is similar to that observed in adults.

  4. A Semi-Supervised Approach for Refining Transcriptional Signatures of Drug Response and Repositioning Predictions.

    Directory of Open Access Journals (Sweden)

    Francesco Iorio

    Full Text Available We present a novel strategy to identify drug-repositioning opportunities. The starting point of our method is the generation of a signature summarising the consensual transcriptional response of multiple human cell lines to a compound of interest (namely the seed compound. This signature can be derived from data in existing databases, such as the connectivity-map, and it is used at first instance to query a network interlinking all the connectivity-map compounds, based on the similarity of their transcriptional responses. This provides a drug neighbourhood, composed of compounds predicted to share some effects with the seed one. The original signature is then refined by systematically reducing its overlap with the transcriptional responses induced by drugs in this neighbourhood that are known to share a secondary effect with the seed compound. Finally, the drug network is queried again with the resulting refined signatures and the whole process is carried on for a number of iterations. Drugs in the final refined neighbourhood are then predicted to exert the principal mode of action of the seed compound. We illustrate our approach using paclitaxel (a microtubule stabilising agent as seed compound. Our method predicts that glipizide and splitomicin perturb microtubule function in human cells: a result that could not be obtained through standard signature matching methods. In agreement, we find that glipizide and splitomicin reduce interphase microtubule growth rates and transiently increase the percentage of mitotic cells-consistent with our prediction. Finally, we validated the refined signatures of paclitaxel response by mining a large drug screening dataset, showing that human cancer cell lines whose basal transcriptional profile is anti-correlated to them are significantly more sensitive to paclitaxel and docetaxel.

  5. L-type calcium channels refine the neural population code of sound level

    Science.gov (United States)

    Grimsley, Calum Alex; Green, David Brian

    2016-01-01

    The coding of sound level by ensembles of neurons improves the accuracy with which listeners identify how loud a sound is. In the auditory system, the rate at which neurons fire in response to changes in sound level is shaped by local networks. Voltage-gated conductances alter local output by regulating neuronal firing, but their role in modulating responses to sound level is unclear. We tested the effects of L-type calcium channels (CaL: CaV1.1–1.4) on sound-level coding in the central nucleus of the inferior colliculus (ICC) in the auditory midbrain. We characterized the contribution of CaL to the total calcium current in brain slices and then examined its effects on rate-level functions (RLFs) in vivo using single-unit recordings in awake mice. CaL is a high-threshold current and comprises ∼50% of the total calcium current in ICC neurons. In vivo, CaL activates at sound levels that evoke high firing rates. In RLFs that increase monotonically with sound level, CaL boosts spike rates at high sound levels and increases the maximum firing rate achieved. In different populations of RLFs that change nonmonotonically with sound level, CaL either suppresses or enhances firing at sound levels that evoke maximum firing. CaL multiplies the gain of monotonic RLFs with dynamic range and divides the gain of nonmonotonic RLFs with the width of the RLF. These results suggest that a single broad class of calcium channels activates enhancing and suppressing local circuits to regulate the sensitivity of neuronal populations to sound level. PMID:27605536

  6. 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

    that occur during development that may be observable even in actual neural systems where these changes are convoluted with changes in synaptic connectivity and intrinsic neural plasticity.

  7. Reliability analysis of a consecutive r-out-of-n: F system based on neural networks

    International Nuclear Information System (INIS)

    Habib, Aziz; Alsieidi, Ragab; Youssef, Ghada

    2009-01-01

    In this paper, we present a generalized Markov reliability and fault-tolerant model, which includes the effects of permanent fault and intermittent fault for reliability evaluations based on neural network techniques. The reliability of a consecutive r-out-of-n: F system was obtained with a three-layer connected neural network represents a discrete time state reliability Markov model of the system. Such that we fed the neural network with the desired reliability of the system under design. Then we extracted the parameters of the system from the neural weights at the convergence of the neural network to the desired reliability. Finally, we obtain simulation results.

  8. Dynamics in a delayed-neural network

    International Nuclear Information System (INIS)

    Yuan Yuan

    2007-01-01

    In this paper, we consider a neural network of four identical neurons with time-delayed connections. Some parameter regions are given for global, local stability and synchronization using the theory of functional differential equations. The root distributions in the corresponding characteristic transcendental equation are analyzed, Pitchfork bifurcation, Hopf and equivariant Hopf bifurcations are investigated by revealing the center manifolds and normal forms. Numerical simulations are shown the agreements with the theoretical results

  9. Flexible neural interfaces with integrated stiffening shank

    Energy Technology Data Exchange (ETDEWEB)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2017-10-17

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  10. Flexible neural interfaces with integrated stiffening shank

    Science.gov (United States)

    Tooker, Angela C.; Felix, Sarah H.; Pannu, Satinderpall S.; Shah, Kedar G.; Sheth, Heeral; Tolosa, Vanessa

    2016-07-26

    A neural interface includes a first dielectric material having at least one first opening for a first electrical conducting material, a first electrical conducting material in the first opening, and at least one first interconnection trace electrical conducting material connected to the first electrical conducting material. A stiffening shank material is located adjacent the first dielectric material, the first electrical conducting material, and the first interconnection trace electrical conducting material.

  11. Learning in Neural Networks: VLSI Implementation Strategies

    Science.gov (United States)

    Duong, Tuan Anh

    1995-01-01

    Fully-parallel hardware neural network implementations may be applied to high-speed recognition, classification, and mapping tasks in areas such as vision, or can be used as low-cost self-contained units for tasks such as error detection in mechanical systems (e.g. autos). Learning is required not only to satisfy application requirements, but also to overcome hardware-imposed limitations such as reduced dynamic range of connections.

  12. Optimal multiple-information integration inherent in a ring neural network

    International Nuclear Information System (INIS)

    Takiyama, Ken

    2017-01-01

    Although several behavioral experiments have suggested that our neural system integrates multiple sources of information based on the certainty of each type of information in the manner of maximum-likelihood estimation, it is unclear how the maximum-likelihood estimation is implemented in our neural system. Here, I investigate the relationship between maximum-likelihood estimation and a widely used ring-type neural network model that is used as a model of visual, motor, or prefrontal cortices. Without any approximation or ansatz, I analytically demonstrate that the equilibrium of an order parameter in the neural network model exactly corresponds to the maximum-likelihood estimation when the strength of the symmetrical recurrent synaptic connectivity within a neural population is appropriately stronger than that of asymmetrical connectivity, that of local and external inputs, and that of symmetrical or asymmetrical connectivity between different neural populations. In this case, strengths of local and external inputs or those of symmetrical connectivity between different neural populations exactly correspond to the input certainty in maximum-likelihood estimation. Thus, my analysis suggests appropriately strong symmetrical recurrent connectivity as a possible candidate for implementing the maximum-likelihood estimation within our neural system. (paper)

  13. Mirror of the refined topological vertex from a matrix model

    CERN Document Server

    Eynard, B

    2011-01-01

    We find an explicit matrix model computing the refined topological vertex, starting from its representation in terms of plane partitions. We then find the spectral curve of that matrix model, and thus the mirror symmetry of the refined vertex. With the same method we also find a matrix model for the strip geometry, and we find its mirror curve. The fact that there is a matrix model shows that the refined topological string amplitudes also satisfy the remodeling the B-model construction.

  14. A refinement methodology for object-oriented programs

    OpenAIRE

    Tafat , Asma; Boulmé , Sylvain; Marché , Claude

    2010-01-01

    International audience; Refinement is a well-known approach for developing correct-byconstruction software. It has been very successful for producing high quality code e.g., as implemented in the B tool. Yet, such refinement techniques are restricted in the sense that they forbid aliasing (and more generally sharing of data-structures), which often happens in usual programming languages. We propose a sound approach for refinement in presence of aliases. Suitable abstractions of programs are d...

  15. Solving the Sophistication-Population Paradox of Game Refinement Theory

    OpenAIRE

    Xiong , Shuo; Tiwary , Parth ,; Iida , Hiroyuki

    2016-01-01

    Part 4: Short Papers; International audience; A mathematical model of game refinement was proposed based on uncertainty of game outcome. This model has been shown to be useful in measuring the entertainment element in the domains such as boardgames and sport games. However, game refinement theory has not been able to explain the correlation between the popularity of a game and the game refinement value. This paper introduces another aspect in the study of game entertainment, the concept of “a...

  16. Refining clean fuels for the future

    International Nuclear Information System (INIS)

    Courty, P.; Gruson, J.F.

    2001-01-01

    To which extent transportation fuels will reasonably be changed in the coming years? LPG and natural gas are expected to challenge conventional fuels, hydrogen and methanol are bounded to possible fuel cells development. Among others, security of supply, competitive economics and environmental protection issues will be the key to the changes in the coming years. But taking into account expected transportation development, liquid fuels from oil should prevail as the reference energy. Though most of technologies and catalysts needed for the future are still existing or under marketing plans, the industry has to cope with the growing share of middle distillates. Indeed future zero heavy fuel-oil refineries are technically feasible through many existing and recent technologies. However their potential profitability is weighed down deeply by the very high investments and operating costs which are tied up. Tomorrow's main gasoline challenges deal with sulfur in FCC gasoline, aromatics and olefins contents together with a possible ban of ethers, hampering future octane demand and its technical feasibility. In a similar way diesel oil issues for the future imply a very deep desulfurization with possible aromatics hydrogenation and rings opening in order to comply with cetane and poly-aromatics ratings. Natural gas upgrading via syngas chemistry is still expected to open the way to clean fuels for the future via improved and integrated FT's GTL technologies which could as a matter provide most of future increases in clean fuels demand without decreasing the related fatal carbon losses as CO 2 . As an overall view, clean fuels production for the future is technically feasible. Advanced hydro-refining and hydro-conversion technologies open the way to clean fuels and allow the best flexibility in the gasoline/middle distillates ratio. However cost reduction remains a key issue since the huge investments needed are faced with low and volatile refining margins. In addition, CO 2

  17. Investigation of efficient features for image recognition by neural networks.

    Science.gov (United States)

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. Copyright © 2011 Elsevier Ltd. All rights reserved.

  18. Refining of raw materials, lignite present economic problems

    Energy Technology Data Exchange (ETDEWEB)

    Schirmer, G.

    1985-06-01

    East Germany seeks an economic intensification program that involves refining raw materials to a higher level. Lignite briquetting prior to liquefaction and gasification illustrates both the theoretical and practical aspects of that goal and also introduces questions of secure supplies. The author describes the special labor processes, use of technology, recycling of waste materials, and other new problems that the approach entails as the refined raw materials become new materials or energy sources. Economics based on the value of the refined product and the cost of the materials determine the degree of refinement. The concept also involves the relationship of producer and user as profits increase.

  19. Method of optimization of the natural gas refining process

    Energy Technology Data Exchange (ETDEWEB)

    Sadykh-Zade, E.S.; Bagirov, A.A.; Mardakhayev, I.M.; Razamat, M.S.; Tagiyev, V.G.

    1980-01-01

    The SATUM (automatic control system of technical operations) system introduced at the Shatlyk field should assure good quality of gas refining. In order to optimize the natural gas refining processes and experimental-analytical method is used in compiling the mathematical descriptions. The program, compiled in Fortran language, in addition to parameters of optimal conditions gives information on the yield of concentrate and water, concentration and consumption of DEG, composition and characteristics of the gas and condensate. The algorithm for calculating optimum engineering conditions of gas refining is proposed to be used in ''advice'' mode, and also for monitoring progress of the gas refining process.

  20. New Process for Grain Refinement of Aluminum. Final Report

    Energy Technology Data Exchange (ETDEWEB)

    Dr. Joseph A. Megy

    2000-09-22

    A new method of grain refining aluminum involving in-situ formation of boride nuclei in molten aluminum just prior to casting has been developed in the subject DOE program over the last thirty months by a team consisting of JDC, Inc., Alcoa Technical Center, GRAS, Inc., Touchstone Labs, and GKS Engineering Services. The Manufacturing process to make boron trichloride for grain refining is much simpler than preparing conventional grain refiners, with attendant environmental, capital, and energy savings. The manufacture of boride grain refining nuclei using the fy-Gem process avoids clusters, salt and oxide inclusions that cause quality problems in aluminum today.

  1. Architectural Refinement for the Design of Survivable Systems

    National Research Council Canada - National Science Library

    Ellison, Robert

    2001-01-01

    This paper describes a process for systematically refining an enterprise system architecture to resist recognize and recover from deliberate, malicious attacks by applying reusable design primitives...

  2. Minimum cost connection networks

    DEFF Research Database (Denmark)

    Hougaard, Jens Leth; Tvede, Mich

    2015-01-01

    In the present paper we consider the allocation of costs in connection networks. Agents have connection demands in form of pairs of locations they want to have connected. Connections between locations are costly to build. The problem is to allocate costs of networks satisfying all connection...... demands. We use a few axioms to characterize allocation rules that truthfully implement cost minimizing networks satisfying all connection demands in a game where: (1) a central planner announces an allocation rule and a cost estimation rule; (2) every agent reports her own connection demand as well...... as all connection costs; (3) the central planner selects a cost minimizing network satisfying reported connection demands based on the estimated costs; and, (4) the planner allocates the true costs of the selected network. It turns out that an allocation rule satisfies the axioms if and only if relative...

  3. Hybrid discrete-time neural networks.

    Science.gov (United States)

    Cao, Hongjun; Ibarz, Borja

    2010-11-13

    Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

  4. 'The Iranian connection': the geo-economics of exporting Central Asian energy via Iran

    International Nuclear Information System (INIS)

    Stauffer, T.R.

    1998-01-01

    Of the possible routes to connect Caucasian and Central Asian oil to market, the 'Iranian connection' is the most interesting. The economic attraction of the Iranian route is clear: large transport capacities exist in the various pieces of Iran's existing network, large refining centers are located near the Caspian and there ate unutilized export terminals [it

  5. Medical image segmentation by means of constraint satisfaction neural network

    International Nuclear Information System (INIS)

    Chen, C.T.; Tsao, C.K.; Lin, W.C.

    1990-01-01

    This paper applies the concept of constraint satisfaction neural network (CSNN) to the problem of medical image segmentation. Constraint satisfaction (or constraint propagation), the procedure to achieve global consistency through local computation, is an important paradigm in artificial intelligence. CSNN can be viewed as a three-dimensional neural network, with the two-dimensional image matrix as its base, augmented by various constraint labels for each pixel. These constraint labels can be interpreted as the connections and the topology of the neural network. Through parallel and iterative processes, the CSNN will approach a solution that satisfies the given constraints thus providing segmented regions with global consistency

  6. Statistical mechanics of attractor neural network models with synaptic depression

    International Nuclear Information System (INIS)

    Igarashi, Yasuhiko; Oizumi, Masafumi; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato

    2009-01-01

    Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.

  7. Online fouling detection in electrical circulation heaters using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Lalot, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Valenciennes (France). LME; Lecoeuche, S. [M.E.T.I.E.R., Longuenesse Cedex (France); Universite de Lille (France). Laboratoire 13D

    2003-06-01

    Here is presented a method that is able to detect fouling during the service of a circulation electrical heater. The neural based technique is divided in two major steps: identification and classification. Each step uses a neural network, the connection weights of the first one being the inputs of the second network. Each step is detailed and the main characteristics and abilities of the two neural networks are given. It is shown that the method is able to discriminate fouling from viscosity modification that would lead to the same type of effect on the total heat transfer coefficient. (author)

  8. Google matrix analysis of C.elegans neural network

    Energy Technology Data Exchange (ETDEWEB)

    Kandiah, V., E-mail: kandiah@irsamc.ups-tlse.fr; Shepelyansky, D.L., E-mail: dima@irsamc.ups-tlse.fr

    2014-05-01

    We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed.

  9. How synapses can enhance sensibility of a neural network

    Science.gov (United States)

    Protachevicz, P. R.; Borges, F. S.; Iarosz, K. C.; Caldas, I. L.; Baptista, M. S.; Viana, R. L.; Lameu, E. L.; Macau, E. E. N.; Batista, A. M.

    2018-02-01

    In this work, we study the dynamic range in a neural network modelled by cellular automaton. We consider deterministic and non-deterministic rules to simulate electrical and chemical synapses. Chemical synapses have an intrinsic time-delay and are susceptible to parameter variations guided by learning Hebbian rules of behaviour. The learning rules are related to neuroplasticity that describes change to the neural connections in the brain. Our results show that chemical synapses can abruptly enhance sensibility of the neural network, a manifestation that can become even more predominant if learning rules of evolution are applied to the chemical synapses.

  10. Google matrix analysis of C.elegans neural network

    International Nuclear Information System (INIS)

    Kandiah, V.; Shepelyansky, D.L.

    2014-01-01

    We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed.

  11. Abnormal synchrony and effective connectivity in patients with schizophrenia and auditory hallucinations

    Directory of Open Access Journals (Sweden)

    Maria de la Iglesia-Vaya

    2014-01-01

    These data indicate that an anomalous process of neural connectivity exists when patients with AH process emotional auditory stimuli. Additionally, a central role is suggested for the cerebellum in processing emotional stimuli in patients with persistent AH.

  12. Neural substrates of decision-making.

    Science.gov (United States)

    Broche-Pérez, Y; Herrera Jiménez, L F; Omar-Martínez, E

    2016-06-01

    Decision-making is the process of selecting a course of action from among 2 or more alternatives by considering the potential outcomes of selecting each option and estimating its consequences in the short, medium and long term. The prefrontal cortex (PFC) has traditionally been considered the key neural structure in decision-making process. However, new studies support the hypothesis that describes a complex neural network including both cortical and subcortical structures. The aim of this review is to summarise evidence on the anatomical structures underlying the decision-making process, considering new findings that support the existence of a complex neural network that gives rise to this complex neuropsychological process. Current evidence shows that the cortical structures involved in decision-making include the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). This process is assisted by subcortical structures including the amygdala, thalamus, and cerebellum. Findings to date show that both cortical and subcortical brain regions contribute to the decision-making process. The neural basis of decision-making is a complex neural network of cortico-cortical and cortico-subcortical connections which includes subareas of the PFC, limbic structures, and the cerebellum. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

  13. Soft Computing Methods for Disulfide Connectivity Prediction.

    Science.gov (United States)

    Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

    2015-01-01

    The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

  14. Refining the ischemic penumbra with topography.

    Science.gov (United States)

    Thirugnanachandran, Tharani; Ma, Henry; Singhal, Shaloo; Slater, Lee-Anne; Davis, Stephen M; Donnan, Geoffrey A; Phan, Thanh

    2018-04-01

    It has been 40 years since the ischemic penumbra was first conceptualized through work on animal models. The topography of penumbra has been portrayed as an infarcted core surrounded by penumbral tissue and an extreme rim of oligemic tissue. This picture has been used in many review articles and textbooks before the advent of modern imaging. In this paper, we review our understanding of the topography of the ischemic penumbra from the initial experimental animal models to current developments with neuroimaging which have helped to further define the temporal and spatial evolution of the penumbra and refine our knowledge. The concept of the penumbra has been successfully applied in clinical trials of endovascular therapies with a time window as long as 24 h from onset. Further, there are reports of "good" outcome even in patients with a large ischemic core. This latter observation of good outcome despite having a large core requires an understanding of the topography of the penumbra and the function of the infarcted regions. It is proposed that future research in this area takes departure from a time-dependent approach to a more individualized tissue and location-based approach.

  15. Oil Production, Refining and Transportation in Canada

    Directory of Open Access Journals (Sweden)

    Igbal A. Guliyev

    2015-01-01

    Full Text Available The article deals with fuel and energy complex of Canada as one of the largest manufacturers of primary energy in the world, which provides up to 6 percent of the world energy supply. Only the Russian Federation, PRC, the United States of America and the Kingdom of Saudi Arabia have larger production volumes. However, oil plays the most significant role in Canada's energy exports. It is estimated that its proven reserves are sufficient to meet the demand for 140 years at current production rate. The relevance of the study, including the analysis of fuel and energy complex of Canada, is due to the fact that such comparison and synthesis of data on the amount of recoverable oil reserves, the volume of its production, imports, exports and transit of oil and oil products, the distribution of oil for transportation (via pipelines, rail, sea, road, strategic oil field, refining and transportation of oil and oil products development projects, as well as implementation of Canada's best practices in the Russian Federation, is being developed for the first time. In addition, the data given in previously published articles on the subject, due to the dynamic development of the industry, are obsolete and do not reflect the real situation.

  16. Linear Prediction Using Refined Autocorrelation Function

    Directory of Open Access Journals (Sweden)

    M. Shahidur Rahman

    2007-07-01

    Full Text Available This paper proposes a new technique for improving the performance of linear prediction analysis by utilizing a refined version of the autocorrelation function. Problems in analyzing voiced speech using linear prediction occur often due to the harmonic structure of the excitation source, which causes the autocorrelation function to be an aliased version of that of the vocal tract impulse response. To estimate the vocal tract characteristics accurately, however, the effect of aliasing must be eliminated. In this paper, we employ homomorphic deconvolution technique in the autocorrelation domain to eliminate the aliasing effect occurred due to periodicity. The resulted autocorrelation function of the vocal tract impulse response is found to produce significant improvement in estimating formant frequencies. The accuracy of formant estimation is verified on synthetic vowels for a wide range of pitch frequencies typical for male and female speakers. The validity of the proposed method is also illustrated by inspecting the spectral envelopes of natural speech spoken by high-pitched female speaker. The synthesis filter obtained by the current method is guaranteed to be stable, which makes the method superior to many of its alternatives.

  17. Refined phase diagram of boron nitride

    International Nuclear Information System (INIS)

    Solozhenko, V.; Turkevich, V.Z.

    1999-01-01

    The equilibrium phase diagram of boron nitride thermodynamically calculated by Solozhenko in 1988 has been now refined on the basis of new experimental data on BN melting and extrapolation of heat capacities of BN polymorphs into high-temperature region using the adapted pseudo-Debye model. As compared with the above diagram, the hBN left-reversible cBN equilibrium line is displaced by 60 K toward higher temperatures. The hBN-cBN-L triple point has been calculated to be at 3480 ± 10 K and 5.9 ± 0.1 GPa, while the hBN-L-V triple point is at T = 3400 ± 20 K and p = 400 ± 20 Pa, which indicates that the region of thermodynamic stability of vapor in the BN phase diagram is extremely small. It has been found that the slope of the cBN melting curve is positive whereas the slope of hBN melting curve varies from positive between ambient pressure and 3.4 GPa to negative at higher pressures

  18. Refining and blending of aviation turbine fuels.

    Science.gov (United States)

    White, R D

    1999-02-01

    Aviation turbine fuels (jet fuels) are similar to other petroleum products that have a boiling range of approximately 300F to 550F. Kerosene and No.1 grades of fuel oil, diesel fuel, and gas turbine oil share many similar physical and chemical properties with jet fuel. The similarity among these products should allow toxicology data on one material to be extrapolated to the others. Refineries in the USA manufacture jet fuel to meet industry standard specifications. Civilian aircraft primarily use Jet A or Jet A-1 fuel as defined by ASTM D 1655. Military aircraft use JP-5 or JP-8 fuel as defined by MIL-T-5624R or MIL-T-83133D respectively. The freezing point and flash point are the principle differences between the finished fuels. Common refinery processes that produce jet fuel include distillation, caustic treatment, hydrotreating, and hydrocracking. Each of these refining processes may be the final step to produce jet fuel. Sometimes blending of two or more of these refinery process streams are needed to produce jet fuel that meets the desired specifications. Chemical additives allowed for use in jet fuel are also defined in the product specifications. In many cases, the customer rather than the refinery will put additives into the fuel to meet their specific storage or flight condition requirements.

  19. Structural refinement of vitreous silica bilayers

    Science.gov (United States)

    Sadjadi, Mahdi; Wilson, Mark; Thorpe, M. F.

    The importance of glasses resides not only in their applications but in fundamental questions that they put forth. The continuous random network model can successfully describe the glass structure, but determining details, like ring statistics, has always been difficult using only diffraction data. But recent atomic images of 2D vitreous silica bilayers can offer valuable new insights which are hard to be observed directly in 3D silica models/experiments (for references see). However, the experimental results are prone to uncertainty in atomic positions, systematic errors, and being finite. We employ special boundary conditions developed for such networks to refine the experimental structures. We show the best structure can be found by using various potentials to maximize information gained from the experimental samples. We find a range of densities, the so-called flexibility window, in which tetrahedra are perfect. We compare results from simulations using harmonic potentials, MD with atomic polarizabilities included and DFT. We should thank David Drabold and Bishal Bhattarai for useful discussions. Support through NSF Grant # DMS 1564468 is gratefully acknowledged.

  20. FPGA Congestion-Driven Placement Refinement

    Energy Technology Data Exchange (ETDEWEB)

    Vicente de, J.

    2005-07-01

    The routing congestion usually limits the complete proficiency of the FPGA logic resources. A key question can be formulated regarding the benefits of estimating the congestion at placement stage. In the last years, it is gaining acceptance the idea of a detailed placement taking into account congestion. In this paper, we resort to the Thermodynamic Simulated Annealing (TSA) algorithm to perform a congestion-driven placement refinement on the top of the common Bounding-Box pre optimized solution. The adaptive properties of TSA allow the search to preserve the solution quality of the pre optimized solution while improving other fine-grain objectives. Regarding the cost function two approaches have been considered. In the first one Expected Occupation (EO), a detailed probabilistic model to account for channel congestion is evaluated. We show that in spite of the minute detail of EO, the inherent uncertainty of this probabilistic model impedes to relieve congestion beyond the sole application of the Bounding-Box cost function. In the second approach we resort to the fast Rectilinear Steiner Regions algorithm to perform not an estimation but a measurement of the global routing congestion. This second strategy allows us to successfully reduce the requested channel width for a set of benchmark circuits with respect to the widespread Versatile Place and Route (VPR) tool. (Author) 31 refs.

  1. A neural flow estimator

    DEFF Research Database (Denmark)

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

    1995-01-01

    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...... 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...

  2. 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...

  3. Connectivities and synchronous firing in cortical neuronal networks

    International Nuclear Information System (INIS)

    Jia, L.C.; Sano, M.; Lai, P.-Y.; Chan, C.K.

    2004-01-01

    Network connectivities (k-bar) of cortical neural cultures are studied by synchronized firing and determined from measured correlations between fluorescence intensities of firing neurons. The bursting frequency (f) during synchronized firing of the networks is found to be an increasing function of k-bar. With f taken to be proportional to k-bar, a simple random model with a k-bar dependent connection probability p(k-bar) has been constructed to explain our experimental findings successfully

  4. Refining and Delegating Strategic Ability in ATL

    Directory of Open Access Journals (Sweden)

    Dimitar P. Guelev

    2014-04-01

    Full Text Available We propose extending Alternating-time Temporal Logic (ATL by an operator F to express that agent i can distribute its powers to a set of sub-agents G in a way which satisfies ATL condition f on the strategic ability of the coalitions they may form, possibly together with others agents. We prove the decidability of model-checking of formulas whose subformulas with this operator as the main connective have the form ... f, with no further occurrences of this operator in f.

  5. Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

    DEFF Research Database (Denmark)

    Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin

    2015-01-01

    correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking...... dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural...... mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online...

  6. Empirical validation of directed functional connectivity.

    Science.gov (United States)

    Mill, Ravi D; Bagic, Anto; Bostan, Andreea; Schneider, Walter; Cole, Michael W

    2017-02-01

    Mapping directions of influence in the human brain connectome represents the next phase in understanding its functional architecture. However, a host of methodological uncertainties have impeded the application of directed connectivity methods, which have primarily been validated via "ground truth" connectivity patterns embedded in simulated functional MRI (fMRI) and magneto-/electro-encephalography (MEG/EEG) datasets. Such simulations rely on many generative assumptions, and we hence utilized a different strategy involving empirical data in which a ground truth directed connectivity pattern could be anticipated with confidence. Specifically, we exploited the established "sensory reactivation" effect in episodic memory, in which retrieval of sensory information reactivates regions involved in perceiving that sensory modality. Subjects performed a paired associate task in separate fMRI and MEG sessions, in which a ground truth reversal in directed connectivity between auditory and visual sensory regions was instantiated across task conditions. This directed connectivity reversal was successfully recovered across different algorithms, including Granger causality and Bayes network (IMAGES) approaches, and across fMRI ("raw" and deconvolved) and source-modeled MEG. These results extend simulation studies of directed connectivity, and offer practical guidelines for the use of such methods in clarifying causal mechanisms of neural processing. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Connected vehicle standards.

    Science.gov (United States)

    2016-01-01

    Connected vehicles have the potential to transform the way Americans travel by : allowing cars, buses, trucks, trains, traffic signals, smart phones, and other devices to : communicate through a safe, interoperable wireless network. A connected vehic...

  8. Neural Networks: Implementations and Applications

    OpenAIRE

    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

  9. Improved Extension Neural Network and Its Applications

    Directory of Open Access Journals (Sweden)

    Yu Zhou

    2014-01-01

    Full Text Available Extension neural network (ENN is a new neural network that is a combination of extension theory and artificial neural network (ANN. The learning algorithm of ENN is based on supervised learning algorithm. One of important issues in the field of classification and recognition of ENN is how to achieve the best possible classifier with a small number of labeled training data. Training data selection is an effective approach to solve this issue. In this work, in order to improve the supervised learning performance and expand the engineering application range of ENN, we use a novel data selection method based on shadowed sets to refine the training data set of ENN. Firstly, we use clustering algorithm to label the data and induce shadowed sets. Then, in the framework of shadowed sets, the samples located around each cluster centers (core data and the borders between clusters (boundary data are selected as training data. Lastly, we use selected data to train ENN. Compared with traditional ENN, the proposed improved ENN (IENN has a better performance. Moreover, IENN is independent of the supervised learning algorithms and initial labeled data. Experimental results verify the effectiveness and applicability of our proposed work.

  10. Refined analysis results for multimedia network costs and profits

    DEFF Research Database (Denmark)

    Tahkokorpi, M.; Falch, Morten; Skouby, Knud Erik

    This deliverable describes the techno-economic business model developed in EURORIM WP3 and presents the refined results of the multimedia service delivery cost-profit calculations......This deliverable describes the techno-economic business model developed in EURORIM WP3 and presents the refined results of the multimedia service delivery cost-profit calculations...

  11. Investment in exploration-production and refining 2015

    International Nuclear Information System (INIS)

    Maisonnier, G.; Hureau, G.; Serbutoviez, S.; Silva, C.

    2016-01-01

    IFPEN analyses in this study the 2015 evolution of global investment in the field of exploration-production and refining: - Changes in oil and gas prices; - Investment in Exploration/Production: the end of an upward cycle; - Drilling and the global drilling market, upstream activities and markets; - 2015, a breath of fresh air for refining

  12. Effect of Chemical Refining on Citrullus Colocynthis and Pongamia ...

    African Journals Online (AJOL)

    Oil from the both plant seeds was evaluated (both before and after refining) for different physico-chemical parameters like free fatty acids, iodine value, peroxide value, saponification value, unsaponifiable matter and fatty acid composition. Oil yield (30-35 %) in both plants was found average. After refining, per cent reduction ...

  13. Optimization of Refining Craft for Vegetable Insulating Oil

    Science.gov (United States)

    Zhou, Zhu-Jun; Hu, Ting; Cheng, Lin; Tian, Kai; Wang, Xuan; Yang, Jun; Kong, Hai-Yang; Fang, Fu-Xin; Qian, Hang; Fu, Guang-Pan

    2016-05-01

    Vegetable insulating oil because of its environmental friendliness are considered as ideal material instead of mineral oil used for the insulation and the cooling of the transformer. The main steps of traditional refining process included alkali refining, bleaching and distillation. This kind of refining process used in small doses of insulating oil refining can get satisfactory effect, but can't be applied to the large capacity reaction kettle. This paper using rapeseed oil as crude oil, and the refining process has been optimized for large capacity reaction kettle. The optimized refining process increases the acid degumming process. The alkali compound adds the sodium silicate composition in the alkali refining process, and the ratio of each component is optimized. Add the amount of activated clay and activated carbon according to 10:1 proportion in the de-colorization process, which can effectively reduce the oil acid value and dielectric loss. Using vacuum pumping gas instead of distillation process can further reduce the acid value. Compared some part of the performance parameters of refined oil products with mineral insulating oil, the dielectric loss of vegetable insulating oil is still high and some measures are needed to take to further optimize in the future.

  14. The Analysis of the Refined Financial Management of Modern Enterprises

    Directory of Open Access Journals (Sweden)

    Li Ran

    2016-01-01

    Full Text Available This paper briefly introduces the concept of the refined financial management, elaborates on its characteristics and puts forward some main points about it. It also comes up with some personal suggestions for reference on effective ways of refining financial management.

  15. Local grid refinement for free-surface flow simulations

    NARCIS (Netherlands)

    van der Plas, Peter

    2017-01-01

    The principal goal of the current study is to explore and investigate the potential of local grid refinement for increasing the numerical efficiency of free-surface flow simulations in a practical context. In this thesis we propose a method for local grid refinement in the free-surface flow model

  16. Using atomic energy in the oil refining and petrochemical industry

    Energy Technology Data Exchange (ETDEWEB)

    Feigin, E.A.; Barashkov, R.Ia.; Raud, E.A.

    1982-01-01

    A short description of the basic large scale processes for oil refining and petrochemistry in which nuclear reactors can be used is given. The possible industrial plans for using nuclear reactors are examined together with the problems in using the advances in atomic technology in oil refining and petrochemical processes.

  17. Cavitation-aided grain refinement in aluminium alloys

    NARCIS (Netherlands)

    Atamanenko, T.V.

    2010-01-01

    This thesis deals with grain refinement under the influence of ultrasonic-driven cavitation in aluminium casting processes. Three major goals of this research were: (1) to identify the mechanism of the cavitation-aided grain refinement at different stages of solidification; (2) to reveal the

  18. RBT—A Tool for Building Refined Buneman Trees

    DEFF Research Database (Denmark)

    Besenbacher, Søren; Mailund; Westh-Nielsen, Lasse

    2005-01-01

    We have developed a tool implementing an efficient algorithm for refined Buneman tree reconstruction. The algorithm—which has the same complexity as the neighbour-joining method and the (plain) Buneman tree construction—enables refined Buneman tree reconstruction on large taxa sets....

  19. Grain refining efficiency of Al-Ti-C alloys

    International Nuclear Information System (INIS)

    Birol, Yuecel

    2006-01-01

    The problems associated with boride agglomeration and the poisoning effect of Zr in Zr-bearing alloys have created a big demand for boron-free grain refiners. The potential benefits of TiC as a direct nucleant for aluminium grains have thus generated a great deal of interest in TiC-bearing alloys in recent years. In Al-Ti-C grain refiners commercially available today, Al 3 Ti particles are introduced into the melt along with the TiC particles. Since the latter are claimed to nucleate α-Al directly, it is of great technological interest to see if reducing the Ti:C ratio further, i.e., increasing the C content of the grain refiner, will produce an increase in the grain refining efficiency of these alloys. A series of grain refiner samples with the Ti concentration fixed at 3% and a range of C contents between 0 and 0.75 were obtained by appropriately mixing an experimental Al-3Ti-0.75C alloy with Al-10Ti alloy and commercial purity aluminium. The grain refining efficiency of these grain refiners was assessed to investigate the role of the insoluble TiC and the soluble Al 3 Ti particles. The optimum chemistry for the Al-Ti-C grain refiners was also identified

  20. Grain refining efficiency of Al-Ti-C alloys

    Energy Technology Data Exchange (ETDEWEB)

    Birol, Yuecel [Materials Institute, Marmara Research Center, TUBITAK, 41470 Gebze, Kocaeli (Turkey)]. E-mail: yucel.birol@mam.gov.tr

    2006-09-28

    The problems associated with boride agglomeration and the poisoning effect of Zr in Zr-bearing alloys have created a big demand for boron-free grain refiners. The potential benefits of TiC as a direct nucleant for aluminium grains have thus generated a great deal of interest in TiC-bearing alloys in recent years. In Al-Ti-C grain refiners commercially available today, Al{sub 3}Ti particles are introduced into the melt along with the TiC particles. Since the latter are claimed to nucleate {alpha}-Al directly, it is of great technological interest to see if reducing the Ti:C ratio further, i.e., increasing the C content of the grain refiner, will produce an increase in the grain refining efficiency of these alloys. A series of grain refiner samples with the Ti concentration fixed at 3% and a range of C contents between 0 and 0.75 were obtained by appropriately mixing an experimental Al-3Ti-0.75C alloy with Al-10Ti alloy and commercial purity aluminium. The grain refining efficiency of these grain refiners was assessed to investigate the role of the insoluble TiC and the soluble Al{sub 3}Ti particles. The optimum chemistry for the Al-Ti-C grain refiners was also identified.

  1. Connecting to Everyday Practices

    DEFF Research Database (Denmark)

    Iversen, Ole Sejer; Smith, Rachel Charlotte

    2012-01-01

    construction and reproduction of cultural heritage creating novel connections between self and others and between past, present and future. We present experiences from a current research project, the Digital Natives exhibition, in which social media was designed as an integral part of the exhibition to connect...... focusing on the connections between audiences practices and the museum exhibition....

  2. Preparation of Al-Ti-B grain refiner by SHS technology[Self-propagating High-temperature Synthesis

    Energy Technology Data Exchange (ETDEWEB)

    Nikitin, V.I.; Wanqi, J.I.E.; Kandalova, E.G.; Makarenko, A.G.; Yong, L.

    2000-02-01

    Since the discovery of the grain refinement effect of aluminum by titanium, especially with the existence of B or C in 1950, grain refiners are widely accepted in industry for microstructure control of aluminum alloys. Research on this topic is to obtain the highest grain refinement efficiency with the lowest possible addition of master alloy. It is widely accepted that the morphology and size of TiAl{sub 3} particles, which are known as heterogeneous nucleation centers, are important factors deterring the grain refinement efficiency. Fine TiAl{sub 3} particles are favorable. The grain refinement process shows a heredity phenomenon, which means that structural information from initial materials transfers through a melt to the final product. It is important to find the connection between microstructural parameters of the master alloy and the final product. To improve the quality of Al-Ti-B master alloys for the use as a grain refiner, a new method based on SHS (self-propagating high-temperature synthesis) technology has been developed in Samara State Technical University to produce the master alloys. SHS, as a new method for preparation of materials, was first utilized by Merzhanov in 1967. This method uses the energy from highly exothermic reactions to sustain the chemical reaction in a combustion wave. The advantages of SHS include simplicity, low energy requirement, and higher product purity. Because SHS reactions can take place between elemental reactants, it is easy to control product composition. The purposes of this investigation were to fabricate an SHS Al-5%Ti-1%B master alloy, to analyze its structure and to test its grain refining performance.

  3. Invariant 2D object recognition using the wavelet transform and structured neural networks

    Science.gov (United States)

    Khalil, Mahmoud I.; Bayoumi, Mohamed M.

    1999-03-01

    This paper applies the dyadic wavelet transform and the structured neural networks approach to recognize 2D objects under translation, rotation, and scale transformation. Experimental results are presented and compared with traditional methods. The experimental results showed that this refined technique successfully classified the objects and outperformed some traditional methods especially in the presence of noise.

  4. 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…

  5. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  6. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  7. Whether integrating refining and petrochemical business can provide opportunities for development of petrochemical industry in Serbia

    Directory of Open Access Journals (Sweden)

    Popović Zoran M.

    2016-01-01

    Full Text Available Since the beginning of 90s of last century both the petroleum industry and petrochemical industry have operated in difficult circumstances. In particularly, margins of petroleum and petrochemical industry were exacerbated during global economic crisis in 2008-2009 years. At that time, as one option that could be the solution, the global analysts had started to more intense investigate the benefits of Refining-Petrochemical Integration. Shortly afterwards, more and more petroleum refineries and petrochemical manufacturers began to see the future in this kind of operational, managerial, marketing and commercial connection. This paper evaluates, in particular, the achieved level of integration of refinery and petrochemical businesses in Central and South-Eastern Europe. And specifically, the paper identifies current capabilities and future chances of linking this kind of integration between Serbian refining and petrochemical players. The viability of integration between possible actors and benefits of every single refining-petrochemical interface in Serbia depend on many factors, and therefore each integrated system is unique and requires prior serious Cost Benefit Analysis.

  8. A parallel adaptive mesh refinement algorithm for predicting turbulent non-premixed combusting flows

    International Nuclear Information System (INIS)

    Gao, X.; Groth, C.P.T.

    2005-01-01

    A parallel adaptive mesh refinement (AMR) algorithm is proposed for predicting turbulent non-premixed combusting flows characteristic of gas turbine engine combustors. The Favre-averaged Navier-Stokes equations governing mixture and species transport for a reactive mixture of thermally perfect gases in two dimensions, the two transport equations of the κ-ψ turbulence model, and the time-averaged species transport equations, are all solved using a fully coupled finite-volume formulation. A flexible block-based hierarchical data structure is used to maintain the connectivity of the solution blocks in the multi-block mesh and facilitate automatic solution-directed mesh adaptation according to physics-based refinement criteria. This AMR approach allows for anisotropic mesh refinement and the block-based data structure readily permits efficient and scalable implementations of the algorithm on multi-processor architectures. Numerical results for turbulent non-premixed diffusion flames, including cold- and hot-flow predictions for a bluff body burner, are described and compared to available experimental data. The numerical results demonstrate the validity and potential of the parallel AMR approach for predicting complex non-premixed turbulent combusting flows. (author)

  9. Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

    Science.gov (United States)

    Peng, Yefei

    2010-01-01

    An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

  10. Network connectivity value.

    Science.gov (United States)

    Dragicevic, Arnaud; Boulanger, Vincent; Bruciamacchie, Max; Chauchard, Sandrine; Dupouey, Jean-Luc; Stenger, Anne

    2017-04-21

    In order to unveil the value of network connectivity, we formalize the construction of ecological networks in forest environments as an optimal control dynamic graph-theoretic problem. The network is based on a set of bioreserves and patches linked by ecological corridors. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs of timber production. We consider a case of complete graph, where the ecological network is fully connected, and a case of incomplete graph, where the ecological network is partially connected. The results show that the network equilibrium depends on the size of the reception zone, while the network connectivity depends on the environmental compatibility between the ecological areas. Through shadow prices, we find that securing connectivity in partially connected networks is more expensive than in fully connected networks, but should be undertaken when the opportunity costs are significant. Copyright © 2017 Elsevier Ltd. All rights reserved.

  11. Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhisheng Zhang

    2016-01-01

    Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

  12. Dynamic effective connectivity of inter-areal brain circuits.

    Directory of Open Access Journals (Sweden)

    Demian Battaglia

    Full Text Available Anatomic connections between brain areas affect information flow between neuronal circuits and the synchronization of neuronal activity. However, such structural connectivity does not coincide with effective connectivity (or, more precisely, causal connectivity, related to the elusive question "Which areas cause the present activity of which others?". Effective connectivity is directed and depends flexibly on contexts and tasks. Here we show that dynamic effective connectivity can emerge from transitions in the collective organization of coherent neural activity. Integrating simulation and semi-analytic approaches, we study mesoscale network motifs of interacting cortical areas, modeled as large random networks of spiking neurons or as simple rate units. Through a causal analysis of time-series of model neural activity, we show that different dynamical states generated by a same structural connectivity motif correspond to distinct effective connectivity motifs. Such effective motifs can display a dominant directionality, due to spontaneous symmetry breaking and effective entrainment between local brain rhythms, although all connections in the considered structural motifs are reciprocal. We show then that transitions between effective connectivity configurations (like, for instance, reversal in the direction of inter-areal interactions can be triggered reliably by brief perturbation inputs, properly timed with respect to an ongoing local oscillation, without the need for plastic synaptic changes. Finally, we analyze how the information encoded in spiking patterns of a local neuronal population is propagated across a fixed structural connectivity motif, demonstrating that changes in the active effective connectivity regulate both the efficiency and the directionality of information transfer. Previous studies stressed the role played by coherent oscillations in establishing efficient communication between distant areas. Going beyond these early

  13. Force field refinement from NMR scalar couplings

    Energy Technology Data Exchange (ETDEWEB)

    Huang Jing [Department of Chemistry, University of Basel, Klingelbergstrasse 80, 4056 Basel (Switzerland); Meuwly, Markus, E-mail: m.meuwly@unibas.ch [Department of Chemistry, University of Basel, Klingelbergstrasse 80, 4056 Basel (Switzerland)

    2012-03-02

    Graphical abstract: We show that two classes of H-bonds are sufficient to quantitatively describe scalar NMR coupling constants in small proteins. Highlights: Black-Right-Pointing-Pointer We present force field refinements based on explicit MD simulations using scalar couplings across hydrogen bonds. Black-Right-Pointing-Pointer This leads to {sup h3}J{sub NC{sup }{sup P}{sup r}{sup i}{sup m}{sup e}} couplings to within 0.03 Hz at best compared to experiment. Black-Right-Pointing-Pointer A classification of H-bonds according to secondary structure is not sufficiently robust. Black-Right-Pointing-Pointer Grouping H-bonds into two classes and reparametrization yields an RMSD of 0.07 Hz. Black-Right-Pointing-Pointer This is an improvement of 50. - Abstract: NMR observables contain valuable information about the protein dynamics sampling a high-dimensional potential energy surface. Depending on the observable, the dynamics is sensitive to different time-windows. Scalar coupling constants {sup h3}J{sub NC{sup }{sup P}{sup r}{sup i}{sup m}{sup e}} reflect the pico- to nanosecond motions associated with the intermolecular hydrogen bond network. Including an explicit H-bond in the molecular mechanics with proton transfer (MMPT) potential allows us to reproduce experimentally determined {sup h3}J{sub NC{sup }{sup P}{sup r}{sup i}{sup m}{sup e}} couplings to within 0.02 Hz at best for ubiquitin and protein G. This is based on taking account of the chemically changing environment by grouping the H-bonds into up to seven classes. However, grouping them into two classes already reduces the RMSD between computed and observed {sup h3}J{sub NC{sup }{sup P}{sup r}{sup i}{sup m}{sup e}} couplings by almost 50%. Thus, using ensemble-averaged data with two classes of H-bonds leads to substantially improved scalar couplings from simulations with accurate force fields.

  14. View from the market : the refiners' perspective

    International Nuclear Information System (INIS)

    Earnest, N.K.

    2003-01-01

    The western Canadian crude market area is vast, ranging from Puget Sound to Oklahoma to western Pennsylvania. The three main challenges facing western Canadian crude oil producers are high crude production volumes of heavy sour grades, strong competition for market share from the United States and offshore crudes, and the inability to access offshore global crude markets in meaningful volumes. The paper includes a map depicting core markets which have historically processed most of the crude produced in western Canada. The map also includes extended market areas where western Canadian crude sales are expected to increase as production increases. These regions will become the price setting areas for Canadian crude. Currently more than 40 per cent of the total crude produced in western Canada flows into the upper Midwest market. This paper summarized Canadian crude pricing mechanisms, noting that more than half of the oil consumed in North America is imported, linking North America with the pricing dynamics and supply/demand considerations of the global crude markets. The two key points that have to be considered in the Canadian crude pricing mechanisms is the economics of refining and parity point location. Crude grades such as light sweet or medium sour, are priced relative to one another depending on their differing values as a feedstock. The greatest determinant of relative crude grade value is the proportion produced of high value, light transportation fuels compared to low value, heavy products such as asphalt and heavy fuel oil. The value of crude oil is also influenced by the level of sulphur and metal content. A section of the paper was devoted to the pricing of heavy sour conventional and bitumen blend crudes, which is different than for lighter crude grades because of the near lack of competition from similar imported crude grades. A market optimization model used to analyze the future price relationships for Canadian crudes showed that the Canadian crude

  15. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-01

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  16. Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.

    Science.gov (United States)

    Pyle, Ryan; Rosenbaum, Robert

    2017-01-06

    Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

  17. Fastest learning in small-world neural networks

    International Nuclear Information System (INIS)

    Simard, D.; Nadeau, L.; Kroeger, H.

    2005-01-01

    We investigate supervised learning in neural networks. We consider a multi-layered feed-forward network with back propagation. We find that the network of small-world connectivity reduces the learning error and learning time when compared to the networks of regular or random connectivity. Our study has potential applications in the domain of data-mining, image processing, speech recognition, and pattern recognition

  18. Directions in refining and upgrading of heavy oil and bitumen

    International Nuclear Information System (INIS)

    Dawson, B.; Parker, R. J.; Flint, L.

    1997-01-01

    The expansion of heavy oil transportation, marketing and refining facilities over the past two decades have been reviewed to show the strides that several Canadian refiners have taken to build up the facilities required to process synthetic crude oil (SCO). Key points made at a conference, convened by the National Centre for Upgrading Technology (NCUT), held in Edmonton during September 1997 to discuss current and future directions in the refining and marketing of heavy oil, bitumen and SCO, were summarized. Among the key points mentioned were: (1) the high entry barriers faced by centralized upgraders, (2) the advantages of integrating SCO or heavy oil production with downstream refining, (3) the stiff competition from Venezuela and Mexico that both SCO and heavy oil will face in the U.S. PADD II market, (4) the differences between Canadian refiners who have profited from hydrocracking and are better able to handle coker-based SCO, and American refiners who rely chiefly on catalytic cracking and are less able to process the highly aromatic SCO, and (5) the disproportionate cost in the upgrading process represented by the conversion of asphaltenes. Challenges and opportunities for key stakeholders, i.e. producers, refiners, marketers and technology licensors also received much attention at the Edmonton conference

  19. New 2D adaptive mesh refinement algorithm based on conservative finite-differences with staggered grid

    Science.gov (United States)

    Gerya, T.; Duretz, T.; May, D. A.

    2012-04-01

    We present new 2D adaptive mesh refinement (AMR) algorithm based on stress-conservative finite-differences formulated for non-uniform rectangular staggered grid. The refinement approach is based on a repetitive cell splitting organized via a quad-tree construction (every parent cell is split into 4 daughter cells of equal size). Irrespective of the level of resolution every cell has 5 staggered nodes (2 horizontal velocities, 2 vertical velocities and 1 pressure) for which respective governing equations, boundary conditions and interpolation equations are formulated. The connectivity of the grid is achieved via cross-indexing of grid cells and basic nodal points located in their corners: four corner nodes are indexed for every cell and up to 4 surrounding cells are indexed for every node. The accuracy of the approach depends critically on the formulation of the stencil used at the "hanging" velocity nodes located at the boundaries between different levels of resolution. Most accurate results are obtained for the scheme based on the volume flux balance across the resolution boundary combined with stress-based interpolation of velocity orthogonal to the boundary. We tested this new approach with a number of 2D variable viscosity analytical solutions. Our tests demonstrate that the adaptive staggered grid formulation has convergence properties similar to those obtained in case of a standard, non-adaptive staggered grid formulation. This convergence is also achieved when resolution boundary crosses sharp viscosity contrast interfaces. The convergence rates measured are found to be insensitive to scenarios when the transition in grid resolution crosses sharp viscosity contrast interfaces. We compared various grid refinement strategies based on distribution of different field variables such as viscosity, density and velocity. According to these tests the refinement allows for significant (0.5-1 order of magnitude) increase in the computational accuracy at the same

  20. Neural complexity: A graph theoretic interpretation

    Science.gov (United States)

    Barnett, L.; Buckley, C. L.; Bullock, S.

    2011-04-01

    One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end, Tononi [Proc. Natl. Acad. Sci. USA.PNASA60027-842410.1073/pnas.91.11.5033 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system’s dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns [Cereb. Cortex53OPAV1047-321110.1093/cercor/10.2.127 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.71.016114 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular, we explicitly establish a dependency of neural complexity on cyclic graph motifs.

  1. 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.

  2. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

    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

  3. 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.

  4. Segmental Refinement: A Multigrid Technique for Data Locality

    KAUST Repository

    Adams, Mark F.; Brown, Jed; Knepley, Matt; Samtaney, Ravi

    2016-01-01

    We investigate a domain decomposed multigrid technique, termed segmental refinement, for solving general nonlinear elliptic boundary value problems. We extend the method first proposed in 1994 by analytically and experimentally investigating its complexity. We confirm that communication of traditional parallel multigrid is eliminated on fine grids, with modest amounts of extra work and storage, while maintaining the asymptotic exactness of full multigrid. We observe an accuracy dependence on the segmental refinement subdomain size, which was not considered in the original analysis. We present a communication complexity analysis that quantifies the communication costs ameliorated by segmental refinement and report performance results with up to 64K cores on a Cray XC30.

  5. Oil refining and product marketing developments in southeast Asia

    International Nuclear Information System (INIS)

    Szabo, A.M.

    1992-01-01

    Views on the future are based on supplies from a relatively stable Middle East and continued economic growth in the southeast Asian and Pacific countries. Under these circumstances the oil market for the Association of Southeast Asian Nations (ASEAN) will expand considerably during the decade of the 90's. Pacific country demand, 5.92 MMB/D, in 1990 is likely to grow to 7.06 MMB/D in 2000. Regional production could supply about 40% of this. The Asia-Pacific shortage of refining capacity could lead to high regional refined product prices and health refining profit margins. (author)

  6. Oil refining in U.S. foreign-trade zones

    International Nuclear Information System (INIS)

    Powell, S.J.; Potter, T.J.

    1991-01-01

    With the crude-oil import supply being as inexpensive as it is today, relative to domestic supply, many independents have been sourcing their crude-oil needs from abroad and have found it an opportune time to step up their level of refining activity. To further enhance their competitive position with respect to foreign refineries, certain domestic refiners have discovered the operational benefits and savings that result from having a refinery designated as a foreign-trade zone (FTZ) under the Foreign-Trade Zones Act of 1934, as amended. This paper examines the history and use of foreign-trade subzones for refining activities

  7. Segmental Refinement: A Multigrid Technique for Data Locality

    KAUST Repository

    Adams, Mark F.

    2016-08-04

    We investigate a domain decomposed multigrid technique, termed segmental refinement, for solving general nonlinear elliptic boundary value problems. We extend the method first proposed in 1994 by analytically and experimentally investigating its complexity. We confirm that communication of traditional parallel multigrid is eliminated on fine grids, with modest amounts of extra work and storage, while maintaining the asymptotic exactness of full multigrid. We observe an accuracy dependence on the segmental refinement subdomain size, which was not considered in the original analysis. We present a communication complexity analysis that quantifies the communication costs ameliorated by segmental refinement and report performance results with up to 64K cores on a Cray XC30.

  8. Anomaly detection in an automated safeguards system using neural networks

    International Nuclear Information System (INIS)

    Whiteson, R.; Howell, J.A.

    1992-01-01

    An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

  9. 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.

  10. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

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

    1996-01-01

    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

  11. Refining the intrinsic chimera flap: a review.

    Science.gov (United States)

    Agarwal, Jayant P; Agarwal, Shailesh; Adler, Neta; Gottlieb, Lawrence J

    2009-10-01

    Reconstruction of complex tissue deficiencies in which each missing component is in a different spatial relationship to each other can be particularly challenging, especially in patients with limited recipient vessels. The chimera flap design is uniquely suited to reconstruct these deformities. Chimera flaps have been previously defined in many ways with 2 main categories: prefabricated or intrinsic. Herein we attempt to clarify the definition of a true intrinsic chimeric flap and provide examples of how these constructs provide a method for reconstruction of complex defects. The versatility of the intrinsic chimera flap and its procurement from 7 different vascular systems is described. A clarification of the definition of a true intrinsic chimera flap is described. In addition, construction of flaps from the lateral femoral circumflex, deep circumflex iliac, inferior gluteal, peroneal, subscapular, thoracodorsal, and radial arterial systems is described to showcase the versatility of these chimera flaps. A true intrinsic chimera flap must consist of more than a single tissue type. Each of the tissue components receives its blood flow from separate vascular branches or perforators that are connected to a single vascular source. These vascular branches must be of appropriate length to allow for insetting with 3-dimensional spatial freedom. There are a multitude of sites from which true intrinsic chimera flaps may be harvested.

  12. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  13. Neural Control of the Lower Urinary Tract

    Science.gov (United States)

    de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

    2015-01-01

    This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

  14. Handbook of networking & connectivity

    CERN Document Server

    McClain, Gary R

    1994-01-01

    Handbook of Networking & Connectivity focuses on connectivity standards in use, including hardware and software options. The book serves as a guide for solving specific problems that arise in designing and maintaining organizational networks.The selection first tackles open systems interconnection, guide to digital communications, and implementing TCP/IP in an SNA environment. Discussions focus on elimination of the SNA backbone, routing SNA over internets, connectionless versus connection-oriented networks, internet concepts, application program interfaces, basic principles of layering, proto

  15. IR wireless cluster synapses of HYDRA very large neural networks

    Science.gov (United States)

    Jannson, Tomasz; Forrester, Thomas

    2008-04-01

    RF/IR wireless (virtual) synapses are critical components of HYDRA (Hyper-Distributed Robotic Autonomy) neural networks, already discussed in two earlier papers. The HYDRA network has the potential to be very large, up to 10 11-neurons and 10 18-synapses, based on already established technologies (cellular RF telephony and IR-wireless LANs). It is organized into almost fully connected IR-wireless clusters. The HYDRA neurons and synapses are very flexible, simple, and low-cost. They can be modified into a broad variety of biologically-inspired brain-like computing capabilities. In this third paper, we focus on neural hardware in general, and on IR-wireless synapses in particular. Such synapses, based on LED/LD-connections, dominate the HYDRA neural cluster.

  16. Functional neural networks underlying response inhibition in adolescents and adults.

    Science.gov (United States)

    Stevens, Michael C; Kiehl, Kent A; Pearlson, Godfrey D; Calhoun, Vince D

    2007-07-19

    This study provides the first description of neural network dynamics associated with response inhibition in healthy adolescents and adults. Functional and effective connectivity analyses of whole brain hemodynamic activity elicited during performance of a Go/No-Go task were used to identify functionally integrated neural networks and characterize their causal interactions. Three response inhibition circuits formed a hierarchical, inter-dependent system wherein thalamic modulation of input to premotor cortex by fronto-striatal regions led to response suppression. Adolescents differed from adults in the degree of network engagement, regional fronto-striatal-thalamic connectivity, and network dynamics. We identify and characterize several age-related differences in the function of neural circuits that are associated with behavioral performance changes across adolescent development.

  17. Neural Activity During The Formation Of A Giant Auditory Synapse

    NARCIS (Netherlands)

    M.C. Sierksma (Martijn)

    2018-01-01

    markdownabstractThe formation of synapses is a critical step in the development of the brain. During this developmental stage neural activity propagates across the brain from synapse to synapse. This activity is thought to instruct the precise, topological connectivity found in the sensory central

  18. Probing the basins of attraction of a recurrent neural network

    NARCIS (Netherlands)

    Heerema, M.; van Leeuwen, W.A.

    2000-01-01

    Analytical expressions for the weights $w_{ij}(b)$ of the connections of a recurrent neural network are found by taking explicitly into account basins of attraction, the size of which is characterized by a basin parameter $b$. It is shown that a network with $b \

  19. A recurrent neural network with ever changing synapses

    NARCIS (Netherlands)

    Heerema, M.; van Leeuwen, W.A.

    2000-01-01

    A recurrent neural network with noisy input is studied analytically, on the basis of a Discrete Time Master Equation. The latter is derived from a biologically realizable learning rule for the weights of the connections. In a numerical study it is found that the fixed points of the dynamics of the

  20. Does Artificial Neural Network Support Connectivism's Assumptions?

    Science.gov (United States)

    AlDahdouh, Alaa A.

    2017-01-01

    Connectivism was presented as a learning theory for the digital age and connectivists claim that recent developments in Artificial Intelligence (AI) and, more specifically, Artificial Neural Network (ANN) support their assumptions of knowledge connectivity. Yet, very little has been done to investigate this brave allegation. Does the advancement…