Reflections on Active Networking

Science.gov (United States)

2005-01-01

with a Software Switch for Active Networks ”. We had initially called the project “ SoftSwitch ”, but after some concerns David Farber raised that this...Reflections on Active Networking Jonathan M. Smith CIS Department, University of Pennsylvania jms@cis.upenn.edu Abstract Interactions among...telecommunications networks , computers, and other peripheral devices have been of interest since the earliest distributed computing systems. A key

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)

Sustained Activity in Hierarchical Modular Neural Networks: Self-Organized Criticality and Oscillations

Science.gov (United States)

Wang, Sheng-Jun; Hilgetag, Claus C.; Zhou, Changsong

2010-01-01

Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. In particular, they are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality (SOC). We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. Previously, it was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We found that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and SOC, which are not present in the respective random networks. The mechanism underlying the sustained activity is that each dense module cannot sustain activity on its own, but displays SOC in the presence of weak perturbations. Therefore, the hierarchical modular networks provide the coupling among subsystems with SOC. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivity of critical states and the predictability and timing of oscillations for efficient information

Consumer Activities and Reactions to Social Network Marketing

Directory of Open Access Journals (Sweden)

Bistra Vassileva

2017-06-01

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

Ripples Make Waves: Binding Structured Activity and Plasticity in Hippocampal Networks

Directory of Open Access Journals (Sweden)

Josef H. L. P. Sadowski

2011-01-01

Full Text Available Establishing novel episodic memories and stable spatial representations depends on an exquisitely choreographed, multistage process involving the online encoding and offline consolidation of sensory information, a process that is largely dependent on the hippocampus. Each step is influenced by distinct neural network states that influence the pattern of activation across cellular assemblies. In recent years, the occurrence of hippocampal sharp wave ripple (SWR oscillations has emerged as a potentially vital network phenomenon mediating the steps between encoding and consolidation, both at a cellular and network level by promoting the rapid replay and reactivation of recent activity patterns. Such events facilitate memory formation by optimising the conditions for synaptic plasticity to occur between contingent neural elements. In this paper, we explore the ways in which SWRs and other network events can bridge the gap between spatiomnemonic processing at cellular/synaptic and network levels in the hippocampus.

Sustained activity in hierarchical modular neural networks: self-organized criticality and oscillations

Directory of Open Access Journals (Sweden)

Sheng-Jun Wang

2011-06-01

Full Text Available Cerebral cortical brain networks possess a number of conspicuous features of structure and dynamics. First, these networks have an intricate, non-random organization. They are structured in a hierarchical modular fashion, from large-scale regions of the whole brain, via cortical areas and area subcompartments organized as structural and functional maps to cortical columns, and finally circuits made up of individual neurons. Second, the networks display self-organized sustained activity, which is persistent in the absence of external stimuli. At the systems level, such activity is characterized by complex rhythmical oscillations over a broadband background, while at the cellular level, neuronal discharges have been observed to display avalanches, indicating that cortical networks are at the state of self-organized criticality. We explored the relationship between hierarchical neural network organization and sustained dynamics using large-scale network modeling. It was shown that sparse random networks with balanced excitation and inhibition can sustain neural activity without external stimulation. We find that a hierarchical modular architecture can generate sustained activity better than random networks. Moreover, the system can simultaneously support rhythmical oscillations and self-organized criticality, which are not present in the respective random networks. The underlying mechanism is that each dense module cannot sustain activity on its own, but displays self-organized criticality in the presence of weak perturbations. The hierarchical modular networks provide the coupling among subsystems with self-organized criticality. These results imply that the hierarchical modular architecture of cortical networks plays an important role in shaping the ongoing spontaneous activity of the brain, potentially allowing the system to take advantage of both the sensitivityof critical state and predictability and timing of oscillations for efficient

Management of synchronized network activity by highly active neurons

International Nuclear Information System (INIS)

Shein, Mark; Raichman, Nadav; Ben-Jacob, Eshel; Volman, Vladislav; Hanein, Yael

2008-01-01

Increasing evidence supports the idea that spontaneous brain activity may have an important functional role. Cultured neuronal networks provide a suitable model system to search for the mechanisms by which neuronal spontaneous activity is maintained and regulated. This activity is marked by synchronized bursting events (SBEs)—short time windows (hundreds of milliseconds) of rapid neuronal firing separated by long quiescent periods (seconds). However, there exists a special subset of rapidly firing neurons whose activity also persists between SBEs. It has been proposed that these highly active (HA) neurons play an important role in the management (i.e. establishment, maintenance and regulation) of the synchronized network activity. Here, we studied the dynamical properties and the functional role of HA neurons in homogeneous and engineered networks, during early network development, upon recovery from chemical inhibition and in response to electrical stimulations. We found that their sequences of inter-spike intervals (ISI) exhibit long time correlations and a unimodal distribution. During the network's development and under intense inhibition, the observed activity follows a transition period during which mostly HA neurons are active. Studying networks with engineered geometry, we found that HA neurons are precursors (the first to fire) of the spontaneous SBEs and are more responsive to electrical stimulations

Active hippocampal networks undergo spontaneous synaptic modification.

Directory of Open Access Journals (Sweden)

Masako Tsukamoto-Yasui

Full Text Available The brain is self-writable; as the brain voluntarily adapts itself to a changing environment, the neural circuitry rearranges its functional connectivity by referring to its own activity. How the internal activity modifies synaptic weights is largely unknown, however. Here we report that spontaneous activity causes complex reorganization of synaptic connectivity without any external (or artificial stimuli. Under physiologically relevant ionic conditions, CA3 pyramidal cells in hippocampal slices displayed spontaneous spikes with bistable slow oscillations of membrane potential, alternating between the so-called UP and DOWN states. The generation of slow oscillations did not require fast synaptic transmission, but their patterns were coordinated by local circuit activity. In the course of generating spontaneous activity, individual neurons acquired bidirectional long-lasting synaptic modification. The spontaneous synaptic plasticity depended on a rise in intracellular calcium concentrations of postsynaptic cells, but not on NMDA receptor activity. The direction and amount of the plasticity varied depending on slow oscillation patterns and synapse locations, and thus, they were diverse in a network. Once this global synaptic refinement occurred, the same neurons now displayed different patterns of spontaneous activity, which in turn exhibited different levels of synaptic plasticity. Thus, active networks continuously update their internal states through ongoing synaptic plasticity. With computational simulations, we suggest that with this slow oscillation-induced plasticity, a recurrent network converges on a more specific state, compared to that with spike timing-dependent plasticity alone.

Networking activities in technology-based entrepreneurial teams

DEFF Research Database (Denmark)

Neergaard, Helle

2005-01-01

Based on social network theoy, this article investigates the distribution of networking roles and responsibilities in entrepreneurial founding teams. Its focus is on the team as a collection of individuals, thus allowing the research to address differences in networking patterns. It identifies six...... central networking activities and shows that not all founding team members are equally active 'networkers'. The analyses show that team members prioritize different networking activities and that one member in particular has extensive networking activities whereas other memebrs of the team are more...

Activity flow over resting-state networks shapes cognitive task activations.

Science.gov (United States)

Cole, Michael W; Ito, Takuya; Bassett, Danielle S; Schultz, Douglas H

2016-12-01

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

Network-level architecture and the evolutionary potential of underground metabolism.

Science.gov (United States)

Notebaart, Richard A; Szappanos, Balázs; Kintses, Bálint; Pál, Ferenc; Györkei, Ádám; Bogos, Balázs; Lázár, Viktória; Spohn, Réka; Csörgő, Bálint; Wagner, Allon; Ruppin, Eytan; Pál, Csaba; Papp, Balázs

2014-08-12

A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.

Assessing sensory versus optogenetic network activation by combining (o)fMRI with optical Ca2+ recordings.

Science.gov (United States)

Schmid, Florian; Wachsmuth, Lydia; Schwalm, Miriam; Prouvot, Pierre-Hugues; Jubal, Eduardo Rosales; Fois, Consuelo; Pramanik, Gautam; Zimmer, Claus; Faber, Cornelius; Stroh, Albrecht

2016-11-01

Encoding of sensory inputs in the cortex is characterized by sparse neuronal network activation. Optogenetic stimulation has previously been combined with fMRI (ofMRI) to probe functional networks. However, for a quantitative optogenetic probing of sensory-driven sparse network activation, the level of similarity between sensory and optogenetic network activation needs to be explored. Here, we complement ofMRI with optic fiber-based population Ca 2+ recordings for a region-specific readout of neuronal spiking activity in rat brain. Comparing Ca 2+ responses to the blood oxygenation level-dependent signal upon sensory stimulation with increasing frequencies showed adaptation of Ca 2+ transients contrasted by an increase of blood oxygenation level-dependent responses, indicating that the optical recordings convey complementary information on neuronal network activity to the corresponding hemodynamic response. To study the similarity of optogenetic and sensory activation, we quantified the density of cells expressing channelrhodopsin-2 and modeled light propagation in the tissue. We estimated the effectively illuminated volume and numbers of optogenetically stimulated neurons, being indicative of sparse activation. At the functional level, upon either sensory or optogenetic stimulation we detected single-peak short-latency primary Ca 2+ responses with similar amplitudes and found that blood oxygenation level-dependent responses showed similar time courses. These data suggest that ofMRI can serve as a representative model for functional brain mapping. © The Author(s) 2015.

Assessing sensory versus optogenetic network activation by combining (o)fMRI with optical Ca2+ recordings

Science.gov (United States)

Schmid, Florian; Wachsmuth, Lydia; Schwalm, Miriam; Prouvot, Pierre-Hugues; Jubal, Eduardo Rosales; Fois, Consuelo; Pramanik, Gautam; Zimmer, Claus; Stroh, Albrecht

2015-01-01

Encoding of sensory inputs in the cortex is characterized by sparse neuronal network activation. Optogenetic stimulation has previously been combined with fMRI (ofMRI) to probe functional networks. However, for a quantitative optogenetic probing of sensory-driven sparse network activation, the level of similarity between sensory and optogenetic network activation needs to be explored. Here, we complement ofMRI with optic fiber-based population Ca2+ recordings for a region-specific readout of neuronal spiking activity in rat brain. Comparing Ca2+ responses to the blood oxygenation level-dependent signal upon sensory stimulation with increasing frequencies showed adaptation of Ca2+ transients contrasted by an increase of blood oxygenation level-dependent responses, indicating that the optical recordings convey complementary information on neuronal network activity to the corresponding hemodynamic response. To study the similarity of optogenetic and sensory activation, we quantified the density of cells expressing channelrhodopsin-2 and modeled light propagation in the tissue. We estimated the effectively illuminated volume and numbers of optogenetically stimulated neurons, being indicative of sparse activation. At the functional level, upon either sensory or optogenetic stimulation we detected single-peak short-latency primary Ca2+ responses with similar amplitudes and found that blood oxygenation level-dependent responses showed similar time courses. These data suggest that ofMRI can serve as a representative model for functional brain mapping. PMID:26661247

Russian State Leveling Network (present and future)

Science.gov (United States)

Mazurova, Elena; Kopeikin, Sergei; Karpik, Aleksander

2017-04-01

In August 2016 the sixth session of the United Nations Committee of Experts of Global Geospatial Information Management (UN-GGIM) endorsed the roadmap for the development of a Global Geodetic Reference Frame (GGRF) and urged countries to join efforts for its creation. In response to the UN appeal in this article describes the current state of the high-precision Leveling Network in Russia and prospects of its development. In this paper, we consider projects related to the construction of new high-precision leveling lines by the classical methods, as well as issues of creating high-precision leveling network, associated with the development and implementation of a fundamentally new method of determining heights in geodesy - chronometric leveling based on the application of quantum metrology of time and the fundamental laws of general relativity. Keywords: leveling network, chronometric leveling, quantum metrology of time, the general theory of relativity.

Constructing level-2 phylogenetic networks from triplets

OpenAIRE

Iersel, Leo; Keijsper, J.C.M.; Kelk, Steven; Stougie, Leen; Hagen, F.; Boekhout, T.; Vingron, M.; Wong, L.

2009-01-01

htmlabstractJansson and Sung showed that, given a dense set of input triplets T (representing hypotheses about the local evolutionary relationships of triplets of taxa), it is possible to determine in polynomial time whether there exists a level-1 network consistent with T, and if so to construct such a network (Inferring a Level-1 Phylogenetic Network from a Dense Set of Rooted Triplets, Theoretical Computer Science, 363, pp. 60-68 (2006)). Here we extend this work by showing that this probl...

Coupling Strength and System Size Induce Firing Activity of Globally Coupled Neural Network

International Nuclear Information System (INIS)

Wei Duqu; Luo Xiaoshu; Zou Yanli

2008-01-01

We investigate how firing activity of globally coupled neural network depends on the coupling strength C and system size N. Network elements are described by space-clamped FitzHugh-Nagumo (SCFHN) neurons with the values of parameters at which no firing activity occurs. It is found that for a given appropriate coupling strength, there is an intermediate range of system size where the firing activity of globally coupled SCFHN neural network is induced and enhanced. On the other hand, for a given intermediate system size level, there exists an optimal value of coupling strength such that the intensity of firing activity reaches its maximum. These phenomena imply that the coupling strength and system size play a vital role in firing activity of neural network

System-level Modeling of Wireless Integrated Sensor Networks

DEFF Research Database (Denmark)

Virk, Kashif M.; Hansen, Knud; Madsen, Jan

2005-01-01

Wireless integrated sensor networks have emerged as a promising infrastructure for a new generation of monitoring and tracking applications. In order to efficiently utilize the extremely limited resources of wireless sensor nodes, accurate modeling of the key aspects of wireless sensor networks...... is necessary so that system-level design decisions can be made about the hardware and the software (applications and real-time operating system) architecture of sensor nodes. In this paper, we present a SystemC-based abstract modeling framework that enables system-level modeling of sensor network behavior...... by modeling the applications, real-time operating system, sensors, processor, and radio transceiver at the sensor node level and environmental phenomena, including radio signal propagation, at the sensor network level. We demonstrate the potential of our modeling framework by simulating and analyzing a small...

Active influence in dynamical models of structural balance in social networks

Science.gov (United States)

Summers, Tyler H.; Shames, Iman

2013-07-01

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of “friendliness levels” in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international-relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.

Infraslow Electroencephalographic and Dynamic Resting State Network Activity.

Science.gov (United States)

Grooms, Joshua K; Thompson, Garth J; Pan, Wen-Ju; Billings, Jacob; Schumacher, Eric H; Epstein, Charles M; Keilholz, Shella D

2017-06-01

A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

Radon levels in a water distribution network

International Nuclear Information System (INIS)

Alabdula'aly, A.I.

1997-01-01

The capital city of Saudi Arabia, Riyadh, relies on both desalinated sea water as well as treated groundwater to meet all its water requirements. About 66% of the water demand is met by desalinated sea water, and the remaining is supplied by six groundwater treatment plants located in the vicinity of the city and supplied with water from 161 wells. The desalinated sea water is blended with only one plant product water and pumped to the distribution network, whereas the other five plants product water is pumped directly to the network. A study of 222 Rn levels in the city distribution network was carried out in which 89 samples were collected from different locations representing the city districts. All samples have shown low radon levels with an average concentration of 0.2 Bq l -1 and a range values of 0.1-1.0 Bq l -1 . The level of radon in different parts of the network was found to be influenced by the water sources to which they are supplied. The lowest radon levels were observed in districts supplied mostly by desalinated sea water. (Author)

Theorizing Network-Centric Activity in Education

Science.gov (United States)

HaLevi, Andrew

2011-01-01

Networks and network-centric activity are increasingly prevalent in schools and school districts. In addition to ubiquitous social network tools like Facebook and Twitter, educational leaders deal with a wide variety of network organizational forms that include professional development, advocacy, informational networks and network-centric reforms.…

Pre-stimulus BOLD-network activation modulates EEG spectral activity during working memory retention

Directory of Open Access Journals (Sweden)

Mara eKottlow

2015-05-01

Full Text Available Working memory (WM processes depend on our momentary mental state and therefore exhibit considerable fluctuations. Here, we investigate the interplay of task-preparatory and task-related brain activity as represented by pre-stimulus BOLD-fluctuations and spectral EEG from the retention periods of a visual WM task. Visual WM is used to maintain sensory information in the brain enabling the performance of cognitive operations and is associated with mental health.We tested 22 subjects simultaneously with EEG and fMRI while performing a visuo-verbal Sternberg task with two different loads, allowing for the temporal separation of preparation, encoding, retention and retrieval periods.Four temporally coherent networks - the default mode network (DMN, the dorsal attention, the right and the left WM network - were extracted from the continuous BOLD data by means of a group ICA. Subsequently, the modulatory effect of these networks’ pre-stimulus activation upon retention-related EEG activity in the theta, alpha and beta frequencies was analyzed. The obtained results are informative in the context of state-dependent information processing.We were able to replicate two well-known load-dependent effects: the frontal-midline theta increase during the task and the decrease of pre-stimulus DMN activity. As our main finding, these two measures seem to depend on each other as the significant negative correlations at frontal-midline channels suggested. Thus, suppressed pre-stimulus DMN levels facilitated later task related frontal midline theta increases. In general, based on previous findings that neuronal coupling in different frequency bands may underlie distinct functions in WM retention, our results suggest that processes reflected by spectral oscillations during retention seem not only to be online synchronized with activity in different attention-related networks but are also modulated by activity in these networks during preparation intervals.

System-Level Design Methodologies for Networked Multiprocessor Systems-on-Chip

DEFF Research Database (Denmark)

Virk, Kashif Munir

2008-01-01

is the first such attempt in the published literature. The second part of the thesis deals with the issues related to the development of system-level design methodologies for networked multiprocessor systems-on-chip at various levels of design abstraction with special focus on the modeling and design...... at the system-level. The multiprocessor modeling framework is then extended to include models of networked multiprocessor systems-on-chip which is then employed to model wireless sensor networks both at the sensor node level as well as the wireless network level. In the third and the final part, the thesis...... to the transaction-level model. The thesis, as a whole makes contributions by describing a design methodology for networked multiprocessor embedded systems at three layers of abstraction from system-level through transaction-level to the cycle accurate level as well as demonstrating it practically by implementing...

Study of active crossover network | Tyona | Nigerian Journal of Physics

African Journals Online (AJOL)

An active crossover network system has been realized using an active component LF356 with a JFET input. The net work has two drives, the low frequency drive (Bass) and the high frequency drive (Treble). It employs high level crossover technique. The circuit performance was adequately verified and the frequency ...

Network analysis of inter-organizational relationships and policy use among active living organizations in Alberta, Canada.

Science.gov (United States)

Loitz, Christina C; Stearns, Jodie A; Fraser, Shawn N; Storey, Kate; Spence, John C

2017-08-09

Coordinated partnerships and collaborations can optimize the efficiency and effectiveness of service and program delivery in organizational networks. However, the extent to which organizations are working together to promote physical activity, and use physical activity policies in Canada, is unknown. This project sought to provide a snapshot of the funding, coordination and partnership relationships among provincial active living organizations (ALOs) in Alberta, Canada. Additionally, the awareness, and use of the provincial policy and national strategy by the organizations was examined. Provincial ALOs (N = 27) answered questions regarding their funding, coordination and partnership connections with other ALOs in the network. Social network analysis was employed to examine network structure and position of each ALO. Discriminant function analysis determined the extent to which degree centrality was associated with the use of the Active Alberta (AA) policy and Active Canada 20/20 (AC 20/20) strategy. The funding network had a low density level (density = .20) and was centralized around Alberta Tourism Parks and Recreation (ATPR; degree centralization = 48.77%, betweenness centralization = 32.43%). The coordination network had a moderate density level (density = .31), and was low-to-moderately centralized around a few organizations (degree centralization = 45.37%, betweenness centrality = 19.92%). The partnership network had a low density level (density = .15), and was moderate-to-highly centralized around ATPR. Most organizations were aware of AA (89%) and AC 20/20 (78%), however more were using AA (67%) compared to AC 20/20 (33%). Central ALOs in the funding network were more likely to use AA and AC 20/20. Central ALOs in the coordination network were more likely to use AC 20/20, but not AA. Increasing formal and informal relationships between organizations and integrating disconnected or peripheral organizations could increase the capacity of the

Memory and pattern storage in neural networks with activity dependent synapses

Science.gov (United States)

Mejias, J. F.; Torres, J. J.

2009-01-01

We present recently obtained results on the influence of the interplay between several activity dependent synaptic mechanisms, such as short-term depression and facilitation, on the maximum memory storage capacity in an attractor neural network [1]. In contrast with the case of synaptic depression, which drastically reduces the capacity of the network to store and retrieve activity patterns [2], synaptic facilitation is able to enhance the memory capacity in different situations. In particular, we find that a convenient balance between depression and facilitation can enhance the memory capacity, reaching maximal values similar to those obtained with static synapses, that is, without activity-dependent processes. We also argue, employing simple arguments, that this level of balance is compatible with experimental data recorded from some cortical areas, where depression and facilitation may play an important role for both memory-oriented tasks and information processing. We conclude that depressing synapses with a certain level of facilitation allow to recover the good retrieval properties of networks with static synapses while maintaining the nonlinear properties of dynamic synapses, convenient for information processing and coding.

Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

KAUST Repository

Bai, Yancheng

2018-01-28

Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.

Contextual Multi-Scale Region Convolutional 3D Network for Activity Detection

KAUST Repository

Bai, Yancheng; Xu, Huijuan; Saenko, Kate; Ghanem, Bernard

2018-01-01

Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMS-RC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activities of different temporal scales. On each level of the temporal feature pyramid, an activity proposal detector and an activity classifier are learned to detect activities of specific temporal scales. Temporal contextual information is fused into activity classifiers for better recognition. More importantly, the entire model at all levels can be trained end-to-end. Our CMS-RC3D detector can deal with activities at all temporal scale ranges with only a single pass through the backbone network. We test our detector on two public activity detection benchmarks, THUMOS14 and ActivityNet. Extensive experiments show that the proposed CMS-RC3D detector outperforms state-of-the-art methods on THUMOS14 by a substantial margin and achieves comparable results on ActivityNet despite using a shallow feature extractor.

Higher frequency network activity flow predicts lower frequency node activity in intrinsic low-frequency BOLD fluctuations.

Science.gov (United States)

Bajaj, Sahil; Adhikari, Bhim Mani; Dhamala, Mukesh

2013-01-01

The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based time-frequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), slow-3 (0.073-0.198 Hz) and slow-2 (0.198-0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow-2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not 'entirely' at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural

The Meso-level Structure of F/OSS Collaboration Network

DEFF Research Database (Denmark)

Conald, Guido; Rullani, Francesco

2010-01-01

Social networks in Free/Open Source Software (F/OSS) have been usually analyzed at the level of the single project e.g., [6], or at the level of a whole ecology of projects, e.g., [33]. In this paper, we also investigate the social network generated by developers who collaborate to one or multiple...... F/OSS projects, but we focus on the less-studied meso-level structure emerging when applying to this network a community-detection technique. The network of ‘communities’ emerging from this analysis links sub-groups of densely connected developers, sub-groups that are smaller than the components...... of the network but larger than the teams working on single projects. Our results reveal the complexity of this meso-level structure, where several dense sub-groups of developers are connected by sparse collaboration among different sub-groups. We discuss the theoretical implications of our findings...

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

Science.gov (United States)

Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

2012-01-01

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

Stochastic cycle selection in active flow networks

Science.gov (United States)

Woodhouse, Francis; Forrow, Aden; Fawcett, Joanna; Dunkel, Jorn

2016-11-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. By connecting concepts from lattice field theory, graph theory and transition rate theory, we show how topology controls dynamics in a generic model for actively driven flow on a network. Through theoretical and numerical analysis we identify symmetry-based rules to classify and predict the selection statistics of complex flow cycles from the network topology. Our conceptual framework is applicable to a broad class of biological and non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ice-type models.

Network-dependent modulation of brain activity during sleep.

Science.gov (United States)

Watanabe, Takamitsu; Kan, Shigeyuki; Koike, Takahiko; Misaki, Masaya; Konishi, Seiki; Miyauchi, Satoru; Miyahsita, Yasushi; Masuda, Naoki

2014-09-01

Brain activity dynamically changes even during sleep. A line of neuroimaging studies has reported changes in functional connectivity and regional activity across different sleep stages such as slow-wave sleep (SWS) and rapid-eye-movement (REM) sleep. However, it remains unclear whether and how the large-scale network activity of human brains changes within a given sleep stage. Here, we investigated modulation of network activity within sleep stages by applying the pairwise maximum entropy model to brain activity obtained by functional magnetic resonance imaging from sleeping healthy subjects. We found that the brain activity of individual brain regions and functional interactions between pairs of regions significantly increased in the default-mode network during SWS and decreased during REM sleep. In contrast, the network activity of the fronto-parietal and sensory-motor networks showed the opposite pattern. Furthermore, in the three networks, the amount of the activity changes throughout REM sleep was negatively correlated with that throughout SWS. The present findings suggest that the brain activity is dynamically modulated even in a sleep stage and that the pattern of modulation depends on the type of the large-scale brain networks. Copyright © 2014 Elsevier Inc. All rights reserved.

Network analysis of inter-organizational relationships and policy use among active living organizations in Alberta, Canada

Directory of Open Access Journals (Sweden)

Christina C. Loitz

2017-08-01

Full Text Available Abstract Background Coordinated partnerships and collaborations can optimize the efficiency and effectiveness of service and program delivery in organizational networks. However, the extent to which organizations are working together to promote physical activity, and use physical activity policies in Canada, is unknown. This project sought to provide a snapshot of the funding, coordination and partnership relationships among provincial active living organizations (ALOs in Alberta, Canada. Additionally, the awareness, and use of the provincial policy and national strategy by the organizations was examined. Methods Provincial ALOs (N = 27 answered questions regarding their funding, coordination and partnership connections with other ALOs in the network. Social network analysis was employed to examine network structure and position of each ALO. Discriminant function analysis determined the extent to which degree centrality was associated with the use of the Active Alberta (AA policy and Active Canada 20/20 (AC 20/20 strategy. Results The funding network had a low density level (density = .20 and was centralized around Alberta Tourism Parks and Recreation (ATPR; degree centralization = 48.77%, betweenness centralization = 32.43%. The coordination network had a moderate density level (density = .31, and was low-to-moderately centralized around a few organizations (degree centralization = 45.37%, betweenness centrality = 19.92%. The partnership network had a low density level (density = .15, and was moderate-to-highly centralized around ATPR. Most organizations were aware of AA (89% and AC 20/20 (78%, however more were using AA (67% compared to AC 20/20 (33%. Central ALOs in the funding network were more likely to use AA and AC 20/20. Central ALOs in the coordination network were more likely to use AC 20/20, but not AA. Conclusions Increasing formal and informal relationships between organizations and integrating disconnected or

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

Science.gov (United States)

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

2015-05-01

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

Levels, Linkages, and Networks in Cross-Cultural Innovation.

Science.gov (United States)

Kennedy, Judith; Kennedy, Chris

1998-01-01

Individuals belong to different cultural networks, and these networks and connections between them play an important role in success or failure of educational innovation and should be included in any model of the management or evaluation of innovation. Looks at functions of the different networks to which individuals belong at three levels,…

The Unified Levelling Network of Sarawak and its Adjustment

Science.gov (United States)

Som, Z. A. M.; Yazid, A. M.; Ming, T. K.; Yazid, N. M.

2016-09-01

The height reference network of Sarawak has seen major improvement in over the past two decades. The most significant improvement was the establishment of extended precise leveling network of which is now able to connect all three major datum points at Pulau Lakei, Original and Bintulu. Datum by following the major accessible routes across Sarawak. This means the leveling network in Sarawak has now been inter-connected and unified. By having such a unified network leads to the possibility of having a common single least squares adjustment been performed for the first time. The least squares adjustment of this unified levelling network was attempted in order to compute the height of all Bench Marks established in the entire levelling network. The adjustment was done by using MoreFix levelling adjustment package developed at FGHT UTM. The computational procedure adopted is linear parametric adjustment by minimum constraint. Since Sarawak has three separate datums therefore three separate adjustments were implemented by utilizing datum at Pulau Lakei, Original Miri and Bintulu Datum respectively. Results of the MoreFix unified adjustment agreed very well with adjustment repeated using Starnet. Further the results were compared with solution given by Jupem and they are in good agreement as well. The difference in height analysed were within 10mm for the case of minimum constraint at Pulau Lakei datum and with much better agreement in the case of Original Miri Datum.

THE UNIFIED LEVELLING NETWORK OF SARAWAK AND ITS ADJUSTMENT

Directory of Open Access Journals (Sweden)

Z. A. M. Som

2016-09-01

Full Text Available The height reference network of Sarawak has seen major improvement in over the past two decades. The most significant improvement was the establishment of extended precise leveling network of which is now able to connect all three major datum points at Pulau Lakei, Original and Bintulu. Datum by following the major accessible routes across Sarawak. This means the leveling network in Sarawak has now been inter-connected and unified. By having such a unified network leads to the possibility of having a common single least squares adjustment been performed for the first time. The least squares adjustment of this unified levelling network was attempted in order to compute the height of all Bench Marks established in the entire levelling network. The adjustment was done by using MoreFix levelling adjustment package developed at FGHT UTM. The computational procedure adopted is linear parametric adjustment by minimum constraint. Since Sarawak has three separate datums therefore three separate adjustments were implemented by utilizing datum at Pulau Lakei, Original Miri and Bintulu Datum respectively. Results of the MoreFix unified adjustment agreed very well with adjustment repeated using Starnet. Further the results were compared with solution given by Jupem and they are in good agreement as well. The difference in height analysed were within 10mm for the case of minimum constraint at Pulau Lakei datum and with much better agreement in the case of Original Miri Datum.

Determining the confidence levels of sensor outputs using neural networks

Energy Technology Data Exchange (ETDEWEB)

Broten, G S; Wood, H C [Saskatchewan Univ., Saskatoon, SK (Canada). Dept. of Electrical Engineering

1996-12-31

This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network`s ability to determine the confidence level is influenced by the complexity of the sensor`s response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

Multi-Level Secure Local Area Network

OpenAIRE

Naval Postgraduate School (U.S.); Center for Information Systems Studies Security and Research (CISR)

2011-01-01

Multi-Level Secure Local Area Network is a cost effective, multi-level, easy to use office environment leveraging existing high assurance technology. The Department of Defense and U.S. Government have an identified need to securely share information classified at differing security levels. Because there exist no commercial solutions to this problem, NPS is developing a MLS LAN. The MLS LAN extends high assurance capabilities of an evaluated multi-level secure system to commercial personal com...

Polarity-specific high-level information propagation in neural networks.

Science.gov (United States)

Lin, Yen-Nan; Chang, Po-Yen; Hsiao, Pao-Yueh; Lo, Chung-Chuan

2014-01-01

Analyzing the connectome of a nervous system provides valuable information about the functions of its subsystems. Although much has been learned about the architectures of neural networks in various organisms by applying analytical tools developed for general networks, two distinct and functionally important properties of neural networks are often overlooked. First, neural networks are endowed with polarity at the circuit level: Information enters a neural network at input neurons, propagates through interneurons, and leaves via output neurons. Second, many functions of nervous systems are implemented by signal propagation through high-level pathways involving multiple and often recurrent connections rather than by the shortest paths between nodes. In the present study, we analyzed two neural networks: the somatic nervous system of Caenorhabditis elegans (C. elegans) and the partial central complex network of Drosophila, in light of these properties. Specifically, we quantified high-level propagation in the vertical and horizontal directions: the former characterizes how signals propagate from specific input nodes to specific output nodes and the latter characterizes how a signal from a specific input node is shared by all output nodes. We found that the two neural networks are characterized by very efficient vertical and horizontal propagation. In comparison, classic small-world networks show a trade-off between vertical and horizontal propagation; increasing the rewiring probability improves the efficiency of horizontal propagation but worsens the efficiency of vertical propagation. Our result provides insights into how the complex functions of natural neural networks may arise from a design that allows them to efficiently transform and combine input signals.

Levelling Profiles and a GPS Network to Monitor the Active Folding and Faulting Deformation in the Campo de Dalias (Betic Cordillera, Southeastern Spain)

Science.gov (United States)

Marín-Lechado, Carlos; Galindo-Zaldívar, Jesús; Gil, Antonio José; Borque, María Jesús; de Lacy, María Clara; Pedrera, Antonio; López-Garrido, Angel Carlos; Alfaro, Pedro; García-Tortosa, Francisco; Ramos, Maria Isabel; Rodríguez-Caderot, Gracia; Rodríguez-Fernández, José; Ruiz-Constán, Ana; de Galdeano-Equiza, Carlos Sanz

2010-01-01

The Campo de Dalias is an area with relevant seismicity associated to the active tectonic deformations of the southern boundary of the Betic Cordillera. A non-permanent GPS network was installed to monitor, for the first time, the fault- and fold-related activity. In addition, two high precision levelling profiles were measured twice over a one-year period across the Balanegra Fault, one of the most active faults recognized in the area. The absence of significant movement of the main fault surface suggests seismogenic behaviour. The possible recurrence interval may be between 100 and 300 y. The repetitive GPS and high precision levelling monitoring of the fault surface during a long time period may help us to determine future fault behaviour with regard to the existence (or not) of a creep component, the accumulation of elastic deformation before faulting, and implications of the fold-fault relationship. PMID:22319309

Activating and inhibiting connections in biological network dynamics

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Knight Rob

2008-12-01

Full Text Available Abstract Background Many studies of biochemical networks have analyzed network topology. Such work has suggested that specific types of network wiring may increase network robustness and therefore confer a selective advantage. However, knowledge of network topology does not allow one to predict network dynamical behavior – for example, whether deleting a protein from a signaling network would maintain the network's dynamical behavior, or induce oscillations or chaos. Results Here we report that the balance between activating and inhibiting connections is important in determining whether network dynamics reach steady state or oscillate. We use a simple dynamical model of a network of interacting genes or proteins. Using the model, we study random networks, networks selected for robust dynamics, and examples of biological network topologies. The fraction of activating connections influences whether the network dynamics reach steady state or oscillate. Conclusion The activating fraction may predispose a network to oscillate or reach steady state, and neutral evolution or selection of this parameter may affect the behavior of biological networks. This principle may unify the dynamics of a wide range of cellular networks. Reviewers Reviewed by Sergei Maslov, Eugene Koonin, and Yu (Brandon Xia (nominated by Mark Gerstein. For the full reviews, please go to the Reviewers' comments section.

A practical algorithm for reconstructing level-1 phylogenetic networks

NARCIS (Netherlands)

Huber, K.T.; Iersel, van L.J.J.; Kelk, S.M.; Suchecki, R.

2011-01-01

Recently, much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic trees in order to accommodate complex evolutionary processes. Here, we present an efficient, practical algorithm for reconstructing level-1 phylogenetic networks-a type of network

GABA and Gap Junctions in the Development of Synchronized Activity in Human Pluripotent Stem Cell-Derived Neural Networks

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Meeri Eeva-Liisa Mäkinen

2018-03-01

Full Text Available The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging and temporal resolution microelectrode array (MEA. We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling.

GABA and Gap Junctions in the Development of Synchronized Activity in Human Pluripotent Stem Cell-Derived Neural Networks

Science.gov (United States)

Mäkinen, Meeri Eeva-Liisa; Ylä-Outinen, Laura; Narkilahti, Susanna

2018-01-01

The electrical activity of the brain arises from single neurons communicating with each other. However, how single neurons interact during early development to give rise to neural network activity remains poorly understood. We studied the emergence of synchronous neural activity in human pluripotent stem cell (hPSC)-derived neural networks simultaneously on a single-neuron level and network level. The contribution of gamma-aminobutyric acid (GABA) and gap junctions to the development of synchronous activity in hPSC-derived neural networks was studied with GABA agonist and antagonist and by blocking gap junctional communication, respectively. We characterized the dynamics of the network-wide synchrony in hPSC-derived neural networks with high spatial resolution (calcium imaging) and temporal resolution microelectrode array (MEA). We found that the emergence of synchrony correlates with a decrease in very strong GABA excitation. However, the synchronous network was found to consist of a heterogeneous mixture of synchronously active cells with variable responses to GABA, GABA agonists and gap junction blockers. Furthermore, we show how single-cell distributions give rise to the network effect of GABA, GABA agonists and gap junction blockers. Finally, based on our observations, we suggest that the earliest form of synchronous neuronal activity depends on gap junctions and a decrease in GABA induced depolarization but not on GABAA mediated signaling. PMID:29559893

Determining the confidence levels of sensor outputs using neural networks

International Nuclear Information System (INIS)

Broten, G.S.; Wood, H.C.

1995-01-01

This paper describes an approach for determining the confidence level of a sensor output using multi-sensor arrays, sensor fusion and artificial neural networks. The authors have shown in previous work that sensor fusion and artificial neural networks can be used to learn the relationships between the outputs of an array of simulated partially selective sensors and the individual analyte concentrations in a mixture of analyses. Other researchers have shown that an array of partially selective sensors can be used to determine the individual gas concentrations in a gaseous mixture. The research reported in this paper shows that it is possible to extract confidence level information from an array of partially selective sensors using artificial neural networks. The confidence level of a sensor output is defined as a numeric value, ranging from 0% to 100%, that indicates the confidence associated with a output of a given sensor. A three layer back-propagation neural network was trained on a subset of the sensor confidence level space, and was tested for its ability to generalize, where the confidence level space is defined as all possible deviations from the correct sensor output. A learning rate of 0.1 was used and no momentum terms were used in the neural network. This research has shown that an artificial neural network can accurately estimate the confidence level of individual sensors in an array of partially selective sensors. This research has also shown that the neural network's ability to determine the confidence level is influenced by the complexity of the sensor's response and that the neural network is able to estimate the confidence levels even if more than one sensor is in error. The fundamentals behind this research could be applied to other configurations besides arrays of partially selective sensors, such as an array of sensors separated spatially. An example of such a configuration could be an array of temperature sensors in a tank that is not in

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.

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.

Associations between Aspects of Friendship Networks, Physical Activity, and Sedentary Behaviour among Adolescents

Science.gov (United States)

McCormack, Gavin R.; Nettel-Aguirre, Alberto; Blackstaffe, Anita; Perry, Rosemary; Hawe, Penelope

2014-01-01

Background. Adolescent friendships have been linked to physical activity levels; however, network characteristics have not been broadly examined. Method. In a cross-sectional analysis of 1061 adolescents (11–15 years), achieving 60 minutes/day of moderate-to-vigorous physical activity (MVPA) and participating in over 2 hours/day of sedentary behaviour were determined based on friendship network characteristics (density; proportion of active/sedentary friends; betweenness centrality; popularity; clique membership) and perceived social support. Results. Adolescents with no friendship nominations participated in less MVPA. For boys and girls, a ten percent point increase in active friends was positively associated with achievement of 60 minutes/day of MVPA (OR 1.11; 95% CI 1.02–1.21, OR 1.14; 95% CI 1.02–1.27, resp.). For boys, higher social support from friends was negatively associated with achieving 60 minutes/day of MVPA (OR 0.63; 95% CI 0.42–0.96). Compared with low density networks, boys in higher density networks were more likely to participate in over 2 hours/day of sedentary behaviour (OR 2.93; 95% CI 1.32–6.49). Social support from friends also modified associations between network characteristics and MVPA and sedentary behaviour. Conclusion. Different network characteristics appeared to have different consequences. The proportion of active close friends was associated with MVPA, while network density was associated with sedentary behaviour. This poses challenges for intervention design. PMID:25328690

Associations between Aspects of Friendship Networks, Physical Activity, and Sedentary Behaviour among Adolescents

Directory of Open Access Journals (Sweden)

Keri Jo Sawka

2014-01-01

Full Text Available Background. Adolescent friendships have been linked to physical activity levels; however, network characteristics have not been broadly examined. Method. In a cross-sectional analysis of 1061 adolescents (11–15 years, achieving 60 minutes/day of moderate-to-vigorous physical activity (MVPA and participating in over 2 hours/day of sedentary behaviour were determined based on friendship network characteristics (density; proportion of active/sedentary friends; betweenness centrality; popularity; clique membership and perceived social support. Results. Adolescents with no friendship nominations participated in less MVPA. For boys and girls, a ten percent point increase in active friends was positively associated with achievement of 60 minutes/day of MVPA (OR 1.11; 95% CI 1.02–1.21, OR 1.14; 95% CI 1.02–1.27, resp.. For boys, higher social support from friends was negatively associated with achieving 60 minutes/day of MVPA (OR 0.63; 95% CI 0.42–0.96. Compared with low density networks, boys in higher density networks were more likely to participate in over 2 hours/day of sedentary behaviour (OR 2.93; 95% CI 1.32–6.49. Social support from friends also modified associations between network characteristics and MVPA and sedentary behaviour. Conclusion. Different network characteristics appeared to have different consequences. The proportion of active close friends was associated with MVPA, while network density was associated with sedentary behaviour. This poses challenges for intervention design.

Achieving Network Level Privacy in Wireless Sensor Networks†

Science.gov (United States)

Shaikh, Riaz Ahmed; Jameel, Hassan; d’Auriol, Brian J.; Lee, Heejo; Lee, Sungyoung; Song, Young-Jae

2010-01-01

Full network level privacy has often been categorized into four sub-categories: Identity, Route, Location and Data privacy. Achieving full network level privacy is a critical and challenging problem due to the constraints imposed by the sensor nodes (e.g., energy, memory and computation power), sensor networks (e.g., mobility and topology) and QoS issues (e.g., packet reach-ability and timeliness). In this paper, we proposed two new identity, route and location privacy algorithms and data privacy mechanism that addresses this problem. The proposed solutions provide additional trustworthiness and reliability at modest cost of memory and energy. Also, we proved that our proposed solutions provide protection against various privacy disclosure attacks, such as eavesdropping and hop-by-hop trace back attacks. PMID:22294881

When BOLD is thicker than water: processing social information about kin and friends at different levels of the social network.

Science.gov (United States)

Wlodarski, Rafael; Dunbar, Robin I M

2016-12-01

The aim of this study was to examine differences in the neural processing of social information about kin and friends at different levels of closeness and social network level. Twenty-five female participants engaged in a cognitive social task involving different individuals in their social network while undergoing functional magnetic resonance imaging scanning to detect BOLD (Blood Oxygen Level Dependent) signals changes. Greater levels of activation occurred in several regions of the brain previously associated with social cognition when thinking about friends than when thinking about kin, including the posterior cingulate cortex (PCC) and the ventral medial prefrontal cortex (vMPFC). Linear parametric analyses across network layers further showed that, when it came to thinking about friends, activation increased in the vMPFC, lingual gyrus, and sensorimotor cortex as individuals thought about friends at closer layers of the network. These findings suggest that maintaining friendships may be more cognitively exacting than maintaining kin relationships. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Concurrent enterprise: a conceptual framework for enterprise supply-chain network activities

Science.gov (United States)

Addo-Tenkorang, Richard; Helo, Petri T.; Kantola, Jussi

2017-04-01

Supply-chain management (SCM) in manufacturing industries has evolved significantly over the years. Recently, a lot more relevant research has picked up on the development of integrated solutions. Thus, seeking a collaborative optimisation of geographical, just-in-time (JIT), quality (customer demand/satisfaction) and return-on-investment (profits), aspects of organisational management and planning through 'best practice' business-process management - concepts and application; employing system tools such as certain applications/aspects of enterprise resource planning (ERP) - SCM systems information technology (IT) enablers to enhance enterprise integrated product development/concurrent engineering principles. This article assumed three main organisation theory applications in positioning its assumptions. Thus, proposing a feasible industry-specific framework not currently included within the SCOR model's level four (4) implementation level, as well as other existing SCM integration reference models such as in the MIT process handbook's - Process Interchange Format (PIF), the TOVE project, etc. which could also be replicated in other SCs. However, the wider focus of this paper's contribution will be concentrated on a complimentary proposed framework to the SCC's SCOR reference model. Quantitative empirical closed-ended questionnaires in addition to the main data collected from a qualitative empirical real-life industrial-based pilot case study were used: To propose a conceptual concurrent enterprise framework for SCM network activities. This research adopts a design structure matrix simulation approach analysis to propose an optimal enterprise SCM-networked value-adding, customised master data-management platform/portal for efficient SCM network information exchange and an effective supply-chain (SC) network systems-design teams' structure. Furthermore, social network theory analysis will be employed in a triangulation approach with statistical correlation analysis

Systems-level mechanisms of action of Panax ginseng: a network pharmacological approach.

Science.gov (United States)

Park, Sa-Yoon; Park, Ji-Hun; Kim, Hyo-Su; Lee, Choong-Yeol; Lee, Hae-Jeung; Kang, Ki Sung; Kim, Chang-Eop

2018-01-01

Panax ginseng has been used since ancient times based on the traditional Asian medicine theory and clinical experiences, and currently, is one of the most popular herbs in the world. To date, most of the studies concerning P. ginseng have focused on specific mechanisms of action of individual constituents. However, in spite of many studies on the molecular mechanisms of P. ginseng , it still remains unclear how multiple active ingredients of P. ginseng interact with multiple targets simultaneously, giving the multidimensional effects on various conditions and diseases. In order to decipher the systems-level mechanism of multiple ingredients of P. ginseng , a novel approach is needed beyond conventional reductive analysis. We aim to review the systems-level mechanism of P. ginseng by adopting novel analytical framework-network pharmacology. Here, we constructed a compound-target network of P. ginseng using experimentally validated and machine learning-based prediction results. The targets of the network were analyzed in terms of related biological process, pathways, and diseases. The majority of targets were found to be related with primary metabolic process, signal transduction, nitrogen compound metabolic process, blood circulation, immune system process, cell-cell signaling, biosynthetic process, and neurological system process. In pathway enrichment analysis of targets, mainly the terms related with neural activity showed significant enrichment and formed a cluster. Finally, relative degrees analysis for the target-disease association of P. ginseng revealed several categories of related diseases, including respiratory, psychiatric, and cardiovascular diseases.

A flow level model for wireless multihop ad hoc network throughput

NARCIS (Netherlands)

Coenen, Tom Johannes Maria; van den Berg, Hans Leo; Boucherie, Richardus J.

2005-01-01

A flow level model for multihop wireless ad hoc networks is presented in this paper. Using a flow level view, we show the main properties and modeling challenges for ad hoc networks. Considering different scenarios, a multihop WLAN and a serial network with a TCP-like flow control protocol, we

PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL

Directory of Open Access Journals (Sweden)

S. Munapo

2012-01-01

Full Text Available

ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.

AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.

Intrinsically-generated fluctuating activity in excitatory-inhibitory networks

Science.gov (United States)

Mastrogiuseppe, Francesca; Ostojic, Srdjan

2017-01-01

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

Linking structure and activity in nonlinear spiking networks.

Directory of Open Access Journals (Sweden)

Gabriel Koch Ocker

2017-06-01

Full Text Available Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

Linking structure and activity in nonlinear spiking networks.

Science.gov (United States)

Ocker, Gabriel Koch; Josić, Krešimir; Shea-Brown, Eric; Buice, Michael A

2017-06-01

Recent experimental advances are producing an avalanche of data on both neural connectivity and neural activity. To take full advantage of these two emerging datasets we need a framework that links them, revealing how collective neural activity arises from the structure of neural connectivity and intrinsic neural dynamics. This problem of structure-driven activity has drawn major interest in computational neuroscience. Existing methods for relating activity and architecture in spiking networks rely on linearizing activity around a central operating point and thus fail to capture the nonlinear responses of individual neurons that are the hallmark of neural information processing. Here, we overcome this limitation and present a new relationship between connectivity and activity in networks of nonlinear spiking neurons by developing a diagrammatic fluctuation expansion based on statistical field theory. We explicitly show how recurrent network structure produces pairwise and higher-order correlated activity, and how nonlinearities impact the networks' spiking activity. Our findings open new avenues to investigating how single-neuron nonlinearities-including those of different cell types-combine with connectivity to shape population activity and function.

ANN multiscale model of anti-HIV drugs activity vs AIDS prevalence in the US at county level based on information indices of molecular graphs and social networks.

Science.gov (United States)

González-Díaz, Humberto; Herrera-Ibatá, Diana María; Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Orbegozo-Medina, Ricardo Alfredo; Pazos, Alejandro

2014-03-24

This work is aimed at describing the workflow for a methodology that combines chemoinformatics and pharmacoepidemiology methods and at reporting the first predictive model developed with this methodology. The new model is able to predict complex networks of AIDS prevalence in the US counties, taking into consideration the social determinants and activity/structure of anti-HIV drugs in preclinical assays. We trained different Artificial Neural Networks (ANNs) using as input information indices of social networks and molecular graphs. We used a Shannon information index based on the Gini coefficient to quantify the effect of income inequality in the social network. We obtained the data on AIDS prevalence and the Gini coefficient from the AIDSVu database of Emory University. We also used the Balaban information indices to quantify changes in the chemical structure of anti-HIV drugs. We obtained the data on anti-HIV drug activity and structure (SMILE codes) from the ChEMBL database. Last, we used Box-Jenkins moving average operators to quantify information about the deviations of drugs with respect to data subsets of reference (targets, organisms, experimental parameters, protocols). The best model found was a Linear Neural Network (LNN) with values of Accuracy, Specificity, and Sensitivity above 0.76 and AUROC > 0.80 in training and external validation series. This model generates a complex network of AIDS prevalence in the US at county level with respect to the preclinical activity of anti-HIV drugs in preclinical assays. To train/validate the model and predict the complex network we needed to analyze 43,249 data points including values of AIDS prevalence in 2,310 counties in the US vs ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4,856 protocols, and 10 possible experimental measures.

Network Communication for Low Level RF Control

International Nuclear Information System (INIS)

Liu Weiqing; Yin Chengke; Zhang Tongxuan; Fu Zechuan; Liu Jianfei

2009-01-01

Low Level RF (LLRF) control system for storage ring of Shanghai Synchrotron Radiation Facility (SSRF) has been built by digital technology. The settings of parameters and the feedback loop status are carried out through the network communication interface, and the local oscillation and clock, which is the important component of the digital LLRF control system, are also configured through network communication. NIOS II processor was employed as a core to build the embedded system with a real-time operating system MicroC/OS-II, finally Lightweight TCP/IP (LwIP) was used to achieve the communication interface. The communication network is stable after a long-term operation. (authors)

PersonA: Persuasive social network for physical Activity.

Science.gov (United States)

Ayubi, Soleh U; Parmanto, Bambang

2012-01-01

Advances in physical activity (PA) monitoring devices provide ample opportunities for innovations in the way the information produced by these devices is used to encourage people to have more active lifestyles. One such innovation is expanding the current use of the information from self-management to social support. We developed a Persuasive social network for physical Activity (PersonA) that combines automatic input of physical activity data, a smartphone, and a social networking system (SNS). This paper describes the motivation for and overarching design of the PersonA and its functional and non-functional features. PersonA is designed to intelligently and automatically receive raw PA data from the sensors in the smartphone, calculate the data into meaningful PA information, store the information on a secure server, and show the information to the users as persuasive and real-time feedbacks or publish the information to the SNS to generate social support. The implementation of self-monitoring, social support, and persuasive concepts using currently available technologies has the potential for promoting healthy lifestyle, greater community participation, and higher quality of life. We also expect that PersonA will enable health professionals to collect in situ data related to physical activity. The platform is currently being used and tested to improve PA level of three groups of users in Pittsburgh, PA, USA.

Meditation leads to reduced default mode network activity beyond an active task.

Science.gov (United States)

Garrison, Kathleen A; Zeffiro, Thomas A; Scheinost, Dustin; Constable, R Todd; Brewer, Judson A

2015-09-01

Meditation has been associated with relatively reduced activity in the default mode network, a brain network implicated in self-related thinking and mind wandering. However, previous imaging studies have typically compared meditation to rest, despite other studies having reported differences in brain activation patterns between meditators and controls at rest. Moreover, rest is associated with a range of brain activation patterns across individuals that has only recently begun to be better characterized. Therefore, in this study we compared meditation to another active cognitive task, both to replicate the findings that meditation is associated with relatively reduced default mode network activity and to extend these findings by testing whether default mode activity was reduced during meditation, beyond the typical reductions observed during effortful tasks. In addition, prior studies had used small groups, whereas in the present study we tested these hypotheses in a larger group. The results indicated that meditation is associated with reduced activations in the default mode network, relative to an active task, for meditators as compared to controls. Regions of the default mode network showing a Group × Task interaction included the posterior cingulate/precuneus and anterior cingulate cortex. These findings replicate and extend prior work indicating that the suppression of default mode processing may represent a central neural process in long-term meditation, and they suggest that meditation leads to relatively reduced default mode processing beyond that observed during another active cognitive task.

Finding quasi-optimal network topologies for information transmission in active networks.

Science.gov (United States)

Baptista, Murilo S; de Carvalho, Josué X; Hussein, Mahir S

2008-01-01

This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Finding quasi-optimal network topologies for information transmission in active networks.

Directory of Open Access Journals (Sweden)

Murilo S Baptista

Full Text Available This work clarifies the relation between network circuit (topology and behaviour (information transmission and synchronization in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Overexpression of cypin alters dendrite morphology, single neuron activity, and network properties via distinct mechanisms

Science.gov (United States)

Rodríguez, Ana R.; O'Neill, Kate M.; Swiatkowski, Przemyslaw; Patel, Mihir V.; Firestein, Bonnie L.

2018-02-01

Objective. This study investigates the effect that overexpression of cytosolic PSD-95 interactor (cypin), a regulator of synaptic PSD-95 protein localization and a core regulator of dendrite branching, exerts on the electrical activity of rat hippocampal neurons and networks. Approach. We cultured rat hippocampal neurons and used lipid-mediated transfection and lentiviral gene transfer to achieve high levels of cypin or cypin mutant (cypinΔPDZ PSD-95 non-binding) expression cellularly and network-wide, respectively. Main results. Our analysis revealed that although overexpression of cypin and cypinΔPDZ increase dendrite numbers and decrease spine density, cypin and cypinΔPDZ distinctly regulate neuronal activity. At the single cell level, cypin promotes decreases in bursting activity while cypinΔPDZ reduces sEPSC frequency and further decreases bursting compared to cypin. At the network level, by using the Fano factor as a measure of spike count variability, cypin overexpression results in an increase in variability of spike count, and this effect is abolished when cypin cannot bind PSD-95. This variability is also dependent on baseline activity levels and on mean spike rate over time. Finally, our spike sorting data show that overexpression of cypin results in a more complex distribution of spike waveforms and that binding to PSD-95 is essential for this complexity. Significance. Our data suggest that dendrite morphology does not play a major role in cypin action on electrical activity.

Enhancement of the FDOT's project level and network level bridge management analysis tools

Science.gov (United States)

2011-02-01

Over several years, the Florida Department of Transportation (FDOT) has been implementing the AASHTO Pontis Bridge Management System to support network-level and project-level decision making in the headquarters and district offices. Pontis is an int...

Network Physics anounces first product to provide business-level management of the most complex and dynamic networks

CERN Multimedia

2003-01-01

Network Physics, provider of business-level, traffic flow-based network management solutions, today announced the introduction of the Network Physics NP/BizFlow-1000. With the NP/BizFlow-1000, Fortune 1000 companies with complex and dynamic networks can analyze the flows that link business groups, critical applications, and network software and hardware (1 page).

Networking activism: implications for Greece

Directory of Open Access Journals (Sweden)

Pantelis Vatikiotis

2011-12-01

Full Text Available The outbreak of December 2008 against police brutality through a wave of demonstrations and street protests in Athens, which was strongly advocated by protest activities and practices across the world, addresses several issues in relation to the transformative potentials of mediated collective action. The paper critically evaluates different accounts of December events, probing then into thevery networking of that movement. From this perspective, it points out another aspect of the local-global interplay in protest culture along new mediating practices (beyond the creation of transnational publics, that of the implications of transnational networking for local social activism and identification, addressing relevant questions in the Greek context.

Using Active Networking to Detect and Troubleshoot Issues in Tactical Data Networks

Science.gov (United States)

2014-06-01

networking (SDN) paradigm, which has gained popularity in recent years, has its roots in the idea of programmable networks [6]. By extending the...278–289, Aug. 2011. 67 [13] M. Hicks, P. Kakkar, J. T. Moore, C. A. Gunter, and S. Nettles , “Plan: A programming language for active networks,” ACM

Adolescents' social environment and depression: social networks, extracurricular activity, and family relationship influences.

Science.gov (United States)

Mason, Michael J; Schmidt, Christopher; Abraham, Anisha; Walker, Leslie; Tercyak, Kenneth

2009-12-01

The present study examined components of adolescents' social environment (social network, extracurricular activities, and family relationships) in association with depression. A total of 332 adolescents presenting for a routine medical check-up were self-assessed for social network risk (i.e., smoking habits of best male and female friends), extracurricular activity level (i.e., participation in organized sports teams, clubs, etc.), family relationship quality (i.e., cohesion and conflict), and symptoms of depression (i.e., minimal, mild, moderate/severe). Results of a forward linear regression modeling indicate that social environment components were associated with a significant proportion of the variance in adolescent depression (Adjusted R (2) = .177, p social network (beta = .107, p depression symptoms. Conversely, adolescents who engaged in more extracurricular activities (beta = -.118, p depressive symptoms. These findings highlight the important role that the social environment plays in adolescent depression, as well as yields new insights into socially-based intervention targets that may ameliorate adolescent depression. These intervention targets may be gender-specific, include positive social network skills training, increase adolescents' engagement in organized activities, and attend to the quality of their family relationships.

A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks

NARCIS (Netherlands)

K.T. Huber; L.J.J. van Iersel (Leo); S.M. Kelk (Steven); R. Suchecki

2010-01-01

htmlabstractRecently much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic trees in order to accommodate complex evolutionary processes. Here we present an efficient, practical algorithm for reconstructing level-1 phylogenetic networks - a type of

Default activity patterns at the neocortical microcircuit level

Directory of Open Access Journals (Sweden)

Artur eLuczak

2012-06-01

Full Text Available Even in absence of sensory stimuli cortical networks exhibit complex, self-organized activity patterns. While the function of those spontaneous patterns of activation remains poorly understood, recent studies both in vivo and in vitro have demonstrated that neocortical neurons activate in a surprisingly similar sequential order both spontaneously and following input into cortex. For example, neurons that tend to fire earlier within spontaneous bursts of activity also fire earlier than other neurons in response to sensory stimuli. These 'default patterns' can last hundreds of milliseconds and are strongly conserved under a variety of conditions. In this paper we will review recent evidence for these default patterns at the local cortical level. We speculate that cortical architecture imposes common constraints on spontaneous and evoked activity flow, which result in the similarity of the patterns.

Development of Human-level Decision Making Algorithm for NPPs through Deep Neural Networks : Conceptual Approach

International Nuclear Information System (INIS)

Kim, Seung Geun; Seong, Poong Hyun

2017-01-01

Development of operation support systems and automation systems are closely related to machine learning field. However, since it is hard to achieve human-level delicacy and flexibility for complex tasks with conventional machine learning technologies, only operation support systems with simple purposes were developed and high-level automation related studies were not actively conducted. As one of the efforts for reducing human error in NPPs and technical advance toward automation, the ultimate goal of this research is to develop human-level decision making algorithm for NPPs during emergency situations. The concepts of SL, RL, policy network, value network, and MCTS, which were applied to decision making algorithm for other fields are introduced and combined with nuclear field specifications. Since the research is currently at the conceptual stage, more research is warranted.

A Web-Based, Social Networking Physical Activity Intervention for Insufficiently Active Adults Delivered via Facebook App: Randomized Controlled Trial.

Science.gov (United States)

Maher, Carol; Ferguson, Monika; Vandelanotte, Corneel; Plotnikoff, Ron; De Bourdeaudhuij, Ilse; Thomas, Samantha; Nelson-Field, Karen; Olds, Tim

2015-07-13

Online social networks offer considerable potential for delivery of socially influential health behavior change interventions. To determine the efficacy, engagement, and feasibility of an online social networking physical activity intervention with pedometers delivered via Facebook app. A total of 110 adults with a mean age of 35.6 years (SD 12.4) were recruited online in teams of 3 to 8 friends. Teams were randomly allocated to receive access to a 50-day online social networking physical activity intervention which included self-monitoring, social elements, and pedometers ("Active Team" Facebook app; n=51 individuals, 12 teams) or a wait-listed control condition (n=59 individuals, 13 teams). Assessments were undertaken online at baseline, 8 weeks, and 20 weeks. The primary outcome measure was self-reported weekly moderate-to-vigorous physical activity (MVPA). Secondary outcomes were weekly walking, vigorous physical activity time, moderate physical activity time, overall quality of life, and mental health quality of life. Analyses were undertaken using random-effects mixed modeling, accounting for potential clustering at the team level. Usage statistics were reported descriptively to determine engagement and feasibility. At the 8-week follow-up, the intervention participants had significantly increased their total weekly MVPA by 135 minutes relative to the control group (P=.03), due primarily to increases in walking time (155 min/week increase relative to controls, Plife or mental health quality of life at either time point. High levels of engagement with the intervention, and particularly the self-monitoring features, were observed. An online, social networking physical activity intervention with pedometers can produce sizable short-term physical activity changes. Future work is needed to determine how to maintain behavior change in the longer term, how to reach at-need populations, and how to disseminate such interventions on a mass scale. Australian New Zealand

Linking Environmental Orientation to Start-ups’ Networking Activities

DEFF Research Database (Denmark)

Dickel, Petra; Ritter, Thomas

Besides for-profit start-ups, an increasing number of firms start their existence with the purpose to “do good” for society – mirrored in an increasing academic discussion of sustainable firms. Yet, there is little research on the networking activities of start-ups that do not have profit...... generation as their primary focus. Addressing this research gap, we develop hypotheses on the different networking activities of environmentally oriented start-ups arguing that their societal focus has a positive impact on the frequency of their networking and the size of their network. For empirically...... investigating such networking differences, we use data from 179 technology-based start-ups and show that start-ups with a strong external environmental orientation have significantly higher networking frequency and build larger networks. On the contrary, strong internal environmental orientation is linked...

Usage of link-level performance indicators for HSDPA network-level simulations in E-UMTS

NARCIS (Netherlands)

Brouwer, Frank; de Bruin, I.C.C.; Silva, João Carlos; Souto, Nuno; Cercas, Francisco; Correia, Américo

2004-01-01

The paper describes integration of HSDPA (high-speed downlink packet access) link-level simulation results into network-level simulations for enhanced UMTS. The link-level simulations model all physical layer features depicted in the 3GPP standards. These include: generation of transport blocks;

PolNet: A Tool to Quantify Network-Level Cell Polarity and Blood Flow in Vascular Remodeling.

Science.gov (United States)

Bernabeu, Miguel O; Jones, Martin L; Nash, Rupert W; Pezzarossa, Anna; Coveney, Peter V; Gerhardt, Holger; Franco, Claudio A

2018-05-08

In this article, we present PolNet, an open-source software tool for the study of blood flow and cell-level biological activity during vessel morphogenesis. We provide an image acquisition, segmentation, and analysis protocol to quantify endothelial cell polarity in entire in vivo vascular networks. In combination, we use computational fluid dynamics to characterize the hemodynamics of the vascular networks under study. The tool enables, to our knowledge for the first time, a network-level analysis of polarity and flow for individual endothelial cells. To date, PolNet has proven invaluable for the study of endothelial cell polarization and migration during vascular patterning, as demonstrated by two recent publications. Additionally, the tool can be easily extended to correlate blood flow with other experimental observations at the cellular/molecular level. We release the source code of our tool under the Lesser General Public License. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Cultured Neural Networks: Optimization of Patterned Network Adhesiveness and Characterization of their Neural Activity

Directory of Open Access Journals (Sweden)

W. L. C. Rutten

2006-01-01

Full Text Available One type of future, improved neural interface is the “cultured probe”. It is a hybrid type of neural information transducer or prosthesis, for stimulation and/or recording of neural activity. It would consist of a microelectrode array (MEA on a planar substrate, each electrode being covered and surrounded by a local circularly confined network (“island” of cultured neurons. The main purpose of the local networks is that they act as biofriendly intermediates for collateral sprouts from the in vivo system, thus allowing for an effective and selective neuron–electrode interface. As a secondary purpose, one may envisage future information processing applications of these intermediary networks. In this paper, first, progress is shown on how substrates can be chemically modified to confine developing networks, cultured from dissociated rat cortex cells, to “islands” surrounding an electrode site. Additional coating of neurophobic, polyimide-coated substrate by triblock-copolymer coating enhances neurophilic-neurophobic adhesion contrast. Secondly, results are given on neuronal activity in patterned, unconnected and connected, circular “island” networks. For connected islands, the larger the island diameter (50, 100 or 150 μm, the more spontaneous activity is seen. Also, activity may show a very high degree of synchronization between two islands. For unconnected islands, activity may start at 22 days in vitro (DIV, which is two weeks later than in unpatterned networks.

Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks

Science.gov (United States)

Amin, Hayder; Maccione, Alessandro; Nieus, Thierry

2017-01-01

Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity. PMID:28749937

Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks.

Directory of Open Access Journals (Sweden)

Davide Lonardoni

2017-07-01

Full Text Available Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs, interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity.

Simulation of Changes of Activity Level of Some Carbohydrazes of Russian Sturgeon by the Influence of Environmental Osmotic Pressure by Means of Hybrid Networks

Directory of Open Access Journals (Sweden)

A. V. Tuktarov

2010-01-01

Full Text Available The article is devoted to the problem of simulation of the influence environmental osmotic pressure to the activity level of maltase and α-amilase of intestinal mucous tunic of Russian sturgeon. For the solving of this problem methods of neural networks and fuzzy logic are used. Create models are rated as the category of adaptive neural-fuzzy inference systems. Regularities of this influence were researched; created models have high approximate property and generalize well.

From network structure to network reorganization: implications for adult neurogenesis

International Nuclear Information System (INIS)

Schneider-Mizell, Casey M; Zochowski, Michal R; Sander, Leonard M; Parent, Jack M; Ben-Jacob, Eshel

2010-01-01

Networks can be dynamical systems that undergo functional and structural reorganization. One example of such a process is adult hippocampal neurogenesis, in which new cells are continuously born and incorporate into the existing network of the dentate gyrus region of the hippocampus. Many of these introduced cells mature and become indistinguishable from established neurons, joining the existing network. Activity in the network environment is known to promote birth, survival and incorporation of new cells. However, after epileptogenic injury, changes to the connectivity structure around the neurogenic niche are known to correlate with aberrant neurogenesis. The possible role of network-level changes in the development of epilepsy is not well understood. In this paper, we use a computational model to investigate how the structural and functional outcomes of network reorganization, driven by addition of new cells during neurogenesis, depend on the original network structure. We find that there is a stable network topology that allows the network to incorporate new neurons in a manner that enhances activity of the persistently active region, but maintains global network properties. In networks having other connectivity structures, new cells can greatly alter the distribution of firing activity and destroy the initial activity patterns. We thus find that new cells are able to provide focused enhancement of network only for small-world networks with sufficient inhibition. Network-level deviations from this topology, such as those caused by epileptogenic injury, can set the network down a path that develops toward pathological dynamics and aberrant structural integration of new cells

A distributed lumped active all-pass network configuration.

Science.gov (United States)

Huelsman, L. P.; Raghunath, S.

1972-01-01

In this correspondence a new and interesting distributed lumped active network configuration that realizes an all-pass network function is described. A design chart for determining the values of the network elements is included.

Goal-congruent default network activity facilitates cognitive control.

Science.gov (United States)

Spreng, R Nathan; DuPre, Elizabeth; Selarka, Dhawal; Garcia, Juliana; Gojkovic, Stefan; Mildner, Judith; Luh, Wen-Ming; Turner, Gary R

2014-10-15

Substantial neuroimaging evidence suggests that spontaneous engagement of the default network impairs performance on tasks requiring executive control. We investigated whether this impairment depends on the congruence between executive control demands and internal mentation. We hypothesized that activation of the default network might enhance performance on an executive control task if control processes engage long-term memory representations that are supported by the default network. Using fMRI, we scanned 36 healthy young adult humans on a novel two-back task requiring working memory for famous and anonymous faces. In this task, participants (1) matched anonymous faces interleaved with anonymous face, (2) matched anonymous faces interleaved with a famous face, or (3) matched a famous faces interleaved with an anonymous face. As predicted, we observed a facilitation effect when matching famous faces, compared with anonymous faces. We also observed greater activation of the default network during these famous face-matching trials. The results suggest that activation of the default network can contribute to task performance during an externally directed executive control task. Our findings provide evidence that successful activation of the default network in a contextually relevant manner facilitates goal-directed cognition. Copyright © 2014 the authors 0270-6474/14/3414108-07$15.00/0.

Systems Biology Graphical Notation: Activity Flow language Level 1 Version 1.2.

Science.gov (United States)

Mi, Huaiyu; Schreiber, Falk; Moodie, Stuart; Czauderna, Tobias; Demir, Emek; Haw, Robin; Luna, Augustin; Le Novère, Nicolas; Sorokin, Anatoly; Villéger, Alice

2015-09-04

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail. The SBGN Activity Flow language represents the influences of activities among various entities within a network. Unlike SBGN PD and ER that focus on the entities and their relationships with others, SBGN AF puts the emphasis on the functions (or activities) performed by the entities, and their effects to the functions of the same or other entities. The nodes (elements) describe the biological activities of the entities, such as protein kinase activity, binding activity or receptor activity, which can be easily mapped to Gene Ontology molecular function terms. The edges (connections) provide descriptions of relationships (or influences) between the activities, e.g., positive influence and negative influence. Among all three languages of SBGN, AF is the closest to signaling pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

The importance of delineating networks by activity type in bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida.

Science.gov (United States)

Gazda, Stefanie; Iyer, Swami; Killingback, Timothy; Connor, Richard; Brault, Solange

2015-03-01

Network analysis has proved to be a valuable tool for studying the behavioural patterns of complex social animals. Often such studies either do not distinguish between different behavioural states of the organisms or simply focus attention on a single behavioural state to the exclusion of all others. In either of these approaches it is impossible to ascertain how the behavioural patterns of individuals depend on the type of activity they are engaged in. Here we report on a network-based analysis of the behavioural associations in a population of bottlenose dolphins (Tursiops truncatus) in Cedar Key, Florida. We consider three distinct behavioural states-socializing, travelling and foraging-and analyse the association networks corresponding to each activity. Moreover, in constructing the different activity networks we do not simply record a spatial association between two individuals as being either present or absent, but rather quantify the degree of any association, thus allowing us to construct weighted networks describing each activity. The results of these weighted activity networks indicate that networks can reveal detailed patterns of bottlenose dolphins at the population level; dolphins socialize in large groups with preferential associations; travel in small groups with preferential associates; and spread out to forage in very small, weakly connected groups. There is some overlap in the socialize and travel networks but little overlap between the forage and other networks. This indicates that the social bonds maintained in other activities are less important as they forage on dispersed, solitary prey. The overall network, not sorted by activity, does not accurately represent any of these patterns.

The Brain on Art: Intense Aesthetic Experience Activates the Default Mode Network

Directory of Open Access Journals (Sweden)

Edward A Vessel

2012-04-01

Full Text Available Aesthetic responses to visual art comprise multiple types of experiences, from sensation and perception to emotion and self-reflection. Moreover, aesthetic experience is highly individual, with observers varying significantly in their responses to the same artwork. Combining fMRI and behavioral analysis of individual differences in aesthetic response, we identify two distinct patterns of neural activity exhibited by different subnetworks. Activity increased linearly with observers’ ratings (4-level scale in sensory (occipito-temporal regions. Activity in the striatum also varied linearly with ratings, with below-baseline activations for low-rated artworks. In contrast, a network of frontal regions showed a step-like increase only for the most moving artworks (4 ratings and non-differential activity for all others. This included several regions belonging to the default mode network previously associated with self-referential mentation. Our results suggest that aesthetic experience involves the integration of sensory and emotional reactions in a manner linked with their personal relevance.

Distributed applications monitoring at system and network level

CERN Document Server

Aderholz, Michael; Augé, E; Bagliesi, G; Banistoni, G; Barone, L; Boschini, M; Brunengo, A; Bunn, J J; Butler, J; Campanella, M; Capiluppi, P; D'Amato, M; Darneri, M; Di Mattia, A; Dorokhov, A E; Gagliardi, F; Gaines, I; Gasparini, U; Ghiselli, A; Gordon, J; Grandi, C; Gálvez, P; Harris, F; Holtman, K; Karimäki, V; Karita, Y; Klem, J T; Legrand, I; Leltchouk, M; Linglin, D; Lubrano, P; Luminari, L; McArthur, I C; Michelotto, M; Morita, Y; Nazarenko, A; Newman, H; O'Dell, Vivian; O'Neale, S W; Osculati, B; Pepé, M; Perini, L; Pinfold, James L; Pordes, R; Prelz, F; Putzer, A; Resconi, S; Robertson, L; Rolli, S; Sasaki, T; Sato, H; Schaffer, R D; Schalk, T L; Servoli, L; Sgaravatto, M; Shiers, J; Silvestris, L; Siroli, G P; Sliwa, K; Smith, T; Somigliana, R; Stanescu, C; Stockinger, H E; Ugolotti, D; Valente, E; Vistoli, C; Wilkinson, R P; Willers, Ian Malcolm; Williams, D O

2001-01-01

Most distributed applications are based on architectural models that do not involve real-time knowledge of network status and of their network usage. Moreover the new "network aware" architectures are still under development and their design is not yet complete. We considered, as a use case, an application using ODBMS (Objectivity /DB) for the distributed analysis of experimental data. The dynamic usage of system and network resources at host and application levels has been measured in different client/server configurations, and on several LAN and WAN layouts. The aim was to study the application efficiency and behavior versus the network characteristics and conditions. The most interesting results of the LAN and WAN tests are described. System bottlenecks and limitations have been identified, and efficient working conditions in the different scenarios have been defined. The behavior observed when moving away from the optimal working conditions is also described.

Neural networks with discontinuous/impact activations

CERN Document Server

Akhmet, Marat

2014-01-01

This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

A Cluster- Based Secure Active Network Environment

Institute of Scientific and Technical Information of China (English)

CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai

2005-01-01

We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

Functional characterization of GABAA receptor-mediated modulation of cortical neuron network activity in microelectrode array recordings

DEFF Research Database (Denmark)

Bader, Benjamin M; Steder, Anne; Klein, Anders Bue

2017-01-01

The numerous γ-aminobutyric acid type A receptor (GABAAR) subtypes are differentially expressed and mediate distinct functions at neuronal level. In this study we have investigated GABAAR-mediated modulation of the spontaneous activity patterns of primary neuronal networks from murine frontal...... of the information extractable from the MEA recordings offers interesting insights into the contributions of various GABAAR subtypes/subgroups to cortical network activity and the putative functional interplay between these receptors in these neurons....... cortex by characterizing the effects induced by a wide selection of pharmacological tools at a plethora of activity parameters in microelectrode array (MEA) recordings. The basic characteristics of the primary cortical neurons used in the recordings were studied in some detail, and the expression levels...

Evaluating Maximum Wind Energy Exploitation in Active Distribution Networks

DEFF Research Database (Denmark)

Siano, Pierluigi; Chen, Peiyuan; Chen, Zhe

2010-01-01

The increased spreading of distributed and renewable generation requires moving towards active management of distribution networks. In this paper, in order to evaluate maximum wind energy exploitation in active distribution networks, a method based on a multi-period optimal power flow (OPF......) analysis is proposed. Active network management schemes such as coordinated voltage control, energy curtailment and power factor control are integrated in the method in order to investigate their impacts on the maximization of wind energy exploitation. Some case studies, using real data from a Danish...... distribution system, confirmed the effectiveness of the proposed method in evaluating the optimal applications of active management schemes to increase wind energy harvesting without costly network reinforcement for the connection of wind generation....

Network-level accident-mapping: Distance based pattern matching using artificial neural network.

Science.gov (United States)

Deka, Lipika; Quddus, Mohammed

2014-04-01

The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily 'transferable' as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in "learning" the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that

Adaptive intelligent power systems: Active distribution networks

International Nuclear Information System (INIS)

McDonald, Jim

2008-01-01

Electricity networks are extensive and well established. They form a key part of the infrastructure that supports industrialised society. These networks are moving from a period of stability to a time of potentially major transition, driven by a need for old equipment to be replaced, by government policy commitments to cleaner and renewable sources of electricity generation, and by change in the power industry. This paper looks at moves towards active distribution networks. The novel transmission and distribution systems of the future will challenge today's system designs. They will cope with variable voltages and frequencies, and will offer more flexible, sustainable options. Intelligent power networks will need innovation in several key areas of information technology. Active control of flexible, large-scale electrical power systems is required. Protection and control systems will have to react to faults and unusual transient behaviour and ensure recovery after such events. Real-time network simulation and performance analysis will be needed to provide decision support for system operators, and the inputs to energy and distribution management systems. Advanced sensors and measurement will be used to achieve higher degrees of network automation and better system control, while pervasive communications will allow networks to be reconfigured by intelligent systems

Google matrix of the world network of economic activities

Science.gov (United States)

Kandiah, Vivek; Escaith, Hubert; Shepelyansky, Dima L.

2015-07-01

Using the new data from the OECD-WTO world network of economic activities we construct the Google matrix G of this directed network and perform its detailed analysis. The network contains 58 countries and 37 activity sectors for years 1995 and 2008. The construction of G, based on Markov chain transitions, treats all countries on equal democratic grounds while the contribution of activity sectors is proportional to their exchange monetary volume. The Google matrix analysis allows to obtain reliable ranking of countries and activity sectors and to determine the sensitivity of CheiRank-PageRank commercial balance of countries in respect to price variations and labor cost in various countries. We demonstrate that the developed approach takes into account multiplicity of network links with economy interactions between countries and activity sectors thus being more efficient compared to the usual export-import analysis. The spectrum and eigenstates of G are also analyzed being related to specific activity communities of countries.

The Political Activity in the Network Environment

Directory of Open Access Journals (Sweden)

Марианна Юрьевна Павлютенкова

2015-12-01

Full Text Available The rapid development and deep penetration into all areas of modern society of information and communication technologies significantly increase the role of network interactions. Network structures represented primarily social networks, embedded in the public policy process and became one of the key political actors. Online communities take the form of public policy, where the formation of public opinion and political decision-making plays the main role. Networking environment opens up new opportunities for the opposition and protest movements, civic participation, and control of public policy in general. The article gives an insight on the political aspects of social networking, concludes on the trend formation and network's strengthening of the political activity in a wide distribution of e-networking and e-communications.

The Impact of the Physical Activity Policy Research Network.

Science.gov (United States)

Manteiga, Alicia M; Eyler, Amy A; Valko, Cheryl; Brownson, Ross C; Evenson, Kelly R; Schmid, Thomas

2017-03-01

Lack of physical activity is one of the greatest challenges of the 21st century. The Physical Activity Policy Research Network (PAPRN) is a thematic network established in 2004 to identify determinants, implementation, and outcomes of policies that are effective in increasing physical activity. The purpose of this study is to describe the products of PAPRN and make recommendations for future research and best practices. A mixed methods approach was used to obtain both quantitative and qualitative data on the network. First, in 2014, PAPRN's dissemination products from 2004 to 2014 were extracted and reviewed, including 57 publications and 56 presentations. Next, semi-structured qualitative interviews were conducted with 25 key network participants from 17 locations around the U.S. The transcripts were transcribed and coded. The results of the interviews indicated that the research network addressed several components of its mission, including the identification of physical activity policies, determinants of these policies, and the process of policy implementation. However, research focusing on physical activity policy outcomes was limited. Best practices included collaboration between researchers and practitioners and involvement of practitioners in research design, data collection, and dissemination of results. PAPRN is an example of a productive research network and has contributed to both the process and content of physical activity policy research over the past decade. Future research should emphasize physical activity policy outcomes. Additionally, increased partnerships with practitioners for collaborative, cross-sectoral physical activity policy research should be developed. Copyright © 2016 American Journal of Preventive Medicine. All rights reserved.

Synaptic model for spontaneous activity in developing networks

DEFF Research Database (Denmark)

Lerchner, Alexander; Rinzel, J.

2005-01-01

Spontaneous rhythmic activity occurs in many developing neural networks. The activity in these hyperexcitable networks is comprised of recurring "episodes" consisting of "cycles" of high activity that alternate with "silent phases" with little or no activity. We introduce a new model of synaptic...... dynamics that takes into account that only a fraction of the vesicles stored in a synaptic terminal is readily available for release. We show that our model can reproduce spontaneous rhythmic activity with the same general features as observed in experiments, including a positive correlation between...

Predictive functional control for active queue management in congested TCP/IP networks.

Science.gov (United States)

Bigdeli, N; Haeri, M

2009-01-01

Predictive functional control (PFC) as a new active queue management (AQM) method in dynamic TCP networks supporting explicit congestion notification (ECN) is proposed. The ability of the controller in handling system delay along with its simplicity and low computational load makes PFC a privileged AQM method in the high speed networks. Besides, considering the disturbance term (which represents model/process mismatches, external disturbances, and existing noise) in the control formulation adds some level of robustness into the PFC-AQM controller. This is an important and desired property in the control of dynamically-varying computer networks. In this paper, the controller is designed based on a small signal linearized fluid-flow model of the TCP/AQM networks. Then, closed-loop transfer function representation of the system is derived to analyze the robustness with respect to the network and controller parameters. The analytical as well as the packet-level ns-2 simulation results show the out-performance of the developed controller for both queue regulation and resource utilization. Fast response, low queue fluctuations (and consequently low delay jitter), high link utilization, good disturbance rejection, scalability, and low packet marking probability are other features of the developed method with respect to other well-known AQM methods such as RED, PI, and REM which are also simulated for comparison.

Technical and economic impacts of active management on distribution network

International Nuclear Information System (INIS)

Zhang, Jietan; Cheng, Haozhong; Wang, Chun

2009-01-01

With the deregulation of energy market and the appeal for environment protection, more and more distributed generation (DG) is embedded in the distribution network. However the approach of connecting DG in most cases is based on a so-called ''fit and forget'' policy and the capacity of DG is limited rigidly by distribution network operator (DNO) to avoid the negative effects of high level penetration. Therefore active management (AM) is put forward as an effective method to network reinforcement for the connection and operation of DG. In this paper, the concept and principle of AM are introduced, and several indices are proposed to evaluate both technical and economic impacts of AM on distribution network with DG. To simplify the simulation fuzzy C-means clustering (FCM) algorithm is introduced. The test results on a sample system represent that AM will lead to decrease of power generation of DG, but it can reduce energy losses and improve voltage profile effectively. Furthermore, AM will take great economic incentives to DG developer as well as DNO with reasonable policy. (author)

Neural Network with Local Memory for Nuclear Reactor Power Level Control

International Nuclear Information System (INIS)

Uluyol, Oender; Ragheb, Magdi; Tsoukalas, Lefteri

2001-01-01

A methodology is introduced for a neural network with local memory called a multilayered local output gamma feedback (LOGF) neural network within the paradigm of locally-recurrent globally-feedforward neural networks. It appears to be well-suited for the identification, prediction, and control tasks in highly dynamic systems; it allows for the presentation of different timescales through incorporation of a gamma memory. A learning algorithm based on the backpropagation-through-time approach is derived. The spatial and temporal weights of the network are iteratively optimized for a given problem using the derived learning algorithm. As a demonstration of the methodology, it is applied to the task of power level control of a nuclear reactor at different fuel cycle conditions. The results demonstrate that the LOGF neural network controller outperforms the classical as well as the state feedback-assisted classical controllers for reactor power level control by showing a better tracking of the demand power, improving the fuel and exit temperature responses, and by performing robustly in different fuel cycle and power level conditions

Developing a network-level structural capacity index for structural evaluation of pavements.

Science.gov (United States)

2013-03-01

The objective of this project was to develop a structural index for use in network-level pavement evaluation to facilitate : the inclusion of the pavements structural condition in pavement management applications. The primary goal of network-level...

Tourist activated networks: Implications for dynamic packaging systems in tourism

DEFF Research Database (Denmark)

Zach, Florian; Gretzel, Ulrike; Fesenmaier, Daniel R.

2008-01-01

This paper discusses tourist activated networks as a concept to inform technological applications supporting dynamic bundling and en-route recommendations. Empirical data was collected from travellers who visited a regional destination in the US and then analyzed with respect to its network...... structure. The results indicate that the tourist activated network for the destination is rather sparse and that there are clearly differences in core and peripheral nodes. The findings illustrate the structure of a tourist activated network and provide implications for technology design and tourism...

Network Layer Protocol Activation for Packet Data Access in UMTS WCDMA Laboratory Network

OpenAIRE

Lakkisto, Erkka

2011-01-01

The purpose of this Bachelor’s Thesis was to set up the UMTS WCDMA network in the laboratory environment of Helsinki Metropolia University of Applied Sciences and to study the network layer protocol activation for packet data access. The development of 3G technology has been very rapid and it can be considered as one of the main technologies in telecommunication. Implementing the laboratory network in Metropolia enables teaching and researching of the modern network technology. Labora...

Information transmission and signal permutation in active flow networks

Science.gov (United States)

Woodhouse, Francis G.; Fawcett, Joanna B.; Dunkel, Jörn

2018-03-01

Recent experiments show that both natural and artificial microswimmers in narrow channel-like geometries will self-organise to form steady, directed flows. This suggests that networks of flowing active matter could function as novel autonomous microfluidic devices. However, little is known about how information propagates through these far-from-equilibrium systems. Through a mathematical analogy with spin-ice vertex models, we investigate here the input–output characteristics of generic incompressible active flow networks (AFNs). Our analysis shows that information transport through an AFN is inherently different from conventional pressure or voltage driven networks. Active flows on hexagonal arrays preserve input information over longer distances than their passive counterparts and are highly sensitive to bulk topological defects, whose presence can be inferred from marginal input–output distributions alone. This sensitivity further allows controlled permutations on parallel inputs, revealing an unexpected link between active matter and group theory that can guide new microfluidic mixing strategies facilitated by active matter and aid the design of generic autonomous information transport networks.

Systems Biology Graphical Notation: Activity Flow language Level 1 Version 1.2

Directory of Open Access Journals (Sweden)

Mi Huaiyu

2015-06-01

Full Text Available The Systems Biological Graphical Notation (SBGN is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD, Entity Relationship (ER and Activity Flow (AF, allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

Localizing potentially active post-transcriptional regulations in the Ewing's sarcoma gene regulatory network

Directory of Open Access Journals (Sweden)

Delyon Bernard

2010-11-01

Full Text Available Abstract Background A wide range of techniques is now available for analyzing regulatory networks. Nonetheless, most of these techniques fail to interpret large-scale transcriptional data at the post-translational level. Results We address the question of using large-scale transcriptomic observation of a system perturbation to analyze a regulatory network which contained several types of interactions - transcriptional and post-translational. Our method consisted of post-processing the outputs of an open-source tool named BioQuali - an automatic constraint-based analysis mimicking biologist's local reasoning on a large scale. The post-processing relied on differences in the behavior of the transcriptional and post-translational levels in the network. As a case study, we analyzed a network representation of the genes and proteins controlled by an oncogene in the context of Ewing's sarcoma. The analysis allowed us to pinpoint active interactions specific to this cancer. We also identified the parts of the network which were incomplete and should be submitted for further investigation. Conclusions The proposed approach is effective for the qualitative analysis of cancer networks. It allows the integrative use of experimental data of various types in order to identify the specific information that should be considered a priority in the initial - and possibly very large - experimental dataset. Iteratively, new dataset can be introduced into the analysis to improve the network representation and make it more specific.

Technical and economic evaluation of voltage level in transmission network expansion planning using GA

International Nuclear Information System (INIS)

Jalilzadeh, S.; Kazemi, A.; Shayeghi, H.; Madavi, M.

2008-01-01

Transmission network expansion planning is an important part of power system planning. Its task is to determine an optimal network configuration according to load growth. It determines where, when and how many new transmission lines should be installed. Up to now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem, but in all of these methods, the STNEP problem has been solved regardless of voltage level of the lines. In this paper, due to different voltage levels in the transmission network, which cause different annual losses, STNEP has been studied considering the voltage level of the transmission lines and the network loss using the genetic algorithm (GA). Finally, the proposed idea has been examined on Garvers 6 bus network. The results show that considering the loss in a network with different voltage levels decreases the operational costs considerably, and the network satisfies the requirement of delivering electric power more safely and reliably to load centers

Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

Science.gov (United States)

Hjorth, Johannes J J; Dawitz, Julia; Kroon, Tim; Pires, Johny; Dassen, Valerie J; Berkhout, Janna A; Emperador Melero, Javier; Nadadhur, Aish G; Alevra, Mihai; Toonen, Ruud F; Heine, Vivi M; Mansvelder, Huibert D; Meredith, Rhiannon M

2016-04-01

Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell migration, to the refinement of synapses, topographic maps, and the mature composition of ion channels. These emergent activity patterns are not present in all cells simultaneously within the network and more immature "silent" cells, potentially correlated with the presence of silent synapses, are prominent in different networks during early developmental periods. Many current network analyses for detection of synchronous cellular activity utilize activity-based pixel correlations to identify cellular-based regions of interest (ROIs) and coincident cell activity. However, using activity-based correlations, these methods first underestimate or ignore the inactive silent cells within the developing network and second, are difficult to apply within cell-dense regions commonly found in developing brain networks. In addition, previous methods may ignore ROIs within a network that shows transient activity patterns comprising both inactive and active periods. We developed analysis software to semi-automatically detect cells within developing neuronal networks that were imaged using calcium-sensitive reporter dyes. Using an iterative threshold, modulation of activity was tracked within individual cells across the network. The distribution pattern of both inactive and active, including synchronous cells, could be determined based on distance measures to neighboring cells and according to different anatomical layers. © 2015 Wiley Periodicals, Inc.

Reduction Method for Active Distribution Networks

DEFF Research Database (Denmark)

Raboni, Pietro; Chen, Zhe

2013-01-01

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

A Network Perspective on Individual-Level Ambidexterity in Organizations

DEFF Research Database (Denmark)

Rogan, Michelle; Mors, Marie Louise

2014-01-01

in the internal and external networks of 79 senior managers in a management consulting firm revealed significant differences in the density, contact heterogeneity, and informality of ties in the networks of senior managers who engaged in both exploration and exploitation compared with managers that predominately......Addressing the call for a deeper understanding of ambidexterity at the individual level, we propose that managers’ networks are an important yet understudied factor in the ability to balance the trade-off between exploring for new business and exploiting existing business. Analyses of 1,449 ties...... explored or exploited. The findings suggest that managers’ networks are important levers for their ability to behave ambidextrously and offer insights into the microfoundations of organizational ambidexterity....

Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks

OpenAIRE

Kaiser, Marcus; Hilgetag, Claus C.

2010-01-01

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show...

Why and how selection patterns in classroom networks differ between students.The potential influence of networks size preferences, level of information, and group membership.

Directory of Open Access Journals (Sweden)

Baerveldt, Chris

2010-12-01

Full Text Available High school students can select class mates for new friendships using a repertoire of patterns. They can actively pursue new friendships, make use of the existing network structure, and/ or use the scarce and often erroneous information about candidates. In this theoretical paper, we argue that such selection patterns should not be studied as the result of general rules, as is usually done in social network studies. Specifically, we state that network size preferences, the level of information about individual attributes of fellow classmates, and group membership are likely to differ among high school students, and that as a result, also their selection patterns are likely to be different. In this paper we sketch the theoretical articulations between these.

Teacher Candidates' Opinions Regarding Instructional and Safe Use of Social Networks and Internet Addiction Risk Levels

Directory of Open Access Journals (Sweden)

Enis Fasli

2018-05-01

Full Text Available In conjunction with the development and advancement of internet technologies, social networking sites have created a socialization environment. Instructors point out that these tools must be used as an active and different form of communication with students. Also, participation of the students through social networking sites should be encouraged. However, the risk of internet addiction has also become widespread on the increase use of social networks. The aim of this research is to “determine the opinions of teacher candidates on the use of social networking sites in education and Internet addiction risk levels”. General survey model was used in this research in order to determine the opinions regarding social networks of teacher candidates from the education faculties in the Turkish Republic of Northern Cyprus and figure out their internet addiction risk levels. “Use of Social Networks in Education Scale” developed by Ozturk and Akgun and “Internet Addiction Test” developed by Young were used in this research. According to the results of the study, it has been figured out that almost all of the teacher candidates think that sharing information through social networking sites is either partially safe or not safe. Besides, most of the teacher candidates feel anxious about keeping information as confidential. Another important result is that teacher candidates are internet users at an average level. It also shows that they might spend too much time on the internet however they use internet in a controlled manner.

Monitoring Malware Activity on the LAN Network

Science.gov (United States)

Skrzewski, Mirosław

Many security related organizations periodically publish current network and systems security information, with the lists of top malware programs. These lists raises the question how these threats spreads out, if the worms (the only threat with own communication abilities) are low or missing on these lists. The paper discuss the research on malware network activity, aimed to deliver the answer to the question, what is the main infection channel of modern malware, done with the usage of virtual honeypot systems on dedicated, unprotected network. Systems setup, network and systems monitoring solutions, results of over three months of network traffic and malware monitoring are presented, along with the proposed answer to our research question.

Nano-engineered intrapores in nanoparticles of PtNi networks for increased oxygen reduction reaction activity

Science.gov (United States)

Ding, Jieting; Ji, Shan; Wang, Hui; Key, Julian; Brett, Dan J. L.; Wang, Rongfang

2018-01-01

Network-like metallic alloys of solid nanoparticles have been frequently reported as promising electrocatalysts for fuel cells. The three-dimensional structure of such networks is rich in pores in the form of voids between nanoparticles, which collectively expose a large surface area for catalytic activity. Herein, we present a novel solution to this problem using a precursor comprising a flocculent core-shell PtNi@Ni to produce PtNi network catalysts with nanoparticle intraporosity after carefully controlled electrochemical dealloying. Physical characterization shows a hierarchical level of nanoporosity (intrapores within nanoparticles and pores between them) evolves during the controlled electrochemical dealloying, and that a Pt-rich surface also forms after 22 cycles of Ni leaching. In ORR cycling, the PtNi networks gain 4-fold activity in both jECSA and jmass over a state of the art Pt/C electrocatalyst, and also significantly exceed previously reported PtNi networks. In ORR degradation tests, the PtNi networks also proved stable, dropping by 30.4% and 62.6% in jECSA and jmass respectively. The enhanced performance of the catalyst is evident, and we also propose that the presented synthesis procedure can be generally applied to developing other metallic networks.

Recognizing Multi-user Activities using Body Sensor Networks

DEFF Research Database (Denmark)

Gu, Tao; Wang, Liang; Chen, Hanhua

2011-01-01

The advances of wireless networking and sensor technology open up an interesting opportunity to infer human activities in a smart home environment. Existing work in this paradigm focuses mainly on recognizing activities of a single user. In this work, we address the fundamental problem...... activity classes of data—for building activity models and design a scalable, noise-resistant, Emerging Pattern based Multi-user Activity Recognizer (epMAR) to recognize both single- and multi-user activities. We develop a multi-modal, wireless body sensor network for collecting real-world traces in a smart...... home environment, and conduct comprehensive empirical studies to evaluate our system. Results show that epMAR outperforms existing schemes in terms of accuracy, scalability and robustness....

Multi-objective optimal power flow for active distribution network considering the stochastic characteristic of photovoltaic

Science.gov (United States)

Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming

2017-05-01

To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.

Active vision and image/video understanding with decision structures based on the network-symbolic models

Science.gov (United States)

Kuvich, Gary

2003-08-01

Vision is a part of a larger information system that converts visual information into knowledge structures. These structures drive vision process, resolve ambiguity and uncertainty via feedback projections, and provide image understanding that is an interpretation of visual information in terms of such knowledge models. The ability of human brain to emulate knowledge structures in the form of networks-symbolic models is found. And that means an important shift of paradigm in our knowledge about brain from neural networks to "cortical software". Symbols, predicates and grammars naturally emerge in such active multilevel hierarchical networks, and logic is simply a way of restructuring such models. Brain analyzes an image as a graph-type decision structure created via multilevel hierarchical compression of visual information. Mid-level vision processes like clustering, perceptual grouping, separation of figure from ground, are special kinds of graph/network transformations. They convert low-level image structure into the set of more abstract ones, which represent objects and visual scene, making them easy for analysis by higher-level knowledge structures. Higher-level vision phenomena are results of such analysis. Composition of network-symbolic models works similar to frames and agents, combines learning, classification, analogy together with higher-level model-based reasoning into a single framework. Such models do not require supercomputers. Based on such principles, and using methods of Computational intelligence, an Image Understanding system can convert images into the network-symbolic knowledge models, and effectively resolve uncertainty and ambiguity, providing unifying representation for perception and cognition. That allows creating new intelligent computer vision systems for robotic and defense industries.

Oscillatory activity in neocortical networks during tactile discrimination near the limit of spatial acuity.

Science.gov (United States)

Adhikari, Bhim M; Sathian, K; Epstein, Charles M; Lamichhane, Bidhan; Dhamala, Mukesh

2014-05-01

Oscillatory interactions within functionally specialized but distributed brain regions are believed to be central to perceptual and cognitive functions. Here, using human scalp electroencephalography (EEG) recordings combined with source reconstruction techniques, we study how oscillatory activity functionally organizes different neocortical regions during a tactile discrimination task near the limit of spatial acuity. While undergoing EEG recordings, blindfolded participants felt a linear three-dot array presented electromechanically, under computer control, and reported whether the central dot was offset to the left or right. The average brain response differed significantly for trials with correct and incorrect perceptual responses in the timeframe approximately between 130 and 175ms. During trials with correct responses, source-level peak activity appeared in the left primary somatosensory cortex (SI) at around 45ms, in the right lateral occipital complex (LOC) at 130ms, in the right posterior intraparietal sulcus (pIPS) at 160ms, and finally in the left dorsolateral prefrontal cortex (dlPFC) at 175ms. Spectral interdependency analysis of activity in these nodes showed two distinct distributed networks, a dominantly feedforward network in the beta band (12-30Hz) that included all four nodes and a recurrent network in the gamma band (30-100Hz) that linked SI, pIPS and dlPFC. Measures of network activity in both bands were correlated with the accuracy of task performance. These findings suggest that beta and gamma band oscillatory networks coordinate activity between neocortical regions mediating sensory and cognitive processing to arrive at tactile perceptual decisions. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Dual-Level Game-Based Energy Efficiency and Fairness for Green Cellular Networks

Directory of Open Access Journals (Sweden)

Sungwook Kim

2016-01-01

Full Text Available In the recent decades, cellular networks have revolutionized the way of next generation communication networks. However, due to the global climate change, reducing the energy consumption of cellular infrastructures is an important and urgent problem. In this study, we propose a novel two-level cooperative game framework for improving the energy efficiency and fairness in cellular networks. For the energy efficiency, base stations (BSs constantly monitor the current traffic load and cooperate with each other to maximize the energy saving. For the energy fairness, renewable energy can be shared dynamically while ensuring the fairness among BSs. To achieve an excellent cellular network performance, the concepts of the Raiffa Bargaining Solution and Jain’s fairness are extended and practically applied to our dual-level cooperative game model. Through system level simulations, the proposed scheme is evaluated and compared with other existing schemes. The simulation results show that our two-level game approach outperforms the existing schemes in providing a better fair-efficient system performance.

A High-Level Petri Net Framework for Genetic Regulatory Networks

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Banks Richard

2007-12-01

Full Text Available To understand the function of genetic regulatory networks in the development of cellular systems, we must not only realise the individual network entities, but also the manner by which they interact. Multi-valued networks are a promising qualitative approach for modelling such genetic regulatory networks, however, at present they have limited formal analysis techniques and tools. We present a flexible formal framework for modelling and analysing multi-valued genetic regulatory networks using high-level Petri nets and logic minimization techniques. We demonstrate our approach with a detailed case study in which part of the genetic regulatory network responsible for the carbon starvation stress response in Escherichia coli is modelled and analysed. We then compare and contrast this multivalued model to a corresponding Boolean model and consider their formal relationship.

Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

OpenAIRE

Johnson, Rie; Zhang, Tong

2016-01-01

This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in Conneau et al., though the results should be interpreted with some consideration due to the unique pre-processing o...

Memory-induced mechanism for self-sustaining activity in networks

Science.gov (United States)

Allahverdyan, A. E.; Steeg, G. Ver; Galstyan, A.

2015-12-01

We study a mechanism of activity sustaining on networks inspired by a well-known model of neuronal dynamics. Our primary focus is the emergence of self-sustaining collective activity patterns, where no single node can stay active by itself, but the activity provided initially is sustained within the collective of interacting agents. In contrast to existing models of self-sustaining activity that are caused by (long) loops present in the network, here we focus on treelike structures and examine activation mechanisms that are due to temporal memory of the nodes. This approach is motivated by applications in social media, where long network loops are rare or absent. Our results suggest that under a weak behavioral noise, the nodes robustly split into several clusters, with partial synchronization of nodes within each cluster. We also study the randomly weighted version of the models where the nodes are allowed to change their connection strength (this can model attention redistribution) and show that it does facilitate the self-sustained activity.

Correlated alpha activity with the facial expression processing network in a simultaneous EEG-fMRI experiment.

Science.gov (United States)

Simoes, Marco; Direito, Bruno; Lima, Joao; Castelhano, Joao; Ferreira, Carlos; Couceiro, Ricardo; Carvalho, Paulo; Castelo-Branco, Miguel

2017-07-01

The relationship between EEG and fMRI data is poorly covered in the literature. Extensive work has been conducted in resting-state and epileptic activity, highlighting a negative correlation between the alpha power band of the EEG and the BOLD activity in the default-mode-network. The identification of an appropriate task-specific relationship between fMRI and EEG data for predefined regions-of-interest, would allow the transfer of interventional paradigms (such as BOLD-based neurofeedback sessions) from fMRI to EEG, enhancing its application range by lowering its costs and improving its flexibility. In this study, we present an analysis of the correlation between task-specific alpha band fluctuations and BOLD activity in the facial expressions processing network. We characterized the network ROIs through a stringent localizer and identified two clusters on the scalp (one frontal, one parietal-occipital) with marked alpha fluctuations, related to the task. We then check whether such power variations throughout the time correlate with the BOLD activity in the network. Our results show statistically significant negative correlations between the alpha power in both clusters and for all the ROIs of the network. The correlation levels have still not met the requirements for transferring the protocol to an EEG setup, but they pave the way towards a better understand on how frontal and parietal-occipital alpha relates to the activity of the facial expressions processing network.

Regulatory networks, legal federalism, and multi-level regulatory systems

OpenAIRE

Kerber, Wolfgang; Wendel, Julia

2016-01-01

Transnational regulatory networks play important roles in multi-level regulatory regimes, as e.g, the European Union. In this paper we analyze the role of regulatory networks from the perspective of the economic theory of legal federalism. Often sophisticated intermediate institutional solutions between pure centralisation and pure decentralisation can help to solve complex tradeoff problems between the benefits and problems of centralised and decentralised solutions. Drawing upon the insight...

Putting age-related task activation into large-scale brain networks: A meta-analysis of 114 fMRI studies on healthy aging.

Science.gov (United States)

Li, Hui-Jie; Hou, Xiao-Hui; Liu, Han-Hui; Yue, Chun-Lin; Lu, Guang-Ming; Zuo, Xi-Nian

2015-10-01

Normal aging is associated with cognitive decline and underlying brain dysfunction. Previous studies concentrated less on brain network changes at a systems level. Our goal was to examine these age-related changes of fMRI-derived activation with a common network parcellation of the human brain function, offering a systems-neuroscience perspective of healthy aging. We conducted a series of meta-analyses on a total of 114 studies that included 2035 older adults and 1845 young adults. Voxels showing significant age-related changes in activation were then overlaid onto seven commonly referenced neuronal networks. Older adults present moderate cognitive decline in behavioral performance during fMRI scanning, and hypo-activate the visual network and hyper-activate both the frontoparietal control and default mode networks. The degree of increased activation in frontoparietal network was associated with behavioral performance in older adults. Age-related changes in activation present different network patterns across cognitive domains. The systems neuroscience approach used here may be useful for elucidating the underlying network mechanisms of various brain plasticity processes during healthy aging. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Application of Artificial Neural Network into the Water Level Modeling and Forecast

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Marzenna Sztobryn

2013-06-01

Full Text Available The dangerous sea and river water level increase does not only destroy the human lives, but also generate the severe flooding in coastal areas. The rapidly changes in the direction and velocity of wind and associated with them sea level changes could be the severe threat for navigation, especially on the fairways of small fishery harbors located in the river mouth. There is the area of activity of two external forcing: storm surges and flood wave. The aim of the work was the description of an application of Artificial Neural Network (ANN methodology into the water level forecast in the case study field in Swibno harbor located is located at 938.7 km of the Wisla River and at a distance of about 3 km up the mouth (Gulf of Gdansk - Baltic Sea.

Spontaneous brain network activity: Analysis of its temporal complexity

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Mangor Pedersen

2017-06-01

Full Text Available The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain’s complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the “cliquiness” of a node and participation coefficients (the between-module connectivity of a node were estimated. For these two network metrics, sample entropy was calculated. The procedure produced a number of results: (1 Entropy is higher for the participation coefficient than for the clustering coefficient. (2 The average clustering coefficient is negatively related to its associated entropy, whereas the average participation coefficient is positively related to its associated entropy. (3 The level of entropy is network-specific to the participation coefficient, but not to the clustering coefficient. High entropy for the participation coefficient was observed in the default-mode, visual, and motor networks. These results were further validated using an independent replication dataset. Our work confirms that brain networks are temporally complex. Entropy is a good candidate metric to explore temporal network alterations in diseases with paroxysmal brain disruptions, including schizophrenia and epilepsy. In recent years, connectomics has provided significant insights into the topological complexity of brain networks. However, the temporal complexity of brain networks still remains somewhat poorly understood. In this study we used entropy analysis to demonstrate that the properties of network segregation (the clustering coefficient and integration (the participation coefficient are temporally complex

Personalized Social Network Activity Feeds for Increased Interaction and Content Contribution

Directory of Open Access Journals (Sweden)

Shlomo eBerkovsky

2015-10-01

Full Text Available Online social networks were originally conceived as means of sharing information and activities with friends, and their success has been one of the primary contributors of the tremendous growth of the Web. Social network activity feeds were devised as a means to aggregate recent actions of friends into a convenient list. But the volume of actions and content generated by social network users is overwhelming, such that keeping users up-to-date with friend activities is an ongoing challenge for social network providers. Personalization has been proposed as a solution to combat social network information overload and help users to identify the nuggets of relevant information in the incoming flood of network activities. In this paper, we propose and thoroughly evaluate a personalized model for predicting the relevance of the activity feed items, which informs the ranking of the feeds and facilitates personalization. Results of a live study show that the proposed feed personalization approach successfully identifies and promotes relevant feed items and boosts the uptake of the feeds. In addition, it increases the contribution of user-generated content to the social network and spurs interaction between users.

Task-dependent changes in cross-level coupling between single neurons and oscillatory activity in multiscale networks.

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Ryan T Canolty

Full Text Available Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC or under direct neural control through a brain-machine interface (Brain Control, BC. In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10-45 Hz during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to

Tourist activated networks: Implications for dynamic bundling and en-route recommendations

DEFF Research Database (Denmark)

Zach, Florian; Gretzel, Ulrike

2011-01-01

This article discusses tourist-activated networks as a concept to inform technological applications supporting dynamic bundling and en route recommendations. Empirical data were collected from travelers who visited a regional destination in the US and then analyzed with respect to its network...... structure. The results indicate that the tourist-activated network for the destination is rather sparse and that there are clearly differences in core and peripheral nodes. The findings illustrate the structure of a tourist-activated network and provide implications for technology design and tourism...

Model for a flexible motor memory based on a self-active recurrent neural network.

Science.gov (United States)

Boström, Kim Joris; Wagner, Heiko; Prieske, Markus; de Lussanet, Marc

2013-10-01

Using recent recurrent network architecture based on the reservoir computing approach, we propose and numerically simulate a model that is focused on the aspects of a flexible motor memory for the storage of elementary movement patterns into the synaptic weights of a neural network, so that the patterns can be retrieved at any time by simple static commands. The resulting motor memory is flexible in that it is capable to continuously modulate the stored patterns. The modulation consists in an approximately linear inter- and extrapolation, generating a large space of possible movements that have not been learned before. A recurrent network of thousand neurons is trained in a manner that corresponds to a realistic exercising scenario, with experimentally measured muscular activations and with kinetic data representing proprioceptive feedback. The network is "self-active" in that it maintains recurrent flow of activation even in the absence of input, a feature that resembles the "resting-state activity" found in the human and animal brain. The model involves the concept of "neural outsourcing" which amounts to the permanent shifting of computational load from higher to lower-level neural structures, which might help to explain why humans are able to execute learned skills in a fluent and flexible manner without the need for attention to the details of the movement. Copyright © 2013 Elsevier B.V. All rights reserved.

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.

Organization of excitable dynamics in hierarchical biological networks.

Directory of Open Access Journals (Sweden)

Mark Müller-Linow

Full Text Available This study investigates the contributions of network topology features to the dynamic behavior of hierarchically organized excitable networks. Representatives of different types of hierarchical networks as well as two biological neural networks are explored with a three-state model of node activation for systematically varying levels of random background network stimulation. The results demonstrate that two principal topological aspects of hierarchical networks, node centrality and network modularity, correlate with the network activity patterns at different levels of spontaneous network activation. The approach also shows that the dynamic behavior of the cerebral cortical systems network in the cat is dominated by the network's modular organization, while the activation behavior of the cellular neuronal network of Caenorhabditis elegans is strongly influenced by hub nodes. These findings indicate the interaction of multiple topological features and dynamic states in the function of complex biological networks.

Kainate-induced network activity in the anterior cingulate cortex.

Science.gov (United States)

Shinozaki, R; Hojo, Y; Mukai, H; Hashizume, M; Murakoshi, T

2016-06-14

Anterior cingulate cortex (ACC) plays a pivotal role in higher order processing of cognition, attention and emotion. The network oscillation is considered an essential means for integration of these CNS functions. The oscillation power and coherence among related areas are often dis-regulated in several psychiatric and pathological conditions with a hemispheric asymmetric manner. Here we describe the network-based activity of field potentials recorded from the superficial layer of the mouse ACC in vitro using submerged type recordings. A short activation by kainic acid administration to the preparation induced populational activities ranging over several frequency bands including theta (3-8Hz), alpha (8-12Hz), beta (13-30Hz), low gamma (30-50Hz) and high gamma (50-80Hz). These responses were repeatable and totally abolished by tetrodotoxin, and greatly diminished by inhibitors of ionotropic and metabotropic glutamate receptors, GABAA receptor or gap-junctions. These observations suggest that the kainate-induced network activity can be a useful model of the network oscillation in the ACC circuit. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Friendship Network Characteristics Are Associated with Physical Activity and Sedentary Behavior in Early Adolescence.

Science.gov (United States)

Marks, Jennifer; de la Haye, Kayla; Barnett, Lisa M; Allender, Steven

2015-01-01

There is limited understanding of the association between peer social networks and physical activity (PA), sedentary and screen-related behaviors. This study reports on associations between personal network characteristics and these important health behaviors for early adolescents. Participants were 310 students, aged 11-13 years, from fifteen randomly selected Victorian primary schools (43% response rate). PA and sedentary behaviors were collected via accelerometer and self-report questionnaire, and anthropometric measures via trained researchers. Participants nominated up to fifteen friends, and described the frequency of interaction and perceived activity intensity of these friends. Personal network predictors were examined using regression modelling for PA and sedentary/screen behavior. Perceived activity levels of friends, and friendships with very frequent interaction were associated with outside-of-school PA and/or sedentary/screen time. Differences according to sex were also observed in the association between network characteristics and PA and sedentary time. A higher number of friends and greater proportion of same sex friends were associated with boys engaging in more moderate-to-vigorous PA outside of school hours. PA intensity during school-day breaks was positively associated with having a greater proportion of friends who played sports for girls, and a greater proportion of male friends for boys. Friendship network characteristics are associated with PA and sedentary/screen time in late childhood/early adolescence, and these associations differ by sex. The positive influence of very active peers may be a promising avenue to strengthen traditional interventions for the promotion of PA and reduction in screen time.

Lipidic profile and the level of physical activity of adolescent scholars

Directory of Open Access Journals (Sweden)

Renato Canevari Dutra da Silva

2011-12-01

Full Text Available Objective: To assess the relationship between lipid profile and physical activity level of adolescent students in Rio Verde-GO, Brazil. Methods: A descriptive and cross-sectionalstudy, conducted in 2006, with a population comprised by 1,229 adolescent students of both genders, aged 15 to 17 years (X = 15.9 years, SD + 0.81, from public and private schools. The level of physical activity was assessed through the International PhysicalActivity Questionnaire (IPAQ. Later, 48 teenagers underwent a lipidogram (lipid profile. Lipid concentrations of total cholesterol (TC, HDL-c (high density lipoprotein and LDL (low density lipoprotein and triglycerides (TGL were determined and assessed according to cutoff points proposed by the III Brazilian Guidelines on dyslipidemias and Guideline of Atherosclerosis, Department of Atherosclerosis of Brazilian Society of Cardiology. Statisticalanalysis was performed by binomial test for proportions and Pearson’s correlation test, adopting p <0.05. Results: Applying IPAQ we found a percentage of 77.7% active adolescents and 22.3% of insufficiently active adolescents, with the highest percentage of active teens inmales (p = 0.0000. Adolescents of both sexes from public network were considered more active than teens from private schools. The lipid profile of the studied adolescents was within normal range. Conclusion: There was no relationship between physical activity level and lipid profile of the adolescents assessed.

Systematic network assessment of the carcinogenic activities of cadmium

International Nuclear Information System (INIS)

Chen, Peizhan; Duan, Xiaohua; Li, Mian; Huang, Chao; Li, Jingquan; Chu, Ruiai; Ying, Hao; Song, Haiyun; Jia, Xudong; Ba, Qian; Wang, Hui

2016-01-01

Cadmium has been defined as type I carcinogen for humans, but the underlying mechanisms of its carcinogenic activity and its influence on protein-protein interactions in cells are not fully elucidated. The aim of the current study was to evaluate, systematically, the carcinogenic activity of cadmium with systems biology approaches. From a literature search of 209 studies that performed with cellular models, 208 proteins influenced by cadmium exposure were identified. All of these were assessed by Western blotting and were recognized as key nodes in network analyses. The protein-protein functional interaction networks were constructed with NetBox software and visualized with Cytoscape software. These cadmium-rewired genes were used to construct a scale-free, highly connected biological protein interaction network with 850 nodes and 8770 edges. Of the network, nine key modules were identified and 60 key signaling pathways, including the estrogen, RAS, PI3K-Akt, NF-κB, HIF-1α, Jak-STAT, and TGF-β signaling pathways, were significantly enriched. With breast cancer, colorectal and prostate cancer cellular models, we validated the key node genes in the network that had been previously reported or inferred form the network by Western blotting methods, including STAT3, JNK, p38, SMAD2/3, P65, AKT1, and HIF-1α. These results suggested the established network was robust and provided a systematic view of the carcinogenic activities of cadmium in human. - Highlights: • A cadmium-influenced network with 850 nodes and 8770 edges was established. • The cadmium-rewired gene network was scale-free and highly connected. • Nine modules were identified, and 60 key signaling pathways related to cadmium-induced carcinogenesis were found. • Key mediators in the network were validated in multiple cellular models.

Systematic network assessment of the carcinogenic activities of cadmium

Energy Technology Data Exchange (ETDEWEB)

Chen, Peizhan; Duan, Xiaohua; Li, Mian; Huang, Chao [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Li, Jingquan; Chu, Ruiai; Ying, Hao; Song, Haiyun [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Jia, Xudong [Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Ba, Qian, E-mail: qba@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); Wang, Hui, E-mail: huiwang@sibs.ac.cn [Key Laboratory of Food Safety Research, Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai (China); Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing (China); School of Life Science and Technology, ShanghaiTech University, Shanghai (China)

2016-11-01

Cadmium has been defined as type I carcinogen for humans, but the underlying mechanisms of its carcinogenic activity and its influence on protein-protein interactions in cells are not fully elucidated. The aim of the current study was to evaluate, systematically, the carcinogenic activity of cadmium with systems biology approaches. From a literature search of 209 studies that performed with cellular models, 208 proteins influenced by cadmium exposure were identified. All of these were assessed by Western blotting and were recognized as key nodes in network analyses. The protein-protein functional interaction networks were constructed with NetBox software and visualized with Cytoscape software. These cadmium-rewired genes were used to construct a scale-free, highly connected biological protein interaction network with 850 nodes and 8770 edges. Of the network, nine key modules were identified and 60 key signaling pathways, including the estrogen, RAS, PI3K-Akt, NF-κB, HIF-1α, Jak-STAT, and TGF-β signaling pathways, were significantly enriched. With breast cancer, colorectal and prostate cancer cellular models, we validated the key node genes in the network that had been previously reported or inferred form the network by Western blotting methods, including STAT3, JNK, p38, SMAD2/3, P65, AKT1, and HIF-1α. These results suggested the established network was robust and provided a systematic view of the carcinogenic activities of cadmium in human. - Highlights: • A cadmium-influenced network with 850 nodes and 8770 edges was established. • The cadmium-rewired gene network was scale-free and highly connected. • Nine modules were identified, and 60 key signaling pathways related to cadmium-induced carcinogenesis were found. • Key mediators in the network were validated in multiple cellular models.

Uniqueness, intractability and exact algorithms: reflections on level-k phylogenetic networks

NARCIS (Netherlands)

L.J.J. van Iersel (Leo); S.M. Kelk (Steven); M. Mnich

2009-01-01

htmlabstractPhylogenetic networks provide a way to describe and visualize evolutionary histories that have undergone so-called reticulate evolutionary events such as recombination, hybridization or horizontal gene transfer. The level k of a network determines how non-treelike the evolution can be,

Cities, Europeanization and Multi-level Governance: Governing Climate Change through Transnational Municipal Networks

NARCIS (Netherlands)

Kern, K.; Bulkeley, H.

2009-01-01

This article focuses on a variant of multi-level governance and Europeanization, i.e. the transnational networking of local authorities. Focusing on local climate change policy, the article examines how transnational municipal networks (TMNs) govern in the context of multi-level European governance.

Consumer Activities and Reactions to Social Network Marketing

OpenAIRE

Bistra Vassileva

2017-01-01

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

Decorrelation of Neural-Network Activity by Inhibitory Feedback

Science.gov (United States)

Einevoll, Gaute T.; Diesmann, Markus

2012-01-01

Correlations in spike-train ensembles can seriously impair the encoding of information by their spatio-temporal structure. An inevitable source of correlation in finite neural networks is common presynaptic input to pairs of neurons. Recent studies demonstrate that spike correlations in recurrent neural networks are considerably smaller than expected based on the amount of shared presynaptic input. Here, we explain this observation by means of a linear network model and simulations of networks of leaky integrate-and-fire neurons. We show that inhibitory feedback efficiently suppresses pairwise correlations and, hence, population-rate fluctuations, thereby assigning inhibitory neurons the new role of active decorrelation. We quantify this decorrelation by comparing the responses of the intact recurrent network (feedback system) and systems where the statistics of the feedback channel is perturbed (feedforward system). Manipulations of the feedback statistics can lead to a significant increase in the power and coherence of the population response. In particular, neglecting correlations within the ensemble of feedback channels or between the external stimulus and the feedback amplifies population-rate fluctuations by orders of magnitude. The fluctuation suppression in homogeneous inhibitory networks is explained by a negative feedback loop in the one-dimensional dynamics of the compound activity. Similarly, a change of coordinates exposes an effective negative feedback loop in the compound dynamics of stable excitatory-inhibitory networks. The suppression of input correlations in finite networks is explained by the population averaged correlations in the linear network model: In purely inhibitory networks, shared-input correlations are canceled by negative spike-train correlations. In excitatory-inhibitory networks, spike-train correlations are typically positive. Here, the suppression of input correlations is not a result of the mere existence of correlations between

Network robustness assessed within a dual connectivity framework: joint dynamics of the Active and Idle Networks.

Science.gov (United States)

Tejedor, Alejandro; Longjas, Anthony; Zaliapin, Ilya; Ambroj, Samuel; Foufoula-Georgiou, Efi

2017-08-17

Network robustness against attacks has been widely studied in fields as diverse as the Internet, power grids and human societies. But current definition of robustness is only accounting for half of the story: the connectivity of the nodes unaffected by the attack. Here we propose a new framework to assess network robustness, wherein the connectivity of the affected nodes is also taken into consideration, acknowledging that it plays a crucial role in properly evaluating the overall network robustness in terms of its future recovery from the attack. Specifically, we propose a dual perspective approach wherein at any instant in the network evolution under attack, two distinct networks are defined: (i) the Active Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN) composed of the affected nodes. The proposed robustness metric considers both the efficiency of destroying the AN and that of building-up the IN. We show, via analysis of well-known prototype networks and real world data, that trade-offs between the efficiency of Active and Idle Network dynamics give rise to surprising robustness crossovers and re-rankings, which can have significant implications for decision making.

Circumpolar Active Layer Monitoring (CALM) Program Network, Version 1

Data.gov (United States)

National Aeronautics and Space Administration — The CALM network includes 168 active sites in both hemispheres with 15 participating countries. This network represents the only coordinated and standardized program...

Distributed state estimation for multi-agent based active distribution networks

NARCIS (Netherlands)

Nguyen, H.P.; Kling, W.L.

2010-01-01

Along with the large-scale implementation of distributed generators, the current distribution networks have changed gradually from passive to active operation. State estimation plays a vital role to facilitate this transition. In this paper, a suitable state estimation method for the active network

Fast neutron spectra determination by threshold activation detectors using neural networks

International Nuclear Information System (INIS)

Kardan, M.R.; Koohi-Fayegh, R.; Setayeshi, S.; Ghiassi-Nejad, M.

2004-01-01

Neural network method was used for fast neutron spectra unfolding in spectrometry by threshold activation detectors. The input layer of the neural networks consisted of 11 neurons for the specific activities of neutron-induced nuclear reaction products, while the output layers were fast neutron spectra which had been subdivided into 6, 8, 10, 12, 15 and 20 energy bins. Neural network training was performed by 437 fast neutron spectra and corresponding threshold activation detector readings. The trained neural network have been applied for unfolding 50 spectra, which were not in training sets and the results were compared with real spectra and unfolded spectra by SANDII. The best results belong to 10 energy bin spectra. The neural network was also trained by detector readings with 5% uncertainty and the response of the trained neural network to detector readings with 5%, 10%, 15%, 20%, 25% and 50% uncertainty was compared with real spectra. Neural network algorithm, in comparison with other unfolding methods, is very fast and needless to detector response matrix and any prior information about spectra and also the outputs have low sensitivity to uncertainty in the activity measurements. The results show that the neural network algorithm is useful when a fast response is required with reasonable accuracy

Microgrids in Active Network Management-Part I

DEFF Research Database (Denmark)

Palizban, Omid; Kauhaniemia, Kimmo; Guerrero, Josep M.

2014-01-01

The microgrid concept has been closely investigated and implemented by numerous experts worldwide. The first part of this paper describes the principles of microgrid design, considering the operational concepts and requirements arising from participation in active network management. Over the las......, energy storage systems, and market participation in both island and grid-connection operation. Finally, control techniques and the principles of energy-storage systems are summarized in a comprehensive flowchart.......The microgrid concept has been closely investigated and implemented by numerous experts worldwide. The first part of this paper describes the principles of microgrid design, considering the operational concepts and requirements arising from participation in active network management. Over the last...

Controlling Voltage Levels of Distribution Network-Radial Feeder after Connecting Wind Turbines to the Network

Directory of Open Access Journals (Sweden)

Muhammad Al Badri

2017-11-01

Full Text Available Several factors in power generation and supply need to be taken into account such as shortages of energy supply, system stability, and energy quality and system disruption due to network losses, industrial development and population expansion. The addition of wind turbines to the distribution network is of great benefit in providing additional power and solving these problems, but this addition is accompanied by the problem of low voltage network. This research found optimal solutions to the problem of low voltage distribution network after connecting wind turbines. The main idea of this paper is to optimize the low-voltage problem as a result of connecting the wind turbines to the "far end" of the radial feeder for a distribution network and to obtain a voltage level within an acceptable and stable range. The problem of low voltage solved by using the load-drop compensation, capacitor-bank and “doubly-fed” induction generators. The results of this study were based on the operation of the entire design of the simulation system which would be compatible with the reality of the energy flow of all network components by using the PSCAD program. The present analysis program revealed an optimum solution for the low voltage profile of the distribution network after connecting the wind turbine.

Mean-field level analysis of epidemics in directed networks

Energy Technology Data Exchange (ETDEWEB)

Wang, Jiazeng [School of Mathematical Sciences, Peking University, Beijing 100871 (China); Liu, Zengrong [Mathematics Department, Shanghai University, Shanghai 200444 (China)], E-mail: wangjiazen@yahoo.com.cn, E-mail: zrongliu@online.sh.cn

2009-09-04

The susceptible-infected-removed spreading model in a directed graph is studied. The mean-field level rate equations are built with the degree-degree connectivity correlation element and the (in, out)-degree distribution. And the outbreak threshold is obtained analytically-it is determined by the combination of connectivity probability and the degree distribution. Furthermore, the methods of calculating the degree-degree correlations in directed networks are presented. The numerical results of the discrete epidemic processes in networks verify our analyses.

Mean-field level analysis of epidemics in directed networks

International Nuclear Information System (INIS)

Wang, Jiazeng; Liu, Zengrong

2009-01-01

The susceptible-infected-removed spreading model in a directed graph is studied. The mean-field level rate equations are built with the degree-degree connectivity correlation element and the (in, out)-degree distribution. And the outbreak threshold is obtained analytically-it is determined by the combination of connectivity probability and the degree distribution. Furthermore, the methods of calculating the degree-degree correlations in directed networks are presented. The numerical results of the discrete epidemic processes in networks verify our analyses.

MGluR5 mediates the interaction between late-LTP, network activity, and learning.

Directory of Open Access Journals (Sweden)

Arthur Bikbaev

2008-05-01

Full Text Available Hippocampal synaptic plasticity and learning are strongly regulated by metabotropic glutamate receptors (mGluRs and particularly by mGluR5. Here, we investigated the mechanisms underlying mGluR5-modulation of these phenomena. Prolonged pharmacological blockade of mGluR5 with MPEP produced a profound impairment of spatial memory. Effects were associated with 1 a reduction of mGluR1a-expression in the dentate gyrus; 2 impaired dentate gyrus LTP; 3 enhanced CA1-LTP and 4 suppressed theta (5-10 Hz and gamma (30-100 Hz oscillations in the dentate gyrus. Allosteric potentiation of mGluR1 after mGluR5 blockade significantly ameliorated dentate gyrus LTP, as well as suppression of gamma oscillatory activity. CA3-lesioning prevented MPEP effects on CA1-LTP, suggesting that plasticity levels in CA1 are driven by mGluR5-dependent synaptic and network activity in the dentate gyrus. These data support the hypothesis that prolonged mGluR5-inactivation causes altered hippocampal LTP levels and network activity, which is mediated in part by impaired mGluR1-expression in the dentate gyrus. The consequence is impairment of long-term learning.

Information Extraction with Character-level Neural Networks and Free Noisy Supervision

OpenAIRE

Meerkamp, Philipp; Zhou, Zhengyi

2016-01-01

We present an architecture for information extraction from text that augments an existing parser with a character-level neural network. The network is trained using a measure of consistency of extracted data with existing databases as a form of noisy supervision. Our architecture combines the ability of constraint-based information extraction systems to easily incorporate domain knowledge and constraints with the ability of deep neural networks to leverage large amounts of data to learn compl...

Real-time Human Activity Recognition using a Body Sensor Network

DEFF Research Database (Denmark)

Wang, Liang; Gu, Tao; Chen, Hanhua

2010-01-01

Real-time activity recognition using body sensor networks is an important and challenging task and it has many potential applications. In this paper, we propose a realtime, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this mo...

Analysis of Amygdalar-Cortical Network Covariance During Pre- versus Post-menopausal Estrogen Levels: Potential Relevance to Resting State Networks, Mood, and Cognition

Science.gov (United States)

Ottowitz, William E.; Derro, David; Dougherty, Darin D.; Lindquist, Martin A.; Fischman, Alan J.; Hall, Janet E.

2014-01-01

Objectives 1.) Expand the scope of neuroendocrine applications of functional neuroimaging techniques. 2.) Compare the covariance of amygdalar activity with that of the rest of the brain during pre- and post-menopausal levels of estrogen (E2). Based on the distribution of cortical E2 receptors and the neocortical regions where E2 has been shown to preferentially accumulate, we predict that E2 infusion will increase covariance of amygdalar activity with that of the temporal and frontal cortices. Design This basic physiology study employed a within-subject design. All participants were post-menopausal women (n =7). Analysis of covariance between whole brain and amygdalar regional cerebral glucose consumption (CMRglc) was conducted in a voxel-wise manner by means of the basic regression option in SPM2 and was applied to FDG-PET scans acquired at baseline and after a 24 hour graded E2 infusion. Setting an academic medical center; Massachusetts General Hospital, Boston, Massachusetts. Results E2 levels (mean ± sem) were significantly greater at 24 hours (257.9 pg/mL ± 29.7) than at 0 hours (28.1 pg/mL ± 3.4). Right amygdalar CMRglc showed a significant covariance with activity of three different regions of the temporal cortex during E2 infusion, but none at baseline. In addition, right amygdalar CMRglc covaried with that of the right medial and superior frontal gyri only during E2 infusion. Conclusions In addition to suggesting changes in amygdalar-cortical network connectivity as a result of short-term E2 exposure, these analyses provide evidence that basic neuroendocrine research may benefit from further use of FDG-PET and other functional neuroimaging modalities for network level analyses. PMID:18766152

Simulating activation propagation in social networks using the graph theory

Directory of Open Access Journals (Sweden)

František Dařena

2010-01-01

Full Text Available The social-network formation and analysis is nowadays one of objects that are in a focus of intensive research. The objective of the paper is to suggest the perspective of representing social networks as graphs, with the application of the graph theory to problems connected with studying the network-like structures and to study spreading activation algorithm for reasons of analyzing these structures. The paper presents the process of modeling multidimensional networks by means of directed graphs with several characteristics. The paper also demonstrates using Spreading Activation algorithm as a good method for analyzing multidimensional network with the main focus on recommender systems. The experiments showed that the choice of parameters of the algorithm is crucial, that some kind of constraint should be included and that the algorithm is able to provide a stable environment for simulations with networks.

Reconstructing Causal Biological Networks through Active Learning.

Directory of Open Access Journals (Sweden)

Hyunghoon Cho

Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

Flexibility and Balancing in Active Distribution Networks

DEFF Research Database (Denmark)

Kordheili, Reza Ahmadi

. Chapter 4 presents the details of the analysis, as well as the details of the MV network. To generalize the analysis, a standard MV network has been used for the studies. The MV network is also an active network, i.e. it involves MV wind turbines and decentralized combined heat and power (DCHP). DCHP...... units play an important role in Danish power system, and they contribute to electricity production as well. Modeling of wind turbines is done considering real data of a Vestas wind turbine. For wind speed, a modified wind speed model has been used for wind turbines, considering the available wind...... measurement. Also, a detailed model of DCHP units has been used in this thesis. Details of wind turbine model, as well as details of DCHP are presented in the thesis. The third objective of the research is to include the LV and MV networks in frequency response of the power system. Considering the increasing...

The European ALARA network. Development, functioning and main activities

International Nuclear Information System (INIS)

Schmitt-Hannig, A.

2009-01-01

The new ICRP recommendations (ICRP 103), and in particular the detailed treatment of optimisation in the ICRP Publication 101, define optimisation of protection as a source-related process aimed at keeping the likelihood of incurred exposures, the number of people exposed and the magnitude of their individual doses as low as reasonably achievable, also below constraints, taking into account economic and societal factors. Practical implementation and further development of the ALARA principle has been achieved for many years now by the successful cooperation of experts from different European organisations; first as pioneers by establishing the European ALARA Network and then by enthusiastically supporting the activities of the network itself. This contribution presents the evolution, operation and key activities of the European ALARA Network (EAN) in the last years; the successful cooperation of experts from different professional backgrounds, advocating the ALARA principle in a range of radiation protection areas, and contributing to its further development by trading experience and networking. The interaction between the EAN and international organisations, which support the ALARA principle by including relevant activities in their work programmes, is described. (orig.)

DReAM: Demand Response Architecture for Multi-level District Heating and Cooling Networks

Energy Technology Data Exchange (ETDEWEB)

Bhattacharya, Saptarshi; Chandan, Vikas; Arya, Vijay; Kar, Koushik

2017-05-19

In this paper, we exploit the inherent hierarchy of heat exchangers in District Heating and Cooling (DHC) networks and propose DReAM, a novel Demand Response (DR) architecture for Multi-level DHC networks. DReAM serves to economize system operation while still respecting comfort requirements of individual consumers. Contrary to many present day DR schemes that work on a consumer level granularity, DReAM works at a level of hierarchy above buildings, i.e. substations that supply heat to a group of buildings. This improves the overall DR scalability and reduce the computational complexity. In the first step of the proposed approach, mathematical models of individual substations and their downstream networks are abstracted into appropriately constructed low-complexity structural forms. In the second step, this abstracted information is employed by the utility to perform DR optimization that determines the optimal heat inflow to individual substations rather than buildings, in order to achieve the targeted objectives across the network. We validate the proposed DReAM framework through experimental results under different scenarios on a test network.

Systems-level organization of non-alcoholic fatty liver disease progression network

Directory of Open Access Journals (Sweden)

K. Shubham

2017-10-01

Full Text Available Non-Alcoholic Fatty Liver Disease (NAFLD is a hepatic metabolic disorder that is commonly associated with sedentary lifestyle and high fat diets. NAFLD is prevalent in individuals with obesity, insulin resistance and Type 2 Diabetes (T2D. The clinical spectrum of NAFLD ranges from simple steatosis to Non-Alcoholic Steatohepatitis (NASH with fibrosis, which can progress to cirrhosis and hepatocellular carcinoma.The pathogenesis of NAFLD is complex, involving crosstalk between multiple organs, cell-types, and environmental and genetic factors. Dysfunction of White Adipose Tissue (WAT plays a central role in the development of NAFLD and other metabolic disorders. WAT is an active endocrine organ that regulates whole-body energy homeostasis, lipid metabolism, insulin sensitivity and food intake by secreting biologically active molecules (lipokines, adipokines and cytokines. WAT dynamically reacts to nutrient excess or deprivation by remodelling the number (called hyperplasia and/or size (called hypertrophy of adipocytes to store fat or supply nutrients to other tissues by lipolysis, respectively. Adipose tissue remodelling is also accompanied by changes in the composition or function of stromal vascular cells and ECM. The major objective of our study was to identify and characterize the metabolic and signaling modules associated with the progression of NAFLD in the VAT. We performed Weighted Gene Co-expression Network Analysis (WGCNA to organize microarray data obtained from the VAT of patients at different stages of NAFLD into functional modules. In order to obtain insights into the metabolism and its regulation at the genome scale, a co-expression network of metabolic genes in the Human Metabolic Network (HMR2 was constructed and compared with the co-expression network constructed based on all the varying genes. We also used the prior network information on adipocyte metabolism (GEM to verify and extract reporter metabolites. Our analysis revealed

The European network of Biosafety-Level-4 laboratories: enhancing European preparedness for new health threats.

Science.gov (United States)

Nisii, C; Castilletti, C; Di Caro, A; Capobianchi, M R; Brown, D; Lloyd, G; Gunther, S; Lundkvist, A; Pletschette, M; Ippolito, G

2009-08-01

Emerging and re-emerging infections and possible bioterrorism acts will continue to challenge both the medical community and civilian populations worldwide, urging health authorities to respond rapidly and effectively. Established in 2005, the European Community (EC)-funded European Network of Biosafety-Level-4 laboratories (Euronet-P4), which brings together the laboratories in Porton Down, London, Hamburg, Marburg, Solna, Lyon and Rome, seeks to increase international collaboration in the areas of high containment laboratory biosafety and viral diagnostic capability, to strengthen Europe's capacity to respond to an infectious disease emergency, and to offer assistance to countries not equipped with such costly facilities. Network partners have agreed on a common strategy to fill the gaps identified in the field of risk group-4 agents' laboratory diagnosis, namely the lack of standardization and of reference samples. The network has received a further 3-year funding, to offer assistance to external laboratories, and to start the planning of field activities.

Effective professional networking.

Science.gov (United States)

Goolsby, Mary Jo; Knestrick, Joyce M

2017-08-01

The reasons for nurse practitioners to develop a professional network are boundless and are likely to change over time. Networking opens doors and creates relationships that support new opportunities, personal development, collaborative research, policy activism, evidence-based practice, and more. Successful professional networking involves shared, mutually beneficial interactions between individuals and/or individuals and groups, regardless of whether it occurs face to face or electronically. This article combines nuggets from the literature with guidance based on the authors' combined experience in networking activities at the local, national, and international levels. ©2017 American Association of Nurse Practitioners.

Integrating Micro-level Interactions with Social Network Analysis in Tie Strength Research

DEFF Research Database (Denmark)

Torre, Osku; Gupta, Jayesh Prakash; Kärkkäinen, Hannu

2017-01-01

of tie strength based on reciprocal interaction from publicly available Facebook data, and suggest that this approach could work as a basis for further tie strength studies. Our approach makes use of weak tie theory, and enables researchers to study micro-level interactions (i.e. discussions, messages......A social tie is a target for ongoing, high-level scientific debate. Measuring the tie strength in social networks has been an important topic for academic studies since Mark Granovetter's seminal papers in 1970's. However, it is still a problematic issue mainly for two reasons: 1) existing tie...... strengthening process in online social networks. Therefore, we suggest a new approach to tie strength research, which focuses on studying communication patterns (edges) more rather than actors (nodes) in a social network. In this paper we build a social network analysis-based approach to enable the evaluation...

Generalized activity equations for spiking neural network dynamics

Directory of Open Access Journals (Sweden)

Michael A Buice

2013-11-01

Full Text Available Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales - the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a classic stiff problem. Spiking neurons are also much more difficult to study analytically. One possible approach to making spiking networks more tractable is to augment mean field activity models with some information about spiking correlations. For example, such a generalized activity model could carry information about spiking rates and correlations between spikes self-consistently. Here, we will show how this can be accomplished by constructing a complete formal probabilistic description of the network and then expanding around a small parameter such as the inverse of the number of neurons in the network. The mean field theory of the system gives a rate-like description. The first order terms in the perturbation expansion keep track of covariances.

Energy Efficient Four Level Cooperative Opportunistic Communication for Wireless Personal Area Networks (WPAN)

DEFF Research Database (Denmark)

Rohokale, Vandana M.; Inamdar, Sandeep; Prasad, Neeli R.

2013-01-01

For wireless sensor networks (WSN),energy is a scarce resource. Due to limited battery resources, the energy consumption is the critical issue for the transmission as well as reception of the signals in the wireless communication. WSNs are infrastructure-less shared network demanding more energy...... consumption due to collaborative transmissions. This paper proposes a new cooperative opportunistic four level model for IEEE 802.15.4 Wireless Personal Area Network (WPAN).The average per node energy consumption is observed merely about 0.17mJ for the cooperative wireless communication which proves...... the proposed mechanism to be energy efficient. This paper further proposes four levels of cooperative data transmission from source to destination to improve network coverage with energy efficiency....

A new chaotic Hopfield network with piecewise linear activation function

International Nuclear Information System (INIS)

Peng-Sheng, Zheng; Wan-Sheng, Tang; Jian-Xiong, Zhang

2010-01-01

This paper presents a new chaotic Hopfield network with a piecewise linear activation function. The dynamic of the network is studied by virtue of the bifurcation diagram, Lyapunov exponents spectrum and power spectrum. Numerical simulations show that the network displays chaotic behaviours for some well selected parameters

Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity

Science.gov (United States)

2008-03-01

warfare, computer network operations, psychological operations, military deception, and operations security, in concert with specified supporting and...you up short—you were subconsciously predicting something else and were surprised by the mismatch” [3]. Notable neurobiologist Horace Barlow of the...malicious network activity is flagged as abnormal . That is, test data should present the N-HTM network with spatial-temporal patterns that do not match 46

SHORT-TERM SOLAR FLARE LEVEL PREDICTION USING A BAYESIAN NETWORK APPROACH

International Nuclear Information System (INIS)

Yu Daren; Huang Xin; Hu Qinghua; Zhou Rui; Wang Huaning; Cui Yanmei

2010-01-01

A Bayesian network approach for short-term solar flare level prediction has been proposed based on three sequences of photospheric magnetic field parameters extracted from Solar and Heliospheric Observatory/Michelson Doppler Imager longitudinal magnetograms. The magnetic measures, the maximum horizontal gradient, the length of neutral line, and the number of singular points do not have determinate relationships with solar flares, so the solar flare level prediction is considered as an uncertainty reasoning process modeled by the Bayesian network. The qualitative network structure which describes conditional independent relationships among magnetic field parameters and the quantitative conditional probability tables which determine the probabilistic values for each variable are learned from the data set. Seven sequential features-the maximum, the mean, the root mean square, the standard deviation, the shape factor, the crest factor, and the pulse factor-are extracted to reduce the dimensions of the raw sequences. Two Bayesian network models are built using raw sequential data (BN R ) and feature extracted data (BN F ), respectively. The explanations of these models are consistent with physical analyses of experts. The performances of the BN R and the BN F appear comparable with other methods. More importantly, the comprehensibility of the Bayesian network models is better than other methods.

Analysis of stationary availability factor of two-level backbone computer networks with arbitrary topology

Science.gov (United States)

Rahman, P. A.

2018-05-01

This scientific paper deals with the two-level backbone computer networks with arbitrary topology. A specialized method, offered by the author for calculation of the stationary availability factor of the two-level backbone computer networks, based on the Markov reliability models for the set of the independent repairable elements with the given failure and repair rates and the methods of the discrete mathematics, is also discussed. A specialized algorithm, offered by the author for analysis of the network connectivity, taking into account different kinds of the network equipment failures, is also observed. Finally, this paper presents an example of calculation of the stationary availability factor for the backbone computer network with the given topology.

Impairment of GABA transporter GAT-1 terminates cortical recurrent network activity via enhanced phasic inhibition

Directory of Open Access Journals (Sweden)

Daniel Simon Razik

2013-09-01

Full Text Available In the central nervous system, GABA transporters (GATs very efficiently clear synaptically released GABA from the extracellular space, and thus exert a tight control on GABAergic inhibition. In neocortex, GABAergic inhibition is heavily recruited during recurrent phases of spontaneous action potential activity which alternate with neuronally quiet periods. Therefore, such activity should be quite sensitive to minute alterations of GAT function. Here, we explored the effects of a gradual impairment of GAT-1 and GAT-2/3 on spontaneous recurrent network activity – termed network bursts and silent periods – in organotypic slice cultures of rat neocortex. The GAT-1 specific antagonist NO-711 depressed activity already at nanomolar concentrations (IC50 for depression of spontaneous multiunit firing rate of 42 nM, reaching a level of 80% at 500-1000 nM. By contrast, the GAT-2/3 preferring antagonist SNAP-5114 had weaker and less consistent effects. Several lines of evidence pointed towards an enhancement of phasic GABAergic inhibition as the dominant activity-depressing mechanism: network bursts were drastically shortened, phasic GABAergic currents decayed slower, and neuronal excitability during ongoing activity was diminished. In silent periods, NO-711 had little effect on neuronal excitability or membrane resistance, quite in contrast to the effects of muscimol, a GABA mimetic which activates GABAA receptors tonically. Our results suggest that an enhancement of phasic GABAergic inhibition efficiently curtails cortical recurrent activity and may mediate antiepileptic effects of therapeutically relevant concentrations of GAT-1 antagonists.

Managing coopetition through horizontal supply chain relations : Linking dyadic and network levels of analysis

NARCIS (Netherlands)

Wilhelm, Miriam M.

2011-01-01

A growing research stream has expanded the level of analysis beyond single buyer-supplier relations to the network, including supplier-supplier relations. These supplier-supplier relations may constitute a missing link between the traditional analysis of the dyadic and the network level of analysis

Managing coopetition through horizontal supply chain relations : Linking dyadic and network levels of analysis

NARCIS (Netherlands)

Wilhelm, Miriam M.

A growing research stream has expanded the level of analysis beyond single buyer-supplier relations to the network, including supplier-supplier relations. These supplier-supplier relations may constitute a missing link between the traditional analysis of the dyadic and the network level of analysis

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

Directory of Open Access Journals (Sweden)

Qiang Yang

2018-01-01

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

Academic Activities Transaction Extraction Based on Deep Belief Network

Directory of Open Access Journals (Sweden)

Xiangqian Wang

2017-01-01

Full Text Available Extracting information about academic activity transactions from unstructured documents is a key problem in the analysis of academic behaviors of researchers. The academic activities transaction includes five elements: person, activities, objects, attributes, and time phrases. The traditional method of information extraction is to extract shallow text features and then to recognize advanced features from text with supervision. Since the information processing of different levels is completed in steps, the error generated from various steps will be accumulated and affect the accuracy of final results. However, because Deep Belief Network (DBN model has the ability to automatically unsupervise learning of the advanced features from shallow text features, the model is employed to extract the academic activities transaction. In addition, we use character-based feature to describe the raw features of named entities of academic activity, so as to improve the accuracy of named entity recognition. In this paper, the accuracy of the academic activities extraction is compared by using character-based feature vector and word-based feature vector to express the text features, respectively, and with the traditional text information extraction based on Conditional Random Fields. The results show that DBN model is more effective for the extraction of academic activities transaction information.

Development of Active External Network Topology Module for Floodlight SDN Controller

Directory of Open Access Journals (Sweden)

A. A. Noskov

2015-01-01

Full Text Available Traditional network architecture is inflexible and complicated. This observation has led to a paradigm shift towards software-defined networking (SDN, where network management level is separated from data forwarding level. This change was made possible by control plane transfer from the switching equipment to software modules that run on a dedicated server, called the controller (or network operating system, or network applications, that work with this controller. Methods of representation, storage and communication interfaces with network topology elements are the most important aspects of network operating systems available to SDN user because performance of some key controller modules is heavily dependent on internal representation of the network topology. Notably, firewall and routing modules are examples of such modules. This article describes the methods used for presentation and storage of network topologies, as well as interface to the corresponding Floodlight modules. An alternative algorithm has been suggested and developed for message exchange conveying network topology alterations between the controller and network applications. Proposed algorithm makes implementation of module alerting based on subscription to the relevant events. API for interaction between controller and network applications has been developed. This algorithm and API formed the base for Topology Tracker module capable to inform network applications about the changes that had occurred in the network topology and also stores compact representation of the network to speed up the interaction process.

Three-Level Z-Source Inverters Using a Single LC Impedance Network

DEFF Research Database (Denmark)

Loh, Poh Chiang; Lim, Sok Wei; Gao, Feng

2007-01-01

two LC impedance networks and two isolated dc sources, which can significantly increase the overall system cost and require a more complex modulator for balancing the network inductive voltage boosting. Offering a number of less costly alternatives, this letter presents the design and control of two...... three-level Z-source inverters, whose output voltage can be stepped down or up using only a single LC impedance network connected between the dc input source and either a neutral-point-clamped (NPC) or dc-link cascaded inverter circuitry. Through careful design of their modulation scheme, both inverters...

Engineering Online and In-person Social Networks for Physical Activity: A Randomized Trial

Science.gov (United States)

Rovniak, Liza S.; Kong, Lan; Hovell, Melbourne F.; Ding, Ding; Sallis, James F.; Ray, Chester A.; Kraschnewski, Jennifer L.; Matthews, Stephen A.; Kiser, Elizabeth; Chinchilli, Vernon M.; George, Daniel R.; Sciamanna, Christopher N.

2016-01-01

Background Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. Purpose To conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively-measured outcomes. Methods Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3% male, 83.4% overweight/obese) were randomized to 1 of 3 groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking, and prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Results Participants increased their MVPA by 21.0 mins/week, 95% CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Conclusions Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. Trial Registration Number NCT01142804 PMID:27405724

Managing nuclear knowledge: IAEA activities and international coordination. Asian Network for Education in Nuclear Technology (ANENT)

International Nuclear Information System (INIS)

2007-07-01

This CD-ROM is attached to the booklet 'Managing nuclear knowledge: IAEA activities and international coordination. Asian Network for Education in Nuclear Technology (ANENT)'. It contains the background material with regard to ANENT in full text, including policy level papers, reports, presentation material made by Member States, and meeting summaries during the period 2002-2005. Further information on the current ANENT activities and related IAEA activities is available at 'http://anent-iaea.org' and 'http://iaea.org/inisnkm'

Photonic Network R&D Activities in Japan-Current Activities and Future Perspectives

Science.gov (United States)

Kitayama, Ken-Ichi; Miki, Tetsuya; Morioka, Toshio; Tsushima, Hideaki; Koga, Masafumi; Mori, Kazuyuki; Araki, Soichiro; Sato, Ken-Ichi; Onaka, Hiroshi; Namiki, Shu; Aoyama, Tomonori

2005-10-01

R&D activities on photonic networks in Japan are presented. First, milestones in current ongoing R&D programs supported by Japanese government agencies are introduced, including long-distance and wavelength division multiplexing (WDM) fiber transmission, wavelength routing, optical burst switching (OBS), and control-plane technology for IP backbone networks. Their goal was set to evolve a legacy telecommunications network to IP-over-WDM networks by introducing technologies for WDM and wavelength routing. We then discuss the perspectives of so-called PHASE II R&D programs for photonic networks over the next 5 years until 2010, by focusing on the report that has been recently issued by the Photonic Internet Forum (PIF), a consortium that has major carriers, telecom vendors, and Japanese academics as members. The PHASE II R&D programs should serve to establish a photonic platform to provide abundant bandwidth on demand, at any time on a real-time basis, through the customer's initiative to promote bandwidth-rich applications, such as grid computing, real-time digital-cinema streaming, medical and educational applications, and network storage in e-commerce.

Engineering Online and In-Person Social Networks for Physical Activity: A Randomized Trial.

Science.gov (United States)

Rovniak, Liza S; Kong, Lan; Hovell, Melbourne F; Ding, Ding; Sallis, James F; Ray, Chester A; Kraschnewski, Jennifer L; Matthews, Stephen A; Kiser, Elizabeth; Chinchilli, Vernon M; George, Daniel R; Sciamanna, Christopher N

2016-12-01

Social networks can influence physical activity, but little is known about how best to engineer online and in-person social networks to increase activity. The purpose of this study was to conduct a randomized trial based on the Social Networks for Activity Promotion model to assess the incremental contributions of different procedures for building social networks on objectively measured outcomes. Physically inactive adults (n = 308, age, 50.3 (SD = 8.3) years, 38.3 % male, 83.4 % overweight/obese) were randomized to one of three groups. The Promotion group evaluated the effects of weekly emailed tips emphasizing social network interactions for walking (e.g., encouragement, informational support); the Activity group evaluated the incremental effect of adding an evidence-based online fitness walking intervention to the weekly tips; and the Social Networks group evaluated the additional incremental effect of providing access to an online networking site for walking as well as prompting walking/activity across diverse settings. The primary outcome was mean change in accelerometer-measured moderate-to-vigorous physical activity (MVPA), assessed at 3 and 9 months from baseline. Participants increased their MVPA by 21.0 min/week, 95 % CI [5.9, 36.1], p = .005, at 3 months, and this change was sustained at 9 months, with no between-group differences. Although the structure of procedures for targeting social networks varied across intervention groups, the functional effect of these procedures on physical activity was similar. Future research should evaluate if more powerful reinforcers improve the effects of social network interventions. The trial was registered with the ClinicalTrials.gov (NCT01142804).

The Dynamics of network and dyad level supply management

DEFF Research Database (Denmark)

Ellegaard, Chris

-supplier relation and its immediate network context, are presented. In analysing the data, the dynamic interdependency between management of one level and management of the other, will be demonstrated. The analysis reveals a need for an alternating approach to supply management, which takes the dynamic complexity...

Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks

KAUST Repository

Douik, Ahmed

2016-03-28

In the context of resource allocation in cloud- radio access networks, recent studies assume either signal-level or scheduling-level coordination. This paper, instead, considers a hybrid level of coordination for the scheduling problem in the downlink of a multi-cloud radio- access network, so as to benefit from both scheduling policies. Consider a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links, and therefore allows joint signal processing between them. Across the multiple clouds, however, only scheduling-level coordination is permitted, as it requires a lower level of backhaul communication. The frame structure of every BS is composed of various time/frequency blocks, called power- zones (PZs), and kept at fixed power level. The paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled, at most, to a single cloud, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs\\' frame. The paper solves the problem using graph theory techniques by constructing the conflict graph. The scheduling problem is, then, shown to be equivalent to a maximum- weight independent set problem in the constructed graph, in which each vertex symbolizes an association of cloud, user, BS and PZ, with a weight representing the utility of that association. Simulation results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

Active Engine Mounting Control Algorithm Using Neural Network

Directory of Open Access Journals (Sweden)

Fadly Jashi Darsivan

2009-01-01

Full Text Available This paper proposes the application of neural network as a controller to isolate engine vibration in an active engine mounting system. It has been shown that the NARMA-L2 neurocontroller has the ability to reject disturbances from a plant. The disturbance is assumed to be both impulse and sinusoidal disturbances that are induced by the engine. The performance of the neural network controller is compared with conventional PD and PID controllers tuned using Ziegler-Nichols. From the result simulated the neural network controller has shown better ability to isolate the engine vibration than the conventional controllers.

Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

Science.gov (United States)

Williams, Tim D; Turan, Nil; Diab, Amer M; Wu, Huifeng; Mackenzie, Carolynn; Bartie, Katie L; Hrydziuszko, Olga; Lyons, Brett P; Stentiford, Grant D; Herbert, John M; Abraham, Joseph K; Katsiadaki, Ioanna; Leaver, Michael J; Taggart, John B; George, Stephen G; Viant, Mark R; Chipman, Kevin J; Falciani, Francesco

2011-08-01

The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

A Qualitative Study to Examine Feasibility and Design of an Online Social Networking Intervention to Increase Physical Activity in Teenage Girls.

Science.gov (United States)

Van Kessel, Gisela; Kavanagh, Madeleine; Maher, Carol

2016-01-01

Online social networks present wide-reaching and flexible platforms through which to deliver health interventions to targeted populations. This study used a social marketing approach to explore teenage girls' perceptions of physical activity and the potential use of online social networks to receive a physical activity intervention. Six focus groups were conducted with 19 Australian teenage girls (ages 13 to 18 years) with varying levels of physical activity and socioeconomic status. A semi-structured format was used, with groups discussion transcribed verbatim. Content analysis identified emergent themes, with triangulation and memos used to ensure accuracy. Physical activity was most appealing when it emphasised sport, exercise and fitness, along with opportunities for socialisation with friends and self-improvement. Participants were receptive to delivery of a physical activity intervention via online social networks, with Facebook the most widely reported site. Participants commonly accessed online social networks via mobile devices and particularly smartphones. Undesirable features included promotion of physical activity in terms of walking; use of cartoon imagery; use of humour; and promotion of the intervention via schools, each of which were considered "uncool". Participants noted that their parents were likely to be supportive of them using an online social networking physical activity intervention, particularly if not promoted as a weight loss intervention. This study identified key features likely to increase the feasibility and retention of an online social networking physical activity intervention for teenage girls. Guidelines for the design of interventions for teenage girls are provided for future applications.

TSUNAMI HAZARD MITIGATION AND THE NOAA NATIONAL WATER LEVEL OBSERVATION NETWORK

Directory of Open Access Journals (Sweden)

James R. Hubbard

2002-01-01

Full Text Available With the renewed interest in regional Tsunami Warning Systems and the potential tsunami threats throughout the Caribbean and West coast of the United States, the National Ocean Service (NOS, National Water Level Observation Network (NWLON consisting of 175 primary stations, is well situated to play a role in the National Hazard Mitigation effort. In addition, information regarding local mean sea level trends and GPS derived geodetic datum relationships at numerous coastal locations is readily available for tsunami hazard assessment and mapping applications.Tsunami inundation maps and modeling are just two of the more important products which may be derived from NWLON data. In addition to the seven water level gauges that are hardwired into the West Coast and Alaska Tsunami Warning Center (WClATWC, NOS has a significant number of gauges with real-time satellite telemetry capabilities located along the Pacific Northwest coastline, the Gulf of Mexico and the Caribbean. These gauges, in concert with near shore buoy systems, have the potential for increasing the effectiveness of the existing tsunami warning system.The recent expansion of the Caribbean Sea Level Gauge Network through the NOS regional partnerships with Central American and Caribbean countries have opened an opportunity for a basin-wide tsunami warning network in a region which is ill prepared for a major tsunami event.

Gap Filling of Daily Sea Levels by Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Lyubka Pashova

2013-06-01

Full Text Available In the recent years, intelligent methods as artificial neural networks are successfully applied for data analysis from different fields of the geosciences. One of the encountered practical problems is the availability of gaps in the time series that prevent their comprehensive usage for the scientific and practical purposes. The article briefly describes two types of the artificial neural network (ANN architectures - Feed-Forward Backpropagation (FFBP and recurrent Echo state network (ESN. In some cases, the ANN can be used as an alternative on the traditional methods, to fill in missing values in the time series. We have been conducted several experiments to fill the missing values of daily sea levels spanning a 5-years period using both ANN architectures. A multiple linear regression for the same purpose has been also applied. The sea level data are derived from the records of the tide gauge Burgas, which is located on the western Black Sea coast. The achieved results have shown that the performance of ANN models is better than that of the classical one and they are very promising for the real-time interpolation of missing data in the time series.

Photonic network R and D activities in Japan

Science.gov (United States)

Kitayama, Ken-ichi; Miki, Tetsuya; Morioka, Toshio; Tsushima, Hideaki; Koga, Masafumi; Mori, Kazuyuki; Araki, Soichiro; Sato, Ken-ichi; Onaka, Hiroshi; Namiki, Shu; Aovama, Tomonori

2005-11-01

R and D activities on photonic networks in Japan are presented. First, milestones in current, ongoing R and D programs supported by Japanese government agencies are introduced, including long-distance and WDM fiber transmission, wavelength routing, optical burst switching, and control plane technology for IP backbone networks. Their goal was set to evolve a legacy telecommunications network to IP over WDM networks by introducing technologies for WDM and wavelength routing. We then discuss the perspectives of so-called PHASE II R and D programs for photonic networks over the next five years until 2010, by focusing on the report which has been recently issued by the Photonic Internet Forum (PIF), a consortium that has major carriers, telecom vendors, and Japanese academics as members. The PHASE II R and D programs should serve to establish a photonic platform to provide abundant bandwidth on demand, at any time on a real-time basis through the customer's initiative, to promote bandwidth-rich applications, such as grid computing, real-time digital-cinema streaming, medical and educational applications, and network storage in e-commerce.

Caribbean Sea Level Network

Science.gov (United States)

von Hillebrandt-Andrade, C.; Crespo Jones, H.

2012-12-01

requirements and factors have been considered for the sustainability of the stations. The sea level stations have to potentially sustain very aggressive conditions of not only tsunamis, but on a more regular basis, hurricanes. Given the requirement that the data be available in near real time, for tsunami and other coastal hazard application, robust communication systems are also essential. For the local operator, the ability to be able to visualize the data is critical and tools like the IOC Sea level Monitoring Facility and the Tide Tool program are very useful. It has also been emphasized the need for these stations to serve multiple purposes. For climate and other research applications the data need to be archived, QC'd and analyzed. Increasing the user base for the sea level data has also been seen as an important goal to gain the local buy in; local weather and meteorological offices are considered as key stakeholders but for whom applications still need to be developed. The CARIBE EWS continues to look forward to working with other IOC partners including the Global Sea Level Observing System (GLOSS) and Sub-Commission for the Caribbean and Adjacent Regions (IOCARIBE)/GOOS, as well as with local, national and global sea level station operators and agencies for the development of a sustainable sea level network.

Tansig activation function (of MLP network) for cardiac abnormality detection

Science.gov (United States)

Adnan, Ja'afar; Daud, Nik Ghazali Nik; Ishak, Mohd Taufiq; Rizman, Zairi Ismael; Rahman, Muhammad Izzuddin Abd

2018-02-01

Heart abnormality often occurs regardless of gender, age and races. This problem sometimes does not show any symptoms and it can cause a sudden death to the patient. In general, heart abnormality is the irregular electrical activity of the heart. This paper attempts to develop a program that can detect heart abnormality activity through implementation of Multilayer Perceptron (MLP) network. A certain amount of data of the heartbeat signals from the electrocardiogram (ECG) will be used in this project to train the MLP network by using several training algorithms with Tansig activation function.

On attracting sets in artificial networks: cross activation

Directory of Open Access Journals (Sweden)

Sadyrbaev Felix

2018-01-01

Full Text Available Mathematical models of artificial networks can be formulated in terms of dynamical systems describing the behaviour of a network over time. The interrelation between nodes (elements of a network is encoded in the regulatory matrix. We consider a system of ordinary differential equations that describes in particular also genomic regulatory networks (GRN and contains a sigmoidal function. The results are presented on attractors of such systems for a particular case of cross activation. The regulatory matrix is then of particular form consisting of unit entries everywhere except the main diagonal. We show that such a system can have not more than three critical points. At least n–1 eigenvalues corresponding to any of the critical points are negative. An example for a particular choice of sigmoidal function is considered.

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

Science.gov (United States)

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

Reduced salience and default mode network activity in women with anorexia nervosa

Science.gov (United States)

McFadden, Kristina L.; Tregellas, Jason R.; Shott, Megan E.; Frank, Guido K.W.

2014-01-01

Background The neurobiology of anorexia nervosa is poorly understood. Neuronal networks contributing to action selection, self-regulation and interoception could contribute to pathologic eating and body perception in people with anorexia nervosa. We tested the hypothesis that the salience network (SN) and default mode network (DMN) would show decreased intrinsic activity in women with anorexia nervosa and those who had recovered from the disease compared to controls. The basal ganglia (BGN) and sensorimotor networks (SMN) were also investigated. Methods Between January 2008 and January 2012, women with restricting-type anorexia nervosa, women who recovered from the disease and healthy control women completed functional magnetic resonance imaging during a conditioned stimulus task. Network activity was studied using independent component analysis. Results We studied 20 women with anorexia nervosa, 24 recovered women and 24 controls. Salience network activity in the anterior cingulate cortex was reduced in women with anorexia nervosa (p = 0.030; all results false-discovery rate–corrected) and recovered women (p = 0.039) compared to controls. Default mode network activity in the precuneus was reduced in women with anorexia compared to controls (p = 0.023). Sensorimotor network activity in the supplementary motor area (SMA; p = 0.008), and the left (p = 0.028) and right (p = 0.002) postcentral gyrus was reduced in women with anorexia compared to controls; SMN activity in the SMA (p = 0.019) and the right postcentral gyrus (p = 0.008) was reduced in women with anorexia compared to recovered women. There were no group differences in the BGN. Limitations Differences between patient and control populations (e.g., depression, anxiety, medication) are potential confounds, but were included as covariates. Conclusion Reduced SN activity in women with anorexia nervosa and recovered women could be a trait-related biomarker or illness remnant, altering the drive to approach

Robust Synchronization in an E/I Network with Medium Synaptic Delay and High Level of Heterogeneity

International Nuclear Information System (INIS)

Han Fang; Wang Zhi-Jie; Gong Tao; Fan Hong

2015-01-01

It is known that both excitatory and inhibitory neuronal networks can achieve robust synchronization only under certain conditions, such as long synaptic delay or low level of heterogeneity. In this work, robust synchronization can be found in an excitatory/inhibitory (E/I) neuronal network with medium synaptic delay and high level of heterogeneity, which often occurs in real neuronal networks. Two effects of post-synaptic potentials (PSP) to network synchronization are presented, and the synaptic contribution of excitatory and inhibitory neurons to robust synchronization in this E/I network is investigated. It is found that both excitatory and inhibitory neurons may contribute to robust synchronization in E/I networks, especially the excitatory PSP has a more positive effect on synchronization in E/I networks than that in excitatory networks. This may explain the strong robustness of synchronization in E/I neuronal networks. (paper)

Network Resilience Analysis: Review Of Concepts And A Country-Level. Case Study

Directory of Open Access Journals (Sweden)

Mariusz Kamola

2014-01-01

Full Text Available This paper presents the rationale behind performing an analysis of Internet resilience in the sense of maintaining a connection of autonomous systems in the presence of failures or attacks — on a level of a single country. Next, the graph of a network is constructed that represents interconnections between autonomous systems. The connectivity of the graph is examined for cases of link or node failure. Resilience metrics are proposed, focusing on a single autonomous system or on overall network reliability. The process of geographic location of networking infrastructure is presented, leading to an analysis of network resilience in the case of a joint failure of neighboring autonomous systems.

Modulation of neuronal network activity with ghrelin

NARCIS (Netherlands)

Stoyanova, Irina; Rutten, Wim; le Feber, Jakob

2012-01-01

Ghrelin is a neuropeptide regulating multiple physiological processes, including high brain functions such as learning and memory formation. However, the effect of ghrelin on network activity patterns and developments has not been studied yet. Therefore, we used dissociated cortical neurons plated

Between "design" and "bricolage": genetic networks, levels of selection, and adaptive evolution.

Science.gov (United States)

Wilkins, Adam S

2007-05-15

The extent to which "developmental constraints" in complex organisms restrict evolutionary directions remains contentious. Yet, other forms of internal constraint, which have received less attention, may also exist. It will be argued here that a set of partial constraints below the level of phenotypes, those involving genes and molecules, influences and channels the set of possible evolutionary trajectories. At the top-most organizational level there are the genetic network modules, whose operations directly underlie complex morphological traits. The properties of these network modules, however, have themselves been set by the evolutionary history of the component genes and their interactions. Characterization of the components, structures, and operational dynamics of specific genetic networks should lead to a better understanding not only of the morphological traits they underlie but of the biases that influence the directions of evolutionary change. Furthermore, such knowledge may permit assessment of the relative degrees of probability of short evolutionary trajectories, those on the microevolutionary scale. In effect, a "network perspective" may help transform evolutionary biology into a scientific enterprise with greater predictive capability than it has hitherto possessed.

Detection of silent cells, synchronization and modulatory activity in developing cellular networks.

NARCIS (Netherlands)

Hjorth, J.J.J.; Dawitz, J.; Kroon, T.; da Silva Dias Pires, J.H.; Dassen, V.J.; Berkhout, J.A.; Emperador Melero, J.; Nadadhur, A.G.; Alevra, M.; Toonen, R.F.G.; Heine, V.M.; Mansvelder, H.D.; Meredith, R.M.

2016-01-01

Developing networks in the immature nervous system and in cellular cultures are characterized by waves of synchronous activity in restricted clusters of cells. Synchronized activity in immature networks is proposed to regulate many different developmental processes, from neuron growth and cell

An event- and network-level analysis of college students' maximum drinking day.

Science.gov (United States)

Meisel, Matthew K; DiBello, Angelo M; Balestrieri, Sara G; Ott, Miles Q; DiGuiseppi, Graham T; Clark, Melissa A; Barnett, Nancy P

2018-04-01

Heavy episodic drinking is common among college students and remains a serious public health issue. Previous event-level research among college students has examined behaviors and individual-level characteristics that drive consumption and related consequences but often ignores the social network of people with whom these heavy drinking episodes occur. The main aim of the current study was to investigate the network of social connections between drinkers on their heaviest drinking occasions. Sociocentric network methods were used to collect information from individuals in the first-year class (N=1342) at one university. Past-month drinkers (N=972) reported on the characteristics of their heaviest drinking occasion in the past month and indicated who else among their network connections was present during this occasion. Average max drinking day indegree, or the total number of times a participant was nominated as being present on another students' heaviest drinking occasion, was 2.50 (SD=2.05). Network autocorrelation models indicated that max drinking day indegree (e.g., popularity on heaviest drinking occassions) and peers' number of drinks on their own maximum drinking occasions were significantly associated with participant maximum number of drinks, after controlling for demographic variables, pregaming, and global network indegree (e.g., popularity in the entire first-year class). Being present at other peers' heaviest drinking occasions is associated with greater drinking quantities on one's own heaviest drinking occasion. These findings suggest the potential for interventions that target peer influences within close social networks of drinkers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Model Integrating Fuzzy Argument with Neural Network Enhancing the Performance of Active Queue Management

Directory of Open Access Journals (Sweden)

Nguyen Kim Quoc

2015-08-01

Full Text Available The bottleneck control by active queue management mechanisms at network nodes is essential. In recent years, some researchers have used fuzzy argument to improve the active queue management mechanisms to enhance the network performance. However, the projects using the fuzzy controller depend heavily on professionals and their parameters cannot be updated according to changes in the network, so the effectiveness of this mechanism is not high. Therefore, we propose a model combining the fuzzy controller with neural network (FNN to overcome the limitations above. Results of the training of the neural networks will find the optimal parameters for the adaptive fuzzy controller well to changes of the network. This improves the operational efficiency of the active queue management mechanisms at network nodes.

Information content of neural networks with self-control and variable activity

International Nuclear Information System (INIS)

Bolle, D.; Amari, S.I.; Dominguez Carreta, D.R.C.; Massolo, G.

2001-01-01

A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations

An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice

Directory of Open Access Journals (Sweden)

Liu Lili

2013-06-01

Full Text Available Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs, protein-protein interactions (PPIs and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

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

Directory of Open Access Journals (Sweden)

Wei Zheng

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

Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study.

Directory of Open Access Journals (Sweden)

Makii Muthalib

Full Text Available Neuroimaging studies have shown neuromuscular electrical stimulation (NMES-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC, premotor cortex (PMC, supplementary motor area (SMA, and secondary somatosensory area (S2, as well as regions of the prefrontal cortex (PFC known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI, and with reference to voluntary (VOL wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb and deoxygenated (HHb hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2. However, the level and area of contralateral sensorimotor network (including PFC activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study.

Science.gov (United States)

Muthalib, Makii; Re, Rebecca; Zucchelli, Lucia; Perrey, Stephane; Contini, Davide; Caffini, Matteo; Spinelli, Lorenzo; Kerr, Graham; Quaresima, Valentina; Ferrari, Marco; Torricelli, Alessandro

2015-01-01

Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions.

A Qualitative Study to Examine Feasibility and Design of an Online Social Networking Intervention to Increase Physical Activity in Teenage Girls.

Directory of Open Access Journals (Sweden)

Gisela Van Kessel

Full Text Available Online social networks present wide-reaching and flexible platforms through which to deliver health interventions to targeted populations. This study used a social marketing approach to explore teenage girls' perceptions of physical activity and the potential use of online social networks to receive a physical activity intervention.Six focus groups were conducted with 19 Australian teenage girls (ages 13 to 18 years with varying levels of physical activity and socioeconomic status. A semi-structured format was used, with groups discussion transcribed verbatim. Content analysis identified emergent themes, with triangulation and memos used to ensure accuracy.Physical activity was most appealing when it emphasised sport, exercise and fitness, along with opportunities for socialisation with friends and self-improvement. Participants were receptive to delivery of a physical activity intervention via online social networks, with Facebook the most widely reported site. Participants commonly accessed online social networks via mobile devices and particularly smartphones. Undesirable features included promotion of physical activity in terms of walking; use of cartoon imagery; use of humour; and promotion of the intervention via schools, each of which were considered "uncool". Participants noted that their parents were likely to be supportive of them using an online social networking physical activity intervention, particularly if not promoted as a weight loss intervention.This study identified key features likely to increase the feasibility and retention of an online social networking physical activity intervention for teenage girls. Guidelines for the design of interventions for teenage girls are provided for future applications.

A distributed water level network in ephemeral river reaches to identify hydrological processes within anthropogenic catchments

Science.gov (United States)

Sarrazin, B.; Braud, I.; Lagouy, M.; Bailly, J. S.; Puech, C.; Ayroles, H.

2009-04-01

In order to study the impact of land use change on the water cycle, distributed hydrological models are more and more used, because they have the ability to take into account the land surface heterogeneity and its evolution due to anthropogenic pressure. These models provide continuous distributed simulations of streamflow, runoff, soil moisture, etc, which, ideally, should be evaluated against continuous distributed measurements, taken at various scales and located in nested sub-catchments. Distributed network of streamflow gauging stations are in general scarce and very expensive to maintain. Furthermore, they can hardly be installed in the upstream parts of the catchments where river beds are not well defined. In this paper, we present an alternative to these standard streamflow gauging stations network, based on self powered high resolution water level sensors using a capacitive water height data logger. One of their advantages is that they can be installed even in ephemeral reaches and from channel head locations to high order streams. Furthermore, these innovative and easily adaptable low cost sensors offer the possibility to develop in the near future, a wireless network application. Such a network, including 15 sensors has been set up on nested watersheds in small and intermittent streams of a 7 km² catchment, located in the mountainous "Mont du Lyonnais" area, close to the city of Lyon, France. The land use of this catchment is mostly pasture, crop and forest, but the catchment is significantly affected by human activities, through the existence of a dense roads and paths network and urbanized areas. The equipment provides water levels survey during precipitation events in the hydrological network with a very accurate time step (2 min). Water levels can be related to runoff production and catchment response as a function of scale. This response will depend, amongst other, on variable soil water storage capacity, physiographic data and characteristics of

Associations within school-based same-sex friendship networks of children's physical activity and sedentary behaviours: a cross-sectional social network analysis.

Science.gov (United States)

Salway, Ruth E; Sebire, Simon J; Solomon-Moore, Emma; Thompson, Janice L; Jago, Russell

2018-02-21

Physical activity in children is associated with better physical and mental health but many children do not meet physical activity guidelines. Friendship groups are potentially an important influence on children's physical activity and sedentary time. This paper examines the association between children of physical activity and sedentary time in school-based same-sex friendship networks, for both moderate-to-vigorous intensity physical activity (MVPA) and sedentary time. Moreover, considering the methodological challenges of conducting and interpreting these analyses, we provide examples of how to analyse these data and interpret results to encourage further work in the area. Accelerometer data for 1223 children, aged 8-9 years, were collected in 2015-2016 and analysed in 2017. Mean accelerometer minutes of MVPA and sedentary time were calculated. Children named up to four school friends and same-sex school-based friendship networks were constructed. Network models, which include correlation between friends, were fitted by sex. Both MVPA and sedentary time were found to be associated via the friendship networks, for both boys and girls. The network autocorrelation was 0.21 (95% CI: 0.15 to 0.26) for boys' MVPA, and 0.14 (95% CI: 0.07 to 0.21) for sedentary time. Network autocorrelation between girls was weaker, with 0.13 (95% CI: 0.06 to 0.19) for MVPA and 0.11 (95% CI: 0.05 to 0.17) for sedentary time. Physical activity and sedentary time of boys and girls are associated with the physical activity and sedentary time respectively of others within same-sex friendship networks, and these associations are comparable to other known factors. In this study, the correlation between friends was stronger for boys than girls, and stronger for MVPA than for sedentary time. These findings suggest that friendship networks play a part in understanding children's physical activity and sedentary time and could play a valuable role in developing effective interventions.

Toward a Theory of Industrial Supply Networks: A Multi-Level Perspective via Network Analysis

Directory of Open Access Journals (Sweden)

Yi Zuo

2017-07-01

Full Text Available In most supply chains (SCs, transaction relationships between suppliers and customers are commonly considered to be an extrapolation from a linear perspective. However, this traditional linear concept of an SC is egotistic and oversimplified and does not sufficiently reflect the complex and cyclical structure of supplier-customer relationships in current economic and industrial situations. The interactional relationships and topological characteristics between suppliers and customers should be analyzed using supply networks (SNs rather than traditional linear SCs. Therefore, this paper reconceptualizes SCs as SNs in complex adaptive systems (CAS, and presents three main contributions. First, we propose an integrated framework of CAS network by synthesizing multi-level network analysis from the network-, community- and vertex-perspective. The CAS perspective enables us to understand the advances of SN properties. Second, in order to emphasize the CAS properties of SNs, we conducted a real-world SN based on the Japanese industry and describe an advanced investigation of SN theory. The CAS properties help in enriching the SN theory, which can benefit SN management, community economics and industrial resilience. Third, we propose a quantitative metric of entropy to measure the complexity and robustness of SNs. The results not only support a specific understanding of the structural outcomes relevant to SNs, but also deliver efficient and effective support to the management and design of SNs.

Character-level neural network for biomedical named entity recognition.

Science.gov (United States)

Gridach, Mourad

2017-06-01

Biomedical named entity recognition (BNER), which extracts important named entities such as genes and proteins, is a challenging task in automated systems that mine knowledge in biomedical texts. The previous state-of-the-art systems required large amounts of task-specific knowledge in the form of feature engineering, lexicons and data pre-processing to achieve high performance. In this paper, we introduce a novel neural network architecture that benefits from both word- and character-level representations automatically, by using a combination of bidirectional long short-term memory (LSTM) and conditional random field (CRF) eliminating the need for most feature engineering tasks. We evaluate our system on two datasets: JNLPBA corpus and the BioCreAtIvE II Gene Mention (GM) corpus. We obtained state-of-the-art performance by outperforming the previous systems. To the best of our knowledge, we are the first to investigate the combination of deep neural networks, CRF, word embeddings and character-level representation in recognizing biomedical named entities. Copyright © 2017 Elsevier Inc. All rights reserved.

Development of a Regional Neural Network for Coastal Water Level Predictions

National Research Council Canada - National Science Library

Huang, Wenrui; Murray, Catherine; Kraus, Nicholas; Rosati, Julie

2003-01-01

.... Fortunately, the US National Oceanographic and Atmospheric Administration (NOAA) has a national network of water level monitoring stations distributed in regional scale that has been operating for several decades...

Modulation of Network Oscillatory Activity and GABAergic Synaptic Transmission by CB1 Cannabinoid Receptors in the Rat Medial Entorhinal Cortex

Directory of Open Access Journals (Sweden)

Nicola H. Morgan

2008-01-01

Full Text Available Cannabinoids modulate inhibitory GABAergic neurotransmission in many brain regions. Within the temporal lobe, cannabinoid receptors are highly expressed, and are located presynaptically at inhibitory terminals. Here, we have explored the role of type-1 cannabinoid receptors (CB1Rs at the level of inhibitory synaptic currents and field-recorded network oscillations. We report that arachidonylcyclopropylamide (ACPA; 10 M, an agonist at CB1R, inhibits GABAergic synaptic transmission onto both superficial and deep medial entorhinal (mEC neurones, but this has little effect on network oscillations in beta/gamma frequency bands. By contrast, the CB1R antagonist/inverse agonist LY320135 (500 nM, increased GABAergic synaptic activity and beta/gamma oscillatory activity in superficial mEC, was suppressed, whilst that in deep mEC was enhanced. These data indicate that cannabinoid-mediated effects on inhibitory synaptic activity may be constitutively active in vitro, and that modulation of CB1R activation using inverse agonists unmasks complex effects of CBR function on network activity.

Connectivity, excitability and activity patterns in neuronal networks

International Nuclear Information System (INIS)

Le Feber, Joost; Stoyanova, Irina I; Chiappalone, Michela

2014-01-01

Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFP i,j ) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFP i,j with the autocorrelation of i (i.e. CFP i,i ), to obtain the single pulse response (SPR i,j )—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression. (papers)

Frequency Count Attribute Oriented Induction of Corporate Network Data for Mapping Business Activity

Directory of Open Access Journals (Sweden)

Tanutama Lukas

2014-03-01

Full Text Available Companies increasingly rely on Internet for effective and efficient business communication. As Information Technology infrastructure backbone for business activities, corporate network connects the company to Internet and enables its activities globally. It carries data packets generated by the activities of the users performing their business tasks. Traditionally, infrastructure operations mainly maintain data carrying capacity and network devices performance. It would be advantageous if a company knows what activities are running in its network. The research provides a simple method of mapping the business activity reflected by the network data. To map corporate users’ activities, a slightly modified Attribute Oriented Induction (AOI approach to mine the network data was applied. The frequency of each protocol invoked were counted to show what the user intended to do. The collected data was samples taken within a certain sampling period. Samples were taken due to the enormous data packets generated. Protocols of interest are only Internet related while intranet protocols are ignored. It can be concluded that the method could provide the management a general overview of the usage of its infrastructure and lead to efficient, effective and secure ICT infrastructure.

Frequency Count Attribute Oriented Induction of Corporate Network Data for Mapping Business Activity

Science.gov (United States)

Tanutama, Lukas

2014-03-01

Companies increasingly rely on Internet for effective and efficient business communication. As Information Technology infrastructure backbone for business activities, corporate network connects the company to Internet and enables its activities globally. It carries data packets generated by the activities of the users performing their business tasks. Traditionally, infrastructure operations mainly maintain data carrying capacity and network devices performance. It would be advantageous if a company knows what activities are running in its network. The research provides a simple method of mapping the business activity reflected by the network data. To map corporate users' activities, a slightly modified Attribute Oriented Induction (AOI) approach to mine the network data was applied. The frequency of each protocol invoked were counted to show what the user intended to do. The collected data was samples taken within a certain sampling period. Samples were taken due to the enormous data packets generated. Protocols of interest are only Internet related while intranet protocols are ignored. It can be concluded that the method could provide the management a general overview of the usage of its infrastructure and lead to efficient, effective and secure ICT infrastructure.

Towards a system level understanding of non-model organisms sampled from the environment: a network biology approach.

Directory of Open Access Journals (Sweden)

Tim D Williams

2011-08-01

Full Text Available The acquisition and analysis of datasets including multi-level omics and physiology from non-model species, sampled from field populations, is a formidable challenge, which so far has prevented the application of systems biology approaches. If successful, these could contribute enormously to improving our understanding of how populations of living organisms adapt to environmental stressors relating to, for example, pollution and climate. Here we describe the first application of a network inference approach integrating transcriptional, metabolic and phenotypic information representative of wild populations of the European flounder fish, sampled at seven estuarine locations in northern Europe with different degrees and profiles of chemical contaminants. We identified network modules, whose activity was predictive of environmental exposure and represented a link between molecular and morphometric indices. These sub-networks represented both known and candidate novel adverse outcome pathways representative of several aspects of human liver pathophysiology such as liver hyperplasia, fibrosis, and hepatocellular carcinoma. At the molecular level these pathways were linked to TNF alpha, TGF beta, PDGF, AGT and VEGF signalling. More generally, this pioneering study has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.

Social network activation: the role of health discussion partners in recovery from mental illness.

Science.gov (United States)

Perry, Brea L; Pescosolido, Bernice A

2015-01-01

In response to health problems, individuals may strategically activate their social network ties to help manage crisis and uncertainty. While it is well-established that social relationships provide a crucial safety net, little is known about who is chosen to help during an episode of illness. Guided by the Network Episode Model, two aspects of consulting others in the face of mental illness are considered. First, we ask who activates ties, and what kinds of ties and networks they attempt to leverage for discussing health matters. Second, we ask about the utility of activating health-focused network ties. Specifically, we examine the consequences of network activation at time of entry into treatment for individuals' quality of life, social satisfaction, ability to perform social roles, and mental health functioning nearly one year later. Using interview data from the longitudinal Indianapolis Network Mental Health Study (INMHS, N = 171), we focus on a sample of new patients with serious mental illness and a group with less severe disorders who are experiencing their first contact with the mental health treatment system. Three findings stand out. First, our results reveal the nature of agency in illness response. Whether under a rational choice or habitus logic, individuals appear to evaluate support needs, identifying the best possible matches among a larger group of potential health discussants. These include members of the core network and those with prior mental health experiences. Second, selective activation processes have implications for recovery. Those who secure adequate network resources report better outcomes than those who injudiciously activate network ties. Individuals who activate weaker relationships and those who are unsupportive of medical care experience poorer functioning, limited success in fulfilling social roles, and lower social satisfaction and quality of life later on. Third, the evidence suggests that social networks matter above and

Cross-Layer Active Predictive Congestion Control Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yinfeng Wu

2009-10-01

Full Text Available In wireless sensor networks (WSNs, there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node‟s neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.

Cross-layer active predictive congestion control protocol for wireless sensor networks.

Science.gov (United States)

Wan, Jiangwen; Xu, Xiaofeng; Feng, Renjian; Wu, Yinfeng

2009-01-01

In wireless sensor networks (WSNs), there are numerous factors that may cause network congestion problems, such as the many-to-one communication modes, mutual interference of wireless links, dynamic changes of network topology and the memory-restrained characteristics of nodes. All these factors result in a network being more vulnerable to congestion. In this paper, a cross-layer active predictive congestion control scheme (CL-APCC) for improving the performance of networks is proposed. Queuing theory is applied in the CL-APCC to analyze data flows of a single-node according to its memory status, combined with the analysis of the average occupied memory size of local networks. It also analyzes the current data change trends of local networks to forecast and actively adjust the sending rate of the node in the next period. In order to ensure the fairness and timeliness of the network, the IEEE 802.11 protocol is revised based on waiting time, the number of the node's neighbors and the original priority of data packets, which dynamically adjusts the sending priority of the node. The performance of CL-APCC, which is evaluated by extensive simulation experiments. is more efficient in solving the congestion in WSNs. Furthermore, it is clear that the proposed scheme has an outstanding advantage in terms of improving the fairness and lifetime of networks.

A Hierarchical Approach to Real-time Activity Recognition in Body Sensor Networks

DEFF Research Database (Denmark)

Wang, Liang; Gu, Tao; Tao, Xianping

2012-01-01

Real-time activity recognition in body sensor networks is an important and challenging task. In this paper, we propose a real-time, hierarchical model to recognize both simple gestures and complex activities using a wireless body sensor network. In this model, we rst use a fast and lightweight al...

Crossmodal semantic congruence can affect visuo-spatial processing and activity of the fronto-parietal attention networks

Directory of Open Access Journals (Sweden)

Serena eMastroberardino

2015-07-01

Full Text Available Previous studies have shown that multisensory stimuli can contribute to attention control. Here we investigate whether irrelevant audio-visual stimuli can affect the processing of subsequent visual targets, in the absence of any direct bottom-up signals generated by low-level sensory changes and any goal-related associations between the multisensory stimuli and the visual targets. Each trial included two pictures (cat/dog, one in each visual hemifield, and a central sound that was semantically congruent with one of the two pictures (i.e. either meow or woof sound. These irrelevant audio-visual stimuli were followed by a visual target that appeared either where the congruent or the incongruent picture had been presented (valid/invalid trials. The visual target was a Gabor patch requiring an orientation discrimination judgment, allowing us to uncouple the visual task from the audio-visual stimuli. Behaviourally we found lower performance for invalid than valid trials, but only when the task demands were high (Gabor target presented together with a Gabor distractor vs. Gabor target alone. The fMRI analyses revealed greater activity for invalid than for valid trials in the dorsal and the ventral fronto-parietal attention networks. The dorsal network was recruited irrespective of task demands, while the ventral network was recruited only when task demands were high and target discrimination required additional top-down control. We propose that crossmodal semantic congruence generates a processing bias associated with the location of congruent picture, and that the presentation of the visual target on the opposite side required updating these processing priorities. We relate the activation of the attention networks to these updating operations. We conclude that the fronto-parietal networks mediate the influence of crossmodal semantic congruence on visuo-spatial processing, even in the absence of any low-level sensory cue and any goal-driven task

Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations

OpenAIRE

Harradon, Michael; Druce, Jeff; Ruttenberg, Brian

2018-01-01

Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then bu...

Communication Networks - Analysis of jitter due to call-level fluctuations

NARCIS (Netherlands)

Mandjes, M.R.H.

2007-01-01

Abstract In communication networks used by constant bit rate applications, call-level dynamics (i.e. entering and leaving calls) lead to fluctuations in the load, and therefore also fluctuations in the delay (jitter). By intentionally delaying the packets at the destination, one can transform the

Knowledge Strategies in Using Social Networks

Directory of Open Access Journals (Sweden)

Contantin BRĂTIANU

2013-05-01

Full Text Available Knowledge strategy selection is a multiple criteria decision-making (MCDM problem, and requires adequate methods to solve it appropriately. Knowledge strategies are also intrinsically linked to individuals and their ability to comprehend the world and leverage their intellectual assets to respond e!ectively to a fast changing environment. the essential features of social networking sites include but are not limited to: blogging, grouping, networking and instant messaging. Since the social networks facilitate communication and interaction among users, there is a continuous need of researches to examine what are the motives that a!ect the acceptance of usage of the social networks. This study aims at examining the role of the knowledge strategies that individuals employ in using social networks with respect to the overall objective of increasing the knowledge level. For this purpose we have used the Analytic Hierarchy Process (AHP mathematical model since it allows us a structuring of the overall objective on the main components. For the present research we considered a structure composed of three levels: L1 – the purpose of networking, L2 – strategies used to achieve that purpose, and L3 – activities needed for strategies implementation. At the upper level (L1, the main objective of a person in using social networks is to increase its knowledge level. To obtain the aforementioned objective we considered for the second level (L2 the following strategies: S1 – to learn from other persons; S2 – to make new friends; S3 – to increase the personal experience and visibility. the implementation of these strategies is realized through the following activities considered at the third hierarchy level (L3: A1– joining general social networks (e.g. Facebook, Google+, MySpace, Hi5 etc.; A2– joining professional social networks (e.g. LinkedIn etc.; A3– creating a personal blog (e.g. Blogster, Wordpress etc.; A4– joining online communities of

Networking in Sport Management: Ideas and Activities to Enhance Student Engagement and Career Development

Directory of Open Access Journals (Sweden)

Alan S. Kornspan

2013-01-01

Full Text Available The primary purpose of this paper is to present information regarding the development of networking skills to enhance the career development of sport management students. Specifically, literature is reviewed which supports the importance of networking in the attainment of employment and career advancement in the sport industry. This is followed by an overview of emerging networking activities that allow opportunities for sport management students to expand their network. Sport industry career fairs and career conferences that students can attend are discussed. Additionally, sport industry professional associations that students can become involved with are presented. This is then followed with information related to the development of sport management clubs and various events that can be promoted to enhance the networking process. Specifically, activities provided by university faculty to enhance the educational experience of sport management students are detailed. Finally, a sample schedule of semester activities focused on student engagement and networking activities is provided.

Alumni Activities : International Alumni Network for TUAS

OpenAIRE

Saarinen, Riikka-Maria

2013-01-01

Turku University of Applied Sciences is currently planning on creating an International Alumni Network for the former exchange students who had their exchange period at TUAS. In this thesis, alumni functions are divided into three sections, i.e. the purpose of the alumni, the activities of the alumni and the management of the communication of the alumni. The research of the alumni functions was conducted by introduction of alumni activities in general and introducing three examples of Amer...

Predicting forest insect flight activity: A Bayesian network approach.

Directory of Open Access Journals (Sweden)

Stephen M Pawson

Full Text Available Daily flight activity patterns of forest insects are influenced by temporal and meteorological conditions. Temperature and time of day are frequently cited as key drivers of activity; however, complex interactions between multiple contributing factors have also been proposed. Here, we report individual Bayesian network models to assess the probability of flight activity of three exotic insects, Hylurgus ligniperda, Hylastes ater, and Arhopalus ferus in a managed plantation forest context. Models were built from 7,144 individual hours of insect sampling, temperature, wind speed, relative humidity, photon flux density, and temporal data. Discretized meteorological and temporal variables were used to build naïve Bayes tree augmented networks. Calibration results suggested that the H. ater and A. ferus Bayesian network models had the best fit for low Type I and overall errors, and H. ligniperda had the best fit for low Type II errors. Maximum hourly temperature and time since sunrise had the largest influence on H. ligniperda flight activity predictions, whereas time of day and year had the greatest influence on H. ater and A. ferus activity. Type II model errors for the prediction of no flight activity is improved by increasing the model's predictive threshold. Improvements in model performance can be made by further sampling, increasing the sensitivity of the flight intercept traps, and replicating sampling in other regions. Predicting insect flight informs an assessment of the potential phytosanitary risks of wood exports. Quantifying this risk allows mitigation treatments to be targeted to prevent the spread of invasive species via international trade pathways.

Multi-level deep supervised networks for retinal vessel segmentation.

Science.gov (United States)

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

Coordinated Voltage Control of Active Distribution Network

Directory of Open Access Journals (Sweden)

Xie Jiang

2016-01-01

Full Text Available This paper presents a centralized coordinated voltage control method for active distribution network to solve off-limit problem of voltage after incorporation of distributed generation (DG. The proposed method consists of two parts, it coordinated primal-dual interior point method-based voltage regulation schemes of DG reactive powers and capacitors with centralized on-load tap changer (OLTC controlling method which utilizes system’s maximum and minimum voltages, to improve the qualified rate of voltage and reduce the operation numbers of OLTC. The proposed coordination has considered the cost of capacitors. The method is tested using a radial edited IEEE-33 nodes distribution network which is modelled using MATLAB.

Forecasting Flare Activity Using Deep Convolutional Neural Networks

Science.gov (United States)

Hernandez, T.

2017-12-01

Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

Constrained Active Learning for Anchor Link Prediction Across Multiple Heterogeneous Social Networks.

Science.gov (United States)

Zhu, Junxing; Zhang, Jiawei; Wu, Quanyuan; Jia, Yan; Zhou, Bin; Wei, Xiaokai; Yu, Philip S

2017-08-03

Nowadays, people are usually involved in multiple heterogeneous social networks simultaneously. Discovering the anchor links between the accounts owned by the same users across different social networks is crucial for many important inter-network applications, e.g., cross-network link transfer and cross-network recommendation. Many different supervised models have been proposed to predict anchor links so far, but they are effective only when the labeled anchor links are abundant. However, in real scenarios, such a requirement can hardly be met and most anchor links are unlabeled, since manually labeling the inter-network anchor links is quite costly and tedious. To overcome such a problem and utilize the numerous unlabeled anchor links in model building, in this paper, we introduce the active learning based anchor link prediction problem. Different from the traditional active learning problems, due to the one-to-one constraint on anchor links, if an unlabeled anchor link a = ( u , v ) is identified as positive (i.e., existing), all the other unlabeled anchor links incident to account u or account v will be negative (i.e., non-existing) automatically. Viewed in such a perspective, asking for the labels of potential positive anchor links in the unlabeled set will be rewarding in the active anchor link prediction problem. Various novel anchor link information gain measures are defined in this paper, based on which several constraint active anchor link prediction methods are introduced. Extensive experiments have been done on real-world social network datasets to compare the performance of these methods with state-of-art anchor link prediction methods. The experimental results show that the proposed Mean-entropy-based Constrained Active Learning (MC) method can outperform other methods with significant advantages.

Assembling the puzzle for promoting physical activity in Brazil: a social network analysis.

Science.gov (United States)

Brownson, Ross C; Parra, Diana C; Dauti, Marsela; Harris, Jenine K; Hallal, Pedro C; Hoehner, Christine; Malta, Deborah Carvalho; Reis, Rodrigo S; Ramos, Luiz Roberto; Ribeiro, Isabela C; Soares, Jesus; Pratt, Michael

2010-07-01

Physical inactivity is a significant public health problem in Brazil that may be addressed by partnerships and networks. In conjunction with Project GUIA (Guide for Useful Interventions for Physical Activity in Brazil and Latin America), the aim of this study was to conduct a social network analysis of physical activity in Brazil. An online survey was completed by 28 of 35 organizations contacted from December 2008 through March 2009. Network analytic methods examined measures of collaboration, importance, leadership, and attributes of the respondent and organization. Leadership nominations for organizations studied ranged from 0 to 23. Positive predictors of collaboration included: south region, GUIA membership, years working in physical activity, and research, education, and promotion/practice areas of physical activity. The most frequently reported barrier to collaboration was bureaucracy. Social network analysis identified factors that are likely to improve collaboration among organizations in Brazil.

Active node determination for correlated data gathering in wireless sensor networks

OpenAIRE

Karasabun, Efe

2009-01-01

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2009. Thesis (Master's) -- Bilkent University, 2009. Includes bibliographical references leaves 53-55. In wireless sensor network applications where data gathered by different sensor nodes is correlated, not all sensor nodes need to be active for the wireless sensor network to be functional. However, the sensor nodes that are selected as active should form a co...

Innovation diffusion on time-varying activity driven networks

Science.gov (United States)

Rizzo, Alessandro; Porfiri, Maurizio

2016-01-01

Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass' model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.

Nonreciprocal signal routing in an active quantum network

Science.gov (United States)

Metelmann, A.; Türeci, H. E.

2018-04-01

As superconductor quantum technologies are moving towards large-scale integrated circuits, a robust and flexible approach to routing photons at the quantum level becomes a critical problem. Active circuits, which contain parametrically driven elements selectively embedded in the circuit, offer a viable solution. Here, we present a general strategy for routing nonreciprocally quantum signals between two sites of a given lattice of oscillators, implementable with existing superconducting circuit components. Our approach makes use of a dual lattice of overdamped oscillators linking the nodes of the main lattice. Solutions for spatially selective driving of the lattice elements can be found, which optimally balance coherent and dissipative hopping of microwave photons to nonreciprocally route signals between two given nodes. In certain lattices these optimal solutions are obtained at the exceptional point of the dynamical matrix of the network. We also demonstrate that signal and noise transmission characteristics can be separately optimized.

Global physical activity levels

DEFF Research Database (Denmark)

Hallal, Pedro C; Andersen, Lars Bo; Bull, Fiona C

2012-01-01

To implement effective non-communicable disease prevention programmes, policy makers need data for physical activity levels and trends. In this report, we describe physical activity levels worldwide with data for adults (15 years or older) from 122 countries and for adolescents (13-15-years......-income countries. The proportion of 13-15-year-olds doing fewer than 60 min of physical activity of moderate to vigorous intensity per day is 80·3% (80·1-80·5); boys are more active than are girls. Continued improvement in monitoring of physical activity would help to guide development of policies and programmes......-old) from 105 countries. Worldwide, 31·1% (95% CI 30·9-31·2) of adults are physically inactive, with proportions ranging from 17·0% (16·8-17·2) in southeast Asia to about 43% in the Americas and the eastern Mediterranean. Inactivity rises with age, is higher in women than in men, and is increased in high...

Associating at the European Level: Civil Society Networks in Brussels

NARCIS (Netherlands)

Brandsen, T.; Sittermann, B.; Freise, M.; Hallmann, T.

2014-01-01

This chapter examines the position of the third sector at the European level, where it has slowly been building a presence. Although great advances have been made, especially from the 1990s onwards, and the sector’s European networks are much stronger than before, it remains institutionally weak and

Two-level Robust Measurement Fusion Kalman Filter for Clustering Sensor Networks

Institute of Scientific and Technical Information of China (English)

ZHANG Peng; QI Wen-Juan; DENG Zi-Li

2014-01-01

This paper investigates the distributed fusion Kalman filtering over clustering sensor networks. The sensor network is partitioned as clusters by the nearest neighbor rule and each cluster consists of sensing nodes and cluster-head. Using the minimax robust estimation principle, based on the worst-case conservative system with the conservative upper bounds of noise variances, two-level robust measurement fusion Kalman filter is presented for the clustering sensor network systems with uncertain noise variances. It can significantly reduce the communication load and save energy when the number of sensors is very large. A Lyapunov equation approach for the robustness analysis is presented, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented, and the robust accuracy relations among the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the two-level weighted measurement fuser is equal to that of the global centralized robust fuser and is higher than those of each local robust filter and each local weighted measurement fuser. A simulation example shows the correctness and effectiveness of the proposed results.

A network-level explanation for the differences in HIV prevalence in ...

African Journals Online (AJOL)

A network-level explanation for the differences in HIV prevalence in South Africa's ... or ethnic groups may help explain the differential spread of HIV in South Africa. ... Keywords: concurrency, epidemiology, ethnicity, HIV/AIDS, race, social ...

Three-dimensional neural cultures produce networks that mimic native brain activity.

Science.gov (United States)

Bourke, Justin L; Quigley, Anita F; Duchi, Serena; O'Connell, Cathal D; Crook, Jeremy M; Wallace, Gordon G; Cook, Mark J; Kapsa, Robert M I

2018-02-01

Development of brain function is critically dependent on neuronal networks organized through three dimensions. Culture of central nervous system neurons has traditionally been limited to two dimensions, restricting growth patterns and network formation to a single plane. Here, with the use of multichannel extracellular microelectrode arrays, we demonstrate that neurons cultured in a true three-dimensional environment recapitulate native neuronal network formation and produce functional outcomes more akin to in vivo neuronal network activity. Copyright © 2017 John Wiley & Sons, Ltd.

Memory effects induce structure in social networks with activity-driven agents

International Nuclear Information System (INIS)

Medus, A D; Dorso, C O

2014-01-01

Activity-driven modelling has recently been proposed as an alternative growth mechanism for time varying networks,displaying power-law degree distribution in time-aggregated representation. This approach assumes memoryless agents developing random connections with total disregard of their previous contacts. Thus, such an assumption leads to time-aggregated random networks that do not reproduce the positive degree-degree correlation and high clustering coefficient widely observed in real social networks. In this paper, we aim to study the incidence of the agents' long-term memory on the emergence of new social ties. To this end, we propose a dynamical network model assuming heterogeneous activity for agents, together with a triadic-closure step as main connectivity mechanism. We show that this simple mechanism provides some of the fundamental topological features expected for real social networks in their time-aggregated picture. We derive analytical results and perform extensive numerical simulations in regimes with and without population growth. Finally, we present an illustrative comparison with two case studies, one comprising face-to-face encounters in a closed gathering, while the other one corresponding to social friendship ties from an online social network. (paper)

Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

Science.gov (United States)

Petrescu-Prahova, Miruna; Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B; Anderson, Lynda A

2015-08-20

Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

Investigating solvability and complexity of linear active networks by means of matroids

DEFF Research Database (Denmark)

Petersen, Bjørn

1979-01-01

The solvability and complexity problems of finear active network are approached from a purely combinatorial point of view, using the concepts of matroid theory. Since the method is purely combinatorial, we take into account the network topology alone. Under this assumption necessary and sufficient...... conditions are given for the unique solvablity of linear active networks. The complexity and the number of dc-eigenfrequencies are also given. The method enables.you to decide if degeneracies are due to the topology alone, or if they are caused by special relations among network parameter values....... If the network parameter values are taken into account, the complexity and number of dc-eigenfrequencies given by the method, are only upper and lower bounds, respectively. The above conditions are fairly easily checked, and the complexity and number of dc-elgenfrequencies are found, using polynomially bounded...

What Motivates Young Adults to Talk About Physical Activity on Social Network Sites?

Science.gov (United States)

Zhang, Ni; Campo, Shelly; Yang, Jingzhen; Eckler, Petya; Snetselaar, Linda; Janz, Kathleen; Leary, Emily

2017-06-22

Electronic word-of-mouth on social network sites has been used successfully in marketing. In social marketing, electronic word-of-mouth about products as health behaviors has the potential to be more effective and reach more young adults than health education through traditional mass media. However, little is known about what motivates people to actively initiate electronic word-of-mouth about health behaviors on their personal pages or profiles on social network sites, thus potentially reaching all their contacts on those sites. This study filled the gap by applying a marketing theoretical model to explore the factors associated with electronic word-of-mouth on social network sites about leisure-time physical activity. A Web survey link was sent to undergraduate students at one of the Midwestern universities and 439 of them completed the survey. The average age of the 439 participants was 19 years (SD=1 year, range: 18-24). Results suggested that emotional engagement with leisure-time physical activity (ie, affective involvement in leisure-time physical activity) predicted providing relevant opinions or information on social network sites. Social network site users who perceived stronger ties with all their contacts were more likely to provide and seek leisure-time physical activity opinions and information. People who provided leisure-time physical activity opinions and information were more likely to seek opinions and information, and people who forwarded information about leisure-time physical activity were more likely to chat about it. This study shed light on the application of the electronic word-of-mouth theoretical framework in promoting health behaviors. The findings can also guide the development of future social marketing interventions using social network sites to promote leisure-time physical activity. ©Ni Zhang, Shelly Campo, Jingzhen Yang, Petya Eckler, Linda Snetselaar, Kathleen Janz, Emily Leary. Originally published in the Journal of Medical

Neural Networks Mediating High-Level Mentalizing in Patients With Right Cerebral Hemispheric Gliomas

Directory of Open Access Journals (Sweden)

Riho Nakajima

2018-03-01

Full Text Available Mentalizing is the ability to understand others’ mental state through external cues. It consists of two networks, namely low-level and high-level metalizing. Although it is an essential function in our daily social life, surgical resection of right cerebral hemisphere disturbs mentalizing processing with high possibility. In the past, little was known about the white matter related to high-level mentalizing, and the conservation of high-level mentalizing during surgery has not been a focus of attention. Therefore, the main purpose of this study was to examine the neural networks underlying high-level mentalizing and then, secondarily, investigate the usefulness of awake surgery in preserving the mentalizing network. A total of 20 patients with glioma localized in the right hemisphere who underwent awake surgery participated in this study. All patients were assigned to two groups: with or without intraoperative assessment of high-level mentalizing. Their high-level mentalizing abilities were assessed before surgery and 1 week and 3 months after surgery. At 3 months after surgery, only patients who received the intraoperative high-level mentalizing test showed the same score as normal healthy volunteers. The tract-based lesion symptom analysis was performed to confirm the severity of damage of associated fibers and high-level mentalizing accuracy. This analysis revealed the superior longitudinal fascicles (SLF III and fronto-striatal tract (FST to be associated with high-level mentalizing processing. Moreover, the voxel-based lesion symptom analysis demonstrated that resection of orbito-frontal cortex (OFC causes persistent mentalizing dysfunction. Our study indicates that damage of the OFC and structural connectivity of the SLF and FST causes the disorder of mentalizing after surgery, and assessing high-level mentalizing during surgery may be useful to preserve these pathways.

Modeling and Visualization of Human Activities for Multicamera Networks

Directory of Open Access Journals (Sweden)

Aswin C. Sankaranarayanan

2009-01-01

Full Text Available Multicamera networks are becoming complex involving larger sensing areas in order to capture activities and behavior that evolve over long spatial and temporal windows. This necessitates novel methods to process the information sensed by the network and visualize it for an end user. In this paper, we describe a system for modeling and on-demand visualization of activities of groups of humans. Using the prior knowledge of the 3D structure of the scene as well as camera calibration, the system localizes humans as they navigate the scene. Activities of interest are detected by matching models of these activities learnt a priori against the multiview observations. The trajectories and the activity index for each individual summarize the dynamic content of the scene. These are used to render the scene with virtual 3D human models that mimic the observed activities of real humans. In particular, the rendering framework is designed to handle large displays with a cluster of GPUs as well as reduce the cognitive dissonance by rendering realistic weather effects and illumination. We envision use of this system for immersive visualization as well as summarization of videos that capture group behavior.

A Bi-Level Programming Model for the Railway Express Cargo Service Network Design Problem

Directory of Open Access Journals (Sweden)

Boliang Lin

2018-06-01

Full Text Available Service network design is fundamentally crucial for railway express cargo transportation. The main challenge is to strike a balance between two conflicting objectives: low network setup costs and high expected operational incomes. Different configurations of these objectives will have different impacts on the quality of freight transportation services. In this paper, a bi-level programming model for the railway express cargo service network design problem is proposed. The upper-level model forms the optimal decisions in terms of the service characteristics, and the low-level model selects the service arcs for each commodity. The rail express cargo is strictly subject to the service commitment, the capacity restriction, flow balance constraints, and logical relationship constraints among the decisions variables. Moreover, linearization techniques are used to convert the lower-level model to a linear one so that it can be directly solved by a standard optimization solver. Finally, a real-world case study based on the Beijing–Guangzhou Railway Line is carried out to demonstrate the effectiveness and efficiency of the proposed solution approach.

Hybrid Scheduling/Signal-Level Coordination in the Downlink of Multi-Cloud Radio-Access Networks

KAUST Repository

Douik, Ahmed; Dahrouj, Hayssam; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2016-01-01

results suggest that the proposed hybrid scheduling strategy provides appreciable gain as compared to the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.

Fast learning of simple perceptual discriminations reduces brain activation in working memory and in high-level auditory regions.

Science.gov (United States)

Daikhin, Luba; Ahissar, Merav

2015-07-01

Introducing simple stimulus regularities facilitates learning of both simple and complex tasks. This facilitation may reflect an implicit change in the strategies used to solve the task when successful predictions regarding incoming stimuli can be formed. We studied the modifications in brain activity associated with fast perceptual learning based on regularity detection. We administered a two-tone frequency discrimination task and measured brain activation (fMRI) under two conditions: with and without a repeated reference tone. Although participants could not explicitly tell the difference between these two conditions, the introduced regularity affected both performance and the pattern of brain activation. The "No-Reference" condition induced a larger activation in frontoparietal areas known to be part of the working memory network. However, only the condition with a reference showed fast learning, which was accompanied by a reduction of activity in two regions: the left intraparietal area, involved in stimulus retention, and the posterior superior-temporal area, involved in representing auditory regularities. We propose that this joint reduction reflects a reduction in the need for online storage of the compared tones. We further suggest that this change reflects an implicit strategic shift "backwards" from reliance mainly on working memory networks in the "No-Reference" condition to increased reliance on detected regularities stored in high-level auditory networks.

Italian retail gasoline activities: inadequate distribution network

International Nuclear Information System (INIS)

Verde, Stefano

2005-01-01

It is common belief that competition in the Italian retail gasoline activities is hindered by oil companies' collusive behaviour. However, when developing a broader analysis of the sector, low efficiency and scarce competition could results as the consequences coming from an inadequate distribution network and from the recognition of international markets and focal point [it

Play Equipment, Physical Activity Opportunities, and Children's Activity Levels at Childcare

Directory of Open Access Journals (Sweden)

Jessica S. Gubbels

2012-01-01

Full Text Available This study investigated the association between physical activity facilities at childcare (e.g., play equipment and physical activity of 2- and 3-year olds. Observations of physical activity intensity were performed among 175 children at 9 childcare centers in The Netherlands, using the OSRAC-P. The physical activity facilities were assessed for indoors and outdoors separately, using the EPAO instrument. Regular (single-level multivariate and multilevel linear regression analyses examined the association of the facilities and child characteristics (age and sex with children's activity levels. Various physical activity facilities were available in all childcare centers (e.g., balls. Riding toys and a small playing area were associated with lower indoor physical activity levels. Outdoor physical activity levels were positively associated with the availability of portable jumping equipment and the presence of a structured track on the playground. Portable slides, fixed swinging equipment, and sandboxes were negatively associated with outdoor activity levels. In addition, the 3-year old children were more active outdoors than the 2-year olds. In conclusion, not all physical activity facilities at childcare were indeed positively associated with children's activity levels. The current findings provide concrete leads for childcare providers regarding which factors they can improve in the physical environment to facilitate children's physical activity.

Synaptic network activity induces neuronal differentiation of adult hippocampal precursor cells through BDNF signaling

Directory of Open Access Journals (Sweden)

Harish Babu

2009-09-01

Full Text Available Adult hippocampal neurogenesis is regulated by activity. But how do neural precursor cells in the hippocampus respond to surrounding network activity and translate increased neural activity into a developmental program? Here we show that long-term potential (LTP-like synaptic activity within a cellular network of mature hippocampal neurons promotes neuronal differentiation of newly generated cells. In co-cultures of precursor cells with primary hippocampal neurons, LTP-like synaptic plasticity induced by addition of glycine in Mg2+-free media for 5 min, produced synchronous network activity and subsequently increased synaptic strength between neurons. Furthermore, this synchronous network activity led to a significant increase in neuronal differentiation from the co-cultured neural precursor cells. When applied directly to precursor cells, glycine and Mg2+-free solution did not induce neuronal differentiation. Synaptic plasticity-induced neuronal differentiation of precursor cells was observed in the presence of GABAergic neurotransmission blockers but was dependent on NMDA-mediated Ca2+ influx. Most importantly, neuronal differentiation required the release of brain-derived neurotrophic factor (BDNF from the underlying substrate hippocampal neurons as well as TrkB receptor phosphorylation in precursor cells. This suggests that activity-dependent stem cell differentiation within the hippocampal network is mediated via synaptically evoked BDNF signaling.

Fragility in dynamic networks: application to neural networks in the epileptic cortex.

Science.gov (United States)

Sritharan, Duluxan; Sarma, Sridevi V

2014-10-01

Epilepsy is a network phenomenon characterized by atypical activity at the neuronal and population levels during seizures, including tonic spiking, increased heterogeneity in spiking rates, and synchronization. The etiology of epilepsy is unclear, but a common theme among proposed mechanisms is that structural connectivity between neurons is altered. It is hypothesized that epilepsy arises not from random changes in connectivity, but from specific structural changes to the most fragile nodes or neurons in the network. In this letter, the minimum energy perturbation on functional connectivity required to destabilize linear networks is derived. Perturbation results are then applied to a probabilistic nonlinear neural network model that operates at a stable fixed point. That is, if a small stimulus is applied to the network, the activation probabilities of each neuron respond transiently but eventually recover to their baseline values. When the perturbed network is destabilized, the activation probabilities shift to larger or smaller values or oscillate when a small stimulus is applied. Finally, the structural modifications to the neural network that achieve the functional perturbation are derived. Simulations of the unperturbed and perturbed networks qualitatively reflect neuronal activity observed in epilepsy patients, suggesting that the changes in network dynamics due to destabilizing perturbations, including the emergence of an unstable manifold or a stable limit cycle, may be indicative of neuronal or population dynamics during seizure. That is, the epileptic cortex is always on the brink of instability and minute changes in the synaptic weights associated with the most fragile node can suddenly destabilize the network to cause seizures. Finally, the theory developed here and its interpretation of epileptic networks enables the design of a straightforward feedback controller that first detects when the network has destabilized and then applies linear state

Default-mode-like network activation in awake rodents.

Directory of Open Access Journals (Sweden)

Jaymin Upadhyay

Full Text Available During wakefulness and in absence of performing tasks or sensory processing, the default-mode network (DMN, an intrinsic central nervous system (CNS network, is in an active state. Non-human primate and human CNS imaging studies have identified the DMN in these two species. Clinical imaging studies have shown that the pattern of activity within the DMN is often modulated in various disease states (e.g., Alzheimer's, schizophrenia or chronic pain. However, whether the DMN exists in awake rodents has not been characterized. The current data provides evidence that awake rodents also possess 'DMN-like' functional connectivity, but only subsequent to habituation to what is initially a novel magnetic resonance imaging (MRI environment as well as physical restraint. Specifically, the habituation process spanned across four separate scanning sessions (Day 2, 4, 6 and 8. At Day 8, significant (p<0.05 functional connectivity was observed amongst structures such as the anterior cingulate (seed region, retrosplenial, parietal, and hippocampal cortices. Prior to habituation (Day 2, functional connectivity was only detected (p<0.05 amongst CNS structures known to mediate anxiety (i.e., anterior cingulate (seed region, posterior hypothalamic area, amygdala and parabracial nucleus. In relating functional connectivity between cingulate-default-mode and cingulate-anxiety structures across Days 2-8, a significant inverse relationship (r = -0.65, p = 0.0004 was observed between these two functional interactions such that increased cingulate-DMN connectivity corresponded to decreased cingulate anxiety network connectivity. This investigation demonstrates that the cingulate is an important component of both the rodent DMN-like and anxiety networks.

Deep Recurrent Neural Networks for Human Activity Recognition

Directory of Open Access Journals (Sweden)

Abdulmajid Murad

2017-11-01

Full Text Available Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM and k-nearest neighbors (KNN. Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs and CNNs.

Deep Recurrent Neural Networks for Human Activity Recognition.

Science.gov (United States)

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

Application of neural networks to seismic active control

International Nuclear Information System (INIS)

Tang, Yu.

1995-01-01

An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads

Tectonic implications of seismic activity recorded by the northern Ontario seismograph network

International Nuclear Information System (INIS)

Wetmiller, R.J.; Cajka, M.G.

1989-01-01

The northern Ontario seismograph network, which has operated under the Canadian Nuclear Fuel Waste Management Program since 1982, has provided valuable data to supplement those recorded by the Canadian national networks on earthquake activity, rockburst activity, the distribution of regional seismic velocities, and the contemporary stress field in northern Ontario. The combined networks recorded the largest earthquake known in northwestern Ontario, M 3.9 near Sioux Lookout on February 11, 1984, and many smaller earthquakes in northeastern Ontario. Focal mechanism solutions of these and older events showed high horizontal stress and thrust faulting to be dominant features of the contemporary tectonics of northern Ontario. The zone of more intense earthquake activity in western Quebec appeared to extend northwestward into the Kapuskasing area of northeastern Ontario, where an area of persistent microearthquake activity had been identified by a seismograph station near Kapuskasing. Controlled explosions of the 1984 Kapuskasing Uplift seismic profile experiment recorded on the northern Ontario seismograph network showed the presence of anomalously high LG velocities in northeastern Ontario (3.65 km/s) that when properly taken into account reduced the mislocation errors of well-recorded seismic events by 50% on average

Nonlinearly Activated Neural Network for Solving Time-Varying Complex Sylvester Equation.

Science.gov (United States)

Li, Shuai; Li, Yangming

2013-10-28

The Sylvester equation is often encountered in mathematics and control theory. For the general time-invariant Sylvester equation problem, which is defined in the domain of complex numbers, the Bartels-Stewart algorithm and its extensions are effective and widely used with an O(n³) time complexity. When applied to solving the time-varying Sylvester equation, the computation burden increases intensively with the decrease of sampling period and cannot satisfy continuous realtime calculation requirements. For the special case of the general Sylvester equation problem defined in the domain of real numbers, gradient-based recurrent neural networks are able to solve the time-varying Sylvester equation in real time, but there always exists an estimation error while a recently proposed recurrent neural network by Zhang et al [this type of neural network is called Zhang neural network (ZNN)] converges to the solution ideally. The advancements in complex-valued neural networks cast light to extend the existing real-valued ZNN for solving the time-varying real-valued Sylvester equation to its counterpart in the domain of complex numbers. In this paper, a complex-valued ZNN for solving the complex-valued Sylvester equation problem is investigated and the global convergence of the neural network is proven with the proposed nonlinear complex-valued activation functions. Moreover, a special type of activation function with a core function, called sign-bi-power function, is proven to enable the ZNN to converge in finite time, which further enhances its advantage in online processing. In this case, the upper bound of the convergence time is also derived analytically. Simulations are performed to evaluate and compare the performance of the neural network with different parameters and activation functions. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

Noise influence on spike activation in a Hindmarsh–Rose small-world neural network

International Nuclear Information System (INIS)

Zhe, Sun; Micheletto, Ruggero

2016-01-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh−Rose (H−R) neural networks. (paper)

Noise influence on spike activation in a Hindmarsh-Rose small-world neural network

Science.gov (United States)

Zhe, Sun; Micheletto, Ruggero

2016-07-01

We studied the role of noise in neural networks, especially focusing on its relation to the propagation of spike activity in a small sized system. We set up a source of information using a single neuron that is constantly spiking. This element called initiator x o feeds spikes to the rest of the network that is initially quiescent and subsequently reacts with vigorous spiking after a transitional period of time. We found that noise quickly suppresses the initiator’s influence and favors spontaneous spike activity and, using a decibel representation of noise intensity, we established a linear relationship between noise amplitude and the interval from the initiator’s first spike and the rest of the network activation. We studied the same process with networks of different sizes (number of neurons) and found that the initiator x o has a measurable influence on small networks, but as the network grows in size, spontaneous spiking emerges disrupting its effects on networks of more than about N = 100 neurons. This suggests that the mechanism of internal noise generation allows information transmission within a small neural neighborhood, but decays for bigger network domains. We also analyzed the Fourier spectrum of the whole network membrane potential and verified that noise provokes the reduction of main θ and α peaks before transitioning into chaotic spiking. However, network size does not reproduce a similar phenomena; instead we recorded a reduction in peaks’ amplitude, a better sharpness and definition of Fourier peaks, but not the evident degeneration to chaos observed with increasing external noise. This work aims to contribute to the understanding of the fundamental mechanisms of propagation of spontaneous spiking in neural networks and gives a quantitative assessment of how noise can be used to control and modulate this phenomenon in Hindmarsh-Rose (H-R) neural networks.

TWO-LEVEL HIERARCHICAL COORDINATION QUEUING METHOD FOR TELECOMMUNICATION NETWORK NODES

Directory of Open Access Journals (Sweden)

M. V. Semenyaka

2014-07-01

Full Text Available The paper presents hierarchical coordination queuing method. Within the proposed method a queuing problem has been reduced to optimization problem solving that was presented as two-level hierarchical structure. The required distribution of flows and bandwidth allocation was calculated at the first level independently for each macro-queue; at the second level solutions obtained on lower level for each queue were coordinated in order to prevent probable network link overload. The method of goal coordination has been determined for multilevel structure managing, which makes it possible to define the order for consideration of queue cooperation restrictions and calculation tasks distribution between levels of hierarchy. Decisions coordination was performed by the method of Lagrange multipliers. The study of method convergence has been carried out by analytical modeling.

INTERCONNECTING NETWORKS WITH DIFFERENT LEVELS OF SECURITY – A PRESENT NATO PROBLEM

Directory of Open Access Journals (Sweden)

LIVIU TATOMIR

2016-07-01

Full Text Available A situation often met in the Romanian Armed Forces in recent years is the need for interconnecting two networks (domains with different levels of classification. Considering that the Romanian armed troops are involved in numerous missions with NATO partners, solutions, already implemented across the organization, are considered to be applied in domestic systems, also. This paper presents the solutions adopted by NATO in order to solve the problem of cross -domains interconnections. We present the maturity level reached by these solutions and the possibility of implementing these solutions in the Romanian Armed Forces, with or without specific adaptation to our own rules and regulations. The goal is to use a NATO already proved solution to our national classified networks.

RelEx: Visualization for Actively Changing Overlay Network Specifications.

Science.gov (United States)

Sedlmair, M; Frank, A; Munzner, T; Butz, A

2012-12-01

We present a network visualization design study focused on supporting automotive engineers who need to specify and optimize traffic patterns for in-car communication networks. The task and data abstractions that we derived support actively making changes to an overlay network, where logical communication specifications must be mapped to an underlying physical network. These abstractions are very different from the dominant use case in visual network analysis, namely identifying clusters and central nodes, that stems from the domain of social network analysis. Our visualization tool RelEx was created and iteratively refined through a full user-centered design process that included a full problem characterization phase before tool design began, paper prototyping, iterative refinement in close collaboration with expert users for formative evaluation, deployment in the field with real analysts using their own data, usability testing with non-expert users, and summative evaluation at the end of the deployment. In the summative post-deployment study, which entailed domain experts using the tool over several weeks in their daily practice, we documented many examples where the use of RelEx simplified or sped up their work compared to previous practices.

Extrinsic and Intrinsic Brain Network Connectivity Maintains Cognition across the Lifespan Despite Accelerated Decay of Regional Brain Activation.

Science.gov (United States)

Tsvetanov, Kamen A; Henson, Richard N A; Tyler, Lorraine K; Razi, Adeel; Geerligs, Linda; Ham, Timothy E; Rowe, James B

2016-03-16

The maintenance of wellbeing across the lifespan depends on the preservation of cognitive function. We propose that successful cognitive aging is determined by interactions both within and between large-scale functional brain networks. Such connectivity can be estimated from task-free functional magnetic resonance imaging (fMRI), also known as resting-state fMRI (rs-fMRI). However, common correlational methods are confounded by age-related changes in the neurovascular signaling. To estimate network interactions at the neuronal rather than vascular level, we used generative models that specified both the neural interactions and a flexible neurovascular forward model. The networks' parameters were optimized to explain the spectral dynamics of rs-fMRI data in 602 healthy human adults from population-based cohorts who were approximately uniformly distributed between 18 and 88 years (www.cam-can.com). We assessed directed connectivity within and between three key large-scale networks: the salience network, dorsal attention network, and default mode network. We found that age influences connectivity both within and between these networks, over and above the effects on neurovascular coupling. Canonical correlation analysis revealed that the relationship between network connectivity and cognitive function was age-dependent: cognitive performance relied on neural dynamics more strongly in older adults. These effects were driven partly by reduced stability of neural activity within all networks, as expressed by an accelerated decay of neural information. Our findings suggest that the balance of excitatory connectivity between networks, and the stability of intrinsic neural representations within networks, changes with age. The cognitive function of older adults becomes increasingly dependent on these factors. Maintaining cognitive function is critical to successful aging. To study the neural basis of cognitive function across the lifespan, we studied a large population

Analysis on the University’s Network Security Level System in the Big Data Era

Science.gov (United States)

Li, Tianli

2017-12-01

The rapid development of science and technology, the continuous expansion of the scope of computer network applications, has gradually improved the social productive forces, has had a positive impact on the increase production efficiency and industrial scale of China's different industries. Combined with the actual application of computer network in the era of large data, we can see the existence of influencing factors such as network virus, hacker and other attack modes, threatening network security and posing a potential threat to the safe use of computer network in colleges and universities. In view of this unfavorable development situation, universities need to pay attention to the analysis of the situation of large data age, combined with the requirements of network security use, to build a reliable network space security system from the equipment, systems, data and other different levels. To avoid the security risks exist in the network. Based on this, this paper will analyze the hierarchical security system of cyberspace security in the era of large data.

Effects of Vertex Activity and Self-organized Criticality Behavior on a Weighted Evolving Network

International Nuclear Information System (INIS)

Zhang Guiqing; Yang Qiuying; Chen Tianlun

2008-01-01

Effects of vertex activity have been analyzed on a weighted evolving network. The network is characterized by the probability distribution of vertex strength, each edge weight and evolution of the strength of vertices with different vertex activities. The model exhibits self-organized criticality behavior. The probability distribution of avalanche size for different network sizes is also shown. In addition, there is a power law relation between the size and the duration of an avalanche and the average of avalanche size has been studied for different vertex activities

Two-level modulation scheme to reduce latency for optical mobile fronthaul networks.

Science.gov (United States)

Sung, Jiun-Yu; Chow, Chi-Wai; Yeh, Chien-Hung; Chang, Gee-Kung

2016-10-31

A system using optical two-level orthogonal-frequency-division-multiplexing (OFDM) - amplitude-shift-keying (ASK) modulation is proposed and demonstrated to reduce the processing latency for the optical mobile fronthaul networks. At the proposed remote-radio-head (RRH), the high data rate OFDM signal does not need to be processed, but is directly launched into a high speed photodiode (HSPD) and subsequently emitted by an antenna. Only a low bandwidth PD is needed to recover the low data rate ASK control signal. Hence, it is simple and provides low-latency. Furthermore, transporting the proposed system over the already deployed optical-distribution-networks (ODNs) of passive-optical-networks (PONs) is also demonstrated with 256 ODN split-ratios.

Passive and Active Monitoring on a High Performance Research Network

International Nuclear Information System (INIS)

Matthews, Warren

2001-01-01

The bold network challenges described in ''Internet End-to-end Performance Monitoring for the High Energy and Nuclear Physics Community'' presented at PAM 2000 have been tackled by the intrepid administrators and engineers providing the network services. After less than a year, the BaBar collaboration has collected almost 100 million particle collision events in a database approaching 165TB (Tera=10 12 ). Around 20TB has been exported via the Internet to the BaBar regional center at IN2P3 in Lyon, France, for processing and around 40 TB of simulated events have been imported to SLAC from Lawrence Livermore National Laboratory (LLNL). An unforseen challenge has arisen due to recent events and highlighted security concerns at DoE funded labs. New rules and regulations suggest it is only a matter of time before many active performance measurements may not be possible between many sites. Yet, at the same time, the importance of understanding every aspect of the network and eradicating packet loss for high throughput data transfers has become apparent. Work at SLAC to employ passive monitoring using netflow and OC3MON is underway and techniques to supplement and possibly replace the active measurements are being considered. This paper will detail the special needs and traffic characterization of a remarkable research project, and how the networking hurdles have been resolved (or not) to achieve the required high data throughput. Results from active and passive measurements will be compared, and methods for achieving high throughput and the effect on the network will be assessed along with tools that directly measure throughput and applications used to actually transfer data

Passive and Active Monitoring on a High Performance Research Network.

Energy Technology Data Exchange (ETDEWEB)

Matthews, Warren

2001-05-01

The bold network challenges described in ''Internet End-to-end Performance Monitoring for the High Energy and Nuclear Physics Community'' presented at PAM 2000 have been tackled by the intrepid administrators and engineers providing the network services. After less than a year, the BaBar collaboration has collected almost 100 million particle collision events in a database approaching 165TB (Tera=10{sup 12}). Around 20TB has been exported via the Internet to the BaBar regional center at IN2P3 in Lyon, France, for processing and around 40 TB of simulated events have been imported to SLAC from Lawrence Livermore National Laboratory (LLNL). An unforseen challenge has arisen due to recent events and highlighted security concerns at DoE funded labs. New rules and regulations suggest it is only a matter of time before many active performance measurements may not be possible between many sites. Yet, at the same time, the importance of understanding every aspect of the network and eradicating packet loss for high throughput data transfers has become apparent. Work at SLAC to employ passive monitoring using netflow and OC3MON is underway and techniques to supplement and possibly replace the active measurements are being considered. This paper will detail the special needs and traffic characterization of a remarkable research project, and how the networking hurdles have been resolved (or not!) to achieve the required high data throughput. Results from active and passive measurements will be compared, and methods for achieving high throughput and the effect on the network will be assessed along with tools that directly measure throughput and applications used to actually transfer data.

Active Queue Management in TCP Networks Based on Fuzzy-Pid Controller

Directory of Open Access Journals (Sweden)

Hossein ASHTIANI

2012-01-01

Full Text Available We introduce a novel and robust active queue management (AQM scheme based on a fuzzy controller, called hybrid fuzzy-PID controller. In the TCP network, AQM is important to regulate the queue length by passing or dropping the packets at the intermediate routers. RED, PI, and PID algorithms have been used for AQM. But these algorithms show weaknesses in the detection and control of congestion under dynamically changing network situations. In this paper a novel Fuzzy-based proportional-integral derivative (PID controller, which acts as an active queue manager (AQM for Internet routers, is proposed. These controllers are used to reduce packet loss and improve network utilization in TCP/IP networks. A new hybrid controller is proposed and compared with traditional RED based controller. Simulations are carried out to demonstrate the effectiveness of the proposed method and show that, the new hybrid fuzzy PID controller provides better performance than random early detection (RED and PID controllers

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

Ethanol affects network activity in cultured rat hippocampus: mediation by potassium channels.

Directory of Open Access Journals (Sweden)

Eduard Korkotian

Full Text Available The effects of ethanol on neuronal network activity were studied in dissociated cultures of rat hippocampus. Exposure to low (0.25-0.5% ethanol concentrations caused an increase in synchronized network spikes, and a decrease in the duration of individual spikes. Ethanol also caused an increase in rate of miniature spontaneous excitatory postsynaptic currents. Higher concentrations of ethanol eliminated network spikes. These effects were reversible upon wash. The effects of the high, but not the low ethanol were blocked by the GABA antagonist bicuculline. The enhancing action of low ethanol was blocked by apamin, an SK potassium channel antagonist, and mimicked by 1-EBIO, an SK channel opener. It is proposed that in cultured hippocampal networks low concentration of ethanol is associated with SK channel activity, rather than the GABAergic receptor.

3D Filament Network Segmentation with Multiple Active Contours

Science.gov (United States)

Xu, Ting; Vavylonis, Dimitrios; Huang, Xiaolei

2014-03-01

Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and microtubules. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we developed a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D TIRF Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy.

Applications of network analysis for adaptive management of artificial drainage systems in landscapes vulnerable to sea level rise

Science.gov (United States)

Poulter, Benjamin; Goodall, Jonathan L.; Halpin, Patrick N.

2008-08-01

SummaryThe vulnerability of coastal landscapes to sea level rise is compounded by the existence of extensive artificial drainage networks initially built to lower water tables for agriculture, forestry, and human settlements. These drainage networks are found in landscapes with little topographic relief where channel flow is characterized by bi-directional movement across multiple time-scales and related to precipitation, wind, and tidal patterns. The current configuration of many artificial drainage networks exacerbates impacts associated with sea level rise such as salt-intrusion and increased flooding. This suggests that in the short-term, drainage networks might be managed to mitigate sea level rise related impacts. The challenge, however, is that hydrologic processes in regions where channel flow direction is weakly related to slope and topography require extensive parameterization for numerical models which is limited where network size is on the order of a hundred or more kilometers in total length. Here we present an application of graph theoretic algorithms to efficiently investigate network properties relevant to the management of a large artificial drainage system in coastal North Carolina, USA. We created a digital network model representing the observation network topology and four types of drainage features (canal, collector and field ditches, and streams). We applied betweenness-centrality concepts (using Dijkstra's shortest path algorithm) to determine major hydrologic flowpaths based off of hydraulic resistance. Following this, we identified sub-networks that could be managed independently using a community structure and modularity approach. Lastly, a betweenness-centrality algorithm was applied to identify major shoreline entry points to the network that disproportionately control water movement in and out of the network. We demonstrate that graph theory can be applied to solving management and monitoring problems associated with sea level rise

Study on application of adaptive fuzzy control and neural network in the automatic leveling system

Science.gov (United States)

Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

2015-04-01

This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Directory of Open Access Journals (Sweden)

Helmut Schmidt

2014-11-01

Full Text Available Graph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz and low-alpha (6-9 Hz bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic

Social Capital, Network Effects and Savings in Rural Vietnam

DEFF Research Database (Denmark)

Newman, Carol; Tarp, Finn; Van Den Broeck, Katleen

social networks in rural Vietnam can play a role in increasing formal savings where knowledge gaps exist. Networks are defined as active membership of women’s unions and the quality of networks is measured by the level of formal savings observed among group members. We find that membership of high...... quality networks leads to higher levels of saving in formal financial institutions and to higher levels of saving for productive investments as compared with other precautionary or lifecycle motives. Our results suggest that transmitting financial information through formal networks could be effective...... in increasing formal savings at grassroots level. We also conclude that ensuring information disseminated by networks is both accurate and desirable as well as important given that behavioural effects are also found in low-quality networks....

Generation of dTALEs and Libraries of Synthetic TALE-Activated Promoters for Engineering of Gene Regulatory Networks in Plants.

Science.gov (United States)

Schreiber, Tom; Tissier, Alain

2017-01-01

Transcription factors with programmable DNA-binding specificity constitute valuable tools for the design of orthogonal gene regulatory networks for synthetic biology. Transcription activator-like effectors (TALEs), as natural transcription regulators, were used to design, build, and test libraries of synthetic TALE-activated promoters (STAPs) that show a broad range of expression levels in plants. In this chapter, we present protocols for the construction of artificial TALEs and corresponding STAPs.

Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

Directory of Open Access Journals (Sweden)

Fernando Moya Rueda

2018-05-01

Full Text Available Human activity recognition (HAR is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs.

NetWorking News - A Method for Engaging Children Actively In Design

DEFF Research Database (Denmark)

Nørregaard, Peter; Dindler, Christian; Fritsch, Jonas

2003-01-01

For many years cooperative design was primarily concerned with the development of IT supported systems for professional users. However, the cooperative design approach can embrace other social practices such as children’s everyday life. At a methodological level there is no difference in designing...... the Networking News workshop, offers an opportunity to make first hand studies of children’s IT supported social activities in an informal classroom setting....... with adults or children. However there is a need for new methods to support communication and collaboration between designers and children. This article proposes a new method for understandings children’s appropriation of new technology in an interactive workshop setting. The method, which we call...

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

OpenAIRE

Francisco Javier Ordóñez; Daniel Roggen

2016-01-01

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we pro...

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

Directory of Open Access Journals (Sweden)

Qian Li

Full Text Available BACKGROUND: Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. METHODOLOGY: We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671 between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. CONCLUSIONS: This article proposes a network-based multi-target computational estimation

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

Science.gov (United States)

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by

The Next Generation of Scientists: Examining the Experiences of Graduate Students in Network-Level Social-Ecological Science

Directory of Open Access Journals (Sweden)

Michele Romolini

2013-09-01

Full Text Available By integrating the research and resources of hundreds of scientists from dozens of institutions, network-level science is fast becoming one scientific model of choice to address complex problems. In the pursuit to confront pressing environmental issues such as climate change, many scientists, practitioners, policy makers, and institutions are promoting network-level research that integrates the social and ecological sciences. To understand how this scientific trend is unfolding among rising scientists, we examined how graduate students experienced one such emergent social-ecological research initiative, Integrated Science for Society and Environment, within the large-scale, geographically distributed Long Term Ecological Research (LTER Network. Through workshops, surveys, and interviews, we found that graduate students faced challenges in how they conceptualized and practiced social-ecological research within the LTER Network. We have presented these conceptual challenges at three scales: the individual/project, the LTER site, and the LTER Network. The level of student engagement with and knowledge of the LTER Network was varied, and students faced different institutional, cultural, and logistic barriers to practicing social-ecological research. These types of challenges are unlikely to be unique to LTER graduate students; thus, our findings are relevant to other scientific networks implementing new social-ecological research initiatives.

Patterns recognition of electric brain activity using artificial neural networks

Science.gov (United States)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

Directory of Open Access Journals (Sweden)

Suryanita Reni

2017-01-01

Full Text Available The strong motion earthquake could cause the building damage in case of the building not considered in the earthquake design of the building. The study aims to predict the damage-level of building due to earthquake using Artificial Neural Networks method. The building model is a reinforced concrete building with ten floors and height between floors is 3.6 m. The model building received a load of the earthquake based on nine earthquake time history records. Each time history scaled to 0,5g, 0,75g, and 1,0g. The Artificial Neural Networks are designed in 4 architectural models using the MATLAB program. Model 1 used the displacement, velocity, and acceleration as input and Model 2 used the displacement only as the input. Model 3 used the velocity as input, and Model 4 used the acceleration just as input. The output of the Neural Networks is the damage level of the building with the category of Safe (1, Immediate Occupancy (2, Life Safety (3 or in a condition of Collapse Prevention (4. According to the results, Neural Network models have the prediction rate of the damage level between 85%-95%. Therefore, one of the solutions for analyzing the structural responses and the damage level promptly and efficiently when the earthquake occurred is by using Artificial Neural Network

Demonstrating the use of web analytics and an online survey to understand user groups of a national network of river level data

Science.gov (United States)

Macleod, Christopher Kit; Braga, Joao; Arts, Koen; Ioris, Antonio; Han, Xiwu; Sripada, Yaji; van der Wal, Rene

2016-04-01

The number of local, national and international networks of online environmental sensors are rapidly increasing. Where environmental data are made available online for public consumption, there is a need to advance our understanding of the relationships between the supply of and the different demands for such information. Understanding how individuals and groups of users are using online information resources may provide valuable insights into their activities and decision making. As part of the 'dot.rural wikiRivers' project we investigated the potential of web analytics and an online survey to generate insights into the use of a national network of river level data from across Scotland. These sources of online information were collected alongside phone interviews with volunteers sampled from the online survey, and interviews with providers of online river level data; as part of a larger project that set out to help improve the communication of Scotland's online river data. Our web analytics analysis was based on over 100 online sensors which are maintained by the Scottish Environmental Protection Agency (SEPA). Through use of Google Analytics data accessed via the R Ganalytics package we assessed: if the quality of data provided by Google Analytics free service is good enough for research purposes; if we could demonstrate what sensors were being used, when and where; how the nature and pattern of sensor data may affect web traffic; and whether we can identify and profile these users based on information from traffic sources. Web analytics data consists of a series of quantitative metrics which capture and summarize various dimensions of the traffic to a certain web page or set of pages. Examples of commonly used metrics include the number of total visits to a site and the number of total page views. Our analyses of the traffic sources from 2009 to 2011 identified several different major user groups. To improve our understanding of how the use of this national

Global Production Networks and International Inequality: Making a Case for a Meso-Level Turn in Macro-Comparative Sociology

Directory of Open Access Journals (Sweden)

Mathew Mahutga

2015-08-01

Full Text Available In this article, I extend recent macro-comparative empirical research on the developmental implications of global production networks. I draw from theories of commodity/value chains, global production networks and economic sociology to identify three contending theoretical perspectives for exactly how the developmental returns to network participants should be distributed-cooperation, exploitation and differential gains-and derive testable hypotheses for each. Adding to recent empirical advances for measuring the average network position of firms at the country level, I evaluate these hypotheses by way of dynamic panel regression models of hourly wage rates in the garment and transportation equipment industries. The results suggest that macro-sociological theories linking underdevelopment to the structure of the world-economy, as well as theories of the distribution of the gains from network participation, miss important variation at the industry level. Cooperation provides a poor account of the distribution of the gains from network participation. Instead, both industries appear to distribute the gains from network participation differentially across network participants. However, the extent of this inequality increases, and the garment industry transitions to exploitation, when global production networks become entrenched organizational logics. Variation in the distribution of the returns to network participation is explicable only by accounting for production-network governance as it varies across industries and over time. I conclude by highlighting the analytical utility to macro-comparative sociology of a turn toward the mesa-level of global industries.

Asymmetric Modulation Gains in Network Coded Relay Networks

DEFF Research Database (Denmark)

Roetter, Daniel Enrique Lucani; Fitzek, Frank

2015-01-01

Wireless relays have usually been considered in two ways. On the one hand, a physical layer approach focused on per-packet reliability and involving the relay on each packet transmission. On the other, recent approaches have relied on the judicious activation of the relay at the network level to ...

Physical and social activities mediate the associations between social network types and ventilatory function in Chinese older adults.

Science.gov (United States)

Cheng, Sheung-Tak; Leung, Edward M F; Chan, Trista Wai Sze

2014-06-01

This study examined the associations between social network types and peak expiratory flow (PEF), and whether these associations were mediated by social and physical activities and mood. Nine hundred twenty-four community-dwelling Chinese older adults, who were classified into five network types (diverse, friend-focused, family-focused, distant family, and restricted), provided data on demographics, social and physical activities, mood, smoking, chronic diseases, and instrumental activities of daily living. PEF and biological covariates, including blood lipids and glucose, blood pressure, and height and weight, were assessed. Two measures of PEF were analyzed: the raw reading in L/min and the reading expressed as percentage of predicted normal value on the basis of age, sex, and height. Diverse, friend-focused, and distant family networks were hypothesized to have better PEF values compared with restricted networks, through higher physical and/or social activities. No relative advantage was predicted for family-focused networks because such networks tend to be associated with lower physical activity. Older adults with diverse, friend-focused, and distant family networks had significantly better PEF measures than those with restricted networks. The associations between diverse network and PEF measures were partially mediated by physical exercise and socializing activity. The associations between friend-focused network and PEF measures were partially mediated by socializing activity. No significant PEF differences between family-focused and restricted networks were found. Findings suggest that social network types are associated with PEF in older adults, and that network-type differences in physical and socializing activity is partly responsible for this relationship. PsycINFO Database Record (c) 2014 APA, all rights reserved.

A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

International Nuclear Information System (INIS)

Amancio, Diego R; Oliveira Jr, Osvaldo N; Costa, Luciano da F

2012-01-01

The mechanisms responsible for containing activity in systems represented by networks are crucial in various phenomena, for example, in diseases such as epilepsy that affect the neuronal networks and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided into modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory, through which containment of activity is achieved by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, namely, random, Barabási–Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not spread out to the entire neuronal network, even when no special topological organization exists. (paper)

Conceptual plan for closer integration of network- and project-level pavement management

Science.gov (United States)

1998-01-01

This report presents an evaluation of current performance modeling concepts and a feasibility study of the possibility of integrating network- and project-level performance prediction. The widely differing modeling methods in use today are reviewed a...

AMETH laboratories network activities; Activites du reseau de Laboratoires AMETH

Energy Technology Data Exchange (ETDEWEB)

Marimbordes, T.; Ould El Moctar, A.; Peerhossaini, H. [Nantes Univ., Ecole Polytechnique, UMR CNRS 6607, Lab. de Thermocinetique, 44 (France)] [and others

2000-07-01

The AMETH laboratories are a network for the improvement of thermal exchanges for one or two phases. This meeting of the 15 november 2000, dealt with the activities of this network of laboratories in the following topics: thermal-hydrodynamic instabilities and control of the limit layer; transfers with change in the liquid-vapor phase; transfers with change in the solid-liquid phase. Ten papers were presented. (A.L.B.)

Contagion processes on the static and activity-driven coupling networks

Science.gov (United States)

Lei, Yanjun; Jiang, Xin; Guo, Quantong; Ma, Yifang; Li, Meng; Zheng, Zhiming

2016-03-01

The evolution of network structure and the spreading of epidemic are common coexistent dynamical processes. In most cases, network structure is treated as either static or time-varying, supposing the whole network is observed in the same time window. In this paper, we consider the epidemics spreading on a network which has both static and time-varying structures. Meanwhile, the time-varying part and the epidemic spreading are supposed to be of the same time scale. We introduce a static and activity-driven coupling (SADC) network model to characterize the coupling between the static ("strong") structure and the dynamic ("weak") structure. Epidemic thresholds of the SIS and SIR models are studied using the SADC model both analytically and numerically under various coupling strategies, where the strong structure is of homogeneous or heterogeneous degree distribution. Theoretical thresholds obtained from the SADC model can both recover and generalize the classical results in static and time-varying networks. It is demonstrated that a weak structure might make the epidemic threshold low in homogeneous networks but high in heterogeneous cases. Furthermore, we show that the weak structure has a substantive effect on the outbreak of the epidemics. This result might be useful in designing some efficient control strategies for epidemics spreading in networks.

Activity-dependent switch of GABAergic inhibition into glutamatergic excitation in astrocyte-neuron networks.

Science.gov (United States)

Perea, Gertrudis; Gómez, Ricardo; Mederos, Sara; Covelo, Ana; Ballesteros, Jesús J; Schlosser, Laura; Hernández-Vivanco, Alicia; Martín-Fernández, Mario; Quintana, Ruth; Rayan, Abdelrahman; Díez, Adolfo; Fuenzalida, Marco; Agarwal, Amit; Bergles, Dwight E; Bettler, Bernhard; Manahan-Vaughan, Denise; Martín, Eduardo D; Kirchhoff, Frank; Araque, Alfonso

2016-12-24

Interneurons are critical for proper neural network function and can activate Ca 2+ signaling in astrocytes. However, the impact of the interneuron-astrocyte signaling into neuronal network operation remains unknown. Using the simplest hippocampal Astrocyte-Neuron network, i.e., GABAergic interneuron, pyramidal neuron, single CA3-CA1 glutamatergic synapse, and astrocytes, we found that interneuron-astrocyte signaling dynamically affected excitatory neurotransmission in an activity- and time-dependent manner, and determined the sign (inhibition vs potentiation) of the GABA-mediated effects. While synaptic inhibition was mediated by GABA A receptors, potentiation involved astrocyte GABA B receptors, astrocytic glutamate release, and presynaptic metabotropic glutamate receptors. Using conditional astrocyte-specific GABA B receptor ( Gabbr1 ) knockout mice, we confirmed the glial source of the interneuron-induced potentiation, and demonstrated the involvement of astrocytes in hippocampal theta and gamma oscillations in vivo. Therefore, astrocytes decode interneuron activity and transform inhibitory into excitatory signals, contributing to the emergence of novel network properties resulting from the interneuron-astrocyte interplay.

Network governance of active employment policy

DEFF Research Database (Denmark)

Damgaard, Bodil; Torfing, Jacob

2010-01-01

The recent reform of the Danish governance system in the field of active employment policy has been subject to fierce criticism, as many commentators fear that it is the beginning of the end of the Danish Model of active stakeholder involvement. Drawing on both quantitative and qualitative data, ......, the tight metagovernance of the LECs does not seem to straightjacket the LECs as there is a considerable scope for local policy making which makes it worthwhile for the social partners to participate in the local networks.......The recent reform of the Danish governance system in the field of active employment policy has been subject to fierce criticism, as many commentators fear that it is the beginning of the end of the Danish Model of active stakeholder involvement. Drawing on both quantitative and qualitative data......, this study aims to analyse the impact of the governance reform by assessing the initial experiences with the Local Employment Councils (LECs). The analysis shows that the LECs are relatively well-functioning and contribute to an effective and democratic governance of local employment policy. Furthermore...

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max.

Directory of Open Access Journals (Sweden)

Yungang Xu

Full Text Available Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN, a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max, due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs, in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional

Risk Evaluation of Railway Coal Transportation Network Based on Multi Level Grey Evaluation Model

Science.gov (United States)

Niu, Wei; Wang, Xifu

2018-01-01

The railway transport mode is currently the most important way of coal transportation, and now China’s railway coal transportation network has become increasingly perfect, but there is still insufficient capacity, some lines close to saturation and other issues. In this paper, the theory and method of risk assessment, analytic hierarchy process and multi-level gray evaluation model are applied to the risk evaluation of coal railway transportation network in China. Based on the example analysis of Shanxi railway coal transportation network, to improve the internal structure and the competitiveness of the market.

A social activity and physical contact-based routing algorithm in mobile opportunistic networks for emergency response to sudden disasters

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Wang, Xiaoming; Lin, Yaguang; Zhang, Shanshan; Cai, Zhipeng

2017-05-01

Sudden disasters such as earthquake, flood and hurricane necessitate the employment of communication networks to carry out emergency response activities. Routing has a significant impact on the functionality, performance and flexibility of communication networks. In this article, the routing problem is studied considering the delivery ratio of messages, the overhead ratio of messages and the average delay of messages in mobile opportunistic networks (MONs) for enterprise-level emergency response communications in sudden disaster scenarios. Unlike the traditional routing methods for MONS, this article presents a new two-stage spreading and forwarding dynamic routing algorithm based on the proposed social activity degree and physical contact factor for mobile customers. A new modelling method for describing a dynamic evolving process of the topology structure of a MON is first proposed. Then a multi-copy spreading strategy based on the social activity degree of nodes and a single-copy forwarding strategy based on the physical contact factor between nodes are designed. Compared with the most relevant routing algorithms such as Epidemic, Prophet, Labelled-sim, Dlife-comm and Distribute-sim, the proposed routing algorithm can significantly increase the delivery ratio of messages, and decrease the overhead ratio and average delay of messages.

Modelling temporal networks of human face-to-face contacts with public activity and individual reachability

Science.gov (United States)

Zhang, Yi-Qing; Cui, Jing; Zhang, Shu-Min; Zhang, Qi; Li, Xiang

2016-02-01

Modelling temporal networks of human face-to-face contacts is vital both for understanding the spread of airborne pathogens and word-of-mouth spreading of information. Although many efforts have been devoted to model these temporal networks, there are still two important social features, public activity and individual reachability, have been ignored in these models. Here we present a simple model that captures these two features and other typical properties of empirical face-to-face contact networks. The model describes agents which are characterized by an attractiveness to slow down the motion of nearby people, have event-triggered active probability and perform an activity-dependent biased random walk in a square box with periodic boundary. The model quantitatively reproduces two empirical temporal networks of human face-to-face contacts which are testified by their network properties and the epidemic spread dynamics on them.

Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses.

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Maxwell R Bennett

Full Text Available Measurements of blood oxygenation level dependent (BOLD signals have produced some surprising observations. One is that their amplitude is proportional to the entire activity in a region of interest and not just the fluctuations in this activity. Another is that during sleep and anesthesia the average BOLD correlations between regions of interest decline as the activity declines. Mechanistic explanations of these phenomena are described here using a cortical network model consisting of modules with excitatory and inhibitory neurons, taken as regions of cortical interest, each receiving excitatory inputs from outside the network, taken as subcortical driving inputs in addition to extrinsic (intermodular connections, such as provided by associational fibers. The model shows that the standard deviation of the firing rate is proportional to the mean frequency of the firing when the extrinsic connections are decreased, so that the mean BOLD signal is proportional to both as is observed experimentally. The model also shows that if these extrinsic connections are decreased or the frequency of firing reaching the network from the subcortical driving inputs is decreased, or both decline, there is a decrease in the mean firing rate in the modules accompanied by decreases in the mean BOLD correlations between the modules, consistent with the observed changes during NREM sleep and under anesthesia. Finally, the model explains why a transient increase in the BOLD signal in a cortical area, due to a transient subcortical input, gives rises to responses throughout the cortex as observed, with these responses mediated by the extrinsic (intermodular connections.

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia.

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Lo, Chun-Yi Zac; Su, Tsung-Wei; Huang, Chu-Chung; Hung, Chia-Chun; Chen, Wei-Ling; Lan, Tsuo-Hung; Lin, Ching-Po; Bullmore, Edward T

2015-07-21

Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.

Reconfigurable network systems and software-defined networking

OpenAIRE

Zilberman, N.; Watts, P. M.; Rotsos, C.; Moore, A. W.

2015-01-01

Modern high-speed networks have evolved from relatively static networks to highly adaptive networks facilitating dynamic reconfiguration. This evolution has influenced all levels of network design and management, introducing increased programmability and configuration flexibility. This influence has extended from the lowest level of physical hardware interfaces to the highest level of network management by software. A key representative of this evolution is the emergence of software-defined n...

Optimisation of groundwater level monitoring networks using geostatistical modelling based on the Spartan family variogram and a genetic algorithm method

Science.gov (United States)

Parasyris, Antonios E.; Spanoudaki, Katerina; Kampanis, Nikolaos A.

2016-04-01

Groundwater level monitoring networks provide essential information for water resources management, especially in areas with significant groundwater exploitation for agricultural and domestic use. Given the high maintenance costs of these networks, development of tools, which can be used by regulators for efficient network design is essential. In this work, a monitoring network optimisation tool is presented. The network optimisation tool couples geostatistical modelling based on the Spartan family variogram with a genetic algorithm method and is applied to Mires basin in Crete, Greece, an area of high socioeconomic and agricultural interest, which suffers from groundwater overexploitation leading to a dramatic decrease of groundwater levels. The purpose of the optimisation tool is to determine which wells to exclude from the monitoring network because they add little or no beneficial information to groundwater level mapping of the area. Unlike previous relevant investigations, the network optimisation tool presented here uses Ordinary Kriging with the recently-established non-differentiable Spartan variogram for groundwater level mapping, which, based on a previous geostatistical study in the area leads to optimal groundwater level mapping. Seventy boreholes operate in the area for groundwater abstraction and water level monitoring. The Spartan variogram gives overall the most accurate groundwater level estimates followed closely by the power-law model. The geostatistical model is coupled to an integer genetic algorithm method programmed in MATLAB 2015a. The algorithm is used to find the set of wells whose removal leads to the minimum error between the original water level mapping using all the available wells in the network and the groundwater level mapping using the reduced well network (error is defined as the 2-norm of the difference between the original mapping matrix with 70 wells and the mapping matrix of the reduced well network). The solution to the

Between “design” and “bricolage”: Genetic networks, levels of selection, and adaptive evolution

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Wilkins, Adam S.

2007-01-01

The extent to which “developmental constraints” in complex organisms restrict evolutionary directions remains contentious. Yet, other forms of internal constraint, which have received less attention, may also exist. It will be argued here that a set of partial constraints below the level of phenotypes, those involving genes and molecules, influences and channels the set of possible evolutionary trajectories. At the top-most organizational level there are the genetic network modules, whose operations directly underlie complex morphological traits. The properties of these network modules, however, have themselves been set by the evolutionary history of the component genes and their interactions. Characterization of the components, structures, and operational dynamics of specific genetic networks should lead to a better understanding not only of the morphological traits they underlie but of the biases that influence the directions of evolutionary change. Furthermore, such knowledge may permit assessment of the relative degrees of probability of short evolutionary trajectories, those on the microevolutionary scale. In effect, a “network perspective” may help transform evolutionary biology into a scientific enterprise with greater predictive capability than it has hitherto possessed. PMID:17494754

The ATLAS Women's Network: one year of activities

CERN Multimedia

Paula Eerola

The idea for an ATLAS Women's Network was born during the ATLAS overview week in October 2005, when a few of us discussed our experiences and were pondering about what we could do. We felt that it was important to increase the visibility of women working in ATLAS in order to make a better and more effective use of the ATLAS human resources, that is, make sure that women are duly included at all levels. Furthermore, it is our belief that making ATLAS a better working environment for female collaborators and other female co-workers will benefit both us and the collaboration as a whole. On the individual level, all of us thought that we could benefit from peer support and experience sharing, and an ATLAS Women's Network could facilitate this by developing contacts between the ATLAS Women in ATLAS Institutes worldwide. Finally, we thought that it was important to increase the number of women studying physics and working in the field of physics research by identifying gender barriers in the career paths of women i...

Simulating ensembles of nonlinear continuous time dynamical systems via active ultra wideband wireless network

Energy Technology Data Exchange (ETDEWEB)

Dmitriev, Alexander S.; Yemelyanov, Ruslan Yu. [V.A. Kotelnikov Institute of Radio Engineering and Electronics of the RAS Mokhovaya 11-7, Moscow, 125009 (Russian Federation); Moscow Institute of Physics and Technology (State University) 9 Institutskiy per., Dolgoprudny, Moscow, 141700 (Russian Federation); Gerasimov, Mark Yu. [V.A. Kotelnikov Institute of Radio Engineering and Electronics of the RAS Mokhovaya 11-7, Moscow, 125009 (Russian Federation); Itskov, Vadim V. [Moscow Institute of Physics and Technology (State University) 9 Institutskiy per., Dolgoprudny, Moscow, 141700 (Russian Federation)

2016-06-08

The paper deals with a new multi-element processor platform assigned for modelling the behaviour of interacting dynamical systems, i.e., active wireless network. Experimentally, this ensemble is implemented in an active network, the active nodes of which include direct chaotic transceivers and special actuator boards containing microcontrollers for modelling the dynamical systems and an information display unit (colored LEDs). The modelling technique and experimental results are described and analyzed.

Electrical responses and spontaneous activity of human iPS-derived neuronal networks characterized for three-month culture with 4096-electrode arrays

Directory of Open Access Journals (Sweden)

Hayder eAmin

2016-03-01

Full Text Available The recent availability of human induced pluripotent stem cells (hiPSCs holds great promise as a novel source of human-derived neurons for cell and tissue therapies as well as for in vitro drug screenings that might replace the use of animal models. However, there is still a considerable lack of knowledge on the functional properties of hiPSC-derived neuronal networks, thus limiting their application. Here, upon optimization of cell culture protocols, we demonstrate that both spontaneous and evoked electrical spiking activities of these networks can be characterized on-chip by taking advantage of the resolution provided by CMOS multielectrode arrays (CMOS-MEAs. These devices feature a large and closely-spaced array of 4096 simultaneously recording electrodes and multi-site on-chip electrical stimulation. Our results show that networks of human-derived neurons can respond to electrical stimulation with a physiological repertoire of spike waveforms after three months of cell culture, a period of time during which the network undergoes the expression of developing patterns of spontaneous spiking activity. To achieve this, we have investigated the impact on the network formation and on the emerging network-wide functional properties induced by different biochemical substrates, i.e. poly-dl-ornithine (PDLO, poly-l-ornithine (PLO, and polyethylenimine (PEI, that were used as adhesion promoters for the cell culture. Interestingly, we found that neuronal networks grown on PDLO coated substrates show significantly higher spontaneous firing activity, reliable responses to low-frequency electrical stimuli, and an appropriate level of PSD-95 that may denote a physiological neuronal maturation profile and synapse stabilization. However, our results also suggest that even three-month culture might not be sufficient for human-derived neuronal network maturation. Taken together, our results highlight the tight relationship existing between substrate coatings

Tunable deformation modes shape contractility in active biopolymer networks

Science.gov (United States)

Stam, Samantha; Banerjee, Shiladitya; Weirich, Kim; Freedman, Simon; Dinner, Aaron; Gardel, Margaret

Biological polymer-based materials remodel under active, molecular motor-driven forces to perform diverse physiological roles, such as force transmission and spatial self-organization. Critical to understanding these biomaterials is elucidating the role of microscopic polymer deformations, such as stretching, bending, buckling, and relative sliding, on material remodeling. Here, we report that the shape of motor-driven deformations can be used to identify microscopic deformation modes and determine how they propagate to longer length scales. In cross-linked actin networks with sufficiently low densities of the motor protein myosin II, microscopic network deformations are predominantly uniaxial, or dominated by sliding. However, longer-wavelength modes are mostly biaxial, or dominated by bending and buckling, indicating that deformations with uniaxial shapes do not propagate across length scales significantly larger than that of individual polymers. As the density of myosin II is increased, biaxial modes dominate on all length scales we examine due to buildup of sufficient stress to produce smaller-wavelength buckling. In contrast, when we construct networks from unipolar, rigid actin bundles, we observe uniaxial, sliding-based contractions on 1 to 100 μm length scales. Our results demonstrate the biopolymer mechanics can be used to tune deformation modes which, in turn, control shape changes in active materials.

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging.

Science.gov (United States)

Voss, Michelle W; Weng, Timothy B; Burzynska, Agnieszka Z; Wong, Chelsea N; Cooke, Gillian E; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P; Olson, Erin A; McAuley, Edward; Kramer, Arthur F

2016-05-01

Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the default mode network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks. Copyright © 2015 Elsevier Inc. All rights reserved.

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging

Science.gov (United States)

Voss, Michelle W.; Weng, Timothy B.; Burzynska, Agnieszka Z.; Wong, Chelsea N.; Cooke, Gillian E.; Clark, Rachel; Fanning, Jason; Awick, Elizabeth; Gothe, Neha P.; Olson, Erin A.; McAuley, Edward; Kramer, Arthur F.

2015-01-01

Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the Default Mode Network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks. PMID:26493108

AmeriFlux Network Data Activities: updates, progress and plans

Science.gov (United States)

Yang, B.; Boden, T.; Krassovski, M.; Song, X.

2013-12-01

The Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory serves as the long-term data repository for the AmeriFlux network. Datasets currently available include hourly or half-hourly meteorological and flux observations, biological measurement records, and synthesis data products. In this presentation, we provide an update of this network database including a comprehensive review and evaluation of the biological data from about 70 sites, development of a new product for flux uncertainty estimates, and re-formatting of Level-2 standard files. In 2013, we also provided data support to two synthesis studies --- 2012 drought synthesis and FACE synthesis. Issues related to data quality and solutions in compiling datasets for these synthesis studies will be discussed. We will also present our work plans in developing and producing other high-level products, such as derivation of phenology from the available measurements at flux sites.

Impacts of optimal energy storage deployment and network reconfiguration on renewable integration level in distribution systems

International Nuclear Information System (INIS)

Santos, Sérgio F.; Fitiwi, Desta Z.; Cruz, Marco R.M.; Cabrita, Carlos M.P.; Catalão, João P.S.

2017-01-01

Highlights: • A dynamic and multi-objective stochastic mixed integer linear programming model is developed. • A new mechanism to quantify the impacts of network flexibility and ESS deployments on RES integration is presented. • Optimal integration of ESSs dramatically increases the level and the optimal exploitation of renewable DGs. • As high as 90% of RES integration level may be possible in distribution network systems. • Joint DG and ESS installations along with optimal network reconfiguration greatly contribute to voltage stability. - Abstract: Nowadays, there is a wide consensus about integrating more renewable energy sources-RESs to solve a multitude of global concerns such as meeting an increasing demand for electricity, reducing energy security and heavy dependence on fossil fuels for energy production, and reducing the overall carbon footprint of power production. Framed in this context, the coordination of RES integration with energy storage systems (ESSs), along with the network’s switching capability and/or reinforcement, is expected to significantly improve system flexibility, thereby increasing the capability of the system in accommodating large-scale RES power. Hence, this paper presents a novel mechanism to quantify the impacts of network switching and/or reinforcement as well as deployment of ESSs on the level of renewable power integrated in the system. To carry out this analysis, a dynamic and multi-objective stochastic mixed integer linear programming (S-MILP) model is developed, which jointly takes the optimal deployment of RES-based DGs and ESSs into account in coordination with distribution network reinforcement and/or reconfiguration. The IEEE 119-bus test system is used as a case study. Numerical results clearly show the capability of ESS deployment in dramatically increasing the level of renewable DGs integrated in the system. Although case-dependent, the impact of network reconfiguration on RES power integration is not

Taurine activates GABAergic networks in the neocortex of immature mice

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Bogdan Aurel Sava

2014-02-01

Full Text Available Although it has been suggested that taurine is the main endogenous neurotransmitter acting on glycine receptors, the implications of glycine receptor-mediated taurine actions on immature neocortical networks have not been addressed yet. To investigate the influence of taurine on the excitability of neuronal networks in the immature neocortex, we performed whole-cell patch-clamp recordings from visually identified pyramidal neurons and interneurons in coronal slices from C57Bl/6 and GAD67-GFP transgenic mice (postnatal days 2-4. In 46 % of the pyramidal neurons bath-application of taurine at concentrations ≥ 300 mM significantly enhanced the frequency of postsynaptic currents (PSCs by 744.3 ± 93.8 % (n = 120 cells. This taurine-induced increase of PSC frequency was abolished by 0.2 mM tetrodotoxine, 1 mM strychnine or 3 mM gabazine, but was unaffected by the glutamatergic antagonists 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX and (± R(--3-(2-carboxypiperazine-4-yl-propyl-1-phosphonic acid (CPP, suggesting that taurine specifically activates GABAergic network activity projecting to pyramidal neurons. Cell-attached recordings revealed that taurine enhanced the frequency of action potentials in pyramidal neurons, indicating an excitatory action of the GABAergic PSCs. In order to identify the presynaptic targets of taurine we demonstrate that bath application of taurine induced in GAD67-GFP labeled interneurons an inward current that is mainly mediated by glycine receptors and can generate action potentials in these cells. We conclude from these results that taurine can enhance network excitability in the immature neocortex by selectively activating GABAergic interneurons via interactions with glycine receptors.

Intrinsic network activity in tinnitus investigated using functional MRI

Science.gov (United States)

Leaver, Amber M.; Turesky, Ted K.; Seydell-Greenwald, Anna; Morgan, Susan; Kim, Hung J.; Rauschecker, Josef P.

2016-01-01

Tinnitus is an increasingly common disorder in which patients experience phantom auditory sensations, usually ringing or buzzing in the ear. Tinnitus pathophysiology has been repeatedly shown to involve both auditory and non-auditory brain structures, making network-level studies of tinnitus critical. In this magnetic resonance imaging (MRI) study, we used two resting-state functional connectivity (RSFC) approaches to better understand functional network disturbances in tinnitus. First, we demonstrated tinnitus-related reductions in RSFC between specific brain regions and resting-state networks (RSNs), defined by independent components analysis (ICA) and chosen for their overlap with structures known to be affected in tinnitus. Then, we restricted ICA to data from tinnitus patients, and identified one RSN not apparent in control data. This tinnitus RSN included auditory-sensory regions like inferior colliculus and medial Heschl’s gyrus, as well as classically non-auditory regions like the mediodorsal nucleus of the thalamus, striatum, lateral prefrontal and orbitofrontal cortex. Notably, patients’ reported tinnitus loudness was positively correlated with RSFC between the mediodorsal nucleus and the tinnitus RSN, indicating that this network may underlie the auditory-sensory experience of tinnitus. These data support the idea that tinnitus involves network dysfunction, and further stress the importance of communication between auditory-sensory and fronto-striatal circuits in tinnitus pathophysiology. PMID:27091485

Optimization based on benefit of regional energy suppliers of distributed generation in active distribution network

Science.gov (United States)

Huo, Xianxu; Li, Guodong; Jiang, Ling; Wang, Xudong

2017-08-01

With the development of electricity market, distributed generation (DG) technology and related policies, regional energy suppliers are encouraged to build DG. Under this background, the concept of active distribution network (ADN) is put forward. In this paper, a bi-level model of intermittent DG considering benefit of regional energy suppliers is proposed. The objective of the upper level is the maximization of benefit of regional energy suppliers. On this basis, the lower level is optimized for each scene. The uncertainties of DG output and load of users, as well as four active management measures, which include demand-side management, curtailing the output power of DG, regulating reactive power compensation capacity and regulating the on-load tap changer, are considered. Harmony search algorithm and particle swarm optimization are combined as a hybrid strategy to solve the model. This model and strategy are tested with IEEE-33 node system, and results of case study indicate that the model and strategy successfully increase the capacity of DG and benefit of regional energy suppliers.

Information Flows in Networked Engineering Design Projects

DEFF Research Database (Denmark)

Parraguez, Pedro; Maier, Anja

Complex engineering design projects need to manage simultaneously multiple information flows across design activities associated with different areas of the design process. Previous research on this area has mostly focused on either analysing the “required information flows” through activity...... networks at the project level or in studying the social networks that deliver the “actual information flow”. In this paper we propose and empirically test a model and method that integrates both social and activity networks into one compact representation, allowing to compare actual and required...... information flows between design spaces, and to assess the influence that these misalignments could have on the performance of engineering design projects....

Automatic adjustment of display window (gray-level condition) for MR images using neural networks

International Nuclear Information System (INIS)

Ohhashi, Akinami; Nambu, Kyojiro.

1992-01-01

We have developed a system to automatically adjust the display window width and level (WWL) for MR images using neural networks. There were three main points in the development of our system as follows: 1) We defined an index for the clarity of a displayed image, and called 'EW'. EW is a quantitative measure of the clarity of an image displayed in a certain WWL, and can be derived from the difference between gray-level with the WWL adjusted by a human expert and with a certain WWL. 2) We extracted a group of six features from a gray-level histogram of a displayed image. We designed two neural networks which are able to learn the relationship between these features and the desired output (teaching signal), 'EQ', which is normalized to 0 to 1.0 from EW. Two neural networks were used to share the patterns to be learned; one learns a variety of patterns with less accuracy, and the other learns similar patterns with accuracy. Learning was performed using a back-propagation method. As a result, the neural networks after learning are able to provide a quantitative measure, 'Q', of the clarity of images displayed in the designated WWL. 3) Using the 'Hill climbing' method, we have been able to determine the best possible WWL for a displaying image. We have tested this technique for MR brain images. The results show that this system can adjust WWL comparable to that adjusted by a human expert for the majority of test images. The neural network is effective for the automatic adjustment of the display window for MR images. We are now studying the application of this method to MR images of another regions. (author)

Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

Science.gov (United States)

Cossalter, Michele; Mengshoel, Ole J.; Selker, Ted

2013-01-01

Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

Sea Level Activities and Changes on the Islands of the Western ...

African Journals Online (AJOL)

1985- 1994), a sea-level study network was established in the Western Indian Ocean (WIO) to monitor sea-level variations. Most of these stations together with additional stations maintained by countries outside the region now form part of the ...

Implementing multiple intervention strategies in Dutch public health-related policy networks.

Science.gov (United States)

Harting, Janneke; Peters, Dorothee; Grêaux, Kimberly; van Assema, Patricia; Verweij, Stefan; Stronks, Karien; Klijn, Erik-Hans

2017-10-13

Improving public health requires multiple intervention strategies. Implementing such an intervention mix is supposed to require a multisectoral policy network. As evidence to support this assumption is scarce, we examined under which conditions public health-related policy networks were able to implement an intervention mix. Data were collected (2009-14) from 29 Dutch public health policy networks. Surveys were used to identify the number of policy sectors, participation of actors, level of trust, networking by the project leader, and intervention strategies implemented. Conditions sufficient for an intervention mix (≥3 of 4 non-educational strategies present) were determined in a fuzzy-set qualitative comparative analysis. A multisectoral policy network (≥7 of 14 sectors present) was neither a necessary nor a sufficient condition. In multisectoral networks, additionally required was either the active participation of network actors (≥50% actively involved) or active networking by the project leader (≥monthly contacts with network actors). In policy networks that included few sectors, a high level of trust (positive perceptions of each other's intentions) was needed-in the absence though of any of the other conditions. If the network actors were also actively involved, an extra requirement was active networking by the project leader. We conclude that the multisectoral composition of policy networks can contribute to the implementation of a variety of intervention strategies, but not without additional efforts. However, policy networks that include only few sectors are also able to implement an intervention mix. Here, trust seems to be the most important condition. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Directory of Open Access Journals (Sweden)

Francisco Javier Ordóñez

2016-01-01

Full Text Available Human activity recognition (HAR tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i is suitable for multimodal wearable sensors; (ii can perform sensor fusion naturally; (iii does not require expert knowledge in designing features; and (iv explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

Science.gov (United States)

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-18

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

Science.gov (United States)

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-01

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

Prediction of SO{sub 2} levels using neural networks

Energy Technology Data Exchange (ETDEWEB)

Belen M. Fernandez de Castro; Jose Manuel Prada Sanchez; Wenceslao Gonzalez Manteiga [and others] [University of Santiago de Compostela, Santiago (Spain). Department of Statistics and Operations Research, Faculty of Mathematics

2003-05-01

The paper presents an adaptation of the air pollution control help system in the neighbourhood of a coal-fired power plant in As Pontes (A Coruna, Spain), property of Endesa Generacion S.A., to the European Council Directive 1999/30/CE. This system contains a statistical prediction made half an hour before the measurement, and it helps the staff in the power plant prevent air quality level episodes. The prediction is made using neural network models. This prediction is compared with one made by a semiparametric model. 11 refs., 6 figs., 4 tabs.

Emergence of gamma motor activity in an artificial neural network model of the corticospinal system.

Science.gov (United States)

Grandjean, Bernard; Maier, Marc A

2017-02-01

Muscle spindle discharge during active movement is a function of mechanical and neural parameters. Muscle length changes (and their derivatives) represent its primary mechanical, fusimotor drive its neural component. However, neither the action nor the function of fusimotor and in particular of γ-drive, have been clearly established, since γ-motor activity during voluntary, non-locomotor movements remains largely unknown. Here, using a computational approach, we explored whether γ-drive emerges in an artificial neural network model of the corticospinal system linked to a biomechanical antagonist wrist simulator. The wrist simulator included length-sensitive and γ-drive-dependent type Ia and type II muscle spindle activity. Network activity and connectivity were derived by a gradient descent algorithm to generate reciprocal, known target α-motor unit activity during wrist flexion-extension (F/E) movements. Two tasks were simulated: an alternating F/E task and a slow F/E tracking task. Emergence of γ-motor activity in the alternating F/E network was a function of α-motor unit drive: if muscle afferent (together with supraspinal) input was required for driving α-motor units, then γ-drive emerged in the form of α-γ coactivation, as predicted by empirical studies. In the slow F/E tracking network, γ-drive emerged in the form of α-γ dissociation and provided critical, bidirectional muscle afferent activity to the cortical network, containing known bidirectional target units. The model thus demonstrates the complementary aspects of spindle output and hence γ-drive: i) muscle spindle activity as a driving force of α-motor unit activity, and ii) afferent activity providing continuous sensory information, both of which crucially depend on γ-drive.

Short-term memory of motor network performance via activity-dependent potentiation of Na+/K+ pump function.

Science.gov (United States)

Zhang, Hong-Yan; Sillar, Keith T

2012-03-20

Brain networks memorize previous performance to adjust their output in light of past experience. These activity-dependent modifications generally result from changes in synaptic strengths or ionic conductances, and ion pumps have only rarely been demonstrated to play a dynamic role. Locomotor behavior is produced by central pattern generator (CPG) networks and modified by sensory and descending signals to allow for changes in movement frequency, intensity, and duration, but whether or how the CPG networks recall recent activity is largely unknown. In Xenopus frog tadpoles, swim bout duration correlates linearly with interswim interval, suggesting that the locomotor network retains a short-term memory of previous output. We discovered an ultraslow, minute-long afterhyperpolarization (usAHP) in network neurons following locomotor episodes. The usAHP is mediated by an activity- and sodium spike-dependent enhancement of electrogenic Na(+)/K(+) pump function. By integrating spike frequency over time and linking the membrane potential of spinal neurons to network performance, the usAHP plays a dynamic role in short-term motor memory. Because Na(+)/K(+) pumps are ubiquitously expressed in neurons of all animals and because sodium spikes inevitably accompany network activity, the usAHP may represent a phylogenetically conserved but largely overlooked mechanism for short-term memory of neural network function. Copyright © 2012 Elsevier Ltd. All rights reserved.

The Use of Meta-Level Control for Coordination in a Distributed Problem Solving Network,

Science.gov (United States)

1983-01-01

crucial aspect of the design organizational structuring in coordinating the local activity of achs decentralized network control policies. It is...TEMTED EXflD.MENTS WITn and ratings of the subgoals." Threshold values indicating ORGANIZATIONAL STRUCIURING IkeA usaw lani of a subal are specif’ied in...the monitoring are. This environmental vehicle, approximate position, time frame, and belief. The scenario was designed to test the networks ability

Birth Order and Activity Level in Children.

Science.gov (United States)

Eaton, Warren O.; And Others

1989-01-01

Studied 7,018 children between birth and 7 years and 81 children of 5-8 years to test the hypothesis that birth order is negatively related to motor activity level. Activity level declined linearly across birth position, so that early-borns were rated as more active than later-borns. (RJC)

Evaluation of the Performance of Feedforward and Recurrent Neural Networks in Active Cancellation of Sound Noise

Directory of Open Access Journals (Sweden)

Mehrshad Salmasi

2012-07-01

Full Text Available Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise. In this paper, performance of the neural networks is evaluated in active cancellation of sound noise. For this reason, feedforward and recurrent neural networks are designed and trained. After training, performance of the feedforwrad and recurrent networks in noise attenuation are compared. We use Elman network as a recurrent neural network. For simulations, noise signals from a SPIB database are used. In order to compare the networks appropriately, equal number of layers and neurons are considered for the networks. Moreover, training and test samples are similar. Simulation results show that feedforward and recurrent neural networks present good performance in noise cancellation. As it is seen, the ability of recurrent neural network in noise attenuation is better than feedforward network.

LEA: An Algorithm to Estimate the Level of Location Exposure in Infrastructure-Based Wireless Networks

Directory of Open Access Journals (Sweden)

Francisco Garcia

2017-01-01

Full Text Available Location privacy in wireless networks is nowadays a major concern. This is due to the fact that the mere fact of transmitting may allow a network to pinpoint a mobile node. We consider that a first step to protect a mobile node in this situation is to provide it with the means to quantify how accurately a network establishes its position. To achieve this end, we introduce the location-exposure algorithm (LEA, which runs on the mobile terminal only and whose operation consists of two steps. In the first step, LEA discovers the positions of nearby network nodes and uses this information to emulate how they estimate the position of the mobile node. In the second step, it quantifies the level of exposure by computing the distance between the position estimated in the first step and its true position. We refer to these steps as a location-exposure problem. We tested our proposal with simulations and testbed experiments. These results show the ability of LEA to reproduce the location of the mobile node, as seen by the network, and to quantify the level of exposure. This knowledge can help the mobile user decide which actions should be performed before transmitting.

Managing nuclear knowledge: IAEA activities and international coordination. Asian Network for Education in Nuclear Technology (ANENT)

International Nuclear Information System (INIS)

2007-07-01

This booklet summarizes the main activities being carried out by the IAEA with regard to the Asian Network for Education in Nuclear Technology (ANENT) and other related activities including those completed during the period 2002-2005. It briefly describes the background information on the events leading to the formation of the ANENT; the terms of reference formulated at the second Coordination Committee meeting held in Vietnam, October 2005; and objectives, strategy and other institutional and managerial policies reaffirmed by the members. The attached CD-ROM contains nearly all of the background material in full text, including policy level papers, reports, presentations made by Member States, and meeting summaries

Inefficient preparatory fMRI-BOLD network activations predict working memory dysfunctions in patients with schizophrenia

Directory of Open Access Journals (Sweden)

Anja eBaenninger

2016-03-01

Full Text Available Patients with schizophrenia show abnormal dynamics and structure of temporally coherent networks (TCNs assessed using fMRI, which undergo adaptive shifts in preparation for a cognitively demanding task. During working memory (WM tasks, patients with schizophrenia show persistent deficits in TCNs as well as EEG indices of WM. Studying their temporal relationship during WM tasks might provide novel insights into WM performance deficits seen in schizophrenia.Simultaneous EEG-fMRI data were acquired during the performance of a verbal Sternberg WM task with two load levels (load 2 & load 5 in 17 patients with schizophrenia and 17 matched healthy controls. Using covariance mapping, we investigated the relationship of the activity in the TCNs before the memoranda were encoded and EEG spectral power during the retention interval. We assessed four TCNs – default mode network (DMN, dorsal attention network (dAN, left and right working memory networks (WMNs – and three EEG bands – theta, alpha, and beta.In healthy controls, there was a load dependent inverse relation between DMN and frontal-midline theta power and an anti-correlation between DMN and dAN. Both effects were not significantly detectable in patients. In addition, healthy controls showed a left-lateralized load-dependent recruitment of the WMNs. Activation of the WMNs was bilateral in patients, suggesting more resources were recruited for successful performance on the WM task.Our findings support the notion of schizophrenia patients showing deviations in their neurophysiological responses before the retention of relevant information in a verbal WM task. Thus, treatment strategies as neurofeedback targeting pre-states could be beneficial as task performance relies on the preparatory state of the brain.

Developing networks to support science teachers work

DEFF Research Database (Denmark)

Sillasen, Martin Krabbe; Valero, Paola

2012-01-01

In educational research literature constructing networks among practitioners has been suggested as a strategy to support teachers’ professional development (Huberman, 1995; Jackson & Temperley, 2007; Van Driel, Beijaard, & Verloop, 2001). The purpose of this paper is to report on a study about how...... networks provide opportunities for teachers from different schools to collaborate on improving the quality of their own science teaching practices. These networks exist at the meso-level of the educational system between the micro-realities of teachers’ individual practice and the macro-level, where...... to develop collaborative activities in primary science teacher communities in schools to improve individual teachers practice and in networks between teachers from different schools in each municipality. Each network was organized and moderated by a municipal science coordinator....

Multi-agent system based active distribution networks

OpenAIRE

Nguyen, H.P.

2010-01-01

This thesis gives a particular vision of the future power delivery system with its main requirements. An investigation of suitable concepts and technologies which creates a road map forward the smart grid has been carried out. They should meet the requirements on sustainability, efficiency, flexibility and intelligence. The so called Active Distribution Network (ADN) is introduced as an important element of the future power delivery system. With an open architecture, the ADN is designed to in...

High level cognitive information processing in neural networks

Science.gov (United States)

Barnden, John A.; Fields, Christopher A.

1992-01-01

Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

Provable network activity for protecting users against false accusation

NARCIS (Netherlands)

Papadopoulos, Panagiotis; Athanasopoulos, Ilias; Kosta, Eleni; Siganos, George; Keromytis, Angelos D.; Markatos, Evangelos P.

2016-01-01

With the proliferation of the World Wide Web, data traces that correspond to users’ network activity can be collected by several Internet actors, including (i) web sites, (ii) smartphone apps, and even (iii) Internet Service Providers. Given that the collection and storage of these data are beyond

Social networks usage implications at the level of medical services consumption in Romania

Directory of Open Access Journals (Sweden)

Daniel Adrian Gardan

2017-03-01

The research results reveal key issues from the perspective of emotional involvement within consumption for the patients and the influence of key variables such as level of education, personality and lifestyle within social networks usage context.

Online social networks that connect users to physical activity partners: a review and descriptive analysis.

Science.gov (United States)

Nakhasi, Atul; Shen, Album Xiaotian; Passarella, Ralph Joseph; Appel, Lawrence J; Anderson, Cheryl Am

2014-06-16

The US Centers for Disease Control and Prevention have identified a lack of encouragement, support, or companionship from family and friends as a major barrier to physical activity. To overcome this barrier, online social networks are now actively leveraging principles of companion social support in novel ways. The aim was to evaluate the functionality, features, and usability of existing online social networks which seek to increase physical activity and fitness among users by connecting them to physical activity partners, not just online, but also face-to-face. In September 2012, we used 3 major databases to identify the website addresses for relevant online social networks. We conducted a Google search using 8 unique keyword combinations: the common keyword "find" coupled with 1 of 4 prefix terms "health," "fitness," "workout," or "physical" coupled with 1 of 2 stem terms "activity partners" or "activity buddies." We also searched 2 prominent technology start-up news sites, TechCrunch and Y Combinator, using 2 unique keyword combinations: the common keyword "find" coupled with 1 of 2 stem terms "activity partners" and "activity buddies." Sites were defined as online social health activity networks if they had the ability to (1) actively find physical activity partners or activities for the user, (2) offer dynamic, real-time tracking or sharing of social activities, and (3) provide virtual profiles to users. We excluded from our analysis sites that were not Web-based, publicly available, in English, or free. Of the 360 initial search results, we identified 13 websites that met our complete criteria of an online social health activity network. Features such as physical activity creation (13/13, 100%) and private messaging (12/13, 92%) appeared almost universally among these websites. However, integration with Web 2.0 technologies such as Facebook and Twitter (9/13, 69%) and the option of direct event joining (8/13, 62%) were not as universally present. Largely

Computational modeling of seizure dynamics using coupled neuronal networks: factors shaping epileptiform activity.